WO2022236751A1 - 电池剩余电量预估方法及装置 - Google Patents

电池剩余电量预估方法及装置 Download PDF

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Publication number
WO2022236751A1
WO2022236751A1 PCT/CN2021/093449 CN2021093449W WO2022236751A1 WO 2022236751 A1 WO2022236751 A1 WO 2022236751A1 CN 2021093449 W CN2021093449 W CN 2021093449W WO 2022236751 A1 WO2022236751 A1 WO 2022236751A1
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energy consumption
information
driving
vehicle
low
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PCT/CN2021/093449
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English (en)
French (fr)
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廖军华
杨凯
周勇有
李帅飞
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华为技术有限公司
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Priority to CN202180002023.4A priority Critical patent/CN113424067A/zh
Priority to PCT/CN2021/093449 priority patent/WO2022236751A1/zh
Publication of WO2022236751A1 publication Critical patent/WO2022236751A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables

Definitions

  • the invention relates to the field of electric vehicles, in particular to a method and device for estimating the remaining power of a battery.
  • the on-board battery is used as the main energy source to drive new energy vehicles, and to provide electrical energy for electrical appliances such as air conditioners in the vehicle.
  • electrical appliances such as air conditioners in the vehicle.
  • the capacity of on-board batteries is limited.
  • users When users use electric vehicles, they generally have mileage anxiety. The longer the mileage, the more obvious the anxiety.
  • the present application provides a method, device, computing device, computer-readable storage medium, etc. for estimating the remaining battery power, so as to improve the accuracy of estimating the battery SOC when the vehicle travels to a destination according to a navigation route.
  • the first aspect of the present application provides a method for estimating the remaining battery power of a vehicle, which is characterized in that it includes: obtaining the driving energy consumption of the vehicle; obtaining the energy consumption of the high-voltage accessories of the vehicle; obtaining the energy consumption of the low-voltage accessories of the vehicle; obtaining the current battery power ; According to the driving energy consumption, the energy consumption of the high-voltage accessories, the energy consumption of the low-voltage accessories and the current battery power, the remaining battery power when reaching the end of the vehicle's navigation path is calculated.
  • the remaining battery power is calculated according to the energy consumption of the drive, the energy consumption of the high-voltage accessories, and the energy consumption of the low-voltage accessories, thereby improving the accuracy and fineness of the estimation of the remaining battery power.
  • the remaining power of the battery is only calculated according to the energy consumption of the drive and the energy consumption of the high-voltage accessories.
  • the above method for estimating the remaining battery power of the present application not only considers the energy consumption of driving and the energy consumption of high-voltage accessories, but also considers the energy consumption of low-voltage accessories, and then estimates the remaining battery power on this basis, thereby improving the accuracy of the remaining battery power. degree and precision.
  • Obtaining the energy consumption of the low-voltage accessories of the vehicle may include: acquiring status information of the low-voltage accessories of the vehicle and estimated time information of the navigation route; and calculating energy consumption of the low-voltage accessories according to the status information of the low-voltage accessories and the estimated time information.
  • the status information of the low-voltage accessories may include one or more of the following items: status information of the heating switch of the main driving seat, information on the heating position of the main driving seat, status information of the heating switch on the passenger seat, information on the switching position of the passenger seat , low beam status information, high beam status information, position marker status information, wiper status information and speaker status information.
  • the battery is calculated according to the state information of the low beam light, the state information of the high beam light, and the position marker light status information. The remaining power, so that the remaining power of the battery can be calculated more accurately and finely.
  • calculating the energy consumption of the low-voltage accessory according to the state information of the low-voltage accessory and the estimated time information includes: obtaining the power information of the low-voltage accessory by using the energy consumption model of the low-voltage accessory according to the status information of the low-voltage accessory; Information and estimated time information to calculate low-voltage accessory energy consumption.
  • the power here refers to the energy consumption per unit time.
  • Energy consumption refers to energy (electricity) consumption.
  • obtaining the driving energy consumption specifically includes: obtaining the distance information and speed information of the navigation path of the vehicle; according to the speed information, using the driving energy consumption model to obtain the driving energy consumption rate information; according to the driving energy consumption Calculate drive energy consumption based on rate information and distance information.
  • the driving energy consumption rate here refers to the driving energy consumption per unit distance.
  • the first aspect also includes: acquiring the driving mode and energy recovery level of the vehicle; and correcting the driving energy consumption according to the driving mode and the energy recovery level.
  • the method further includes: acquiring acceleration information of the vehicle on the navigation path; and correcting the driving energy consumption according to the acceleration information.
  • the energy consumption generated by different drivers driving the vehicle is different.
  • some drivers have more aggressive driving styles, which have a relatively large impact on energy consumption. Therefore , in the above implementation manner of the present application, the driving energy consumption is corrected according to the acceleration information reflecting different driving styles, so that the remaining battery power can be estimated more accurately.
  • the acceleration information may include one or more of the following items: average acceleration information in the acceleration segment, average deceleration information in the deceleration segment, rapid acceleration ratio information, and rapid deceleration ratio information.
  • the remaining power of the battery can be calculated more finely and accurately.
  • the above method can further improve the estimation accuracy of the remaining battery power.
  • correcting the driving energy consumption according to the driving mode and the energy recovery level includes: using the driving energy consumption correction coefficient model to obtain the driving energy consumption correction coefficient according to the driving mode and the energy recovery level;
  • the energy consumption correction factor corrects the drive energy consumption.
  • correcting the driving energy consumption according to the acceleration information includes: using the driving energy consumption correction coefficient model to obtain the driving energy consumption correction coefficient according to the navigation planning acceleration information; Energy consumption is corrected.
  • the driving energy consumption correction coefficient model is established based on the single vehicle data of the vehicle.
  • the driving energy consumption correction coefficient model reflects the impact of acceleration information on driving energy consumption, and the acceleration and deceleration operations when driving a vehicle vary from person to person, so using single vehicle data to establish a driving energy consumption correction coefficient model can improve the accuracy of model prediction Based on this, the driving energy consumption is corrected to improve the prediction accuracy of the driving energy consumption, thereby improving the prediction accuracy of the remaining battery power.
  • the method further includes: obtaining slope information of the navigation path; and correcting driving energy consumption according to the slope information.
  • the road slope also has a relatively large impact on driving energy consumption. Therefore, using the above method, the slope information is used to correct the driving energy consumption, so that the driving energy consumption can be estimated more accurately, and the remaining battery power can be estimated more accurately.
  • obtaining the energy consumption of the high-voltage accessories of the vehicle specifically includes: obtaining the status information of the high-voltage accessories of the vehicle; obtaining the power information of the high-voltage accessories by using the energy consumption model of the high-voltage accessories according to the status information of the high-voltage accessories; Power information and estimated time information calculate the energy consumption of high-voltage accessories.
  • the method for estimating the remaining battery power also includes: when the navigation path is a frequently-traveled route, using the frequently-traveled route to drive the energy consumption model to calculate the driving energy consumption, and the frequently-traveled route to drive the energy consumption model is It is established based on the bicycle data of the vehicle. Frequently-traveled routes are typically commuting routes, and whether they are frequently-traveled routes can be judged according to whether the starting point and end point of the navigation path are home or company.
  • the second aspect of the present application provides a device for estimating the remaining battery power of a vehicle, including an acquisition module and a processing module.
  • the processing module is used to obtain driving energy consumption, high-voltage accessory energy consumption, and low-voltage accessory energy consumption; Power; the processing module is also used to calculate the remaining battery power when reaching the end of the navigation path of the vehicle according to the driving energy consumption, high-voltage accessory energy consumption, low-voltage accessory energy consumption and current battery power.
  • the acquiring module is also used to acquire the vehicle's low-voltage accessory status information and the estimated time information of the navigation route; the processing module is also used to calculate the low-voltage accessory energy consumption according to the low-voltage accessory status information and the estimated time information.
  • the status information of the low-voltage accessories includes one or more of the following items: status information of the heating switch of the main driving seat, information on the heating gear of the main driving seat, heating switch of the passenger seat Status information, passenger seat switch gear information, low beam status information, high beam status information, position marker status information, wiper status information and speaker status information.
  • calculating the energy consumption of the low-voltage accessory according to the status information and estimated time information of the low-voltage accessory includes: obtaining the power information of the low-voltage accessory by using the energy consumption model of the low-voltage accessory according to the status information of the low-voltage accessory; Information and estimated time information to calculate low-voltage accessory energy consumption.
  • the obtaining module is also used to obtain the distance information and speed information of the navigation path of the vehicle; the processing module is also used to obtain the driving energy consumption rate information by using the driving energy consumption model according to the speed information; the processing The module is also used to calculate driving energy consumption according to driving energy consumption rate information and distance information.
  • the acquisition module is also used to acquire the driving mode and energy recovery level of the vehicle; the processing module is also used to correct the driving energy consumption according to the driving mode and energy recovery level.
  • the acquiring module is further configured to acquire acceleration information of the vehicle on the navigation route; the processing module is further configured to correct the driving energy consumption according to the acceleration information.
  • the acceleration information includes one or more of the following items: average acceleration information in the acceleration segment, average deceleration information in the deceleration segment, rapid acceleration ratio information, and rapid deceleration ratio information.
  • correcting the driving energy consumption according to the driving mode and the energy recovery level includes: using the driving energy consumption correction coefficient model to obtain the driving energy consumption correction coefficient according to the driving mode and the energy recovery level;
  • the energy consumption correction factor corrects the drive energy consumption.
  • correcting the driving energy consumption according to the acceleration information includes: using the driving energy consumption correction coefficient model to obtain the driving energy consumption correction coefficient according to the navigation planning acceleration information; Energy consumption is corrected.
  • the driving energy consumption correction coefficient model is established based on the single vehicle data of the vehicle.
  • the acquiring module is further configured to acquire slope information of the navigation path; the processing module is further configured to correct the driving energy consumption according to the slope information.
  • the obtaining module is also used to obtain the status information of the high-voltage accessories of the vehicle; the processing module is also used to obtain the power information of the high-voltage accessories by using the energy consumption model of the high-voltage accessories according to the status information of the high-voltage accessories; the processing module is also used to It is used to calculate the energy consumption of high-voltage accessories according to the power information and estimated time information of high-voltage accessories.
  • the third aspect of the present application provides a computing device, which includes a processor and a memory, where a computer program is stored in the memory, and when the computer program is run by the processor, any one of the methods for estimating the remaining battery power described above is executed.
  • the fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is run by a computer, any one of the methods for estimating the remaining battery power mentioned above is executed.
  • the present application provides a method for establishing a navigation trip energy consumption model, the method including the following content: receiving the historical navigation trip energy consumption related data of the current vehicle (the first vehicle), and the historical navigation trip energy consumption related data is collected by the current vehicle , including the trip energy consumption, driving mode, and energy recovery gear of each round of navigation trip; the driving style energy consumption coefficient model is trained with the energy consumption related data of the historical navigation trip as a sample, and the driving style energy consumption coefficient model is used to represent the current vehicle
  • the corresponding relationship between the driving style and the degree of influence on the energy consumption of the trip, the degree of influence is represented by the energy consumption coefficient of the driving style; wherein, the driving style is at least determined according to the driving mode and the energy recovery gear.
  • the technical solution of the present application considers the impact of driving style on vehicle energy consumption, and establishes a special model for driving personalization. At least the two dimensions of driving mode and energy recovery gear are used as factors to determine driving style, thereby increasing driving efficiency. Style defines simplicity and certainty. Moreover, the model trained by the energy consumption related data of each round of navigation can ensure the accurate expression of the real energy consumption, and quantify the influence of driving style on driving energy consumption in the form of driving style energy consumption coefficient , so that the change of driving energy consumption with driving style can be reflected by a linear relationship.
  • the driving style coefficient can be determined only by the vehicle’s own vehicle condition without considering the acceleration characteristics described in detail below, which is enough to ensure that the driving style coefficient model can predict the energy consumption. The practicability of the estimation, thereby increasing the estimation accuracy of the destination battery SOC of the vehicle navigation trip.
  • the method also includes that the cloud receives road condition information of the navigation itinerary, and the road condition information is opened by the navigation service party, including acceleration characteristics, which are the information provided by the navigation service party on the current vehicle. Acceleration statistics in each round of navigation trip; and, further use road condition information as a sample to train the driving style energy consumption coefficient model
  • the driving style is also determined according to the road type and acceleration characteristics.
  • Road condition information can be obtained based on the map SDK, and driving energy consumption can be integrated into road condition information.
  • the acceleration in the information has a great influence on the prediction accuracy. Therefore, when establishing the driving style energy consumption coefficient model, the acceleration characteristics collected by the navigation service provider in real time are simultaneously considered as another more important dimension to limit the driving style, so that based on the driving style
  • the driving style energy consumption coefficient model of the driving style has a smaller discrimination granularity, which improves the accuracy of the model. Therefore, when using such a driving style energy consumption coefficient model, when more dimensions are matched, the corresponding query driving style energy consumption coefficient will be More reliable, making energy consumption estimation more accurate.
  • the road condition information may also include the road type, which is determined by the navigation service provider according to the road grade; the driving style is also determined according to the road type.
  • various judgment factors road type and acceleration characteristics from the navigation service provider, driving mode from the vehicle end and the arrangement and combination of energy recovery gears can all exist as a driving style , and define their corresponding driving style energy consumption coefficients separately, which further embodies the richness and individualization of driving styles, thereby strengthening the high fitting degree of the driving style coefficient model with driving style as an influencing variable.
  • the model building method also includes iterating the driving style energy consumption coefficient model through reinforcement learning.
  • the establishment of the driving style energy consumption coefficient model is iterated through reinforcement learning, and the corresponding relationship between driving style and coefficients in the driving style energy consumption coefficient model can be gradually corrected according to the navigation itinerary and the accumulation of large data samples in the navigation itinerary.
  • Data training enables the driving style energy consumption coefficient model to timely and truly reflect the slight impact of small changes in driving style on driving energy consumption, so as to improve the accuracy of prediction.
  • Iteration through reinforcement learning further includes: the driving style energy consumption coefficient of the current round of navigation trip adopts Q-table, which is updated on the basis of the driving style energy consumption coefficient of the previous round of navigation trip with the same driving style.
  • the establishment of the driving style energy consumption coefficient model uses the Q-learning algorithm to strengthen the learning.
  • the driving style energy consumption coefficient under the same driving style in the previous round is successively corrected and used as the same as the latest round.
  • the driving style energy consumption coefficient of the driving style is used for estimating energy consumption and battery SOC, and is used as one of the parameters in the next iteration.
  • This iterative strategy is relatively simple, the calculation burden is small, and it makes the establishment of the driving style energy consumption coefficient
  • the model does not exclude receiving a wider range of data as samples, which has good scalability.
  • the data related to the energy consumption of historical navigation trips can also include the estimated SOC and real SOC of each round of navigation trips; the driving style energy consumption coefficient of this round is determined by the driving style energy consumption coefficient and the previous The unit distance absolute error SOC of the round navigation trip is calculated; wherein, the unit distance absolute error SOC is equal to the absolute error between the last round of estimated SOC and the last round of real SOC divided by the distance of the last round of navigation trip.
  • the unit distance absolute error SOC is used as the iterative change amount.
  • the energy consumption error is corrected with the absolute error SOC per unit distance, so that the prediction accuracy is kept within a certain range.
  • the driving style energy consumption coefficient of the first round of navigation trips with the same driving style is initialized to 1.
  • the driving style energy consumption coefficient of the first round of navigation trips with the same driving style is initialized to 1, so that the initial value of the coefficient in each round of navigation trips under the same driving style is minimized, and the amount of iterative calculation is reduced.
  • the historical low-voltage accessory-related energy consumption data of each vehicle of the same model is received as a sample.
  • the historical low-voltage accessory-related energy consumption data includes the status of the low-voltage accessory and the power of the low-voltage accessory, and the low-voltage accessory includes the DC-DC of the vehicle.
  • Electrical appliances establish a low-voltage accessory energy consumption model, which is used to represent the corresponding relationship between the status of the low-voltage accessory and the power of the low-voltage accessory.
  • this application further considers the energy consumption of low-voltage accessories, and establishes a special low-voltage accessory energy consumption model for the energy consumption of low-voltage accessories to include the energy consumption values corresponding to the status of each accessory, so as to ensure the Comprehensive analysis and accurate estimation of energy consumption.
  • the data related to the energy consumption of historical navigation trips also includes the speed of the frequently-traveled routes in the navigation trip.
  • the starting point and destination in the commuting settings provided by the party are determined; the driving energy consumption model of the frequent route is established, and the energy consumption model of the frequent route driving is used to represent the correspondence between the current vehicle speed of the frequent route and the driving energy consumption of the frequent route relation.
  • the embodiment of the present application also provides a method for estimating battery SOC of a navigation itinerary, which includes the following content: according to the navigation itinerary, the road condition information of each road section is obtained, and the road condition information of each road section includes the distance of each road section and its vehicle speed;
  • the driving energy consumption model and the driving style energy consumption coefficient model obtained by any of the above-mentioned methods for establishing the energy consumption model of the navigation trip, wherein the multiple driving energy consumption models are established by using the data related to the driving energy consumption of each vehicle of the same model as a sample,
  • the plurality of driving energy consumption models at least include a driving energy consumption model, and the driving energy consumption model is used to represent the relationship between the vehicle speed of each navigation trip of each vehicle of the same model and the driving energy consumption of each navigation trip of the vehicle.
  • Correspondence query the driving style energy consumption coefficient corresponding to the driving style of the current vehicle in the driving style energy consumption coefficient model; and query the driving style corresponding to the road condition information of each road section in multiple driving energy consumption models
  • the query value of energy consumption based on the distance of each road section and using the driving style energy consumption coefficient and the query value of various driving energy consumption, calculate the estimated energy consumption value of the current vehicle for the whole journey; and use the estimated total energy consumption value to calculate the battery SOC, the estimated total energy consumption value at least includes the estimated energy consumption value of the whole journey.
  • the driving style energy consumption coefficient model provided by this application is included in the energy consumption estimation range, so that the battery SOC estimation scheme of the batteries between the vehicles of the same model is not only applied to the general energy consumption
  • the model is also applied to a personalized and dynamically adjustable energy consumption model, which fully considers the difference in energy consumption brought about by the driver or user's personal driving behavior of each vehicle, so that every navigation in the future will During the trip, it can effectively improve the estimation accuracy of the remaining battery power.
  • the estimated energy consumption value of the whole driving is the sum of the estimated energy consumption values of the segmented driving of each road segment, wherein the segmented driving estimated energy consumption of each road segment
  • the energy consumption value is the product of the segmental driving energy consumption value of the road segment, the distance of the road segment and the driving style energy consumption coefficient; where the segmental driving energy consumption value is calculated by using the query values of various driving energy consumptions.
  • the driving style energy consumption coefficient of this application is used as a quantitative expression of the impact on driving energy consumption.
  • it is equivalent to adding restrictions on the basis of general driving energy consumption, so that the driving energy consumption in the navigation itinerary
  • the energy consumption estimation has a more realistic performance, thereby improving the estimation accuracy of the destination battery SOC.
  • the multiple driving energy consumption models also include an additional energy consumption model, which is used to represent the road gradient and the driving force of each vehicle of the same model.
  • the road slope will become an important factor affecting driving energy consumption. Therefore, in addition to the driving energy consumption coefficient, the energy consumption should also be considered The deviation effect of the increase on the ideal driving energy consumption trend, so as to ensure that the estimated energy consumption of vehicles driving under complex road conditions is close to the real energy consumption on the whole, so as to accurately estimate the power consumption of the battery, and then calculate Correct battery SOC.
