CN117818361A - Method, device, equipment and storage medium for predicting remaining endurance mileage of vehicle - Google Patents
Method, device, equipment and storage medium for predicting remaining endurance mileage of vehicle Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for predicting the remaining range of a vehicle, wherein the method, the device, the equipment and the storage medium are used for obtaining the available remaining capacity of a battery of the vehicle, determining the historical average energy consumption and the real-time predicted average energy consumption of the vehicle, determining the first average energy consumption of the vehicle according to the historical average energy consumption and the real-time predicted average energy consumption, and then taking the ratio of the available remaining energy of the battery to the first average energy consumption as a predicted value of the remaining range of the vehicle. According to the method and the device for predicting the remaining range of the vehicle, when the remaining range of the vehicle is predicted, the energy consumption condition of the vehicle under the historical working condition and the future working condition is comprehensively considered, and the accuracy of the prediction result is improved.
Description
Technical Field
The application belongs to the field of new energy automobiles, and particularly relates to a method, a device, equipment and a storage medium for predicting the remaining endurance mileage of a vehicle.
Background
With the development of new energy technology, new energy automobiles are increasingly used, and the new energy automobiles generally refer to automobiles provided with an electric drive system, such as electric automobiles, hybrid electric automobiles and the like. The new energy automobile can drive the automobile to run through the motor by utilizing the energy provided by the battery through the electric driving system. Since the energy that the battery can store is limited and the remaining energy in the battery varies as the vehicle is operated, the remaining range of the vehicle also varies. The remaining range of the vehicle represents the number of mileage which can be travelled by the vehicle before the battery energy is exhausted under the condition that the vehicle only takes the battery as a power source, and the remaining range of the vehicle can remind a vehicle owner to prepare energy before or in the process of driving the vehicle, so that the phenomenon that the travel plan is influenced due to the fact that the half-way energy is exhausted is avoided.
Currently, the conventional manner of determining the remaining range of a vehicle is mainly to estimate the remaining range according to the historical data of the vehicle and the current remaining energy. However, this approach only considers the historical data of the vehicle, resulting in inaccurate final estimation results.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for predicting the remaining range of a vehicle, which comprehensively consider the energy consumption condition of the vehicle under the historical working condition and the future working condition when predicting the remaining range of the vehicle, and improve the accuracy of a prediction result.
In a first aspect, an embodiment of the present application provides a method for predicting a remaining range of a vehicle, including:
acquiring available residual energy of a battery of the vehicle;
determining historical average energy consumption and real-time prediction average energy consumption of the vehicle;
determining a first average energy consumption of the vehicle according to the historical average energy consumption and the real-time prediction average energy consumption;
and taking the ratio of the available residual energy of the battery to the first average energy consumption as a predicted value of the residual range of the vehicle.
As one possible implementation, determining a historical average energy consumption of the vehicle includes:
acquiring a characteristic parameter value corresponding to a vehicle, wherein the characteristic parameter is a parameter representing the state of the vehicle;
Acquiring target driving state data matched with the characteristic parameter value from prestored driving state data of the vehicle according to the characteristic parameter value;
and determining the historical average energy consumption of the vehicle according to the target driving state data.
As a possible implementation manner, before the target driving state data matched with the characteristic parameter value is obtained from the pre-stored driving state data of the vehicle according to the characteristic parameter value, the method further includes:
judging whether a preset target driving state data acquisition condition is met or not;
obtaining target driving state data matched with the characteristic parameter value from the pre-stored driving state data of the vehicle, wherein the target driving state data comprises the following steps:
in the case where it is determined that the target driving state data acquisition condition is satisfied, target driving state data matching the characteristic parameter value is acquired from the driving state data of the vehicle stored in advance.
As one possible implementation manner, determining whether the preset target driving state data acquisition condition is satisfied includes:
determining target driving state data matched with the characteristic parameter value in the driving state data of the vehicle stored in advance;
determining whether the target driving state data can represent an energy consumption performance of the vehicle;
Under the condition that the target driving state data can represent the energy consumption performance condition of the vehicle, determining that the preset target driving state data acquisition condition is met;
in the case where it is determined that the target driving state data may not represent the energy consumption performance situation of the vehicle, it is determined that the preset target driving state data acquisition condition is not satisfied.
As one possible implementation, determining the historical average energy consumption of the vehicle further includes:
under the condition that the preset target driving state data acquisition condition is not met, acquiring second average energy consumption of the vehicle under standard working condition simulation;
the second average energy consumption is taken as the historical average energy consumption of the vehicle.
As one possible implementation, determining a real-time predicted average energy consumption of the vehicle includes:
predicting a future running path of the vehicle and working condition data of the vehicle in the future running path according to map navigation information and path planning information of the vehicle;
controlling a pre-constructed dynamic simulation model of the vehicle to perform simulation operation according to the future driving path and the working condition data to obtain third average energy consumption;
and taking the third average energy consumption as the real-time predicted average energy consumption of the vehicle.
