CN117775000A - Vehicle remaining endurance mileage estimation method and device, storage medium and electronic device - Google Patents
Vehicle remaining endurance mileage estimation method and device, storage medium and electronic device Download PDFInfo
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Abstract
The invention discloses a vehicle remaining endurance mileage estimation method and device, a storage medium and an electronic device. Wherein the method comprises the following steps: acquiring historical use data of a target vehicle in at least one driving cycle, wherein the historical use data comprises vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information corresponding to the at least one driving cycle; carrying out working condition prediction based on historical use data to obtain a target prediction result; calculating a first power consumption value of the target vehicle by using the target prediction result, wherein the first power consumption value is used for representing the comprehensive power consumption degree of the target vehicle; and determining the remaining range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used for representing the available energy of the battery of the target vehicle in the current state. The method solves the technical problem of low accuracy in estimating the remaining range of the vehicle in the related art.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a vehicle remaining endurance mileage estimation method and device, a storage medium and an electronic device.
Background
The endurance mileage of an electric vehicle is affected by various factors, including driving conditions, temperature, and the like. Therefore, it is very important for the driver to accurately display the remaining range. At present, the calculation of the remaining range of the vehicle mostly adopts national standard working conditions and normal temperature conditions, and has larger difference with the actual driving range of the user. In addition, on the basis of a remaining range display strategy, the remaining range initially displayed by the vehicle under the condition of full charge is generally a numerical value advertised according to national standards. And along with the power consumption during running, the residual endurance mileage is reduced proportionally according to the power consumption under the normal-temperature national standard working condition. However, the initial value and the change value of the remaining range of the vehicle in the above manner are greatly different from the actual range of the user, and cannot reflect the actual remaining range.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a vehicle remaining range estimation method, a device, a storage medium and an electronic device, which are used for at least solving the technical problem of low accuracy in estimating the vehicle remaining range in the related technology.
According to one embodiment of the present invention, there is provided a vehicle remaining range estimation method, including: acquiring historical use data of a target vehicle in at least one driving cycle, wherein the historical use data comprises vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information corresponding to the at least one driving cycle; carrying out working condition prediction based on historical use data to obtain a target prediction result; calculating a first power consumption value of the target vehicle by using the target prediction result, wherein the first power consumption value is used for representing the comprehensive power consumption degree of the target vehicle; and determining the remaining range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used for representing the available energy of the battery of the target vehicle in the current state.
Optionally, performing the working condition prediction based on the historical usage data, and obtaining the target prediction result includes: and carrying out working condition prediction on the historical use data by using a Markov prediction model to obtain a target prediction result.
Optionally, calculating the first power consumption value of the target vehicle using the target prediction result includes: determining a second power consumption value based on the target prediction result, wherein the second power consumption value is used for representing the predicted power consumption degree of the target vehicle, and determining a third power consumption value based on the average power consumption information, and the third power consumption value is used for representing the historical power consumption degree of the target vehicle; and determining the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient.
Optionally, determining the second power consumption value based on the target prediction result includes: determining a battery instantaneous power of the target vehicle based on the target prediction result; integrating the battery instantaneous power to obtain a second energy value, wherein the second energy value is used for representing the total energy of the battery consumed by the target vehicle in the driving process; a second power consumption value is determined using the second energy value and the target travel distance.
Optionally, determining the battery instantaneous power of the target vehicle based on the target prediction result includes: acquiring a plurality of attribute parameters corresponding to a target vehicle; a battery instantaneous power is determined based on the plurality of attribute parameters and the target prediction result.
Optionally, the plurality of attribute parameters includes: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area and whole vehicle rotation conversion coefficient.
Optionally, the vehicle remaining range estimation method further includes: acquiring a battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used for representing available battery energy of the target vehicle in a full-charge state; the first energy value is determined using the battery state of charge value and the third energy value.
According to one embodiment of the present invention, there is also provided a vehicle remaining range estimation apparatus, including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical use data of a target vehicle in at least one driving cycle, wherein the historical use data comprises vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information corresponding to the at least one driving cycle; the prediction module is used for predicting working conditions based on the historical use data to obtain a target prediction result, wherein the target prediction result is used for determining the battery instantaneous power of the target vehicle; and the determining module is used for determining the remaining endurance mileage of the target vehicle according to the target prediction result.
Optionally, the prediction module is further configured to: and carrying out working condition prediction on the historical use data by using a Markov prediction model to obtain a target prediction result.
Optionally, the determining module is further configured to: determining a second power consumption value based on the target prediction result, wherein the second power consumption value is used for representing the predicted power consumption degree of the target vehicle, and determining a third power consumption value based on the average power consumption information, and the third power consumption value is used for representing the historical power consumption degree of the target vehicle; and determining the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient.
Optionally, the determining module is further configured to: determining a battery instantaneous power of the target vehicle based on the target prediction result; integrating the battery instantaneous power to obtain a second energy value, wherein the second energy value is used for representing the total energy of the battery consumed by the target vehicle in the driving process; a second power consumption value is determined using the second energy value and the target travel distance.
Optionally, the obtaining module is further configured to: acquiring a plurality of attribute parameters corresponding to a target vehicle; the determination module is also for: a battery instantaneous power is determined based on the plurality of attribute parameters and the target prediction result.
Optionally, the plurality of attribute parameters includes: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area and whole vehicle rotation conversion coefficient.
Optionally, the obtaining module is further configured to: acquiring a battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used for representing available battery energy of the target vehicle in a full-charge state; the determination module is also for: the first energy value is determined using the battery state of charge value and the third energy value.