  • the method further includes the following steps: obtaining the whole estimated time according to the navigation trip;
  • General accessories are vehicle components that consume electricity for operations other than driving.
  • the energy consumption model of general accessories is used to represent the corresponding relationship between the state of general accessories and the power of general accessories of each vehicle of the same model;
  • general accessories The energy consumption model includes the high-voltage accessory energy consumption model, which is used to represent the corresponding relationship between the high-voltage accessory state and the high-voltage accessory power of each vehicle of the same model; query the general accessory energy consumption model and the current vehicle The query value of the accessory power corresponding to the status of the accessory;
  • the estimated total energy consumption value also includes the energy consumption of the accessory in the whole process, and the energy consumption of the accessory in the whole process is the product of the estimated time of the whole process and the query value of the accessory power.
  • the consumption of battery energy by each accessory on the vehicle cannot be ignored. While the aforementioned driving energy consumption coefficient participates in estimating the type of energy consumption of driving energy consumption, the energy consumption of the accessories in the whole process corresponding to the status of the accessories in the current vehicle's navigation journey is considered. , improve the accuracy of destination battery SOC estimation, improve the certainty of vehicle users on the whole process of power consumption, and alleviate the mileage anxiety of vehicle users.
  • the general accessory energy consumption model also includes a low-voltage accessory energy consumption model; the query value of the accessory power is equal to the sum of the high-voltage accessory power and the low-voltage accessory power.
  • This application establishes the corresponding relationship between the energy consumption of the low-voltage accessories widely distributed in the vehicle and the energy consumption under the combination of different accessory states, so as to ensure the comprehensive analysis of energy consumption and the accurate estimation of battery SOC, and the way of vehicle-cloud information fusion , to obtain the real energy consumption to calculate the cruising range.
  • the navigation trip is a frequently traveled route; multiple driving energy consumption models also include a frequently traveled route driving energy consumption model; query the frequently traveled route driving energy consumption model The query value of driving energy consumption of the conventional route corresponding to the speed of the frequently-traveled route; based on the distance of each road section and using the driving style energy consumption coefficient and the query value of driving energy consumption of the conventional route, calculate the estimated energy consumption of the current vehicle for the entire journey value.
  • the number of data samples is significantly larger than that of other navigation itineraries, so that the energy consumption estimation is more accurate than that of the non-traveling route, and better reflects the effectiveness of battery SOC prediction. Help to improve user experience.
  • the embodiment of the present application also provides a device for establishing a navigation trip energy consumption model, including: a receiving unit, configured to receive the historical navigation trip energy consumption related data of the current vehicle as a sample, and the historical navigation trip energy consumption related data is obtained by the current vehicle Acquisition, including trip energy consumption, driving mode and energy recovery gear of each round of navigation trip; model building unit, used to train the driving style energy consumption coefficient model with the historical navigation trip energy consumption related data as a sample, and the driving style energy consumption coefficient
  • the model is used to represent the corresponding relationship between the driving style of the current vehicle and the degree of influence on the trip energy consumption, and the degree of influence is represented by the driving style energy consumption coefficient; wherein, the driving style is at least determined according to the driving mode and the energy recovery gear.
  • the receiving unit is also used to receive road condition information of the navigation itinerary.
  • the road condition information is opened by the navigation service party and includes acceleration characteristics. Acceleration statistical value in each round of navigation journey of the vehicle; the model building unit is further used to use road condition information as a sample to train the driving style energy consumption coefficient model, and the driving style is also determined according to the acceleration characteristics.
  • the road condition information also includes a road type, which is determined by the navigation service provider according to the road grade; and the driving style is also determined according to the road type.
  • the establishing device further includes an iteration unit, configured to iterate the driving style energy consumption coefficient model through reinforcement learning.
  • the iterative unit is further used to use the Q-table to update the current round based on the driving style energy consumption coefficient of the previous round of navigation trip with the same driving style Driving style energy consumption factor for navigation trips.
  • the historical navigation trip energy consumption related data also includes the estimated SOC and the real SOC of each round of navigation trip;
  • the driving style energy consumption coefficient of one navigation trip and the unit distance absolute error SOC of the previous round of navigation trip are used to calculate the driving style energy consumption coefficient of the current round; where the unit distance absolute error SOC is equal to the estimated SOC of the previous round and the previous round The absolute error of the true SOC of a round divided by the distance of the previous round of navigation travel.
  • the iteration unit is configured to initialize the driving style energy consumption coefficient of the first round of navigation trips with the same driving style to 1.
  • the receiving unit is also used to receive historical low-voltage accessory-related energy consumption data of each vehicle of the same model as a sample, and the historical low-voltage accessory-related energy consumption data includes low-voltage accessories.
  • the state of the accessory and the power of the low-voltage accessory, the low-voltage accessory includes the DC-DC electrical appliances of the vehicle;
  • the model building unit is also used to establish the energy consumption model of the low-voltage accessory, and the energy consumption model of the low-voltage accessory is used to represent the correspondence between the state of the low-voltage accessory and the power of the low-voltage accessory relation.
  • the receiving unit is also used to receive the vehicle speed of the frequently-traveled route in the navigation trip, and the trip energy consumption of each round of navigation trip includes the driving energy consumption of the frequently-traveled route, The frequently traveled route is determined based on the departure place and destination in the commuting settings provided by the navigation service provider; the model building unit is also used to establish a frequently traveled route driving energy consumption model, and the frequently traveled route driven energy consumption model is used to represent the current vehicle The corresponding relationship between the vehicle speed of the frequently-traveled route and the driving energy consumption of the frequently-traveled route.
  • FIG. 1 is an explanatory diagram of an application scenario of the present application.
  • Fig. 2 is an explanatory diagram of vehicle factors that affect driving energy consumption considered in the embodiment of the present application
  • Fig. 3 is a schematic diagram of an SOC estimation process involved in an embodiment of the present application.
  • FIG. 4 is a schematic diagram of an SOC estimation process involved in another embodiment of the present application.
  • FIG. 5 is a schematic flowchart of a battery SOC estimation method involved in an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of a battery SOC estimation device involved in an embodiment of the present application.
  • Fig. 7 is a schematic structural diagram of an electronic control unit provided in an embodiment of the present application.
  • the driving energy consumption is estimated according to the vehicle speed information and distance information in the navigation information, according to
  • the estimated travel time in the navigation information and the state of the air conditioner (high-voltage accessory) estimate the energy consumption of the high-voltage accessory, estimate the total energy consumption based on the obtained energy consumption and the energy consumption of the high-voltage accessory, and calculate the remaining battery power when arriving at the destination based on this.
  • the embodiment of the present application provides a battery SOC estimation method and device, etc., which more comprehensively consider the energy consumption of each link of the vehicle and its influencing factors, and then establish a variety of Energy consumption models, and by combining these energy consumption models, the destination battery SOC for the entire navigation process is finally calculated.
  • FIG 1 is an explanatory diagram of an application scenario of the embodiment of the present application.
  • the vehicle when the driver sets the destination, the vehicle provides a navigation route according to the driver's operation, and at the same time, uses the battery described in detail later
  • the SOC estimation method calculates the battery SOC when arriving at the destination (destination), and displays it (“25%”) at the destination position on the map.
  • the technical solution of the present application can be applied in the vehicle-to-cloud (V2C, Vehicle to Cloud) interaction of the Internet of Vehicles.
  • Vehicle-cloud interaction is a communication technology for data transmission between the vehicle terminal and the cloud, which can be realized by means of cellular mobile communication or the Internet.
  • the vehicle end provides the cloud with data reflecting vehicle operating characteristics.
  • the data of vehicle operating characteristics is sensed, collected and processed by various on-board sensor devices. These data include but are not limited to vehicle driving data, vehicle status, driving operation behavior, and vehicle environment data.
  • vehicle driving data may include but not limited to, for example, vehicle speed, etc.; vehicle status may be the switch status of electric appliances such as lights and wipers, battery SOC, etc.; driving operation behavior may be the driver’s driving preset mode for the vehicle, including Driving mode, energy recovery gear and so on.
  • the vehicle environment data is about the vehicle environment data, such as ambient temperature, light, rain and snow, etc.
  • the energy consumption-related data in the vehicle operating characteristics include actual energy consumption values and influencing factors attributed to influence energy consumption.
  • the energy consumption model is created by using the corresponding relationship between quantified influencing factors (or called influencing variables) and actual energy consumption.
  • the cloud serves as a platform to receive data on vehicle operating characteristics including the above-mentioned energy-related data from the vehicle end, and can further combine map navigation routes or traffic big data to provide information services, calculations, decision-making and other functions based on artificial intelligence algorithms.
  • Driving energy consumption is the energy consumed when the vehicle is running, and general accessories are all vehicle components that consume electricity for operations other than driving. Therefore, the energy consumption of general accessories is All of these vehicle components are energy consumed in operation.
  • the general accessory energy consumption includes high-voltage accessory energy consumption and low-voltage accessory energy consumption.
  • the driving energy consumption can include the driving energy consumption when the vehicle drives itself, and can be regarded as the driving energy consumption in an ideal state, and its main influencing factor is the vehicle speed.
  • the faster the vehicle speed the higher the driving energy consumption.
  • driving energy consumption also includes additional energy consumption, and the main influencing factors can include vehicle weight, tire pressure, road gradient, wind resistance, road type, etc., as an increase or decrease on the basis of driving energy consumption .
  • the greater the uphill slope the greater the additional energy consumption; the greater the self-weight, the greater the additional energy consumption; the greater the wind resistance, the greater the additional energy consumption; and vice versa.
  • the driving style can be determined according to the acceleration characteristics.
  • the acceleration reflects the intensity of the driver's operation of the accelerator pedal, and the deceleration reflects the intensity of the driver's braking.
  • the greater the absolute value of the acceleration or deceleration the greater the driving style.
  • the acceleration feature is used as a key feature to determine the driving style.
  • the driving style can also be determined according to the preset driving mode.
  • the preset driving mode can be a combination of driving mode and energy recovery gear, wherein, the driving mode is from high to low according to the degree of intensity, for example, including but not limited to sports mode, economy mode, comfort mode, etc.; energy recovery gears can be divided into high, medium and low gears, the higher the energy recovery gear, the milder the driving style.
  • the driving style can also be determined in combination with the road type.
  • the road type is determined by the navigation service provider according to the road grade. For example, according to the speed limit grade, it can be divided into expressway, town road, country road, asphalt road, etc. or according to the road quality grade. Divided into hard roads, gravel roads, muddy roads, etc.
  • the factors for judging the driving style will be determined according to the type of collected vehicle operation characteristic data and/or the authority to obtain road condition information in the navigation trip. For details, please refer to the description of the embodiment below.
  • the energy consumption of high-voltage accessories also known as thermal management energy consumption, refers to the energy consumed by thermal management accessories such as air conditioners, high-pressure heaters, and high-pressure defrosters when they are started and used.
  • factors affecting the energy consumption of the high-voltage accessories of the vehicle include but are not limited to ambient temperature, air conditioner switch, air conditioner temperature, and the like.
  • the energy consumption of the high-voltage accessories in the car mainly comes from the starting and operating power of the air conditioner, and the power of the air conditioner is closely related to the set temperature of the air conditioner and the difference between the set temperature of the air conditioner and the ambient temperature.
  • the set temperature of the air conditioner (or called the target temperature ) and the ambient temperature, the greater the energy consumption of the high-voltage accessories.
  • the energy consumption of low-voltage accessories it is the energy consumed by the 12V or 24V load or electrical appliances that convert the vehicle's high-voltage direct current into low-voltage direct current (DC-DC) when starting and using.
  • the influencing factors affecting the energy consumption of the low-voltage accessories of the vehicle come from the activation and possible adjustment of various low-voltage accessories.
  • the influencing factors of the energy consumption of the low-voltage accessories include but are not limited to, and/or marker lamp switch, driver and/or co-driver seat heating switch, ventilation switch, speaker switch, wiper switch, electric sunroof, etc.
  • various types of energy consumption models are respectively established by the cloud based on historical driving data and environmental data related to energy consumption of multiple vehicles of the same model, as well as actual energy consumption.
  • the navigation service party plans the navigation itinerary according to the current location of the current vehicle or the information of the departure place and destination, and queries the energy consumption through the vehicle operation characteristic data of the current vehicle model, obtain multiple query values in various energy consumption models, and then modify the query values in combination with route and road condition information in the navigation itinerary, so as to calculate the energy consumption value of the whole journey to the destination by road sections, so as to The available battery state of energy (SOE, State of Energy), the available remaining power percentage (SOC, State of Charge) and the energy consumption value of the whole process are used to calculate the battery SOC of the destination.
  • SOE State of Energy
  • SOC State of Charge
  • the application provides a battery SOC estimation method for a navigation trip, comprising the following steps: obtaining road condition information of each road section according to the navigation trip, the road condition information of each road section including the distance of each road section and its vehicle speed; calling a plurality of driving energy consumption models and the driving style energy consumption coefficient model, wherein multiple driving energy consumption models are established using the driving energy consumption related data of each vehicle of the same model as a sample, and the multiple driving energy consumption models at least include a driving energy consumption model, and a driving energy consumption model It is used to represent the correspondence between the vehicle speed of each round of navigation trip of each vehicle of the same model and the driving energy consumption of each round of navigation trip of the vehicle; query the driving style energy consumption coefficient model and the driving of the current vehicle The driving style energy consumption coefficient corresponding to the style; and the query value of each driving energy consumption corresponding to the road condition information of each road section in multiple driving energy consumption models; based on the distance of each road section and using the driving style energy consumption Coefficients and various driving energy consumption query values, calculate the estimated
  • the navigation service is usually provided by the server of the navigation service provider or the map service provider (hereinafter also referred to as the map terminal), including route planning and road condition information, specifically, optionally according to the settings of the user or driver of the vehicle,
  • the route of the navigation itinerary is planned by way of sections.
  • the navigation information in the navigation itinerary includes but not limited to the distance of the whole journey, the estimated time of the whole journey, etc.
  • the traffic information in the navigation itinerary includes but not limited to the distance of each road section, the average speed of each road section, Road type, road slope, etc. However, due to reasons such as data security and service permissions, the traffic information of the navigation service provider may not be open to the car end or the cloud.
  • the car end and the cloud have the authority to call or obtain the map software development kit (SDK, Software Development Kit) It will lead to different types of data samples received by the cloud to establish the energy consumption model, so that the knowledge of the road condition information, which is indispensable when estimating the destination battery SOC, is able to perform calculations based on the energy consumption model, that is to say , when the car end or the cloud has no right to obtain the map SDK, the estimated destination battery SOC is implemented by the navigation service provider. When the car end or the cloud has the right to obtain the map SDK, the estimated destination battery SOC can be estimated by the car end or the cloud. to implement.
  • SDK Map software development Kit
  • FIG. 2 is an explanatory diagram of vehicle factors that affect driving energy consumption considered in the embodiment of the present application.
  • driving energy consumption mainly includes three parts: driving energy consumption (energy consumption required to drive the vehicle), high-voltage accessories (mainly air-conditioning) energy consumption, and low-voltage accessories energy consumption.
  • Factors affecting driving energy consumption include vehicle speed, road gradient, road system, driving style, traffic conditions, wind resistance, weather, etc.
  • Factors affecting the energy consumption of high-voltage accessories include ambient temperature, air conditioner switch, and air conditioner set temperature.
  • Factors affecting energy consumption of low-voltage accessories include headlight switch, seat heating/ventilation switch, speaker switch, wiper switch, etc.
  • the voltage of the low-voltage accessory is generally 12V
  • the low voltage of the high-voltage accessory is generally greater than 12V (such as 380v).
  • the samples used for establishing the energy consumption model do not include route segment information and road condition information.
  • Figure 3 shows the interaction process between the map terminal (navigation module) and the vehicle terminal (vehicle machine controller or cockpit domain controller, etc.) and the cloud when estimating the battery SOC in this embodiment without using the map SDK .
  • the map terminal and multiple vehicles are shown independently, however, the map terminal is installed on each vehicle. It can be understood that the map terminal (navigation module) can be integrated in the vehicle-machine controller and the cockpit domain controller, etc.
  • Step S10 all vehicles of the same model, such as car 1, car 2 ...
  • step S12 the cloud is based on big data training, establish and iteratively update the general energy consumption model for vehicles of the same type, and establish a driving style energy consumption coefficient model for each single vehicle;
  • step S13 the current vehicle such as car 1 or car 2 initiates a navigation request, and the map terminal determines the navigation trip details but does not Open road condition information;
  • step S14 send the energy consumption model to the current vehicle such as car 1 or car 2 so as to be called by the map terminal or directly sent to the map terminal;
  • step S15 send the current vehicle's accessories including high-voltage accessories and low-voltage accessories
  • the status is sent to the map terminal, and the map terminal invokes the energy consumption model and receives the status information of the accessories, and queries the corresponding query value in each energy consumption model according to the current vehicle accessory status;
  • S16 The map terminal combines the road section and road condition information on the navigation itinerary To calculate the energy consumption of each road section, and finally estimate the battery SOC
  • the on-board system of each vehicle collects all data related to energy consumption, including but not limited to data related to driving energy consumption, such as road gradient, average vehicle speed, driving energy consumption, etc.; data related to thermal management energy consumption, such as ambient temperature, air conditioner switch Status, set temperature, high-voltage energy consumption, etc.; low-voltage accessory energy consumption related data, low-voltage accessory switch status, low-voltage accessory mode status, DCDC low-voltage accessory energy consumption; in addition, data related to energy consumption also includes attitude axis acceleration, the attitude axis Acceleration is the raw data from which the acceleration characteristics are calculated.
  • Vehicle energy consumption-related data can come from the following on-board components: body electronic stability system (ESP, Electronic Stability Program); motor controller (MCU, Motor Control Unit); cockpit domain controller (CDC, Cockpit Domain Controller); low voltage (DC-DC) accessory system; battery management system (BMS, Battery Management System); thermal management system (TMS, Thermal management system); power domain control unit (PDCU, Powertrain Domain control Unit); and car box T-Box, T-Box includes a sensor module.
  • the attitude axis sensor in the sensor module perceives the acceleration of the attitude axis. For example, the acceleration of the vehicle can be collected.
  • the T-Box includes a satellite positioning system, it can position the vehicle.
  • the vehicle control unit obtains the energy consumption related data of each vehicle component, and performs preprocessing to convert the original power signal into energy consumption per kilometer, such as converting the air conditioner power signal into energy consumption per kilometer Energy consumption, or the average value of setting time, to reduce the upload of invalid data. Then upload the pre-processed vehicle-side energy consumption-related data to the cloud via the gateway and T-box, and the T-box can also upload the vehicle location information to the cloud.
  • the navigation trip energy consumption model building device can be set in the cloud, including a receiving unit and a model building unit.
  • the receiving unit receives energy-related data provided by the VCU of each vehicle of the same model, including but not limited to driving energy-related data, general accessories Energy consumption-related data
  • the model building unit takes the energy consumption-related data as a sample, and establishes a variety of general energy consumption models applicable to all vehicles of the same model.
  • the various energy consumption models include drive energy consumption models, high-voltage accessories
  • the energy consumption model (or thermal management energy consumption model), the low-voltage accessory energy consumption model and the additional energy consumption model, the drive energy consumption model and the additional energy consumption model all belong to the driving energy consumption model.
  • the receiving unit also receives the energy consumption related data of each single vehicle’s historical navigation trip, and the model building unit uses the energy consumption related data of each single vehicle’s historical navigation trip as a sample to establish a single current vehicle’s personalized navigation trip.
  • Energy consumption models such as driving style energy consumption coefficient models. The establishment process of each energy consumption model will be described in detail below.