As one possible implementation, determining a first average energy consumption of the vehicle from the historical average energy consumption and the real-time predicted average energy consumption includes:
Determining a first weight corresponding to the historical average energy consumption and a second weight corresponding to the real-time prediction average energy consumption, wherein the sum of the first weight and the second weight is 1;
calculating a first product of the first weight and the historical average energy consumption and a second product of the second weight and the real-time prediction average energy consumption;
the sum of the first product and the second product is taken as a first average energy consumption of the vehicle.
In a second aspect, an embodiment of the present application provides a device for predicting a remaining range of a vehicle, including:
an acquisition unit configured to acquire remaining energy available from a battery of the vehicle;
a historical average energy consumption determination unit configured to determine a historical average energy consumption of the vehicle;
a future average energy consumption determining unit for determining a real-time predicted average energy consumption of the vehicle;
the comprehensive average energy consumption determining unit is used for determining first average energy consumption of the vehicle according to the historical average energy consumption and the real-time prediction average energy consumption;
and the predicted value determining unit is used for taking the ratio of the available residual energy of the battery to the first average energy consumption as a predicted value of the residual endurance mileage of the vehicle.
In a third aspect, an embodiment of the present application provides a device for predicting a remaining range of a vehicle, including: a processor and a memory storing computer program instructions;
The processor, when executing the computer program instructions, implements the method for predicting the remaining range of the vehicle of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where computer program instructions are stored on the computer readable storage medium, where the computer program instructions, when executed by a processor, implement the method for predicting remaining range of a vehicle according to the first aspect.
According to the method, the device, the equipment and the storage medium for predicting the remaining range of the vehicle, available remaining capacity of a battery of the vehicle is obtained, historical average energy consumption and real-time predicted average energy consumption of the vehicle are determined, first average energy consumption of the vehicle is determined according to the historical average energy consumption and the real-time predicted average energy consumption, and then the ratio of the available remaining energy of the battery to the first average energy consumption is used as a predicted value of the remaining range of the vehicle. According to the method and the device, the energy consumption conditions of the vehicle under the historical working condition and the future working condition are comprehensively considered, and the accuracy of the prediction result is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
Fig. 1 is an application scenario schematic diagram of a method for testing a remaining range of a vehicle according to an embodiment of the present application;
fig. 2 is a schematic diagram of cloud platform data processing according to an embodiment of the present application;
fig. 3 is a flow chart of a method for testing a remaining range of a vehicle according to an embodiment of the present application;
FIG. 4 is a flow chart of a method of determining historical average energy consumption of a vehicle provided in an embodiment of the present application;
fig. 5 is a schematic diagram of target driving state data matching by using a cloud platform according to an embodiment of the present application;
FIG. 6 is a flow chart of a method of determining a real-time predicted average energy consumption of a vehicle according to an embodiment of the present application;
fig. 7 is a schematic diagram of determining a real-time predicted average energy consumption by a cloud platform according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a device for testing a remaining range of a vehicle according to an embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of a test device for remaining range of a vehicle according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application are described in detail below to make the objects, technical solutions and advantages of the present application more apparent, and to further describe the present application in conjunction with the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative of the application and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
Firstly, it should be noted that, in the following embodiments of the present application, all the remaining range of the vehicle refers to the remaining range corresponding to the battery of the vehicle, that is, the range that the vehicle can continue to run under the condition that only the battery provides power.
In the prior art, for a vehicle provided with an electric drive system, when predicting the remaining range of the vehicle, a large amount of historical data of the vehicle is generally collected, then a range prediction model is trained by using the large amount of historical data, and the trained prediction model is used for predicting the remaining range of the vehicle. However, the accuracy of the prediction model constructed by the artificial intelligence algorithm has great influence on the prediction accuracy of the remaining range, and if the model accuracy is low, the prediction result is inaccurate. Moreover, the method is completely based on historical data of the vehicle, and when the residual endurance mileage of the vehicle is predicted by adopting the method due to uncertainty of future working conditions of the vehicle, the used energy consumption may have a large difference from the actual energy consumption of the future working conditions, so that the prediction result is inaccurate.
In view of this, the embodiment of the application provides a novel method for predicting the remaining range of a vehicle, which predicts the remaining range of the vehicle according to the historical average energy consumption and the predicted real-time average energy consumption of the vehicle, wherein the historical average energy consumption refers to the average energy consumption of the vehicle under the historical working condition, and the predicted real-time average energy consumption refers to the average energy consumption of the vehicle under the future working condition obtained through real-time prediction. In this way, when the remaining range of the vehicle is predicted, the average energy consumption of the vehicle under the historical working condition and the future working condition is comprehensively considered, compared with the method for predicting the remaining range by only adopting the average energy consumption under the historical working condition, the method for predicting the remaining range by combining the average energy consumption under the future working condition is more in line with the actual condition of the vehicle, so that the accuracy of the prediction result is improved.