  • the driving energy consumption model includes but is not limited to the driving energy consumption model, and also includes an additional energy consumption model that has an additional increase or decrease in driving energy consumption, the following point 5 Detailed instructions will be given.
  • the driving energy consumption model here is established based on the data of multiple vehicles of the same model as a sample, and the same driving energy consumption model is established for all vehicles of the same model, and the driving energy consumption model is the corresponding average unit of the influencing variable—average vehicle speed V Drive energy consumption E (the unit drive energy consumption can also be called the drive energy consumption rate, the physical meaning is the power consumption per unit distance), specifically in the form of the following table 1:
  • V can be different values based on different designs, and the interval is also variable.
  • vehicle speed values in Table 1-2 above may also be values in km/h.
  • An average vehicle speed range corresponds to an average driving energy consumption
  • the driving energy consumption model is established in tabular form.
  • Table 1 the range of V1-V2 corresponds to the average driving energy consumption of E1.
  • the database ID of the driving energy consumption model can be numbered according to different ranges of the average vehicle speed.
  • the current vehicle can correspondingly query the average driving energy consumption E in the driving energy consumption model according to its own vehicle speed on the current road section, so as to determine the value of the average driving energy consumption of the current vehicle on the current road section.
  • the driving energy consumption model represents the corresponding relationship between the average speed and the average driving energy consumption, but it can be understood that the value is not limited to the average value, and the median value can also be considered. Both the median and the average are statistically significant parameters, and the present application is not limited to these two, and all features with statistically significant parameters can be used.
  • the driving style of each vehicle user will present personalized driving characteristic data, the impact of vigorous or moderate driving on energy consumption is very different, and the driving style can be judged by key characteristic quantities of acceleration, such as the average acceleration value
  • the more aggressive driving style the more frequent the acceleration.
  • the more aggressive driving style the energy consumption is higher than the average driving energy consumption;
  • the consumption should be low.
  • Establish a separate driving style energy consumption coefficient model for each vehicle which can add personalized considerations in driving style on the basis of the general driving energy consumption model for all vehicles, and can make the specific data of a single car used for correction And process general-purpose driving energy consumption models to increase the accuracy of energy consumption estimates for each vehicle.
  • the map SDK may not be used, and the average acceleration of the acceleration section and the average deceleration of the deceleration section in the navigation itinerary section may not be sent to the cloud as data samples to establish a driving style energy consumption coefficient model, or the cloud may not start from
  • the map side obtains the road condition information of the navigation trip, so in the establishment of the driving style energy consumption coefficient model, the navigation trip energy consumption model establishment device also includes an iterative unit, which is used to indirectly use the reinforcement learning Q-table to correct the driving style under the same driving style.
  • the driving energy consumption coefficient dynamically corrects the driving style energy consumption coefficient in an iterative (or called filtering) manner, and fills in the corresponding information in the following learning table for each iteration, and the battery SOC result of one navigation to the destination corresponds to one iteration.
  • the determination of the driving style selects the driving mode and the energy recovery gear as judging factors, and when the driving mode and the energy recovery gear are respectively the same, it is determined that the driving style is the same.
  • the driving style energy consumption coefficient under various combinations of driving mode and energy recovery gear is initially set to 1 and is also recorded in the learning table as an iteration round.
  • the new navigation journey is taken as the current round, and the SOC absolute error per kilometer and the driving energy consumption coefficient of the same driving mode and energy recovery gear of the previous round are used to calculate the driving style energy consumption coefficient of the current round.
  • the specific calculation method is:
  • the energy consumption coefficient of the driving style of the current wheel the energy consumption coefficient of the same style of the previous wheel*(1+ ⁇ ) (Formula 1)
  • ⁇ is the absolute error of SOC per kilometer based on the same driving mode and energy recovery gear in the previous round.
  • S estimate is the estimated battery SOC to the destination in the previous round
  • S is the actual battery SOC to the destination in the previous round
  • K previous round is the total distance of the whole navigation trip in the previous round.
  • the driving mode is divided into sports mode, economical mode and comfort mode, and the energy recovery gear is divided into high, medium and low gears, so that driving mode and energy recovery gear can form 9 different combinations, namely 9
  • the energy consumption coefficients of the driving styles under these combinations are all initially set to 1, and recorded as the number of rounds respectively, so as to facilitate the search for the information of the corresponding number of rounds in subsequent iterative rounds.
  • the driving mode used by the current vehicle user is the sports mode, and the energy recovery gear is high
  • the driving style energy consumption coefficient of the 9th round is equal to 1
  • the driving energy consumption coefficient of the 0th round is 1
  • the energy consumption coefficient of the 10th round is determined by the same driving Mode and energy recovery gear combination corresponding to the driving style energy consumption coefficient of the 9th wheel to filter, that is, the driving style energy consumption coefficient of the 10th wheel is equal to the driving style energy consumption coefficient of the 9th wheel and (1+(S9 The product of -S99)/K9).
  • the energy consumption coefficient of the driving style of the 11th round is equal to that of the previous round, that is, The product of the driving style energy consumption coefficient of the first round and (1+(S1-S11)/K1).
  • the energy consumption coefficient of the 12th round of driving style is equal to that of the previous round, that is, the 2nd round.
  • thermal management energy consumption mainly depends on the operation of high-voltage accessories, so the thermal management energy consumption model can also be called the high-voltage accessory energy consumption model.
  • the thermal management energy consumption is an influencing variable, such as the thermal management power Pheat corresponding to the state of high-voltage accessories such as the state of the air conditioner switch, the ambient temperature outside the vehicle, the temperature value setting of the main driver, the temperature value setting of the co-driver, etc.
  • the specific form is:
  • Air conditioner switch status Ambient temperature outside the car Driver temperature setting Passenger temperature setting Thermal management power (W) 3 open T ring T main setting T auxiliary equipment P hot 1 3 open T ring T main setting none P hot 2 3 open T ring none T auxiliary equipment P hot 3 3 close T ring none none P hot 4 3 ... ... ... ... ... ...
  • the switch status of the air conditioner has two states: on and off.
  • the ambient temperature T ring outside the car, the set temperature T of the main driver and the set temperature of the co-driver can be discrete values according to the arithmetic difference, or they can be installed in different ranges. set up. Each combination of each influencing variable with other influencing factors corresponds to a thermal management power.
  • the samples of the high-voltage accessory energy consumption model used in this embodiment are mainly built around the air conditioner, but the high-voltage accessories include not only the air conditioner, but also the status of other high-voltage electrical appliances in vehicles, such as high-voltage heaters, high-voltage defrosters, etc.
  • the switch, set temperature, etc. can be further used as the influencing factors of the energy consumption model of high-voltage accessories.
  • thermal management energy consumption models Some specific numerical examples of thermal management energy consumption models are given in the table below.
  • the low-voltage accessory energy consumption model is also established based on the historical low-voltage accessory-related energy consumption data of each vehicle of the same model. All vehicles of the same model build the same low-voltage accessory energy consumption model. Direct current accessories (DCDC) will generate energy consumption, and can be classified as low-voltage accessory energy consumption.
  • DCDC Direct current accessories
  • the receiving unit of the navigation trip energy consumption model establishment device receives the historical low-voltage accessory-related energy consumption data of each vehicle of the same model, and the model building unit sets the low-voltage accessory energy consumption model as the main low-voltage accessory state as an influencing variable ——For example, the state of the main driver's seat heating switch, the position of the main driver's seat heating, the state of the passenger's seat heating switch, the position of the passenger's seat heating, the state of the low beam light, the state of the high beam light, and the state of the marker lights
  • the specific form is:
  • Low-voltage accessories can also include many electronic control devices controlled by Body Controller Module (BCM), including far and near lights, position lights, turn signal lights, horns, windows and doors, defrosters, wipers, Speakers and more.
  • BCM Body Controller Module
  • a part of low-voltage accessory states that have a more significant impact on energy consumption are provided as data samples.
  • Table 4 all combinations of states of a plurality of low-voltage accessories are used (Table Not all combinations are listed in ) to establish the energy consumption model of low-voltage accessories by corresponding low-voltage accessory power.
  • Plow in KW
  • the additional energy consumption model is established based on the data of multiple vehicles of the same type, and the same additional energy consumption model is established for all vehicles of the same type.
  • the additional energy consumption model is an influencing variable—average road gradient (%)—corresponding
  • the increase in energy consumption Aux based on the road gradient of 0%, is based on the historical data of multiple vehicles, and the relationship between the average road gradient and the average driving energy consumption of all vehicles of the same model is calculated, as shown in the following table for details:
  • the road slope is identified and processed by the slope sensor on the vehicle. When it is negative, it means downhill, and when it is positive, it means uphill. Therefore, the energy consumption increase Aux in the table can be positive or negative.
  • the corresponding relationship between the change of the road gradient and the increase in driving energy consumption can be listed with 1% as the gradient difference, or can be listed with other gradient differences according to the requirements of the model fitting degree.
  • the road slope of each road segment in the road condition information can be queried from Table 5 to obtain the query value corresponding to the increase of the road slope of each road segment.
  • the additional energy consumption model can also be a combination of multiple influencing variables of road slope, vehicle weight, and tire pressure. Energy consumption increases corresponding to various combinations. In this embodiment, only the slope of the road that has a significant impact on additional energy consumption is selected as an influencing variable to model, so as to reduce the calculation burden and ensure the accuracy of overall driving energy consumption estimation to the greatest extent.
  • the map terminal plans the navigation itinerary according to the departure and destination of the vehicle terminal. Since the cloud cannot obtain the road condition information and thus cannot combine the current path traffic information and the above-mentioned models to estimate the battery SOC from the departure point to the destination, the map terminal (navigation device, navigation module) is responsible for the estimation in this embodiment. Battery SOC.
  • the cloud sends multiple energy consumption models to the cockpit domain controller (CDC, Cokpit Domain Controller) of the current vehicle.
  • CDC is responsible for querying the high-voltage accessory energy consumption model and the low-voltage accessory energy consumption model according to the current vehicle's high-voltage accessory status and low-voltage accessory status to obtain the high-voltage accessory power and low-voltage accessory power, and is responsible for querying the current vehicle
  • the driving style energy consumption coefficient and can also convert the power query value into the energy consumption per kilometer, and then the CDC at the car end, together with the driving energy consumption model and the additional energy consumption model, will use the driving style energy consumption coefficient, the currently available remaining energy SOE (unit is KWH), currently available battery SOC (unit is %), and accessory power including thermal management power and low-voltage accessory power are sent to the map terminal together.
  • the map terminal receives the current vehicle's driving style energy consumption coefficient, accessory power, driving energy consumption model and additional energy consumption model, etc., and stores them as configuration files, combined with the road condition information of the road sections from the departure point to the destination, such as each road section.
  • the average vehicle speed and the road gradient of each road section are used to obtain the average driving energy consumption and energy consumption increase corresponding to each road section, calculate the remaining power to the destination, and then present it on the map interface of the car.
  • the cloud queries relevant models according to the request of the current vehicle CDC to obtain the driving style coefficient corresponding to the current vehicle and the general accessory power corresponding to its accessory status; the map side directly calls the cloud information about the current vehicle according to the request of the current vehicle CDC Driving style coefficient and general accessory power, as well as directly calling the driving energy consumption model and additional energy consumption model, etc., and storing them as configuration files, and querying each model according to the high-voltage, low-voltage accessory status and vehicle information sent by the current vehicle to obtain Corresponds to the energy consumption query value, energy consumption coefficient value and power query value of the current vehicle, and converts the power query value into energy consumption per kilometer, and then combines the current road condition information from the starting point to the destination to calculate the destination to send to the map interface of the car.
  • the high-voltage accessory energy consumption model and the low-voltage accessory energy consumption model according to the current vehicle accessory status or query the driving style energy consumption coefficient model according to the current vehicle ID, which is not limited to the implementation subject, it can be the vehicle terminal, the cloud, or a map terminal, which is not limited in this embodiment.
  • the map terminal plans the navigation route from the starting point to the destination and determines the road condition information of each section on the navigation route of the current round, and obtains the total distance (total distance) L and the estimated time T of the whole journey according to the navigation route.
  • the road condition information of the road section includes the distance (distance) l i of each road section, the average speed v i of each road section, and the slope i of each road section, where i is the number of each road section in the whole navigation process.
  • the map side invokes multiple driving energy consumption models including driving energy consumption models and additional energy consumption models, driving style energy consumption coefficient models, and general accessory energy consumption models including high-voltage accessory energy consumption models and low-voltage accessory energy consumption models, according to For the average speed v i of each section along the way, query the driving energy consumption model in the configuration file to obtain the query average driving energy consumption E i of each section; according to the slope i of each section, query the additional energy consumption model in the configuration file to obtain the query Additional energy consumption increase Aux i ; and read the accessory energy consumption query value and driving style energy consumption coefficient from the configuration file, the accessory energy consumption query value is equal to the high-voltage accessory energy consumption query value (power) P heat and the low-voltage accessory energy consumption query The sum of the value (power) P low .
  • Estimated total energy consumption value estimated energy consumption value of the whole journey + energy consumption of accessories for the whole journey (Formula 5)
  • the total distance (total distance) L of the whole journey and the distance li of each road section correspond to the distance information in this application
  • the estimated time T of the whole journey corresponds to the estimated time information in this application.
  • the battery SOC estimation method provided in the embodiment of the present application can be applied to a vehicle domain controller (Vehicle Domain Controller, VDC) or a smart cockpit domain controller (Cockpit Domain Controller, CDC). Applied to the controller of BMS, etc.
  • VDC Vehicle Domain Controller
  • CDC Clickpit Domain Controller
  • the data samples used by the device for establishing the energy consumption model of the navigation journey to establish the energy consumption model include, in addition to the driving characteristic data and accessory status collected by the vehicle end as described in the above embodiment, data samples obtained from the map end are also included. Traffic information for the segments of the navigation trip, such as road type and/or acceleration characteristics in the segment.
  • Fig. 4 shows the interaction process between the map terminal, the vehicle terminal and the cloud when estimating the battery SOC when the vehicle terminal uses the map SDK in this embodiment.
  • Step S20 similar to the above-mentioned embodiment, each car of the same model, such as car 1, car 2 ... car n collects driving characteristic data and corresponding energy consumption, and uploads the data after preprocessing the data related to energy consumption to the cloud, but the difference is that in step S21, the map end uploads the road condition information directly to the cloud or via the vehicle end; Energy consumption-related data and corresponding road condition information.
  • the model building unit also uses road condition information as a sample to establish an energy consumption model, including driving energy consumption model, driving style energy consumption model, thermal management Energy consumption model, high-voltage accessory energy consumption model, low-voltage accessory energy consumption model, and additional energy consumption model; step S23, the current vehicle such as car 1 initiates a navigation request; step S24, the map terminal determines the navigation itinerary details of car 1 and releases road condition information to The cloud and/or the car terminal; step S25, send the accessory status, driving characteristics and road condition information of the car 1 to the cloud; step S26, the cloud receives the accessory status, driving characteristic data, and road condition information of the car 1 and inquires based on these information
  • the energy consumption model obtains the energy consumption query values of the current vehicle in order to calculate the energy consumption of each road section, and finally estimates the battery SOC of the destination after the whole navigation, and sends the estimated results to the car terminal and the map terminal ; Step S27, after the vehicle terminal updates the SOC result, it is displayed on
  • the data sample collected for establishing the energy consumption model further includes the road condition information of the navigation trip as an influencing variable, so that the road and vehicle speed statistics data on the map side are also involved in the establishment of the energy consumption model during the model establishment process, expanding the scope of construction.
  • the large data sample size of the model further increases the accuracy of the model and improves the accuracy of predicting battery SOC.
  • the model building method of this embodiment is basically the same as the model building method of the above embodiment, the difference lies in the establishment of the driving style energy consumption coefficient model, and the establishment of the driving style energy consumption coefficient model in this embodiment will be described in detail below.
  • the iterative update of the driving style energy consumption coefficient model in addition to considering the above-mentioned factors to determine the driving style - driving mode, energy recovery gear -, also further consider the road type and acceleration characteristics as The driving style judgment factor, the acceleration feature is the acceleration statistical value of each round of the navigation journey of the current vehicle by the navigation service provider, which can reflect the intensity of the driving style of the driver or user of the current vehicle.
  • the absolute value of the acceleration is large , the driver or user's driving style is more aggressive, and vice versa.
  • the driving style is determined according to the road type and acceleration characteristics in addition to the driving mode and energy recovery gear in the aforementioned embodiments, so that the granularity of the distinction between driving styles is smaller, so that the driving style
  • the fitting degree of the energy consumption model is higher, so that the energy consumption prediction of the navigation trip is more accurate, which in turn makes the SOC prediction more accurate.
  • Acceleration features include, but are not limited to, the average acceleration level of the acceleration section, the average deceleration level of the deceleration section, the ratio of rapid acceleration, the ratio of rapid deceleration, the standard deviation of vehicle speed, and the like. This information is obtained based on the vehicle's historical navigation driving data. Specifically, when the vehicle completes a road section according to the navigation information, these acceleration characteristics and road section information are stored in a memory (either on the vehicle or in the cloud, stored The database can be referred to as the acceleration feature-road section database), for future query.
  • acceleration feature is specifically selected as the driving style judgment factor for establishing the driving style energy consumption model, and those skilled in the art can choose arbitrarily according to practical needs.
  • This article only uses the following possible implementation methods for illustration.
  • a driving energy consumption coefficient model is established for the historical data of each vehicle of the same model and the corresponding road condition information, and the driving energy consumption coefficient model is road type, driving mode, energy The recovery gear, the average acceleration of the acceleration section, and the average deceleration of the deceleration section are all used as the driving energy consumption coefficient of the influencing variables.
  • the average acceleration of the acceleration section refers to taking the average value of the acceleration within the sampling period of the acceleration section, for example, the average acceleration of the acceleration section is in the range of 0 to 2m/s 2 .
  • the average deceleration of the deceleration section refers to taking the average value of the deceleration within the sampling period of the deceleration section, for example, in the range of -2 to 0m/s 2 .
  • the driving mode is divided into sports mode, economical mode and comfort mode
  • the energy recovery gear is divided into high, medium and low gears, so that driving mode and energy recovery gear can form 9 different combinations
  • this embodiment divides the average acceleration of the acceleration section and the average deceleration of the deceleration section into multiple grades according to the preset equivalent acceleration range. One grade, 0.2-0.3 is another grade..., and then each grade is recombined with the combination of driving mode and energy recovery gear, so as to determine the driving style of each single vehicle.
  • the combination of road type, driving mode, energy recovery gear, average acceleration level of each level of acceleration section, average deceleration level of each deceleration section, and SOC absolute error ⁇ per kilometer is used as a driving Style
  • the driving style energy consumption coefficient under each driving style is initially set to 1, and is recorded as the number of rounds, which is used for the next iterative update of the driving style energy consumption under the same driving style starting from 1.
  • the driving style energy consumption coefficient of the third round is equal to the driving energy consumption coefficient of the previous round of the same driving style, that is, the 0th round 1 and (1+(S0-S00)/ K0) product.
  • the fifth round of navigation is in economic mode and the energy recovery gear is high, the average acceleration level of the acceleration section is 0.2 ⁇ 0.3, and the average deceleration level of the deceleration section is -0.3 ⁇ 0.2, then the driving style energy consumption coefficient of the fifth round It is equal to the product of the driving style energy consumption coefficient 1 and (1+(S4-S44)/K4) of the previous round of the same driving style, that is, the fourth round.
  • the acceleration characteristics specifically include not only the average acceleration in the acceleration section and the average deceleration in the deceleration section, but also the rapid acceleration proportional level , Rapid deceleration ratio level.
  • the rapid acceleration ratio refers to the ratio of the time when the acceleration is greater than the set value to the entire statistical segment, for example, 5%-6%.
  • Rapid deceleration ratio refers to the ratio of the time when the deceleration is less than the set value to the entire statistical segment, for example, 2%-3%.
  • the number of influencing variable combinations is also increased, and the judgment granularity of driving style is smaller, which further increases the accuracy of the model.
  • the standard deviation of vehicle speed in the acceleration characteristics can also be included in the above table as an influencing factor for establishing the driving style energy consumption coefficient model, so that the number of combinations between various influencing factors can be further increased, and the aspect that characterizes the driving style energy consumption coefficient More comprehensive, so as to improve the accuracy of driving style energy consumption coefficient.
  • a model is jointly established using the acceleration feature together with the driving mode, energy recovery level and road type, however, a model may also be independently established for the acceleration feature.
  • acceleration characteristics as direct influencing factors and the absolute error ⁇ of SOC per kilometer as indirect influencing factors can be arbitrarily combined or independently used as influencing variables according to the acquisition of road condition information, requirements for model accuracy, and computing power. Establish the corresponding driving style energy consumption model.
  • a dedicated driving style energy consumption coefficient model is established for each vehicle using its historical data (single-vehicle historical data). Since driving style is a personalized matter, establishing a dedicated driving style energy consumption coefficient model for each vehicle can improve the accuracy of energy consumption estimation.
  • a general driving style energy consumption coefficient model may also be established by using the historical data of multiple vehicles.
  • Table 7 shows a model example obtained by the above model building method (learning method).
  • this model example is only the result of a certain moment, and it will be continuously updated as time goes by. Data changes may occur.
  • Table 7 only shows some combinations of road types, driving modes, energy recovery gears and acceleration characteristics, but not all combinations.
  • the map terminal plans the navigation itinerary according to the departure and destination of the vehicle terminal. Since the map side has opened up the road condition information of the navigation itinerary, the map side sends the road condition information of the current round of navigation itinerary, including the average speed of each road section, the road gradient of each road section, and the itinerary to the cloud.
  • the battery SOC estimation method is performed by a navigation trip battery SOC estimation system based on car-cloud interaction.
  • the estimation system can be set in the cloud, and includes a trip acquisition unit, a model call unit, and a result query unit. , a driving energy consumption calculation unit and an estimation unit.
  • the itinerary obtaining unit obtains the road condition information of each road section and the estimated time of the whole navigation journey according to the navigation itinerary, and the road condition information of each road section includes the distance of each road section and the vehicle speed thereof.
  • the model invoking unit invokes a plurality of driving energy consumption models and the driving style energy consumption coefficient model in this embodiment, wherein the multiple driving energy consumption models are established using the driving energy consumption related data of each vehicle of the same model as a sample, for example including But not limited to the driving energy consumption model, the driving energy consumption model is used to represent the corresponding relationship between the speed of each navigation trip of each vehicle of the same model and the driving energy consumption of each navigation trip of the vehicle, the model calls
  • the unit also invokes general additional energy consumption models including the energy consumption model of the high-voltage accessory and the energy consumption model of the low-voltage accessory.
  • the result query unit queries the driving style energy consumption coefficient corresponding to the driving style of the current vehicle in the driving style energy consumption coefficient model described in the above embodiment or this embodiment;
  • the query value of each driving energy consumption corresponding to the road condition information of the road section.
  • the driving energy consumption calculation unit calculates the estimated energy consumption value of the current vehicle for the entire journey based on the distance of each road section and using the driving style energy consumption coefficient and the query values of various driving energy consumption. You can refer to formula 3 to formula 5 in the above embodiment description of.
  • the estimating unit calculates the battery SOC at the destination by using the estimated total energy consumption value including at least the estimated energy consumption value of the whole journey, and reference may be made to the description of Equation 6 to Equation 8 in the above embodiment.
  • the C cloud sends the destination battery SOC result obtained by the estimation system to the map display interface of CDC for presentation.
  • the current practice of the driving energy consumption model is not to distinguish frequent routes, such as routes between home and company, and unusual routes.
  • frequent routes such as routes between home and company
  • unusual routes such as routes between home and company
  • the number of samples of frequently traveled routes is obviously more than that of unusually traveled routes, which will make the running energy consumption model established on the frequently traveled routes with a higher degree of fitting. Therefore, in commuting scenarios, the navigation travel energy consumption model establishment
  • the driving energy consumption model of the driving route is separately established as another personalized model in the driving energy consumption model.
  • the frequent route driving energy consumption that is, the trip energy consumption includes the frequent route driving energy consumption.
  • the receiving unit receives the vehicle speed and driving energy consumption of frequently-traveled routes in the navigation itinerary, and uses the frequently-traveled route to drive the energy consumption model to separately calculate the battery SOC of the frequently-traveled route to the destination, which can improve the accuracy of SOC estimation and improve user experience .
  • the specific implementation steps are as follows:
  • the map terminal Based on the commuting settings provided by the map terminal, obtain the location points of the starting point and destination, such as home location and company location. When any of the following conditions are met, it is judged as a frequent route:
  • the starting point is the home location and the ending point is the company location;
  • the starting point is the company location, and the end point is the home location.
  • the model building unit establishes a frequent route driving energy consumption model, and the frequent route driving energy consumption model is used to represent the corresponding relationship between the current vehicle's frequent route speed and frequent route driving energy consumption.
  • the energy consumption data model driven by frequent routes is established for a single vehicle using single vehicle data, as shown in Table 8.
  • the estimated energy consumption tends to be closer to the actual energy consumption, thereby improving the SOC estimation accuracy of frequently traveled routes.
  • the battery SOC estimation system for the navigation itinerary based on car-cloud interaction no longer adopts the driving energy consumption model common to all vehicles of the same model in Table 1, but according to The driving energy consumption of the usual route in the driving energy consumption model of a single vehicle of the current vehicle and the increase in the additional driving energy consumption model are used to calculate the overall driving energy consumption.
  • the model invoking unit invokes the energy consumption model driven by the frequent route described in this embodiment;
  • the result query unit inquires the query value of the energy consumption for driving the conventional route corresponding to the vehicle speed of the frequent route in the energy consumption model for the frequent route;
  • the driving energy consumption calculation unit calculates the current vehicle's overall driving forecast according to formula 3 to formula 5 based on the distance of each road section and using the driving style energy consumption coefficient in the above-mentioned embodiment or this embodiment and the query value of conventional route driving energy consumption. Estimate energy value.
  • This embodiment provides a battery SOC estimation method, which includes S100, acquiring navigation information and various state parameter information; S200, calculating driving energy consumption; S300, calculating energy consumption of high-voltage accessories; S400, calculating energy consumption of low-voltage accessories; S500 , Calculate the battery SOC.
  • the execution order of S200, S300, and S400 in this embodiment can be adjusted freely.
  • the information in S100 does not have to be acquired at one point in time, and can be interspersed among or among S200, S300, S400, and S500 as required.
  • the information to be obtained may include the starting point, end point, road sections passed, distance information of each road section, total distance information, estimated time information, slope information, speed information, driving mode information, and energy recovery level of the navigation route.