The method for predicting the remaining range of the vehicle, which is provided by the embodiment of the application, can be used for predicting the remaining range of any vehicle provided with an electric drive system, including but not limited to a pure electric vehicle, a hybrid electric vehicle and the like.
The method for predicting the remaining range of the vehicle, which is provided by the embodiment of the application, can be executed by the cloud platform or the vehicle controller.
The cloud platform is also called a cloud computing platform, and is a service based on hardware resources and software resources, provides computing, networking and storage capabilities, and is composed of a cloud server on which cloud platform server-side software is installed, a cloud computer on which cloud platform client-side software is installed, and a network component.
The vehicle controller can be a whole vehicle computer, a whole vehicle controller, a domain controller and the like.
The following describes an example of execution by the cloud platform.
Referring to fig. 1, a schematic diagram of an application scenario of a method for predicting a remaining range of a vehicle according to an embodiment of the present application is shown in fig. 1, where the application scenario includes: vehicle 110 and cloud platform 120, wherein communication is performed between vehicle 110 and cloud platform 120 through the internet of vehicles.
The cloud platform 120 may be a storage type cloud platform, a computing type cloud platform, a comprehensive cloud computing platform, and the like.
The internet of vehicles is a large system network which is based on an in-vehicle network, an inter-vehicle network and a vehicle-mounted mobile internet and performs wireless communication and information interaction among vehicles, roads, vehicles, people, the vehicle-internet and the like according to agreed communication protocols and data interaction standards, and is an integrated network capable of realizing intelligent traffic management, intelligent dynamic information service and intelligent vehicle control. May be a wireless wide area network (Wireless Wide Area Network, WWAN), a wireless local area network (Wireless Local Area Network, WLAN), a wireless metropolitan area network (Wireless Metropolitan AreaNetwork, WMAN), a wireless personal area network (Wireless Personal Area Network, WPAN), etc., without limitation.
In the running process of the vehicle 110, a vehicle controller in the vehicle 110 collects driving state data and battery state data of the vehicle 110 in real time, and reports the collected data to the cloud platform 120 in real time, and the cloud platform 120 can store the data reported by the vehicle controller in real time. The driving State data may include real-time energy consumption data, a power system temperature, an air conditioning State, a vehicle speed, mileage, motor torque and rotation speed, tire pressure, cabin interior and exterior temperature, energy consumption states Of each component, fault states Of each component, and the like, and the battery State data may include a battery available remaining capacity (SOC) and a battery Health (SOH).
The cloud platform 120 adopts classified storage when storing data reported by the vehicle controller. When the cloud platform 120 acquires the data reported by the vehicle controller, the cloud platform can acquire corresponding weather information, season information, traffic road condition information and the like in real time, wherein the weather information can be acquired through set weather software, the season information can be determined according to time, and the traffic road condition information can be acquired from map navigation information of the vehicle 110 or automatic driving path planning. Based on the above, when the data is classified and stored, the characteristic parameters can be set according to weather information, season information, traffic information and the like, then the data is classified according to the set characteristic parameters, and the data of different categories is stored to different positions. For example, the characteristic parameters may include one or more of the following: road conditions, weather, time periods, ambient temperature, air conditioning states, seasons and the like, wherein parameter values corresponding to road conditions can be divided into cities, suburbs, high speeds and the like, parameter values corresponding to weather can be divided into sunny days, cloudy days, rainy days and the like, parameter values corresponding to time periods can be divided into daytime, evening and the like, parameter values corresponding to ambient temperature can be divided into high temperature, normal temperature, low temperature and the like, parameter values corresponding to air conditioning states can be divided into refrigeration, heating, defrosting, defogging, closing and the like, and parameter values corresponding to seasons can be divided into spring, summer, autumn, winter and the like. And arranging and combining the set M characteristic parameters to form N categories, classifying the data according to the N categories to obtain N data packets, wherein different data packets can form different data storage blocks for storage respectively.
Referring to fig. 2, a schematic diagram of data processing of the cloud platform 120 includes real-time data collection and data classification storage, in which 3 examples of N data packets are listed, namely class 1, class 2, and class 3.
When predicting the remaining range of the vehicle 110, the vehicle controller collects the remaining battery capacity of the vehicle 110 and uploads the remaining battery capacity to the cloud platform 120, and the cloud platform 120 calculates the historical average energy consumption of the vehicle 110 according to the stored data and predicts the real-time predicted average energy consumption of the vehicle 110. The cloud platform 120 then calculates a remaining range of the vehicle 110 from the battery available remaining capacity, the historical average energy consumption, and the real-time predicted average energy consumption, and then may send the calculated remaining range of the vehicle 110 to a vehicle controller, which may send the remaining range to a vehicle meter to cause the meter to display the remaining range of the vehicle.
Of course, the method for predicting the remaining range of the vehicle may also be executed by the vehicle controller, and when the vehicle controller is adopted to execute the method, the steps executed by the cloud platform are directly put into the vehicle controller to execute the steps, and detailed descriptions are omitted.
However, considering that a large amount of data is required to be used when predicting the remaining range of the vehicle, and various calculations are involved, a large storage space and running memory are required, if the calculation is performed by the vehicle controller, a large amount of memory may be occupied to affect the vehicle performance, and if the calculation is performed by the cloud platform, the problem that the vehicle memory is occupied in a large amount can be exactly solved. Therefore, the cloud platform is preferably adopted for execution.
Referring to fig. 3, a flowchart of a method for predicting a remaining range of a vehicle according to an exemplary embodiment of the present application, as shown in fig. 3, the method for predicting a remaining range of a vehicle according to the present embodiment may include the following steps:
s31, acquiring available residual energy of a battery of the vehicle.
And obtaining the available residual capacity of the battery of the vehicle, namely obtaining the SOC value of the battery.
S32, determining historical average energy consumption and real-time prediction average energy consumption of the vehicle.
The historical average energy consumption refers to average energy consumption of the vehicle under the historical working condition, and the real-time prediction average energy consumption refers to average energy consumption of the vehicle under the predicted future working condition.
S33, determining first average energy consumption of the vehicle according to the historical average energy consumption and the real-time prediction average energy consumption.
S34, the ratio of the available residual energy of the battery to the first average energy consumption is used as a predicted value of the residual range of the vehicle.
According to the method for predicting the remaining range of the vehicle, the available remaining capacity of the battery of the vehicle is obtained, the historical average energy consumption and the real-time predicted average energy consumption of the vehicle are determined, the first average energy consumption of the vehicle is determined according to the historical average energy consumption and the real-time predicted average energy consumption, and then the ratio of the available remaining energy of the battery to the first average energy consumption is used as a predicted value of the remaining range of the vehicle. According to the method and the device, when the remaining endurance mileage of the vehicle is predicted, the energy consumption condition of the vehicle under the historical working condition and the future working condition is comprehensively considered, and the accuracy of the prediction result is improved.
The implementation manner of each step is described below with reference to the application scenario shown in fig. 1.
In some embodiments, in S31, when determining that the vehicle needs to be predicted for the remaining range, the vehicle controller may obtain the available remaining battery capacity of the vehicle from the battery management system BMS of the vehicle, and then report the available remaining battery capacity to the cloud platform in real time, so that the cloud platform may obtain the available remaining battery capacity of the vehicle.
Because battery degradation performance has been comprehensively considered before the BMS provides the battery usable remaining capacity, the accuracy of the battery usable remaining capacity of the vehicle obtained from the BMS is high.
In one example, a control for triggering measurement of the remaining range may be provided in the vehicle, and when the user needs to measure the remaining range of the vehicle, the control may be triggered, and the vehicle controller may detect a triggering state of the control, and after detecting that the control is triggered, determine that prediction of the remaining range of the vehicle is needed. Meanwhile, in order to enable the cloud platform to start a measurement flow of the remaining range, when the control is detected to be triggered, or when available remaining capacity of the battery is reported to the cloud platform, the vehicle controller sends a first instruction for indicating the cloud platform to predict the remaining range.
In another example, the cloud platform may periodically send a second instruction to the vehicle controller indicating that a remaining range prediction is to be made. Based on this, the vehicle controller may determine that a remaining range prediction for the vehicle is required after receiving the second instruction.
In some embodiments, in S32, as shown in fig. 4, when determining the historical average energy consumption of the vehicle, the following steps may be included:
S41, obtaining a corresponding characteristic parameter value of the vehicle, wherein the characteristic parameter is a parameter representing the state of the vehicle.
The characteristic parameters are consistent with the characteristic parameters adopted when the cloud platform performs classified storage on the data uploaded by the vehicle controller. One or more of the following parameters may be included: road conditions, weather, time periods, ambient temperature of the vehicle, air conditioning conditions of the vehicle or seasons. Under the condition that the characteristic parameters comprise road conditions, the parameter values corresponding to the road conditions can be determined according to map navigation data of the vehicle or path planning information of automatic driving, wherein the parameter values corresponding to the road conditions can be urban, suburban or high-speed and the like.
Under the condition that the characteristic parameters comprise weather, the parameter values corresponding to the weather can be obtained in real time in weather software, wherein the parameter values corresponding to the weather can be in sunny days, cloudy days or rainy days.
Under the condition that the characteristic parameters comprise time periods, the cloud platform can acquire clock data, and parameter values corresponding to the time periods are determined according to the clock data, wherein the parameter values corresponding to the time periods can be daytime, evening and the like.
Under the condition that the characteristic parameters comprise the environmental temperature of the vehicle, the cloud platform can send a third instruction for indicating the vehicle to acquire the environmental temperature to the vehicle controller, the vehicle controller responds to the third instruction, the temperature acquisition device arranged in the vehicle is used for acquiring the environmental temperature value of the environment of the vehicle, and the acquired environmental temperature value is uploaded to the cloud platform, so that the cloud platform can determine the parameter value corresponding to the parameter of the environmental temperature of the vehicle according to the environmental temperature value uploaded by the vehicle controller, wherein the parameter value corresponding to the environmental temperature of the vehicle can be high temperature, normal temperature or low temperature and the like.