  • Information (setting parameters of the vehicle), high-voltage accessory status information, low-voltage accessory status information, current battery power information, etc.
  • its content includes calculating driving energy consumption according to speed information and distance information, and also includes correcting driving energy consumption according to driving mode, energy recovery level, road type or acceleration information (that is, obtaining driving style energy consumption in the above embodiment) coefficient and calculate the driving energy consumption based on this), in addition, it also includes correcting the driving energy consumption according to the additional energy consumption (that is, calculating the driving energy consumption according to the gradient information in the above embodiment).
  • its content mainly includes calculating the energy consumption of the air conditioner based on the ambient temperature and the status of the air conditioner switch.
  • S400 its content includes calculating the energy consumption of low-voltage accessories based on the status information and estimated time information of low-voltage accessories.
  • its content includes obtaining the current battery SOC, and calculating the battery SOC when reaching the end of the navigation path based on the current battery SOC and driving energy consumption (corrected if there is a correction), high-voltage accessory energy consumption, and low-voltage accessory energy consumption.
  • An embodiment of the present application provides a device for establishing an energy consumption model.
  • the device for establishing an energy consumption model can be set on the cloud or on a vehicle, and is used to establish the above-mentioned drive energy consumption model, driving style energy consumption coefficient model, and high-voltage accessories.
  • the specific action and processing of the energy consumption model, the low-voltage accessory energy consumption model, and the additional energy consumption model have been described above, and will not be repeated here.
  • the device for estimating the remaining battery capacity can be set on the cloud or on the vehicle (the above-described cloud-based model building and other processing can also be performed by the vehicle), and is used to calculate driving energy consumption, high-voltage accessory energy consumption, low-voltage Accessories energy consumption, battery SOC, etc.
  • the device 100 for estimating the remaining battery capacity includes a processing module 10 and an obtaining module 20 .
  • the processing module 10 is mainly used to execute S200-S500 in FIG. 5
  • the acquisition module is mainly used to execute S100 in FIG. 5 .
  • the driving energy consumption is calculated based on the initial driving energy consumption and the driving style energy consumption coefficient. Specifically, it is calculated based on navigation information (including estimated navigation distance, estimated navigation time, rail planning speed, navigation planning acceleration, and navigation path slope information)
  • the driving style energy consumption coefficient is calculated according to the driving mode, energy recovery gear, road type in navigation information, acceleration information, etc., and then the driving energy consumption is calculated according to the initial driving energy consumption and the driving style energy consumption coefficient.
  • the remaining battery power estimation device calculates the energy consumption of the high-voltage accessories according to the status information of the high-voltage accessories and the estimated driving time in the navigation information. set information, etc.
  • the remaining battery power estimation device calculates the energy consumption of the low-voltage accessories based on the status information of the low-voltage accessories and the estimated driving time in the navigation information. , Passenger seat heating switch status information, passenger seat switch gear information, low beam status information, high beam status information, position marker status information, wiper status information and speaker status information, etc. In addition, the substantive content of the actions and processing of the device for estimating the remaining battery power has been described above, and will not be repeated here.
  • the battery remaining power estimating device can show the driver the estimated remaining battery power when arriving at the destination, and can also directly show the driver the expected consumption. How much battery power (especially when the current battery power is less than the expected battery power consumption). In addition, how much mileage the vehicle can still travel can be estimated and presented to the driver.
  • the device for estimating the remaining battery power can be a multi-domain controller or a controller of a navigation device, etc., and the respective functions above can be realized by hardware or software.
  • the device for estimating the remaining battery power can use an electronic control unit (electronic control unit, ECU) implementation
  • ECU refers to a control device composed of integrated circuits used to implement a series of functions such as data analysis, processing and transmission.
  • the embodiment of the present application provides an electronic control unit ECU, the ECU includes a microcomputer (microcomputer), an input circuit, an output circuit and an analog-to-digital (analog-to-digital, A/D) converter .
  • the main function of the input circuit is to preprocess the input signal (such as the signal from the sensor), and the processing method is different for different input signals.
  • the input circuit may include an input circuit that processes analog signals and an input circuit that processes digital signals.
  • the main function of the A/D converter is to convert the analog signal into a digital signal. After the analog signal is preprocessed by the corresponding input circuit, it is input to the A/D converter for processing and converted into a digital signal accepted by the microcomputer.
  • the output circuit is a device that establishes a connection between the microcomputer and the actuator. Its function is to convert the processing results sent by the microcomputer into control signals to drive the actuators to work.
  • the output circuit generally uses a power transistor, which controls the electronic circuit of the actuator by turning on or off according to the instructions of the microcomputer.
  • Microcomputer includes central processing unit (central processing unit, CPU), memory and input/output (input/output, I/O) interface, CPU is connected with memory, I/O interface through bus, can communicate with each other through bus exchange.
  • the memory may be a memory such as a read-only memory (ROM) or a random access memory (RAM).
  • the I/O interface is a connection circuit for exchanging information between the central processor unit (CPU) and the input circuit, output circuit or A/D converter. Specifically, the I/O interface can be divided into a bus interface and a communication interface .
  • the memory stores a program, and the CPU calls the program in the memory to execute the estimation method described in the embodiments corresponding to FIG. 3 and FIG. 4 .
  • the disclosed systems, devices and methods may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components can be combined or integrated. to another system, or some features may be ignored, or not implemented.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

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Abstract

本申请涉及电动车与智能车领域，特别提供一种电池剩余电量预估方法及装置，其中，获取导航路径的路程信息；根据路程信息计算驱动能耗；获取车辆在导航路径上行驶的预估时间信息；获取车辆的高压附件状态信息；根据高压附件状态信息与预估时间信息计算高压附件能耗；获取车辆的低压附件状态信息；根据低压附件状态信息与预估时间信息计算低压附件能耗；获取当前电池电量；根据驱动能耗、高压附件能耗、低压附件能耗与当前电池电量计算到达导航路径的终点时的电池剩余电量。采用如上方式，不但考虑行驶能耗与高压附件能耗，还考虑了低压附件能耗，再此基础上预估电池剩余电量，从而能够提高电池剩余电量的准确度与精细度。

Description

电池剩余电量预估方法及装置 技术领域
本发明涉及电动车辆领域，尤其涉及一种电池剩余电量预估方法及装置。
背景技术
车载电池作为主要能量源来驱动新能源汽车，并为汽车内的电器例如空调等提供电能。但是由于受到电池技术发展缓慢以及安全因素的制约，车载电池容量是有限的，用户在使用电动汽车时，普遍存在里程焦虑，里程越长，焦虑越明显。近来，对部分电动汽车用户关于电池续航能力和里程评价方面的调研显示，在上下班代步场景下，被调查的所有用户都是满意的，认为使用此场景时电池续航的“里程足够”；在短途出行场景下，大多数的用户是满意的，认为使用此场景时电池续航“勉强够用”；在长途出行场景下，只有三分之一左右的用户是满意的，很多用户认为使用此场景时电池续航“里程不足”。
为缓解里程焦虑，现有技术中存在一种基于导航行程目的地的电池剩余电量(也被称为SOC,State of charge)预估方法，帮助电动车主掌握续航能力，及时应对续航里程不足问题。
然而，汽车在行驶过程中，对电池电量的消耗程度受到多方面的影响，例如驾驶过程中是否开空调、是否交通拥堵等等对电池的能耗影响差异是巨大的，因此如何精确预估导航行程目的地的电池SOC是亟待解决的问题。
发明内容
本申请提供一种电池剩余电量预估方法、装置、计算设备、计算机可读存储介质等，以提高车辆按照导航路径行驶到达目的地时的电池SOC的预估准确度。
本申请第一方面提供一种车辆的电池剩余电量预估方法，其特征在于，包括：获取车辆的驱动能耗；获取车辆的高压附件能耗；获取车辆的低压附件能耗；获取当前电池电量；根据驱动能耗、高压附件能耗、低压附件能耗与当前电池电量计算到达车辆的导航路径的终点时的电池剩余电量。
采用上述的电池剩余电量预估方法，根据驱动能耗、高压附件能耗与低压附件能耗来计算电池剩余电量，从而能够提高电池剩余电量的预估准确度、精细度。具体而言，现有技术中仅根据驱动能耗与高压附件能耗来计算电池剩余电量。然而，车辆上还有许多分散配置的低压附件，这些低压附件的使用虽然相对而言比较随机，但是对电池剩余电量的预估准确度还是有一定程度的影响的。例如，夜间行驶时需要开启车外照明灯，这与白天行驶时的能耗是不同的，或者环境温度低时，乘员可能会开启座椅加热功能，这也很对能耗造成影响。因此，本申请的上述电池剩余电量预估方法，不但考虑行驶能耗与高压附件能耗，还考虑了低压附件能耗，再此基础上预估电池剩余电量，从而能够提高电池剩余电量的准确度与精细度。
获取车辆的低压附件能耗可以包括：获取车辆的低压附件状态信息和导航路径的预估时间信息；根据低压附件状态信息与预估时间信息计算低压附件能耗。
低压附件状态信息可以包括下述项中的一项或多项：主驾座椅加热开关状态信息、主驾座椅加热挡位信息、副驾座椅加热开关状态信息、副驾座椅开关挡位信息、近光灯状态信息、远光灯状态信息、示廓灯状态信息、雨刮器状态信息与音箱状态信息。
采用如上方式，不但考虑低压附件的开关状态，还考虑到例如主驾座椅加热挡位信息，根据不同的加热挡位来计算电池剩余电量，从而能够更加准确、精细化地计算电池剩余电量。另外，对于车外照明灯，考虑到远光灯、近光灯、示廓灯的功率通常是不同的，因此根据近光灯状态信息、远光灯状态信息、示廓灯状态信息来计算电池剩余电量，从而能够更加准确、精细化地计算电池剩余电量。
作为第一方面的一个可能的实现方式，根据低压附件状态信息与预估时间信息计算低压附件能耗包括：根据低压附件状态信息，利用低压附件能耗模型获得低压附件功率信息；根据低压附件功率信息与预估时间信息计算低压附件能耗。这里的功率是指单位时间的能耗。能耗是指能量(电量)消耗量。
作为第一方面的一个可能的实现方式，获取驱动能耗具体包括：获取车辆的导航路径的路程信息与速度信息；根据速度信息，利用驱动能耗模型获得驱动能耗率信息；根据驱动能耗率信息与路程信息计算驱动能耗。这里的驱动能耗率是指单位路程的驱动能耗。
作为第一方面的一个可能的实现方式，还包括：获取车辆的驾驶模式与能量回收等级；根据驾驶模式与能量回收等级对驱动能耗进行修正。
作为第一方面的一个可能的实现方式，还包括：获取车辆在导航路径上的加速度信息；根据加速度信息对驱动能耗进行修正。
即便是相同的道路、相同的路程等，不同的驾驶员驾驶车辆所产生的能耗也是不同的，例如，有的驾驶员的驾驶风格比较激进，这对能耗的影响也是比较大的，因此，本申请的如上实现方式中，根据体现不同驾驶风格的加速度信息来修正驱动能耗，从而能够更加准确地预估电池剩余电量。
加速度信息可以包括下述项中的一项或多项：加速段平均加速度信息、减速段平均减速度信息、急加速比例信息和急减速比例信息。
采用如上方式，能够更加精细化、准确地计算电池剩余电量。另外，不仅仅是平均加速度，急加速和急减速所占的比例对能耗的影响也是很大的，因此，采用如上方式能够进一步提高电池剩余电量的预估准确度。
作为第一方面的一个可能的实现方式，根据驾驶模式与能量回收等级对驱动能耗进行修正包括：根据驾驶模式与能量回收等级，利用驱动能耗修正系数模型获得驱动能耗修正系数；根据驱动能耗修正系数对驱动能耗进行修正。
作为第一方面的一个可能的实现方式，根据加速度信息对驱动能耗进行修正包括：根据导航规划加速度信息，利用驱动能耗修正系数模型获得驱动能耗修正系数；根据驱动能耗修正系数对驱动能耗进行修正。
作为第一方面的一个可能的实现方式，驱动能耗修正系数模型是根据车辆 的单车数据建立的。