Under the condition that the characteristic parameters comprise the air conditioning state of the vehicle, the cloud platform can send a fourth instruction for indicating the vehicle controller to acquire the air conditioning running state, the vehicle controller responds to the fourth instruction to acquire the state information of the vehicle-mounted air conditioner and upload the acquired air conditioning state information to the cloud platform, so that the cloud platform can determine the parameter value corresponding to the air conditioning state of the vehicle according to the air conditioning state information uploaded by the vehicle controller, wherein the parameter value corresponding to the air conditioning state of the vehicle can be refrigeration, heating, defrosting, defogging or closing.
Under the condition that the characteristic parameters comprise seasons, the cloud platform can acquire real-time date information, and then determine parameter values corresponding to the seasons according to the acquired date information, wherein the parameter values corresponding to the seasons can be spring, summer, autumn or winter.
S42, acquiring target driving state data matched with the characteristic parameter value from the pre-stored driving state data of the vehicle according to the characteristic parameter value.
In the running process of the vehicle, the vehicle controller can upload driving state data of the vehicle to the cloud platform in real time, and the cloud platform can store the driving state data uploaded by the vehicle controller in a classified mode according to the characteristic parameters to obtain N groups of data.
After the characteristic parameter value corresponding to the vehicle is acquired, the cloud platform searches the data corresponding to the characteristic parameter value from the stored N groups of data to serve as target driving state data matched with the acquired characteristic parameter value.
As shown in fig. 5, a schematic diagram of target driving state data matching performed by the cloud platform is shown.
And the cloud platform can screen out target driving state data closest to the current state of the vehicle from stored historical driving state data according to the characteristic parameter values corresponding to the vehicle. For example, when the current vehicle runs on the expressway in a rainy day and the vehicle speed is more than 80km/h, the cloud platform can call historical driving state data meeting three states of rainy day, expressway and vehicle speed more than 80km/h from prestored driving state data of the current vehicle according to the special constant parameter value of the current vehicle to calculate historical average energy consumption.
S43, determining historical average energy consumption of the vehicle according to the target driving state data.
The driving state data stored by the cloud platform comprises vehicle energy consumption data, mileage and the like, the average energy consumption can be calculated according to the vehicle energy consumption data and the corresponding mileage, and the average energy consumption calculated according to the target driving state data is used as the historical average energy consumption of the vehicle.
According to the implementation mode, the cloud platform can screen target driving state data closest to the current state of the vehicle from stored historical driving state data according to the characteristic parameter values corresponding to the vehicle, so that historical average energy consumption determined according to the target driving state is more accurate. The method solves the problem that single historical driving data cannot show different energy consumption performances under different environments, vehicle conditions and the like, and the accuracy performance difference of the residual range estimation under different conditions of the vehicle (such as high temperature or low temperature, expressway or urban road) is large.
In some embodiments, before S42, it may further include: judging whether a preset target driving state data acquisition condition is met, and acquiring target driving state data matched with the characteristic parameter value from prestored driving state data of the vehicle under the condition that the target driving state data acquisition condition is met.
When judging whether the preset target driving state data acquisition condition is met, the target driving state data matched with the characteristic parameter value in the pre-stored driving state data of the vehicle can be determined first, then whether the target driving state data can represent the energy consumption performance condition of the vehicle is determined, the preset target driving state data acquisition condition is determined to be met under the condition that the target driving state data can represent the energy consumption performance condition of the vehicle is determined, and the preset target driving state data acquisition condition is determined not to be met under the condition that the target driving state data cannot represent the energy consumption performance condition of the vehicle is determined. In this way, it can be determined whether the maturity of the current target driving state data meets the computational requirements of historical average energy consumption.
In one example, when determining whether the target driving state data may represent the energy consumption performance of the vehicle, the data amount of the target driving state data may be determined, and then the data amount may be compared with a preset data amount threshold value, and if the data amount is greater than or equal to the data amount threshold value, it may be determined that the target driving state data may represent the energy consumption performance of the vehicle; in the case where the data amount is smaller than the data amount threshold, it is determined that the target driving state data is not yet sufficient to represent the energy consumption behavior of the vehicle. The data amount threshold may be set according to actual situations.
In another example, when determining whether the target driving state data may represent the energy consumption performance of the vehicle, a corresponding accumulated mileage may be determined according to the determined target driving state data, the accumulated mileage may be compared with a preset mileage threshold value, the target driving state data may represent the energy consumption performance of the vehicle when the accumulated mileage is greater than or equal to the mileage threshold value, and the target driving state data may not represent the energy consumption performance of the vehicle when the accumulated mileage is less than the mileage threshold value. The mileage threshold may be set according to actual situations.
Through the implementation manner, the finally obtained target driving state data can be ensured to reflect the energy consumption performance of the vehicle, and the accuracy of historical average energy consumption calculated according to the target driving state data is ensured.
In some embodiments, in the case where it is determined that the target driving state data matching the characteristic parameter value cannot be acquired from the pre-stored driving state data, the historical average energy consumption of the vehicle may be determined in the following manner:
and obtaining second average energy consumption of the vehicle under standard working condition simulation, and taking the second average energy consumption as historical average energy consumption of the vehicle.
The second average energy consumption may be average energy consumption under simulation using standard working conditions, such as NEDC (New-European-Driving-Cycle), CLTC (China Light Vehicle Test Cycle, driving condition of chinese light vehicle), etc., at the initial stage of running of the New vehicle.
By the method, the prediction of the remaining range of the vehicle with insufficient historical driving state data can be guaranteed.
In some embodiments, in S32, in determining a real-time predicted average energy time for the vehicle, the following steps may be included as shown in fig. 6:
S61, predicting a future running path of the vehicle and working condition data of the vehicle in the future running path according to map navigation information and path planning information of the vehicle.
The cloud platform may send a fifth instruction for instructing to acquire map navigation information and path planning information of the vehicle to the vehicle controller when predicting the real-time predicted average energy consumption of the vehicle, and the vehicle controller may upload the map navigation information and the path planning information of the vehicle to the cloud platform in response to the fifth instruction. The route planning information is generated by the automatic driving system based on map navigation information.
The cloud platform determines the current position and the final position of the vehicle according to the acquired map navigation information and path planning information of the vehicle, takes the path from the current position to the final position as the future running path of the vehicle, and acquires vehicle working condition data corresponding to the future running path from the map navigation information and the path planning information of the vehicle as the working condition data of the vehicle in the future running path, wherein the working condition data comprises the relation between the vehicle speed and time and the relation between the road gradient and time.
S62, controlling a pre-constructed dynamic simulation model of the vehicle to perform simulation operation according to the future driving path and working condition data of the vehicle in the future driving path, and obtaining third average energy consumption.
The dynamic simulation model of the vehicle is preset and stored in the cloud platform, the cloud platform operates in a simulation environment according to the future driving path and the working condition data of the vehicle in the future driving path by controlling the dynamic simulation model of the vehicle, so that the total energy consumption required by the vehicle to operate according to the future driving path and the working condition data of the vehicle in the future driving path can be obtained, then the corresponding future driving mileage can be determined according to the future driving path, and the total energy consumption obtained through simulation is divided by the future driving mileage, so that the third average energy consumption can be obtained.
And S63, taking the third average energy consumption as the real-time prediction average energy consumption of the vehicle.
According to the implementation mode, the future working condition of the vehicle is predicted according to the navigation data and the path planning of the vehicle, so that the predicted working condition is closer to the future actual running working condition of the vehicle, the accuracy is higher, the real-time prediction average energy consumption can be determined in a simulation mode, the operation is simple, the accuracy is high, and the implementation is easy.
Referring to fig. 7, a schematic diagram of real-time predicted average energy consumption is determined for a cloud platform.
In some embodiments, in S33, a weighted sum calculation may be performed on the historical average energy consumption and the real-time predicted average energy consumption, and the calculation result is taken as the first average energy consumption of the vehicle.
When calculating the first average energy consumption, a first weight corresponding to the historical average energy consumption and a second weight corresponding to the real-time prediction average energy consumption can be determined, wherein the sum of the first weight and the second weight is 1, then, the first product of the first weight and the historical average energy consumption and the second product of the second weight and the real-time prediction average energy consumption are calculated respectively, and finally, the sum of the first product and the second product is taken as the first average energy consumption of the vehicle. The corresponding calculation formula is as follows:
first average energy consumption = a real-time predicted average energy consumption + (1-a) historical average energy consumption, a e 0, 1.
The first weight and the second weight may be dynamically changed values, and the first weight and the second weight are used for adjusting weights of real-time prediction average energy consumption and historical average energy consumption.
In some embodiments, the value of α depends on the future operating range for which the average energy consumption is predicted in real time. Under the condition that the future working condition mileage is closer to the predicted remaining endurance mileage, the real-time predicted average energy consumption is more accurate, namely, the value of alpha is closer to 1, and more calculation of the remaining endurance mileage by using the real-time predicted average energy consumption is indicated; in contrast, under the condition that the future working condition mileage is smaller than the predicted remaining range mileage, the value of alpha is closer to 0, and when the future working condition data cannot be predicted, alpha is equal to 0, and the remaining range mileage of the vehicle is estimated completely based on the historical average energy consumption data.
According to the method, under the condition that the average energy consumption accuracy of the predicted vehicle under the future working condition is high, the prediction of the remaining range is performed by adopting the larger proportion of real-time predicted average energy consumption, and the accuracy of the prediction result of the remaining range can be further improved because the amount of the remaining range is greatly influenced by the future working condition of the vehicle.