驱动能耗修正系数模型体现的是加速度信息对驱动能耗的影响，而驾驶车辆时的加减速操作是因人而异的，因此利用单车数据建立驱动能耗修正系数模型能够提高模型预测准确度，基于此来修正驱动能耗从而能够提高驱动能耗的预测准确度，进而提高电池剩余电量预测准确度。
作为第一方面的一个可能的实现方式，还包括：获取导航路径的坡度信息；根据坡度信息对驱动能耗进行修正。
道路坡度对驱动能耗也是有比较大的影响的，因此，采用如上方式，坡度信息来修正驱动能耗，从而能够更加准确的预估驱动能耗，进而能够更加准确地预估电池剩余电量。
作为第一方面的一个可能的实现方式，获取车辆的高压附件能耗具体包括：获取车辆的高压附件状态信息；根据高压附件状态信息，利用高压附件能耗模型获得高压附件功率信息；根据高压附件功率信息与预估时间信息计算高压附件能耗。
作为第一方面的一个可能的实现方式，电池剩余电量预估方法还包括：当导航路径是常走路线时，利用常走路线驱动能耗模型计算驱动能耗，常走路线驱动能耗模型是根据车辆的单车数据建立的。常走路线典型的是通勤路线，可以根据导航路径的起点与终点是否为家和公司来判断是否为常走路线。
本申请第二方面提供一种车辆的电池剩余电量预估装置，包括获取模块与处理模块，处理模块用于获取驱动能耗、高压附件能耗与低压附件能耗；获取模块用于获取当前电池电量；处理模块还用于根据驱动能耗、高压附件能耗、低压附件能耗与当前电池电量计算到达车辆的导航路径的终点时的电池剩余电量。
作为一个可能的实现方式，获取模块还用于获取车辆的低压附件状态信息和导航路径的预估时间信息；处理模块还用于根据低压附件状态信息与预估时间信息计算低压附件能耗。
作为第二方面的一个可能的实现方式，低压附件状态信息包括下述项中的一项或多项：主驾座椅加热开关状态信息、主驾座椅加热挡位信息、副驾座椅加热开关状态信息、副驾座椅开关挡位信息、近光灯状态信息、远光灯状态信息、示廓灯状态信息、雨刮器状态信息与音箱状态信息。
作为第二方面的一个可能的实现方式，根据低压附件状态信息与预估时间信息计算低压附件能耗包括：根据低压附件状态信息，利用低压附件能耗模型获得低压附件功率信息；根据低压附件功率信息与预估时间信息计算低压附件能耗。
作为第二方面的一个可能的实现方式，获取模块还用于获取车辆的导航路径的路程信息与速度信息；处理模块还用于根据速度信息，利用驱动能耗模型获得驱动能耗率信息；处理模块还用于根据驱动能耗率信息与路程信息计算驱动能耗。
作为第二方面的一个可能的实现方式，获取模块还用于获取车辆的驾驶模式与能量回收等级；处理模块还用于根据驾驶模式与能量回收等级对驱动能耗进行修正。
作为第二方面的一个可能的实现方式，获取模块还用于获取车辆在导航路径上的加速度信息；处理模块还用于根据加速度信息对驱动能耗进行修正。
作为第二方面的一个可能的实现方式，加速度信息包括下述项中的一项或多项：加速段平均加速度信息、减速段平均减速度信息、急加速比例信息和急减速比例信息。
作为第二方面的一个可能的实现方式，根据驾驶模式与能量回收等级对驱动能耗进行修正包括：根据驾驶模式与能量回收等级，利用驱动能耗修正系数模型获得驱动能耗修正系数；根据驱动能耗修正系数对驱动能耗进行修正。
作为第二方面的一个可能的实现方式，根据加速度信息对驱动能耗进行修正包括：根据导航规划加速度信息，利用驱动能耗修正系数模型获得驱动能耗修正系数；根据驱动能耗修正系数对驱动能耗进行修正。
作为第二方面的一个可能的实现方式，驱动能耗修正系数模型是根据车辆的单车数据建立的。
作为第二方面的一个可能的实现方式，获取模块还用于获取导航路径的坡度信息；处理模块还用于根据坡度信息对驱动能耗进行修正。
作为第二方面的一个可能的实现方式，获取模块还用于获取车辆的高压附件状态信息；处理模块还用于根据高压附件状态信息，利用高压附件能耗模型获得高压附件功率信息；处理模块还用于根据高压附件功率信息与预估时间信息计算高压附件能耗。
本申请第三方面提供一种计算设备，其包括处理器与存储器，存储器存储有计算机程序，计算机程序当被处理器运行时执行上述任一种电池剩余电量预估方法。
本申请第四方面提供一种计算机可读存储介质，其存储有计算机程序，计算机程序当被计算机运行时执行上述任一种电池剩余电量预估方法。
另外，本申请提供了一种导航行程能耗模型建立方法，该方法包括以下内容：接收当前车辆(第一车辆)的历史导航行程能耗相关数据，历史导航行程能耗相关数据由当前车辆采集，包括每一轮导航行程的行程能耗、驾驶模式和能量回收挡位；以历史导航行程能耗相关数据作为样本训练驾驶风格能耗系数模型，驾驶风格能耗系数模型用于表示当前车辆的驾驶风格和对行程能耗的影响程度之间的对应关系，影响程度用驾驶风格能耗系数表示；其中，驾驶风格至少根据驾驶模式和能量回收挡位确定。
本申请的技术方案考虑了驾驶风格对车辆能耗的影响，针对驾驶个性化来建立特别的模型，至少将驾驶模式和能量回收挡位这两个维度作为确定驾驶风格的因素，从而增加了驾驶风格定义简易性和确定性。并且，通过每一轮导航行程的行程能耗相关数据训练出的模型能够保证对真实能耗情况始终准确的表达，并将驾驶风格对行驶能耗的影响程度量化为驾驶风格能耗系数的形式，从而使得对行驶能耗随驾驶风格的变化用线性关系来体现。另外，即使导航服务方未开放与导航信息相关的路况信息，驾驶风格系数也可以不考虑下文将详述的加速度特征，仅通过车辆自身的车况来确定，足以保证驾驶风格系数模型对能耗预估的实用性，进而增加了车辆导航行程的目的地电池SOC的预估准确度。
在导航行程能耗模型建立方法的一种可能的实现方式中，方法还包括，云端接收导航行程的路况信息，路况信息由导航服务方开放，包括加速度特征，加速度 特征是导航服务方对当前车辆的每一轮导航行程中的加速度统计值；以及，进一步以路况信息作为样本训练驾驶风格能耗系数模型驾驶风格还根据道路类型和加速度特征来确定。
路况信息可以基于地图SDK获取，行驶能耗可以融入路况信息，也就是，车辆行驶过程中行驶能耗相关信息就可以同步结合导航路径中的路况信息来一起训练驾驶风格能耗系数模型，由于路况信息中的加速度对预估准确度影响较大，因此在建立驾驶风格能耗系数模型时同步考虑由导航服务方实时统计的加速度特征作为另一更重要的维度来限定驾驶风格，使得基于驾驶风格的驾驶风格能耗系数模型的区分粒度更小，提升该模型的精度，因此在使用这样的驾驶风格能耗系数模型时，更多维度上均匹配时，对应查询出的驾驶风格能耗系数将更可靠，使得能耗预估更加准确。
路况信息还可以包括道路类型，道路类型是由导航服务方根据道路等级确定的；驾驶风格还根据道路类型来确定。
当对驾驶风格的限定进一步增加判断维度时，多种判断因素：来自导航服务方的道路类型和加速度特征、来自车端的驾驶模式和能量回收挡位的排列组合，均可以作为一种驾驶风格存在，并单独定义各自对应的驾驶风格能耗系数，进一步体现了驾驶风格的丰富和个性化，从而加强了以驾驶风格作为影响变量的驾驶风格系数模型的高拟合度。
模型建立方法还包括驾驶风格能耗系数模型通过强化学习迭代。
驾驶风格能耗系数模型的建立通过强化学习来迭代，可以根据导航行程和导航行程中大数据样本的积累，逐渐修正驾驶风格能耗系数模型中驾驶风格和系数的对应关系，随着持续的大数据训练，使得驾驶风格能耗系数模型及时地、真实地反映驾驶风格的微小变化对行驶能耗的微小影响，以提升预估准确度。
通过强化学习迭代进一步包括：本轮导航行程的驾驶风格能耗系数采用Q-table，在相同驾驶风格的上一轮导航行程的驾驶风格能耗系数的基础上更新。
驾驶风格能耗系数模型的建立通过Q-learning算法来强化学习，在Q-table(或称为Q表)中逐次修正上一轮相同驾驶风格下的驾驶风格能耗系数并作为最新一轮相同驾驶风格的驾驶风格能耗系数以供预估能耗和电池SOC使用，并作为下一轮迭代时的参数之一，这种迭代策略较为简易，计算负担小，并且使得建立驾驶风格能耗系数模型时不排除接收更广泛的数据范围作为样本，具有良好的扩展性。
历史导航行程能耗相关数据还可以包括每一轮导航行程的预估SOC和真实SOC；本轮的驾驶风格能耗系数由相同驾驶风格的上一轮导航行程的驾驶风格能耗系数和上一轮导航行程的单位距离绝对误差SOC计算得到；其中，单位距离绝对误差SOC等于上一轮预估SOC和上一轮真实SOC的绝对误差除以上一轮导航行程的距离。
在用Q-table进行迭代时，将单位距离绝对误差SOC作为迭代改变量，前一轮的误差越大，迭代改变量越大。用单位距离绝对误差SOC来修正能耗误差，使得预估准确度保持在一定范围内。
作为一种可能的实现方式，将相同驾驶风格的第一轮导航行程的驾驶风格能耗系数初始化为1。
将相同驾驶风格的第一轮导航行程的驾驶风格能耗系数初始化为1，使得对之后的相同驾驶风格下的各轮导航行程中系数起始值最小化，减小迭代计算量。
作为一种可能的实现方式，接收同一车型的每个车辆的历史低压附件相关能耗数据作为样本，历史低压附件相关能耗数据包括低压附件状态和低压附件功率，低压附件包括车辆的DC-DC用电器；建立低压附件能耗模型，低压附件能耗模型用于表示低压附件状态和低压附件功率之间的对应关系。
由于车辆的低压附件多点分散，能耗发生较为随机，现有技术中对低压附件的能耗缺乏有效的分析，在预估SOC时容易忽略这方面的能耗影响。本申请除了考虑驾驶风格对行驶能耗的影响外，还进一步考虑低压附件的能耗，为低压附件能耗建立专门的低压附件能耗模型以囊括各个附件状态所对应的能耗值，确保对能耗的全面分析和准确预估。
作为一种可能的实现方式，历史导航行程能耗相关数据还包括导航行程中的常走路线车速，每一轮导航行程的行程能耗包括常走路线驱动能耗，其中常走路线基于导航服务方提供的通勤设置中的出发地和目的地来确定；建立常走路线驱动能耗模型，常走路线驱动能耗模型用于表示当前车辆的常走路线车速和常走路线驱动能耗的对应关系。
由于每个单个车辆在常走路线上的样本数明显比其它导航行程的样本数多，并且常走路线上路况和车况通常较稳定，因此为每个单个车辆建立单独的常走路线驱动能耗模型，可以使得能耗预估的结果相对于非常走路线更加真实可靠，充分满足每个车辆的个性化预估需要，提高用户体验。
另外，本申请实施例还提供了一种导航行程电池SOC预估方法，包括以下内容：根据导航行程获取各路段的路况信息，各路段的路况信息包括各路段的距离和其车速；调用多个行驶能耗模型和上述任一种导航行程能耗模型建立方法得到的驾驶风格能耗系数模型，其中多个行驶能耗模型以同一车型的每个车辆的行驶能耗相关数据作为样本来建立，多个行驶能耗模型至少包括驱动能耗模型，驱动能耗模型用于表示同一车型的每个车辆的每一轮导航行程的车速和该车辆的每一轮导航行程的驱动能耗之间的对应关系；查询驾驶风格能耗系数模型中的与当前车辆的驾驶风格相对应的驾驶风格能耗系数；以及查询多个行驶能耗模型中的分别与各路段的路况信息相对应的各项行驶能耗的查询值；基于各路段的距离并使用驾驶风格能耗系数和各项行驶能耗的查询值，计算当前车辆的全程行驶预估能耗值；以及使用预估总能耗值计算电池SOC，预估总能耗值至少包括全程行驶预估能耗值。
在预估导航行程电池SOC时，将本申请提供的驾驶风格能耗系数模型纳入能耗预估范围，使得同一车型的各个车辆之间的电池的电池SOC预估方案不仅应用到通用的能耗模型，还应用到个性化的并且可动态调整的能耗模型，充分考虑了每一辆车的驾驶员或用户的个人驾驶行为习惯所带来的能耗区别，从而在未来发生的每一次导航行程中，都有效提升电池剩余电量的预估准确度。
在导航行程电池SOC预估方法的一种可能的实现方式中，全程行驶预估能耗值是各个路段的分段行驶预估能耗值之和，其中，每一路段的分段行驶预估能耗值是该路段的分段行驶能耗值、该路段的距离和驾驶风格能耗系数的乘积；其中，分 段行驶能耗值是利用各项行驶能耗的查询值计算得到。
本申请的驾驶风格能耗系数作为对行驶能耗的影响程度的量化表现，在预估全程行驶能耗时，相当于在通用的行驶能耗的基础上增加了限制条件，使导航行程中的能耗预估情况具有更真实表现，进而提升了目的地电池SOC的预估准确度。
在导航行程电池SOC预估方法的一种可能的实现方式中，多个行驶能耗模型还包括附加能耗模型，附加能耗模型用于表示同一车型的各个车辆所处的道路坡度和对驱动能耗的增加量的对应关系；各路段的路况信息还包括各路段的道路坡度；查询附加能耗模型以获得对应于各路段的道路坡度的增加量的查询值；分段行驶能耗值为驱动能耗的查询值和增加量的查询值之和。
在导航行程的路线中出现路况复杂，比如路线中具有立交桥、盘山路等路段的情况下，道路坡度会成为影响行驶能耗的重要因素，因此除了驾驶能耗系数外，还要考虑能耗的增加量对理想状态的行驶能耗趋势的偏离作用，以确保对复杂路况下行驶的车辆的预估能耗在整体上接近真实能耗，以便对电池的电量消耗有准确预估，从而计算出正确的电池SOC。
在导航行程电池SOC预估方法的一种可能的实现方式中，方法还包括以下步骤：根据导航行程获取全程预估时间；调用通用附件能耗模型，以同一车型的每个车辆的通用附件能耗相关数据作为样本来建立，通用附件是消耗电量以进行驱动之外操作的车辆部件，通用附件能耗模型用于表示同一车型的每个车辆通用附件状态和通用附件功率的对应关系；通用附件能耗模型包括高压附件能耗模型，高压附件能耗模型用于表示同一车型的每个车辆的高压附件状态和其高压附件功率之间的对应关系；查询通用附件能耗模型中的与当前车辆的附件状态相对应的附件功率的查询值；预估总能耗值还包括全程附件能耗，全程附件能耗是全程预估时间和附件功率的查询值的乘积。
车辆上的各个附件对电池能量的消耗不可忽略，在前述驾驶能耗系数参与预估行驶能耗的能耗类型的同时，考虑与当前车辆在导航行程中的附件状态相对应的全程附件能耗，提升目的地电池SOC预估的准确度，提高车辆用户对全程用电情况的确定性，缓解车辆用户的里程焦虑。
在导航行程电池SOC预估方法的一种可能的实现方式中，通用附件能耗模型还包括低压附件能耗模型；附件功率的查询值等于高压附件功率和低压附件功率之和。
本申请为车内分布广泛的低压附件能耗建立不同附件状态的组合下分别和能耗的对应关系，确保对能耗的全面分析以及对电池SOC的准确预估，以车云信息融合的方式，获取真实能耗以计算续航里程。
在导航行程电池SOC预估方法的一种可能的实现方式中，导航行程为常走路线；多个行驶能耗模型中还包括常走路线驱动能耗模型；查询常走路线驱动能耗模型中的与常走路线车速相对应的常规路线驱动能耗的查询值；基于各路段的距离并使用驾驶风格能耗系数和常规路线驱动能耗的查询值，计算当前车辆的全程行驶预估能耗值。
当导航行程是常走路线时，数据样本明显比其它导航行程的样本数多，从 而使得能耗预估比非常走路线的预估准确度更高，更好地体现电池SOC预测的有效性，有利于提高用户体验。
相应地，本申请实施例还提供一种导航行程能耗模型建立装置，包括：接收单元，用于接收当前车辆的历史导航行程能耗相关数据作为样本，历史导航行程能耗相关数据由当前车辆采集，包括每一轮导航行程的行程能耗、驾驶模式和能量回收挡位；模型建立单元，用于以历史导航行程能耗相关数据作为样本训练驾驶风格能耗系数模型，驾驶风格能耗系数模型用于表示当前车辆的驾驶风格和对行程能耗的影响程度之间的对应关系，影响程度用驾驶风格能耗系数表示；其中，驾驶风格至少根据驾驶模式和能量回收挡位确定。
在导航行程能耗模型建立装置的第一种可能的实现方式中，接收单元还用于接收导航行程的路况信息，路况信息由导航服务方开放，包括加速度特征，加速度特征是导航服务方对当前车辆的每一轮导航行程中的加速度统计值；模型建立单元进一步用于以路况信息作为样本训练驾驶风格能耗系数模型，驾驶风格还根据加速度特征来确定。
在导航行程能耗模型建立装置的第二种可能的实现方式中，路况信息还包括道路类型，道路类型是由导航服务方根据道路等级确定的；驾驶风格还根据道路类型来确定。
在导航行程能耗模型建立装置的第三种可能的实现方式中，建立装置还包括迭代单元，用于通过强化学习迭代驾驶风格能耗系数模型。
在导航行程能耗模型建立装置的第四种可能的实现方式中，迭代单元进一步用于采用Q-table，在相同驾驶风格的上一轮导航行程的驾驶风格能耗系数的基础上更新本轮导航行程的驾驶风格能耗系数。
在导航行程能耗模型建立装置的第五种可能的实现方式中，历史导航行程能耗相关数据还包括每一轮导航行程的预估SOC和真实SOC；迭代单元用于通过相同驾驶风格的上一轮导航行程的驾驶风格能耗系数和上一轮导航行程的单位距离绝对误差SOC来计算得到本轮的驾驶风格能耗系数；其中，单位距离绝对误差SOC等于上一轮预估SOC和上一轮真实SOC的绝对误差除以上一轮导航行程的距离。
在导航行程能耗模型建立装置的第六种可能的实现方式中，迭代单元用于将相同驾驶风格的第一轮导航行程的驾驶风格能耗系数初始化为1。
在导航行程能耗模型建立装置的第七种可能的实现方式中，接收单元还用于接收同一车型的每个车辆的历史低压附件相关能耗数据作为样本，历史低压附件相关能耗数据包括低压附件状态和低压附件功率，低压附件包括车辆的DC-DC用电器；模型建立单元还用于建立低压附件能耗模型，低压附件能耗模型用于表示低压附件状态和低压附件功率之间的对应关系。
在导航行程能耗模型建立装置的第八种可能的实现方式中，接收单元还用于接收导航行程中的常走路线车速，每一轮导航行程的行程能耗包括常走路线驱动能耗，其中常走路线基于导航服务方提供的通勤设置中的出发地和目的地来确定；模型建立单元还用于建立常走路线驱动能耗模型，常走路线驱动能耗模型用于表示当前车辆的常走路线车速和常走路线驱动能耗的对应关系。
本发明的这些和其它方面在以下(多个)实施例的描述中会更加简明易懂。
附图说明
以下参照附图来进一步说明本发明的各个特征和各个特征之间的联系。附图均为示例性的，一些特征并不以实际比例示出，并且一些附图中可能省略了本申请所涉及领域的惯常的且对于本申请非必要的特征，或是额外示出了对于本申请非必要的特征，附图所示的各个特征的组合并不用以限制本申请。另外，在本说明书全文中，相同的附图标记所指代的内容也是相同的。具体的附图说明如下：
图1是本申请的一种应用场景说明图。
图2是本申请实施例中考虑到的对行驶能耗有影响的车辆因素的说明图
图3是本申请一个实施例涉及的SOC预估流程示意图。
图4是本申请另一个实施例涉及的SOC预估流程示意图。
图5是本申请一个实施例涉及的电池SOC预估方法的一种流程示意图；
图6是本申请一个实施例涉及的电池SOC预估装置的一种结构示意图；
图7是本申请一个实施例中提供的电子控制单元的一种结构示意图。
具体实施方式
在以下的描述中，所涉及的表示步骤的标号，如S10、S12……等，并不表示一定会按此步骤执行，在允许的情况下可以互换前后步骤的顺序，或同时执行。
说明书和权利要求书中使用的术语“包括”不应解释为限制于其后列出的内容；它不排除其它的元件或步骤。因此，其应当诠释为指定所提到的所述特征、整体、步骤或部件的存在，但并不排除存在或添加一个或更多其它特征、整体、步骤或部件及其组群。
本说明书中提到的“一个实施例”或“实施例”意味着与该实施例结合描述的特定特征、结构或特性包括在本发明的至少一个实施例中。因此，在本说明书各处出现的用语“在一个实施例中”或“在实施例中”并不一定都指同一实施例，但可以指同一实施例。此外，在一个或多个实施例中，能够以任何适当的方式组合各特定特征、结构或特性，如从本公开对本领域的普通技术人员显而易见的那样。
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。如有不一致，以本说明书中所说明的含义或者根据本说明书中记载的内容得出的含义为准。另外，本文中所使用的术语只是为了描述本申请实施例的目的，不是旨在限制本申请。[0093]为缓解里程焦虑，现有技术中存在一种基于导航行程目的地的电池剩余电量预估方法，在该方法中，根据导航信息中的车速信息与路程信息预估驱动能耗，根据导航信息中的预估行驶时间与空调(高压附件)的状态预估高压附件能耗，根据取得能耗与高压附件能耗预估总能耗，基于此计算到达目的地时的电池剩余电量。
然而，这种方法仅仅根据考虑了通常对能耗有影响的驱动能耗与高压附件能耗，其预估结果比较粗略，还存在改进的余地。
基于现有技术所存在的缺陷，本申请实施例提供了一种电池SOC预估方 法和装置等，更全面地考虑车辆各个环节的能耗和其影响因素，然后基于多个影响因素建立多种能耗模型，并通过结合这些能耗模型，最终计算出导航全程的目的地电池SOC。
图1是本申请实施例的一个应用场景的说明图，如图1所示，当驾驶员设定了目的地，车辆根据驾驶员的操作给出了导航路径，同时，利用后面详细描述的电池SOC预估方法计算到达目的地(终点)时的电池SOC，并在地图上的目的地位置处进行显示(“25％”)。本申请的技术方案可以应用在车联网的车云(V2C，Vehicle to Cloud)交互中。车云交互是车端与云端进行数据传输的通信技术，可以采用蜂窝移动通信或者互联网等方式实现。
车端向云端提供反映车辆运行特征的数据，车辆运行特征的数据由各种车载传感设备感知、收集并处理，这些数据包括但不限于车辆行驶数据、车辆状态、驾驶操作行为、车辆环境数据等，车辆行驶数据可以包括但不限于例如车速等；车辆状态可以是诸如车灯、雨刮器等用电器的开关状态、电池SOC等；驾驶操作行为可以是驾驶员对车辆的驾驶预设方式，包括驾驶模式、能量回收档位等等。车辆环境数据是关于车辆环境数据，比如周边环境温度、光线、雨雪等。车辆运行特征中的能耗相关数据包括实际能耗值和被归为影响能耗的影响因素。用量化后的影响因素(或称为影响变量)和实际能耗之间的对应关系来创建能耗模型。
云端作为平台接收来自车端的包括上述能耗相关数据的车辆运行特征的数据，并且还可以进一步结合地图导航路线或交通大数据，根据人工智能算法来提供信息服务、计算、决策等功能。
能耗也根据来源分为行驶能耗和通用附件能耗，行驶能耗是车辆行驶时所消耗的能量，通用附件是消耗电量以进行驱动之外操作的所有车辆部件，所以通用附件能耗是这些所有车辆部件在操作是所消耗的能量。在本实施例中，通用附件能耗包括高压附件能耗和低压附件能耗。
对于行驶能耗，影响车辆的行驶能耗的影响因素包括但不限于，车辆的车速、怠速比例、道路坡度、道路类型、驾驶风格、交通状况、风阻、天气等。因此，行驶能耗可以包括车辆驱动自身时的驱动能耗，可以视为理想状态下的行驶能耗，其主要影响因素是车速。大体而言，车速越快，驱动能耗越高。然而实际中，行驶能耗还包括附加能耗，主要影响因素可以包括车的自重、胎压、道路坡度、风阻、道路类型等等，作为在驱动能耗的基础上的增加量或减小量。大体而言，上坡坡度越大，附加能耗越大；自重越大，附加能耗越大；风阻越大，附加能耗越大；反之亦然。
需要特别说明的是，驾驶风格对行驶能耗的影响也不容忽略。驾驶风格为激烈驾驶时，对行驶能耗的影响较大，温柔驾驶时，对行驶能耗的影响较小。在本实施例中，驾驶风格可以根据加速度特征来确定，加速度反映了驾驶员操作加速踏板的激烈程度，减速度反映了驾驶员刹车的激烈程度，加速度或减速度的绝对值越大，驾驶风格越激烈，所以本实施例的一种实现方式中，加速度特征作为关键特征量来确定驾驶风格。在本实施例中，驾驶风格还可以根据驾驶预设方式确定，驾驶预设方式可以是驾驶模式和能量回收挡位的组合，其中，驾驶模式按照激烈程度从大到小例如包括但不限于运动模式、经济模式、舒适模式等；能量回收挡位可以分为高、中、低挡， 能量回收挡位越高，驾驶风格越温和。另外，还可以结合道路类型来确定驾驶风格，道路类型是由导航服务方根据道路等级确定的，比如按照限速等级可以分为高速路、城镇路、乡村路、柏油路等或者按照公路质量等级分为硬地路面、砂砾路、泥泞路等，道路类型的不同对驾驶员情绪继而对驾驶行为产生影响，从而也对驾驶风格具有影响。因此，本实施例中，将根据采集的车辆运行特征数据的种类和/或对导航行程中的路况信息的获取权限来确定驾驶风格的判断因素有哪些，具体请参见下文对实施例的说明。
对于高压附件能耗，也称为热管理能耗，是热管理附件例如空调、高压加热器、高压除霜器等启动和使用时消耗的能量。本实施例中，影响车辆的高压附件能耗的影响因素包括但不限于环境温度、空调开关、空调温度等等。车内的高压附件能耗主要来自于空调的启动和运行功率，而空调的功率与空调设定温度以及空调设定温度与环境温度的差异等密切有关，空调设定温度(或者称为目标温度)与环境温度差异越大，高压附件能耗就越大。
对于低压附件能耗，是车辆高压直流电转为低压直流电(DC-DC)的12V或者24V的负载或用电器在启动和使用时消耗的能量。影响车辆的低压附件能耗的影响因素来自于各种低压附件的启动和可能的调节档位，在本实施例中，低压附件能耗的影响因素包括但不限于，远光灯、近光灯和/或示廓灯的开关、主驾和/或副驾座椅加热开关、通风开关、音箱开关、雨刮开关、电动天窗等等。