Based on the method for predicting the remaining range of the vehicle provided by the embodiment, correspondingly, the embodiment of the application also provides an implementation mode of the device for predicting the remaining range of the vehicle.
Referring to fig. 8, a schematic structural diagram of a device for predicting remaining range of a vehicle according to an embodiment of the present application, as shown in fig. 8, the device for predicting remaining range of a vehicle according to the present embodiment may include the following units:
an acquisition unit 801 for acquiring battery usable remaining energy of the vehicle.
A historical average energy consumption determination unit 802 for determining a historical average energy consumption of the vehicle.
A future average energy consumption determination unit 803 for determining a real-time predicted average energy consumption of the vehicle.
The integrated average energy consumption determining unit 804 is configured to determine a first average energy consumption of the vehicle according to the historical average energy consumption and the real-time predicted average energy consumption.
The predicted value determining unit 805 is configured to use a ratio of the available remaining battery energy to the first average energy consumption as a predicted value of a remaining range of the vehicle.
According to the prediction device for the remaining range of the vehicle, the available remaining capacity of the battery of the vehicle is obtained, the historical average energy consumption and the real-time predicted average energy consumption of the vehicle are determined, the first average energy consumption of the vehicle is determined according to the historical average energy consumption and the real-time predicted average energy consumption, and then the ratio of the available remaining energy of the battery to the first average energy consumption is used as a predicted value of the remaining range of the vehicle. According to the method and the device, the energy consumption conditions of the vehicle under the historical working condition and the future working condition are comprehensively considered, and the accuracy of the prediction result is improved.
As a possible implementation manner, the historical average energy consumption determining unit 802 may include:
the parameter value acquisition subunit is used for acquiring a characteristic parameter value corresponding to the vehicle, wherein the characteristic parameter is a parameter representing the state of the vehicle;
the data matching subunit is used for acquiring target driving state data matched with the characteristic parameter value from prestored driving state data of the vehicle according to the characteristic parameter value;
And the calculating subunit is used for determining the historical average energy consumption of the vehicle according to the target driving state data.
As a possible implementation manner, the historical average energy consumption determining unit 802 may further include:
a judging subunit, configured to judge whether a preset target driving state data acquisition condition is satisfied before target driving state data matched with the feature parameter value is acquired from prestored driving state data of the vehicle according to the feature parameter value;
a data matching subunit for:
and acquiring target driving state data matched with the characteristic parameter value from the pre-stored driving state data of the vehicle when the preset target driving state data acquisition condition is determined to be met.
As a possible implementation manner, the judging subunit is specifically configured to:
determining target driving state data matched with the characteristic parameter value in the driving state data of the vehicle stored in advance;
determining whether the target driving state data can represent an energy consumption performance of the vehicle;
under the condition that the target driving state data can represent the energy consumption performance condition of the vehicle, determining that the preset target driving state data acquisition condition is met;
In the case where it is determined that the target driving state data may not represent the energy consumption performance situation of the vehicle, it is determined that the preset target driving state data acquisition condition is not satisfied.
As a possible implementation manner, the historical average energy consumption determining unit 802 further includes:
the average energy consumption acquisition subunit is used for acquiring second average energy consumption of the vehicle under standard working condition simulation under the condition that the preset target driving state data acquisition condition is not met, and taking the second average energy consumption as historical average energy consumption of the vehicle.
As a possible implementation manner, the future average energy consumption determining unit 803 may include:
the working condition prediction subunit is used for predicting a future running path of the vehicle and working condition data of the vehicle in the future running path according to map navigation information and path planning information of the vehicle;
the simulation subunit is used for controlling a pre-constructed dynamic simulation model of the vehicle to perform simulation operation according to the future driving path and the working condition data to obtain third average energy consumption;
and the average energy consumption determining subunit is used for taking the third average energy consumption as the real-time prediction average energy consumption of the vehicle.
As a possible implementation manner, the integrated average energy consumption determining unit 804 may include:
The weight obtaining subunit is used for determining a first weight corresponding to the historical average energy consumption and a second weight corresponding to the real-time prediction average energy consumption, and the sum of the first weight and the second weight is 1;
the product calculating subunit is used for calculating a first product of the first weight and the historical average energy consumption and a second product of the second weight and the real-time prediction average energy consumption;
and a sum value calculation subunit configured to take a sum value of the first product and the second product as a first average energy consumption of the vehicle.
Fig. 9 shows a hardware structure schematic diagram of a device for predicting remaining range of a vehicle according to an embodiment of the present application.
The prediction device of the remaining range of the vehicle may include a processor 901 and a memory 902 storing computer program instructions.
In particular, the processor 901 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
Memory 902 may include mass storage for data or instructions. By way of example, and not limitation, the memory 902 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. The memory 902 may include removable or non-removable (or fixed) media, where appropriate. The memory 902 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 902 is a non-volatile solid state memory.
Memory 902 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory 902 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and which, when executed (e.g., by one or more processors), can perform the operations described by the methods of the first aspect of the present disclosure.