可以理解，市场上不同车型的车辆的能耗在相同影响因素的条件是不完全相同的，不同车型车辆之间的能耗参考作用不大，因此，分析能耗影响因素和建立能耗模型时，以同一车型的多车辆的数据和实际能耗作为样本来源，将提高实际能耗的能耗模型的分析价值和能耗模型的准确度，从而使电池SOC的计算更接近实际的电池SOC。
本技术方案中，由云端根据同一车型的多车辆历史上的与能耗相关的驾驶数据和环境数据以及实际能耗，分别建立多种类型的能耗模型。在用户开启地图端(导航装置、导航模块)进行导航时，导航服务方根据当前车辆的当前位置或出发地和目的地的信息来规划导航行程，通过当前车辆的车辆运行特征数据来查询能耗模型，获得各种能耗模型中的多个查询值，再结合导航行程中路径和路况信息对查询值进行修正，以分路段地计算到目的地处的全程能耗值，从而根据出发地当前的电池可用能量状态(SOE,State of Energy)、可用的剩余电量百分比(SOC，State of Charge)和全程能耗值来计算目的地的电池SOC。
为此本申请提供一种导航行程电池SOC预估方法，包括以下步骤：根据导航行程获取各路段的路况信息，各路段的路况信息包括各路段的距离和其车速；调用多个行驶能耗模型和驾驶风格能耗系数模型，其中多个行驶能耗模型以同一车型的每个车辆的行驶能耗相关数据作为样本来建立，多个行驶能耗模型至少包括驱动能耗模型，驱动能耗模型用于表示同一车型的每个车辆的每一轮导航行程的车速和该车辆的每一轮导航行程的驱动能耗之间的对应关系；查询驾驶风格能耗系数模型中的与当前车辆的驾驶风格相对应的驾驶风格能耗系数；以及查询多个行驶能耗模型中的分别与各路段的路况信息相对应的各项行驶能耗的查询值；基于各路段的距离并使用驾驶风 格能耗系数和各项行驶能耗的查询值，计算当前车辆的全程行驶预估能耗值；以及使用预估总能耗值计算电池SOC，预估总能耗值至少包括全程行驶预估能耗值。
另外，导航服务通常由导航服务方或地图服务商的服务器(以下也被称为地图端)来提供，包括路径规划和路况信息，具体地，可选地根据车辆的用户或驾驶员的设置，以分路段的方式规划导航行程的路径，导航行程中导航信息包括但不限于全程距离、全程预估时间等，导航行程中的路况信息包括但不限于各路段的距离、各路段的平均车速、道路类型、道路坡度等等。但基于数据安全、服务权限等原因，导航服务方的路况信息不一定会对车端或云端开放，车端和云端是否具有调用或获取地图软件开发工具包(SDK，Software Development Kit)的权限，会导致云端为建立能耗模型所接收的数据样本类别有所不同，从而在预估目的地电池SOC时所不可或缺的路况信息的知晓方才有能力依据能耗模型来实施计算，也就是说，当车端或云端无权获取地图SDK时，预估目的地电池SOC由导航服务方来实施，当车端或云端有权获取地图SDK时，预估目的地电池SOC可以由车端或云端来实施。
图2是本申请实施例中考虑到的对行驶能耗有影响的车辆因素的说明图。如图2所示，行驶能耗主要包括三部分：驱动能耗(为驱动车辆行驶所需要的能耗)、高压附件(主要是空调)能耗与低压附件能耗。驱动能耗的影响因素包括车速、道路坡度、道路系、驾驶风格、交通状况、风阻、天气等。高压附件能耗的影响因素包括环境温度、空调开关、空调设定温度等。低压附件能耗的影响因素包括大灯开关、座椅加热/通风开关、音箱开关、雨刮器开关等。本领域技术人员知晓，低压附件的电压一般为12V，高压附件的低压一般是大于12V(比如380v)。
下面对本申请的一个实施例进行描述。
当导航服务方未开放路径分段路况信息的情况下，为建立能耗模型所使用的样本不包括路径分段信息和路况信息。
图3示出了不使用地图SDK的情况下，本实施例中的地图端(导航模块)与车端(车机控制器或座舱域控制器等)和云端在预估电池SOC时的交互过程。另外，图3中地图端和多个车独立进行表示，然而，地图端是安装在各个车上的。可以理解，地图端(导航模块)可以集成在车机控制器和座舱域控制器等中。步骤S10，同一车型的所有车辆，例如车1、车2……车n采集行驶特征数据，对其中与能耗相关的数据预处理后，上传至云端；步骤S12，云端基于大数据训练，建立和迭代更新对同一车型的车辆通用的能耗模型，并针对每个单车建立驾驶风格能耗系数模型；步骤S13，当前车辆例如车1或车2启动导航请求，地图端确定导航行程详情但不开放路况信息；步骤S14，将能耗模型下发至当前车辆例如车1或车2以便被地图端调用或直接下发至地图端；步骤S15，将当前车辆的包括高压附件和低压附件的附件状态发送给地图端，地图端调用能耗模型并接收附件状态的信息，根据当前车辆的附件状态在各个能耗模型中查询对应的查询值；S16地图端通过结合导航行程上的路段和路况信息来计算出各个路段的能耗，最终预估出导航全程后目的地的电池SOC，并将这一预估结果在车端的地图界面呈现。具体过程如下。
样本采集和预处理：
各个车的车载系统采集所有与能耗相关的数据，包括但不限于，驱动能耗 相关数据，例如道路坡度、平均车速、驱动能耗等；热管理能耗相关数据，例如环境温度、空调开关状态、设定温度、高压能耗等；低压附件能耗相关数据，低压附件开关状态、低压附件模式状态、DCDC低压附件能耗；另外与能耗相关的数据还包括姿态轴加速度，该姿态轴加速度是计算加速度特征的原始数据。
车辆的与能耗相关的数据可以来自以下车载部件：车身电子稳定系统(ESP，Electronic Stability Program)；电机控制器(MCU，Motor Control Unit)；座舱域控制器(CDC,Cockpit Domain Controller)；低压(DC-DC)附件系统；电池管理系统(BMS，Battery Management System)；热管理系统(TMS，Thermal management system)；动力域控制单元(PDCU，Powertrain Domain control Unit)；以及汽车盒子T-Box，T-Box包括传感器模块，其传感器模块中姿态轴传感器感知姿态轴加速度，例如可以采集到车辆的加速度，T-Box包括卫星定位系统时，其可对车辆定位。
整车控制单元(VCU，Vehicle Control Unit)获取各个车载部件的能耗相关数据，并进行预处理，将原始功率信号转换为单位公里消耗的能耗，例如将空调功率信号转换为单位公里消耗的能耗，或者设定时间的均值，以减少无效数据的上传。然后把经过预处理的车端能耗相关数据经由网关和T-box上传至云端，同时T-Box也可以将车辆位置信息上传给云端。
需要说明的是，用于采集能耗相关数据的传感设备的设置位置和对这些数据预处理的处理模块都不必限定在上述提及的车载部件中。
能耗模型建立和更新：
导航行程能耗模型建立装置可以设置在云端，包括接收单元和模型建立单元，接收单元接收同一车型的每个车辆的VCU提供的能耗相关数据，包括但不限于行驶能耗相关数据、通用附件能耗相关数据，模型建立单元以能耗相关数据作为样本，建立同一车型所有车辆都适用的通用的多种能耗模型，本实施例中，多种能耗模型包括驱动能耗模型、高压附件能耗模型(或称为热管理能耗模型)、低压附件能耗模型和附加能耗模型，驱动能耗模型和附加能耗模型都属于行驶能耗模型。接收单元还接收每个单车各自的历史导航行程能耗相关数据，模型建立单元以每个单车的历史导航行程能耗相关数据作为样本，用于建立单个的当前车辆在具体导航行程时的个性化能耗模型，例如驾驶风格能耗系数模型。各能耗模型的建立过程将在下文进行详细描述。
驱动能耗模型的建立
首先需要说明的是，在本实施例中，行驶能耗模型中包括但不限于驱动能耗模型，还包括对驱动能耗有附加的增量或减量的附加能耗模型，下面第5点将给予详细说明。这里的驱动能耗模型基于同一车型的多车辆的数据作为样本来建立，同一车型的所有车辆建立同一个驱动能耗模型，驱动能耗模型为影响变量——平均车速V——对应的平均单位驱动能耗E(单位驱动能耗也可以称之为驱动能耗率，物理含义是单位距离下所消耗的电量)，具体例如为下表1的形式：
表1-1驱动能耗模型例
数据库ID 平均车速 平均单位驱动能耗
1 V1～V2 E1
1 V2～V3 E2
1 …… ……
V基于不同设计可以是不同的值，区间也可变。
表1-2驱动能耗模型例
车速(m/s) 单位驱动能耗(kW)
10-15 9.1
15-20 11.67
20-25 15.6
25-30 22
可以理解，上表1-2中的车速值也可以是以km/h为单位的值。
一个平均车速范围对应一个平均驱动能耗，驱动能耗模型以表格形式建立，例如表1中，V1～V2的范围对应与E1的平均驱动能耗。驱动能耗模型的数据库ID可以按照平均车速的不同范围进行编号。当前车辆可以依照自身在当前路段下的车速在驱动能耗模型中对应地查询到平均驱动能耗E，从而确定当前车辆当前路段下的平均驱动能耗的值。本实施例中驱动能耗模型表示的是速度的平均值和驱动能耗的平均值的对应关系，但可以理解的是取值并不限于平均值，还可以考虑取值为中位数等。中位数和平均值，都是统计学意义的参量，本申请不仅限制于这两个，所有有着统计学意义参量的特征，都可以采用。
驾驶风格能耗系数(驱动能耗修正系数)模型的建立
由于每辆车的用户的驾驶风格会呈现出个性化的行驶特征数据，激烈还是温和地驾驶对能量消耗的影响差异很大，而驾驶风格可以通过加速度的关键特征量来判断，比如平均加速度值较大的倾向于激烈驾驶的风格，加速度出现越频繁的倾向于激烈驾驶的驾驶风格，能耗比平均驱动能耗要高；反过来，则倾向于温和驾驶的风格，能耗比平均驱动能耗要低。针对每一辆车分别建立各自的驾驶风格能耗系数模型，可以在针对所有车辆的通用的行驶能耗模型的基础上在驾驶风格方面增加个性化考虑，可以使单个车的特定数据用于修正和处理通用的行驶能耗模型，以增加每辆车的能耗预估准确度。
在本实施例中，可以不利用地图SDK，不将导航行程路段中加速段的平均加速度、减速段平均减速度等作为数据样本发送给云端来建立驾驶风格能耗系数模型，或者云端并不从地图端获取到导航行程的路况信息，因此在驾驶风格能耗系数模型的建立上，导航行程能耗模型建立装置还包括迭代单元，用于间接地采用强化学习Q-table修正相同驾驶风格下的驾驶能耗系数，以迭代(或称为滤波)的方式动态修正驾驶风格能耗系数，每轮迭代将相应信息填入下面学习表，一次导航到达目的地的电池SOC结果对应一轮迭代。在本实施例中，驾驶风格的确定选取驾驶模式和能量回收挡位这两个作为判断因素，驾驶模式和能量回收档位分别相同时则确定驾驶风格相同。这里，在迭代单元中，将驾驶模式、能量回收挡位的各种不同组合下的驾驶风格能耗系数均初始设置为1并且也作为一次迭代轮记录在学习表中，迭代单元还将每次新的导航行程作为当前一轮，采用前一轮相同驾驶模式和能量回收挡位的每公里SOC绝对误差和驾驶能耗系数来计算这当前一轮的驾驶风格能耗系数。具体计算方法为:
当前轮的驾驶风格能耗系数＝前一轮相同风格下的能耗系数*(1+△)   (式1)
其中△是基于前一轮相同驾驶模式和能量回收挡位的每公里SOC绝对误差。
△＝(S 估-S 实)/K 前一轮        (式2)
其中，S 估是前一轮到达目的地的预估电池SOC，S 实是前一轮到达目的地的实际的电池SOC，K 前一轮是前一轮导航行程的全程总距离。前一轮的绝对误差(S 估-S 实)越大，△值越大。
表2驾驶风格能耗系数模型的Q-table学习表例
Figure PCTCN2021093449-appb-000001
在上面表2中，驾驶模式分为运动模式、经济模式和舒适模式，能量回收挡位分为高、中、低挡，从而驾驶模式和能量回收挡位可以形成9种不同的组合，即9种驾驶风格，将这些组合下的驾驶风格能耗系数均初始设置为1，并分别记为一次轮数，便于后续的迭代轮的查找对应轮数的信息。例如，在导航的第9轮时，当前车辆用户使用的驾驶模式为运动模式，能量回收挡位为高时，则第9轮的驾驶风格能耗系数等于，第0轮的驾驶能耗系数1与(1+(S0-S00)/K0)的乘积。例如，导航的第10轮仍是运动模式和能量回收挡位为高的组合，与前一轮即第9轮的驾驶风格相同，则该第10轮的驾驶风格能耗系数由这一相同驾驶模式和能量回收挡位组合所对应即第9轮的驾驶风格能耗系数来滤波，也就是该第10轮的驾驶风格能耗系数等于第9轮的驾驶风格能耗系数与(1+(S9-S99)/K9)的乘积。例如，导航的第11轮是运动模式和能量回收挡位为中的组合，与前一轮的第1轮的驾驶风格相同，则该第11轮的驾驶风格能耗系数等于其前一轮即第1轮的驾驶风格能耗系数与(1+(S1-S11)/K1)的乘积。例如，导航的第12轮是运动模式和能量回收挡位为低的组合，与第2轮的驾驶风格相同，则该第12轮的驾驶风格能耗系数等于其前一轮即第2轮的驾驶风格能耗系数与(1+(S2-S22)/K2)的乘积。
热管理能耗模型(高压附件能耗模型)的建立
同一车型的所有车辆建立同一个热管理能耗模型，热管理附件能耗主要取决于作为高压附件的运行情况，因此热管理能耗模型也可以称为高压附件能耗模型。在本实施例中，热管理能耗为影响变量，比如空调开关状态、车外环境温度、主驾温度值设定、副驾温度值设定等等高压附件状态所对应的热管理功率P 热，具体形式为：
表3-1热管理能耗模型例
数据库ID 空调开关状态 车外环境温度 主驾温度值设定 副驾温度值设定 热管理功率(W)
3 开 T 环 T 主设 T 副设 P 热1
3 开 T 环 T 主设 无 P 热2
3 开 T 环 无 T 副设 P 热3
3 关 T 环 无 无 P 热4
3 …… …… …… …… ……
空调开关状态有开、关两种状态，车外环境温度T 环、主驾的设定温度T主设和副驾的设定温度分别可以是按照等差的离散值，也可以是安装不同范围来设置。每个影响变量与其它影响因素的各个组合分别对应于一个热管理功率。当前车辆可以依照自身当前的高压附件的状态来查询热管理能耗模型，以确定与当前车辆的高压附件状态对应的热管理功率的查询值P 热(单位为KW)，然后可以根据能耗时间T和热管理功率查询值P 热，得到该能耗时间T下的能耗值E 热(单位为KWh)，即E 热＝P 热*T。可以理解，本实施例中所使用的高压附件能耗模型的样本主要围绕空调的来建立，但高压附件不仅包括空调，其他车辆高压用电器的状态，比如高压加热器、高压除霜器等等的开关、设定温度等均可以进一步作为高压附件能耗模型的影响因素。
下表中给出了热管理能耗模型的一些具体数值例。
表3-2热管理能耗模型例
车外环境温度 主驾设定温度 副驾设定温度 热管理能耗功率
-10度 24度 24度 4.8kW
-10度 24度 24度 5.2Kw
低压附件能耗模型的建立
低压附件能耗模型也是基于同一车型的每个车辆的历史低压附件相关能耗数据建立，同一车型的所有车辆建立同一个低压附件能耗模型，车辆内的所有使用了由电池高压直流电转换的低压直流电的用电附件(DCDC)均会发生能耗，并可以被归入低压附件能耗。本实施例中，导航行程能耗模型建立装置的接收单元接收同一车型的每个车辆的历史低压附件相关能耗数据，模型建立单元将低压附件能耗模型设置为主要的低压附件状态作为影响变量——例如主驾座椅加热开关状态、主驾座椅加热档位、副驾座椅加热开关状态、副驾座椅加热档位、近光灯状态、远光灯状态、示廓灯状态所对应的低压附件功率，具体形式为：
表4低压附件能耗模型例
Figure PCTCN2021093449-appb-000002
低压附件还可以包括车身控制模块(BCM，Body controller Module)所控制的诸多电控装置，包括远近灯、示廓灯、转向灯的灯光系统、喇叭、车窗车门、除霜器、雨刮、音箱等等。本实施例中，仅以举例的方式，提供了一部分对能耗有较显著影响的低压附件状态作为数据样本，如表4中所列那样，用多个低压附件的各状态的所有组合(表中未列出所有组合)各自对应的低压附件功率来建立低压附件能耗模型。当前车辆可以依照自身当前的低压附件的状态来查询低压附件能耗模型，以确定与当前车辆的低压附件状态对应的低压附件功率的查询值P 低(单位为KW)，然后可以根据能耗时间T和低压附件功率查询值P 低，计算得到该能耗时间T下的能耗值E 低(单位为KWh)，即E 低＝P 低*T。作为一些具体的数值例，例如在远光灯开启时，低压附件能耗功率增加20W，雨刮开启时，低压附件能耗功率增加50W，座椅加热开启时，低压附件能耗功率增加40W。
附加能耗模型的建立
附加能耗模型基于同一车型的多车辆的数据建立，同一车型的所有车辆建立同一个附加能耗模型，本实施例中，附加能耗模型为影响变量——平均道路坡度(％)——对应的能耗增加量Aux，以道路坡度0％为基线，基于多车的历史数据，统计同一车型的所有车辆平均道路坡度与平均驱动能耗之间的关系，具体见如下表形式：
表5附加能耗模型例
数据库ID 平均道路坡度 坡度变化导致的能耗增加量
5 -20％ Aux1
5 -19％ Aux2
5 …… ……
5 0％ 0
5 20％ Aux4
道路坡度由车辆上的坡度传感器来识别和处理，为负数时表示下坡，为正数时表示上坡，因此表格里能耗增加量Aux可以是正值也可以是负值。本实施例中，可以以1％为坡度差来列举道路坡度的变化和对驱动能耗的增加量的对应关系，也可以根据模型拟合程度要求以其它的坡度差来列举。在预估未来的附加能耗时，通过路况信息中的各个路段的道路坡度，可以从表5中对应地查询，以获得对应于各路段的道路坡度的增加量的查询值。
可以想到，除了坡度之外，还有其他因素也可能对驱动能耗造成影响，例如车重、胎压等，因此附加能耗模型还可以是道路坡度、车重、胎压的多重影响变量的各种组合所对应的能耗增加量。本实施例中仅仅选取了对附加能耗影响较明显的道路坡度作为影响变量来建模，以减少计算负担并最大程度保证了整体上对行驶能耗预估准确性。
能耗模型预处理和电池SOC预估：
当前车辆的用户开启导航时，则地图端按照车端的出发地和目的地规划导航行程。由于云端无法获得路况路段信息从而无法结合当前路径路况信息和上述的各个模型来预估出发地到目的地的电池SOC，因此本实施例中由地图端(导航装置、导航模块)来负责预估电池SOC。
云端将多种能耗模型下发至当前车辆的座舱域控制器(CDC，Cokpit Domain Controller)。CDC作为地图端在车端的接入点，负责根据当前车辆的高压附件状态和低压附件状态查询高压附件能耗模型和低压附件能耗模型以获得高压附件功率和低压附件功率，以及负责查询当前车辆的驾驶风格能耗系数，并且还可以对功率查询值转换为单位公里消耗的能耗，然后车端的CDC连同驱动能耗模型和附加能耗模型，将驾驶风格能耗系数、当前可用的剩余能量SOE(单位是KWH)、当前可用的电池SOC(单位是％)和包括热管理功率和低压附件功率的附件功率一起发送给地图端。地图端接收当前车辆的驾驶风格能耗系数、附件功率以及驱动能耗模型和附加能耗模型等，并将它们存储为配置文件，结合出发地至目的地的分路段的路况信息，比如各路段的平均车速和各路段的道路坡度，来获取各路段对应的平均驱动能耗和能耗增加量，计算到达目的地的剩余电量，再在车端的地图界面呈现。
可替换地，云端根据当前车辆CDC的请求，查询相关模型得到对应当前车辆的驾驶风格系数以及对应其附件状态的通用附件功率；地图端根据当前车辆CDC的请求，直接调用云端的关于当前车辆的驾驶风格系数和通用附件功率，以及直接调用驱动能耗模型和附加能耗模型等，并将它们存储为配置文件，以及根据当前车辆发送来的高压、低压附件状态和车辆信息查询各个模型以获得对应于当前车辆的能耗查询值、能耗系数值和功率查询值，并对功率查询值转换为单位公里消耗的能耗，然后再结合起点地至目的地的当前路况信息，计算到达目的地的剩余电量，以发送至车端的 地图界面。
根据当前车辆的附件状态查询高压附件能耗模型和低压附件能耗模型，或者根据当前车辆的ID查询驾驶风格能耗系数模型，并不限于实施主体，可以是车端、云端，也可以是地图端来实施，本实施例中对此不做限制。
以下以地图端能够直接从云端调用获取当前车辆的驾驶风格能耗系数、附件功率的情况为例，详细说明地图端计算导航行程目的地电池SOC的过程。
用户开启导航时，地图端根据起始点至目的地规划导航路径并确定本轮导航路径上的各路段的路况信息，根据导航路径获得全程总距离(总路程)L和全程预估时间T，各路段的路况信息包括各路段距离(路程)l i、各路段平均速度v i和各路段坡度slope i，其中i为导航全程各路段的编号。
地图端调用包括驱动能耗模型和附加能耗模型的多个行驶能耗模型，驾驶风格能耗系数模型，还有包括高压附件能耗模型和低压附件能耗模型的通用附件能耗模型，根据沿途各段的平均速度v i，查询配置文件中的驱动能耗模型，得到各路段的查询平均驱动能耗E i；根据各路段的坡度slope i，查询配置文件中附加能耗模型，得到查询附加能耗增加量Aux i；并从配置文件中读取附件能耗查询值和驾驶风格能耗系数，附件能耗查询值等于高压附件能耗查询值(功率)P 热和低压附件能耗查询值(功率)P 低之和。
根据以下公式计算全程消耗能量(导航路径全程能耗)：
全程行驶预估驱动能耗值＝∑((E i+Aux i)*l i*驾驶风格能耗系数)     (式3)
全程附件能耗＝附件能耗查询值*T＝(P 热+P 低)*T               (式4)
预估总能耗值＝全程行驶预估能耗值+全程附件能耗                 (式5)
根据以下公式计算目的地电池SOC(百分比)：
满电可用能量＝当前可用能量SOE/出发地电池SOC                (式6)
目的地剩余可用能量＝当前可用能量SOE–全程消耗能量          (式7)
目的地电池SOC＝目的地剩余可用能量/满电可用能量＝出发地电池SOC-全程消耗能量/当前可用能量SOE*出发地电池SOC                         (式8)
这里的全程总距离(总路程)L和各路段距离l i对应于本申请中的路程信息，全程预估时间T对应本申请中的预估时间信息。
作为一个例子，可以理解，本申请实施例提供的电池SOC预估方法可以应用于车控域控制器(Vehicle Domain Controller，VDC)或者智能座舱域控制器(Cockpit Domain Controller，CDC)，此外还可以应用于BMS的控制器等。
下面参照图4等对本申请的另一个实施例进行描述。
本实施例中，导航行程能耗模型建立装置为建立能耗模型所使用的数据样本除了如上述实施例描述的由车端采集的行驶特征数据和附件状态之外，还包括从地图端获取的导航行程的分路段的路况信息，例如道路类型和/或路段中的加速度特征。
图4示出了本实施例中车端利用地图SDK的情况下，地图端与车端和云端在预估电池SOC时的交互过程。步骤S20，与上述实施例类似地，同一车型的每辆车，例如车1、车2……车n采集行驶特征数据和相对应的能耗，对与能耗相关的数据预处理后，上传至云端，但有区别的地方在于，步骤S21，地图端将路况信息直接上传 至云端或者经由车端上传至云端；步骤S22，可以设置在云端的导航行程能耗模型建立装置的接收单元获取与能耗相关的数据和对应的路况信息，模型建立单元除了以能耗相关的数据作为样本外，还以路况信息作为样本建立能耗模型，包括驱动能耗模型、驾驶风格能耗模型、热管理能耗模型、高压附件能耗模型、低压附件能耗模型、附加能耗模型；步骤S23，当前车辆例如车1启动导航请求；步骤S24，地图端确定车1的导航行程详情并开放路况信息给云端和/或车端；步骤S25，将车1的附件状态、行驶特征连同路况信息一起发送给云端；步骤S26，云端接收车1的附件状态、行驶特征数据、路况信息并根据这些信息来查询能耗模型以获得当前车辆的各项能耗查询值，以便计算出各个路段的能耗，最终预估出导航全程后目的地的电池SOC，并将预估结果下发至车端和地图端；步骤S27，车端将SOC结果更新后，在地图界面呈现。
建立能耗模型所采集的数据样本进一步包括了导航行程的路况信息作为影响变量，从而使得模型建立过程时，将地图端的道路和车速统计数据也参与到能耗模型的建立过程中，扩大了建模的大数据样本量，进一步增加了模型的精确度，提高了预测电池SOC的准确度。
本实施例的模型建立方法基本与上述实施例的模型建立方法相同，区别在于对驾驶风格能耗系数模型的建立，以下将对本实施例中的驾驶风格能耗系数模型的建立进行详细说明。
驾驶风格能耗系数模型的建立
在本实施例中，驾驶风格能耗系数模型的迭代更新，除了考虑上述实施例确定驾驶风格的判断因素——驾驶模式、能量回收挡位——之外，还进一步考虑道路类型和加速度特征作为驾驶风格的判断因素，加速度特征是导航服务方对当前车辆的每一轮导航行程中的加速度统计值，可以体现出当前车辆的驾驶员或用户的驾驶风格的激烈程度，当加速度绝对值较大时，驾驶员或用户的驾驶风格较激烈，反之亦然。通过结合地图端实时共享的路况信息，可以获知每个车辆在各个路段中的加/减速度段的平均加/减速度的历史数据。
本实施例中，驾驶风格除了根据前述上述实施例中的驾驶模式和能量回收挡位之外，还根据道路类型和加速度特征来确定，这样使得对驾驶风格的区分粒度更加小，从而使得驾驶风格能耗模型拟合度更高，从而使得导航行程的能耗预测更加准确，继而使得SOC预测更加准确。加速度特征包括但不限于加速段平均加速度等级、减速段平均减速度等级、急加速比例等级、急减速比例等级、车速标准差等等。这些信息根据车辆历史导航行驶数据获取，具体也就是说，当车辆按照导航信息行驶完成一个路段时，将这些加速度特征与路段信息关联性地存储在存储器(可以在车端也可以在云端，存储的数据库可以称之为加速度特征-路段数据库)中，以供以后查询。
本申请不限于具体选取哪种加速度特征作为建立驾驶风格能耗模型的驾驶风格判断因素，本领域技术人员可以根据实践需要来任意选择，本文仅以下列可能的实现方式举例说明。
在本实施例的一种可能的实现方式中，针对同一车型的每个车辆的历史数据和所对应的路况信息建立驾驶能耗系数模型，该驾驶能耗系数模型是道路类型、驾驶模式、能量回收档位、加速段平均加速度、减速段平均减速度都作为影响变量的驾 驶能耗系数。
加速度段平均加速度，是指对加速度段的采样周期内的加速度取均值，例如在加速度段平均加速度在0～2m/s 2的范围。减速度段平均减速度，是指对减速度段的采样周期内的减速度取均值，例如在-2～0m/s 2的范围。
表6驾驶风格能耗系数模型的Q-table学习表例
Figure PCTCN2021093449-appb-000003
在上面表6中，将驾驶模式分为了运动模式、经济模式和舒适模式，将能 量回收挡位分为高、中、低挡，从而驾驶模式和能量回收挡位可以形成9种不同的组合，另外，本实施例按照预设的等量加速度范围，对加速度段平均加速度和减速段平均减速度划分多个等级，例如表6中每0.1m/s 2范围作为一个等级，如0.1～0.2为一个等级，0.2～0.3为另一个等级……，然后每个等级分别与驾驶模式和能量回收挡位的组合进行再组合，以用于确定每个单车辆的驾驶风格。
本实施例中，将道路类型、驾驶模式、能量回收挡位、各个等级的加速度段平均加速度等级、各个减速段平均减速度等级和每公里SOC绝对误差△的不同影响变量的组合分别作为一个驾驶风格，每个驾驶风格下的驾驶风格能耗系数均初始设置为1，并分别记为一次轮数，用于下一次相同驾驶风格下的驾驶风格能耗以1为起始进行迭代更新。例如，在导航的第3轮时，当前车辆用户使用的驾驶模式为运动模式，能量回收挡位为高时，并且地图端路况信息道路类型为高速，加速段平均加速度等级为0.1～0.2，减速段平均减速度等级-0.1～0，则第3轮的驾驶风格能耗系数等于，其相同驾驶风格的前一轮即第0轮的驾驶能耗系数1与(1+(S0-S00)/K0)的乘积。例如，导航的第5轮是经济模式和能量回收挡位为高，加速段平均加速度等级为0.2～0.3，减速段平均减速度等级-0.3～0.2，则该第5轮的驾驶风格能耗系数等于其相同驾驶风格的前一轮即第4轮的驾驶风格能耗系数1与(1+(S4-S44)/K4)的乘积。
在本实施例的另一种可能的实现方式中，建立驾驶能耗系数模型的影响变量中，加速度特征具体地除了包括加速段平均加速度、减速段平均减速度外，还包括了急加速比例等级、急减速比例等级。急加速比例，是指加速度大于设定值的时间占整个统计段的比例，例如5％-6％。急减速比例，是指减速度小于设定值的时间占整个统计段的比例，例如2％-3％。在这一实现方式中，由于增加了两个影响变量，因此影响变量组合的数量也增加了，驾驶风格的判断粒度更小，使得模型精确性进一步增加。
应当理解，加速度特征中的车速标准差也可以作为建立驾驶风格能耗系数模型的影响因素纳入上述表格中，使得各个影响因素不同之间的组合数量进一步增加，对表征驾驶风格能耗系数的方面更全面，从而提高驾驶风格能耗系数的精准程度。另外，在上面的描述中，利用加速度特征同驾驶模式、能量回收等级和道路类型共同建立一个模型，然而，也可以针对加速度特征独立建立一个模型。
应当理解，作为直接影响因素的以上各项加速度特征和作为间接影响因素的每公里SOC绝对误差△可以根据路况信息的获得、对模型精确度要求、以及计算能力，任意组合或独立作为影响变量来建立对应的驾驶风格能耗模型。
另外，在本实施例中，针对每一个车辆利用其历史数据(单车历史数据)单独建立专用的驾驶风格能耗系数模型。由于驾驶风格属于个性化的事项，因此，针对每一个车辆单独建立专用的驾驶风格能耗系数模型，能够提高能耗的预估准确率。另外，作为其他实施例，也可以利用多车历史数据建立通用的驾驶风格能耗系数模型。
表7中示出了通过上述模型建立方法(学习方法)得到的一个模型例，另外，可以理解，这个模型例仅仅是某一时刻的结果，随着时间的推移，其会被不断更新，有可能发生数据变化。另外，囿于篇幅，在表7中仅仅示出了道路类型、驾驶模式、能量回收挡位与加速度特征的一部分组合，而并不是全部组合。
表7驾驶风格能耗系数模型例
Figure PCTCN2021093449-appb-000004
能耗模型预处理和预估SOC：
当前车辆的用户开启导航时，则地图端按照车端的出发地和目的地规划导航行程。由于地图端开放了导航行程的路况信息，地图端将本轮导航行程的路况信息，包括各路段的平均车速、各路段的道路坡度、行程发送至云端。在本实施例中，由一种基于车云交互的导航行程电池SOC预估系统来执行电池SOC预估方法，该预估系统可以设置在云端，包括行程获取单元、模型调用单元、结果查询单元、行驶能耗计算单元和预估单元。行程获取单元根据导航行程获取各路段的路况信息和导航行程的全程预估时间，各路段的路况信息包括各路段的距离和其车速。模型调用单元调用多个行驶能耗模型和本实施例中的驾驶风格能耗系数模型，其中多个行驶能耗模型以同一车型的每个车辆的行驶能耗相关数据作为样本来建立，例如包括但不限于驱动能耗模型，驱动能耗模型用于表示同一车型的每个车辆的每一轮导航行程的车速和该车辆的每一轮导航行程的驱动能耗之间的对应关系，模型调用单元还调用包括高压附件能耗模型和低压附件能耗模型在内的通用附加能耗模型，高压附件能耗模型和低压附件能耗模型可参考上述实施例中的说明。结果查询单元查询上述实施例或本实施例中描述的驾驶风格能耗系数模型中的与当前车辆的驾驶风格相对应的驾驶风格能耗系数；以及查询多个行驶能耗模型中的分别与各路段的路况信息相对应的各项行驶能耗的查询值。行驶能耗计算单元基于各路段的距离并使用驾驶风格能耗系数和各项行驶能耗的查询值，计算当前车辆的全程行驶预估能耗值，可以参考上述实施例的式3至式5的描述。预估单元使用至少包括全程行驶预估能耗值的预估总能耗值计算到达目的地的电池SOC，可以参考上述实施例的式6至式8的描述。C云端将预估系统得出的目的地电池SOC结果发送至CDC的地图显示界面来呈现。
下面对本申请的又一个实施例进行描述。
行驶能耗模型当前的做法是未区分常走路线，例如家至公司之间的路线，和非常走路线。然而常走路线的样本数明显比非常走路线多，会使得行驶能耗模型在常走路线上被建立得拟合度更高，因此，在通勤场景下，导航行程能耗模型建立装置针对常走路线单独建立单独常走路线驱动能耗模型作为行驶能耗模型中的另一种个性化模型。每个车辆所采集的能耗相关数据中，当行程能耗是在常走路线上发生时其可以被称为常走路线驱动能耗，即行程能耗包括常走路线驱动能耗。接收单元接收导航行程中的常走路线车速和常走路线驱动能耗，利用常走路线驱动能耗模型单独计算常走路线到达目的地电池SOC，可以提升SOC预估准确度，提高用户体验感受。具体实施步骤如下：
基于地图端提供的通勤设置，获取出发点和目的地的位置点，例如家位置点和公司位置点，当满足如下任一条件时，判断为常走路线：
(1)起点为家位置点，终点为公司位置点；或者
(2)起点为公司位置点，终点为家位置点。
模型建立单元建立常走路线驱动能耗模型，常走路线驱动能耗模型用于表示当前车辆的常走路线车速和常走路线驱动能耗的对应关系。常走路线驱动能耗数据模型针对单个车辆使用单车数据建立，如表8所示。
表8常走路线驱动能耗数据模型例
数据库ID 平均车速 平均驱动能耗
7 V1～V2 E7
7 V2～V3 E8
由于常走路线样本量大，模型更新频繁，使得预估能耗更趋向于实际能耗，从而提升了常走路线的SOC预估准确度。
因此，在本实施例中，当导航行程为常走路线时，基于车云交互的导航行程电池SOC预估系统不再采用表1中的同一车型所有车辆通用的驱动能耗模型，而是按照当前车辆的单个车的常走路线驱动能耗模型中的常走路线驱动能耗，以及附加驱动能耗模型中的增加量，来计算整体上的行驶能耗。其中，模型调用单元调用本实施例中描述的常走路线驱动能耗模型；结果查询单元查询常走路线驱动能耗模型中的与常走路线车速相对应的常规路线驱动能耗的查询值；行驶能耗计算单元基于各路段的距离并使用上述实施例或本实施例中的驾驶风格能耗系数以及常规路线驱动能耗的查询值，根据式3至式5来计算当前车辆的全程行驶预估能耗值。
下面参照图5对本申请一个实施例进行描述。该实施例提供一种电池SOC预估方法，其包括S100、获取导航信息与各种状态参数信息；S200、计算驱动能耗；S300、计算高压附件能耗；S400、计算低压附件能耗；S500、计算电池SOC。
本实施例中的S200、S300、S400间的执行顺序是可以自由调整的。另外，S100中的信息不必在一个时间点获取完成，可以根据需要穿插在S200、S300、S400、S500间或其中执行。
另外，S100-S500中的一些具体内容可以采用上述实施例中所描述的技术特征，下面举例进行说明，更详细的内容可以参见上述实施例。
关于S100，其中的要获取的信息可以包括导航路径的起点、终点、经过的路段、各路段的路程信息、总路程信息、预估时间信息、坡度信息、速度信息、驾驶模式信息、能量回收等级信息(车辆的设定参数)、高压附件状态信息、低压附件状态信息、当前电池电量信息等等。
关于S200，其,内容包括根据速度信息、路程信息计算驱动能耗，还包括根据驾驶模式、能量回收等级、道路类型或加速度信息等修正驱动能耗(即上述实施例中的获取驾驶风格能耗系数并基于此计算驱动能耗)，此外还包括根据附加能耗修正驱动能耗(即上述实施例中的根据坡度信息等来计算驱动能耗)。
关于S300，其内容主要包括根据环境温度、空调开关状态等计算空调能耗。
关于S400，其内容包括根据低压附件状态信息与预估时间信息计算低压附件能耗，低压附件状态信息可以包括主驾座椅加热开关状态信息、主驾座椅加热挡位信息、副驾座椅加热开关状态信息、副驾座椅开关挡位信息、近光灯状态信息、远光灯状态信息、示廓灯状态信息、雨刮器状态信息与音箱状态信息等。
关于S500，其内容包括获取当前电池SOC，根据当前电池SOC与驱动能耗(有修正时为修正后的)、高压附件能耗、低压附件能耗计算到达导航路径终点时的电池SOC。
本申请一个实施例提供一种能耗模型建立装置，该能耗模型建立装置可以设置在云端，也可以设置在车辆上，用于建立上述驱动能耗模型、驾驶风格能耗系数模型、高压附件能耗模型、低压附件能耗模型、附加能耗模型，其具体的动作与处理的实质内容已在上面进行了描述，这里不再重复描述。
下面参照图6、7对本申请一个实施例提供的一种电池剩余电量预估装置进行描述。该电池剩余电量预估装置可以设置在云端，也可以设置在车辆上(上面描述的云端进行的模型建立等处理也可以由车端进行)，用于计算驱动能耗、高压附件能耗、低压附件能耗、电池SOC等。如图6所示，该电池剩余电量预估装置100包括处理模块10与获取模块20。处理模块10主要用于执行图5中的S200-S500，获取模块主要用于执行图5中的S100。
这里简略地进行描述一下。驱动能耗根据初始驱动能耗和驾驶风格能耗系数计算，具体而言，根据导航信息(包括导航预估路程、导航预估时间、导轨规划速度、导航规划加速度、导航路径坡度信息)等计算初始驱动能耗，根据驾驶模式、能量回收挡位、导航信息中的道路类型、加速度信息等计算驾驶风格能耗系数，之后，根据初始驱动能耗和驾驶风格能耗系数计算驱动能耗。电池剩余电量预估装置根据高压附件状态信息与导航信息中的预估行驶时间等计算高压附件能耗，高压附件状态信息可以包括空调开关状态信息、主驾温度值设定信息、副驾温度值设定信息等。电池剩余电量预估装置根据低压附件状态信息与导航信息中的预估行驶时间等计算低压附件能耗，抵押附件状态信息可以包括主驾座椅加热开关状态信息、主驾座椅加热挡位信息、副驾座椅加热开关状态信息、副驾座椅开关挡位信息、近光灯状态信息、远光灯状态信息、示廓灯状态信息、雨刮器状态信息与音箱状态信息等。另外，该电池剩余电量预估装置的动作与处理的实质内容在上面已经进行了描述，这里不再重复描述。
另外，在电池剩余电量预估装置计算出预估驾驶能耗后，电池剩余电量预估装置可以向驾驶员显示到达目的地时预估的电池剩余电量，也可以直接向驾驶员显示预计需要消耗多少电池电量(特别是在电池当前电量小于预计需要消耗的电池电量时)。此外，还可以预估车辆还能行驶多少里程，并呈现给驾驶员。
另外，电池剩余电量预估装置可以是多域控制器或导航装置的控制器等，上述各自功能可以由硬件实现，也可以由软件实现，典型地，电池剩余电量预估装置可以采用电子控制单元(electronic control unit，ECU)实现，ECU是指由集成电路组成的用于实现对数据的分析处理发送等一系列功能的控制装置。如图7所示，本申请实施例提供了一种电子控制单元ECU，该ECU包括微型计算机(microcomputer)、 输入电路、输出电路和模/数(analog-to-digital，A/D)转换器。
输入电路的主要功能是对输入信号(例如来自传感器的信号)进行预处理，输入信号不同，处理方法也不同。具体地，因为输入信号有两类：模拟信号和数字信号，所以输入电路可以包括处理模拟信号的输入电路和处理数字信号的输入电路。
A/D转换器的主要功能是将模拟信号转变为数字信号，模拟信号经过相应输入电路预处理后输入A/D转换器进行处理转换为微型计算机接受的数字信号。
输出电路是微型计算机与执行器之间建立联系的一个装置。它的功能是将微型计算机发出的处理结果转变成控制信号，以驱动执行器工作。输出电路一般采用的是功率晶体管，根据微型计算机的指令通过导通或截止来控制执行元件的电子回路。
微型计算机包括中央处理器(central processing unit，CPU)、存储器和输入/输出(input/output，I/O)接口，CPU通过总线与存储器、I/O接口相连，彼此之间可以通过总线进行信息交换。存储器可以是只读存储器(read-only memory，ROM)或随机存取存储器(random access memory，RAM)等存储器。I/O接口是中央处理单元(central processor unit，CPU)与输入电路、输出电路或A/D转换器之间交换信息的连接电路，具体的，I/O接口可以分为总线接口和通信接口。存储器存储有程序，CPU调用存储器中的程序可以执行图3、图4对应实施例描述的预估方法。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
注意，上述仅为本申请的较佳实施例及所运用的技术原理。本领域技术人员会理解，本发明不限于这里的特定实施例，对本领域技术人员来说能够进行各种明显的变化、重新调整和替代而不会脱离本发明的保护范围。因此，虽然通过以上实施例对本申请进行了较为详细的说明，但是本发明不仅仅限于以上实施例，在不脱离本发明的构思的情况下，还可以包括更多其他等效实施例，均属于本发明的保护范畴。

Claims (28)

  1. 一种车辆的电池剩余电量预估方法，其特征在于，包括：
    获取所述车辆的驱动能耗；
    获取所述车辆的高压附件能耗；
    获取所述车辆的低压附件能耗；
    获取当前电池电量；
    根据所述驱动能耗、所述高压附件能耗、所述低压附件能耗与所述当前电池电量计算到达所述车辆的导航路径的终点时的电池剩余电量。
  2. 根据权利要求1所述的电池剩余电量预估方法，其特征在于，
    所述获取所述车辆的低压附件能耗包括：
    获取所述车辆的低压附件状态信息和所述导航路径的预估时间信息；
    根据所述低压附件状态信息与所述预估时间信息计算所述低压附件能耗。
  3. 根据权利要求2所述的电池剩余电量预估方法，其特征在于，
    所述低压附件状态信息包括下述项中的一项或多项：
    主驾座椅加热开关状态信息、主驾座椅加热挡位信息、副驾座椅加热开关状态信息、副驾座椅加热开关挡位信息、近光灯状态信息、远光灯状态信息、示廓灯状态信息、雨刮器状态信息与音箱状态信息。
  4. 根据权利要求2或3所述的电池剩余电量预估方法，其特征在于，所述根据所述低压附件状态信息与所述预估时间信息计算低压附件能耗包括：
    根据所述低压附件状态信息，利用低压附件能耗模型获得低压附件功率信息；
    根据所述低压附件功率信息与所述预估时间信息计算所述低压附件能耗。
  5. 根据权利要求1-4中任一项所述的电池剩余电量预估方法，其特征在于，所述获取所述车辆的驱动能耗包括：
    获取所述车辆的所述导航路径的路程信息与速度信息；
    根据所述速度信息，利用驱动能耗模型获得驱动能耗率信息；
    根据所述驱动能耗率信息与所述路程信息计算所述驱动能耗。
  6. 根据权利要求1-5中任一项所述的电池剩余电量预估方法，其特征在于，还包括：
    获取所述车辆的驾驶模式与能量回收等级；
    根据所述驾驶模式与所述能量回收等级对所述驱动能耗进行修正。
  7. 根据权利要求1-6中任一项所述的电池剩余电量预估方法，其特征在于，还包括：
    获取所述车辆在所述导航路径上的加速度信息；
    根据所述加速度信息对所述驱动能耗进行修正。
  8. 根据权利要求7所述的电池剩余电量预估方法，其特征在于，所述加速度信息包括下述项中的一项或多项：
    加速段平均加速度信息、减速段平均减速度信息、急加速比例信息和急减速比例信息。
  9. 根据权利要求6所述的电池剩余电量预估方法，其特征在于，所述根据所述驾驶模式与所述能量回收等级对所述驱动能耗进行修正包括：
    根据所述驾驶模式与所述能量回收等级，利用驱动能耗修正系数模型获得驱动能耗修正系数；
    根据所述驱动能耗修正系数对所述驱动能耗进行修正。
  10. 根据权利要求7或8所述的电池剩余电量预估方法，其特征在于，所述根据所述加速度信息对所述驱动能耗进行修正包括：
    根据所述导航规划加速度信息，利用驱动能耗修正系数模型获得驱动能耗修正系数；
    根据所述驱动能耗修正系数对所述驱动能耗进行修正。
  11. 根据权利要求9或10所述的电池剩余电量预估方法，其特征在于，所述驱动能耗修正系数模型是根据所述车辆的单车数据建立的。
  12. 根据权利要求1-11中任一项所述的电池剩余电量预估方法，其特征在于，还包括：
    获取所述导航路径的坡度信息；
    根据所述坡度信息对所述驱动能耗进行修正。
  13. 根据权利要求1-12中任一项所述的电池剩余电量预估方法，其特征在于，所述获取所述车辆的高压附件能耗包括：
    获取所述车辆的高压附件状态信息；
    根据所述高压附件状态信息，利用高压附件能耗模型获得高压附件功率信息；
    根据所述高压附件功率信息与所述预估时间信息计算所述高压附件能耗。
  14. 一种车辆的电池剩余电量预估装置，其特征在于，包括获取模块与处理模块，
    所述处理模块用于获取驱动能耗、高压附件能耗与低压附件能耗；
    所述获取模块用于获取当前电池电量；
    所述处理模块还用于根据所述驱动能耗、所述高压附件能耗、所述低压附件能耗 与所述当前电池电量计算到达所述车辆的导航路径的终点时的电池剩余电量。
  15. 根据权利要求14所述的电池剩余电量预估装置，其特征在于，
    所述获取模块还用于获取所述车辆的低压附件状态信息和所述导航路径的预估时间信息；
    所述处理模块还用于根据所述低压附件状态信息与所述预估时间信息计算所述低压附件能耗。
  16. 根据权利要求15所述的电池剩余电量预估装置，其特征在于，
    所述低压附件状态信息包括下述项中的一项或多项：
    主驾座椅加热开关状态信息、主驾座椅加热挡位信息、副驾座椅加热开关状态信息、副驾座椅开关挡位信息、近光灯状态信息、远光灯状态信息、示廓灯状态信息、雨刮器状态信息与音箱状态信息。
  17. 根据权利要求15或16所述的电池剩余电量预估装置，其特征在于，所述根据所述低压附件状态信息与所述预估时间信息计算低压附件能耗包括：
    根据所述低压附件状态信息，利用低压附件能耗模型获得低压附件功率信息；
    根据所述低压附件功率信息与所述预估时间信息计算所述低压附件能耗。
  18. 根据权利要求14-17中任一项所述的电池剩余电量预估装置，其特征在于，
    所述获取模块还用于获取所述车辆的所述导航路径的路程信息与速度信息；
    所述处理模块还用于根据所述速度信息，利用驱动能耗模型获得驱动能耗率信息；
    所述处理模块还用于根据所述驱动能耗率信息与所述路程信息计算所述驱动能耗。
  19. 根据权利要求14-18中任一项所述的电池剩余电量预估装置，其特征在于，
    所述获取模块还用于获取所述车辆的驾驶模式与能量回收等级；
    所述处理模块还用于根据所述驾驶模式与所述能量回收等级对所述驱动能耗进行修正。
  20. 根据权利要求14-19中任一项所述的电池剩余电量预估装置，其特征在于，
    所述获取模块还用于获取所述车辆在所述导航路径上的加速度信息；
    所述处理模块还用于根据所述加速度信息对所述驱动能耗进行修正。
  21. 根据权利要求20所述的电池剩余电量预估装置，其特征在于，所述加速度信息包括下述项中的一项或多项：
    加速段平均加速度信息、减速段平均减速度信息、急加速比例信息和急减速比例信息。
  22. 根据权利要求19所述的电池剩余电量预估装置，其特征在于，所述根据所述驾驶模式与所述能量回收等级对所述驱动能耗进行修正包括：
    根据所述驾驶模式与所述能量回收等级，利用驱动能耗修正系数模型获得驱动能耗修正系数；
    根据所述驱动能耗修正系数对所述驱动能耗进行修正。
  23. 根据权利要求20或21所述的电池剩余电量预估装置，其特征在于，所述根据所述加速度信息对所述驱动能耗进行修正包括：
    根据所述导航规划加速度信息，利用驱动能耗修正系数模型获得驱动能耗修正系数；
    根据所述驱动能耗修正系数对所述驱动能耗进行修正。
  24. 根据权利要求22或23所述的电池剩余电量预估装置，其特征在于，所述驱动能耗修正系数模型是根据所述车辆的单车数据建立的。
  25. 根据权利要求14-24中任一项所述的电池剩余电量预估装置，其特征在于，
    所述获取模块还用于获取所述导航路径的坡度信息；
    所述处理模块还用于根据所述坡度信息对所述驱动能耗进行修正。
  26. 根据权利要求14-25中任一项所述的电池剩余电量预估装置，其特征在于，
    所述获取模块还用于获取所述车辆的高压附件状态信息；
    所述处理模块还用于根据所述高压附件状态信息，利用高压附件能耗模型获得高压附件功率信息；
    所述处理模块还用于根据所述高压附件功率信息与所述预估时间信息计算所述高压附件能耗。
  27. 一种计算设备，其特征在于，包括处理器与存储器，所述存储器存储有计算机程序，所述计算机程序当被所述处理器运行时执行权利要求1-13中任一项所述的电池剩余电量预估方法。
  28. 一种计算机可读存储介质，其特征在于，存储有计算机程序，所述计算机程序当被计算机运行时执行权利要求1-13中任一项所述的电池剩余电量预估方法。
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