The processor 901 reads and executes the computer program instructions stored in the memory 902 to implement any one of the methods for predicting the remaining range of the vehicle in the above embodiments.
In one example, the device for predicting the remaining range of the vehicle may further include a communication interface 903 and a bus 910. As shown in fig. 9, the processor 901, the memory 902, and the communication interface 903 are connected to each other via a bus 910, and communicate with each other.
The communication interface 903 is mainly used to implement communication between each module, device, unit, and/or apparatus in the embodiments of the present application.
Bus 910 includes hardware, software, or both, that couple the components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 910 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
In addition, in combination with the method for predicting the remaining range of the vehicle in the above embodiment, the embodiment of the application may provide a computer storage medium for implementation. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement a method for predicting remaining range of a vehicle in any of the above embodiments.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, which are intended to be included in the scope of the present application.
Claims (10)
1. The method for predicting the remaining range of the vehicle is characterized by comprising the following steps of:
acquiring available residual energy of a battery of the vehicle;
determining historical average energy consumption and real-time predicted average energy consumption of the vehicle;
determining a first average energy consumption of the vehicle according to the historical average energy consumption and the real-time predicted average energy consumption;
and taking the ratio of the available residual energy of the battery to the first average energy consumption as a predicted value of the residual range of the vehicle.
2. The method of claim 1, wherein the determining the historical average energy consumption of the vehicle comprises:
Acquiring a characteristic parameter value corresponding to the vehicle, wherein the characteristic parameter is a parameter representing the state of the vehicle;
acquiring target driving state data matched with the characteristic parameter value from prestored driving state data of the vehicle according to the characteristic parameter value;
and determining historical average energy consumption of the vehicle according to the target driving state data.
3. The method according to claim 2, wherein before the target driving state data matching the characteristic parameter value is acquired from the driving state data of the vehicle stored in advance according to the characteristic parameter value, the method further comprises:
judging whether a preset target driving state data acquisition condition is met or not;
the obtaining target driving state data matched with the characteristic parameter value from the pre-stored driving state data of the vehicle includes:
and acquiring target driving state data matched with the characteristic parameter value from the pre-stored driving state data of the vehicle under the condition that the target driving state data acquisition condition is met.
4. A method according to claim 3, wherein said determining whether a preset target driving state data acquisition condition is satisfied comprises:
Determining target driving state data matched with the characteristic parameter value in the pre-stored driving state data of the vehicle;
determining whether the target driving state data may represent an energy consumption performance of the vehicle;
determining that a preset target driving state data acquisition condition is met under the condition that the target driving state data can represent the energy consumption performance condition of the vehicle;
and under the condition that the target driving state data is determined to not represent the energy consumption performance condition of the vehicle, determining that the preset target driving state data acquisition condition is not met.
5. The method of claim 3, wherein said determining the historical average energy consumption of the vehicle further comprises:
under the condition that the preset target driving state data acquisition condition is not met, acquiring second average energy consumption of the vehicle under standard working condition simulation;
and taking the second average energy consumption as the historical average energy consumption of the vehicle.
6. The method of claim 1, wherein said determining a real-time predicted average energy consumption of said vehicle comprises:
predicting a future running path of the vehicle and working condition data of the vehicle in the future running path according to the map navigation information and the path planning information of the vehicle;
Controlling a pre-constructed dynamic simulation model of the vehicle to perform simulation operation according to the future driving path and the working condition data to obtain third average energy consumption;
and taking the third average energy consumption as the real-time predicted average energy consumption of the vehicle.
7. The method of claim 1, wherein the determining a first average energy consumption of the vehicle based on the historical average energy consumption and the real-time predicted average energy consumption comprises:
determining a first weight corresponding to the historical average energy consumption and a second weight corresponding to the real-time prediction average energy consumption, wherein the sum of the first weight and the second weight is 1;
calculating a first product of the first weight and the historical average energy consumption and a second product of the second weight and the real-time predicted average energy consumption;
and taking the sum value of the first product and the second product as a first average energy consumption of the vehicle.
8. The device for predicting the remaining range of the vehicle is characterized by comprising the following components:
an acquisition unit configured to acquire remaining energy available for a battery of the vehicle;
a historical average energy consumption determination unit configured to determine a historical average energy consumption of the vehicle;
A future average energy consumption determining unit configured to determine a real-time predicted average energy consumption of the vehicle;
the comprehensive average energy consumption determining unit is used for determining first average energy consumption of the vehicle according to the historical average energy consumption and the real-time prediction average energy consumption;
and the predicted value determining unit is used for taking the ratio of the available residual energy of the battery to the first average energy consumption as a predicted value of the residual endurance mileage of the vehicle.
9. A prediction apparatus of a remaining range of a vehicle, the apparatus comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method for predicting remaining range of a vehicle as set forth in any one of claims 1-7.
10. A computer readable storage medium, wherein computer program instructions are stored on the computer readable storage medium, which when executed by a processor, implement the method for predicting remaining range of a vehicle according to any one of claims 1-7.
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