According to an embodiment of the present invention, there is also provided a non-volatile storage medium in which a computer program is stored, wherein the computer program is configured to execute the vehicle remaining range estimation method in any one of the above-described claims when run.
According to one embodiment of the present invention, there is also provided an electronic device including a memory, in which a computer program is stored, and a processor configured to run the computer program to perform the vehicle remaining range estimation method of any one of the above.
In the embodiment of the invention, the method of acquiring the historical use data of the target vehicle in at least one driving cycle and carrying out working condition prediction based on the historical use data to obtain the target prediction result is adopted, the first power consumption value of the target vehicle is calculated by utilizing the target prediction result, and the residual range of the target vehicle is determined according to the first energy value and the first power consumption value, so that the purpose of accurately estimating the residual range of the vehicle is achieved, the technical effect of improving the accuracy of estimating the residual range of the vehicle is realized, and the technical problem that the accuracy of estimating the residual range of the vehicle in the related technology is low is solved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a method for estimating remaining range of a vehicle according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of a vehicle range estimation system according to one embodiment of the present invention;
FIG. 3 is a flow chart of yet another vehicle remaining range estimation method according to one embodiment of the present invention;
fig. 4 is a block diagram illustrating a structure of a vehicle remaining range estimation apparatus according to one embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present invention, a method embodiment of a vehicle remaining range estimation method is provided, and it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that illustrated herein.
The method embodiments may be performed in an electronic device or similar computing device that includes a memory and a processor. Taking an example of operation on a vehicle terminal, the vehicle terminal may include one or more processors (the processors may include, but are not limited to, central processing units (Central Processing Unit, CPU), graphics processing units (Graphics Processing Unit, GPU), digital signal processing (Digital Signal Processing, DSP) chips, microprocessors (Micro Controller Unit, MCU), programmable logic devices (Field Programmable Gate Array, FPGA), neural-network processors (Neural-network Processor Unit, NPU), tensor processors (Tensor Processing Unit, TPU), artificial intelligence (Artificial Intelligence, AI) type processors, and the like processing means for storing data. Alternatively, the vehicle terminal may further include a transmission device, an input-output device, and a display device for a communication function. It will be appreciated by those skilled in the art that the above description of the structure is merely illustrative and is not intended to limit the structure of the vehicle terminal. For example, the vehicle terminal may also include more or fewer components than the above structural description, or have a different configuration than the above structural description.
The memory may be used to store a computer program, for example, a software program of an application software and a module, for example, a computer program corresponding to the vehicle remaining range estimation method in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the computer program stored in the memory, that is, implements the vehicle remaining range estimation method described above. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the memory may further include memory remotely located with respect to the processor, the remote memory being connectable to the mobile terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission means comprises a network adapter (Network Interface Controller, simply referred to as NIC) that can be connected to other network devices via a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.
Display devices may be, for example, touch screen type liquid crystal displays (Liquid Crustal Display, LCDs) and touch displays (also referred to as "touch screens" or "touch display screens"). The liquid crystal display may enable a user to interact with a user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (Graphical User Interface, GUI) with which a user can interact with the GUI by touching finger contacts and/or gestures on the touch-sensitive surface, where the human-machine interaction functionality optionally includes the following interactions: executable instructions for performing the above-described human-machine interaction functions, such as creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving electronic mail, talking interfaces, playing digital video, playing digital music, and/or web browsing, are configured/stored in a computer program product or readable storage medium executable by one or more processors.
Fig. 1 is a flowchart of a vehicle remaining range estimation method according to one embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
step S10, historical use data of a target vehicle in at least one driving cycle is obtained, wherein the historical use data comprises vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information corresponding to the at least one driving cycle;
In step S10, the driving cycle refers to the travel information of the target vehicle over a period of time. For example, the remaining range display modes in the meter are divided into "urban", "suburban", "high speed"By integrating the four modes, if the user sets the urban mode, one driving cycle refers to the driving information of the target vehicle from the start to the end of the driving in the current urban mode. The above-mentioned vehicle navigation information is used to record the vehicle start position, the vehicle end position and the total distance of each driving cycle, wherein the vehicle start position is denoted An, the vehicle end position Bn, and the total distance of each driving cycle is denoted S totaln . The above-mentioned vehicle running information refers to the average vehicle speed and average acceleration of the vehicle in each driving cycle, wherein the average vehicle speed is noted asThe average acceleration of the vehicle is recorded as->The above ambient temperature information refers to the average ambient temperature of the outside world in each driving cycle, and is denoted +.>The average power consumption information refers to the average power consumption per driving cycle, and is denoted as EC avgn 。
Specifically, historical usage data of the target vehicle in at least one driving cycle is obtained, and table 1 records the historical usage data of the target vehicle in the driving cycle, as shown in table 1, and records the historical usage data of n driving cycles, wherein the usage data of each driving cycle includes vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information.
Step S12, working condition prediction is carried out based on historical use data, and a target prediction result is obtained;
specifically, the working condition prediction is performed based on the historical use data, and a predicted speed value of the target vehicle is obtained.
Table 1 historical usage data in a target vehicle drive cycle
Step S14, calculating a first power consumption value of the target vehicle by using the target prediction result, wherein the first power consumption value is used for representing the comprehensive power consumption degree of the target vehicle;
in the above step S14, the first power consumption value is the overall power consumption of the target vehicle, and is denoted as ec_combined.
And S16, determining the remaining range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used for representing the available energy of the battery of the target vehicle in the current state.
In the above step S16, the first energy value refers to the energy available to the battery pack of the current target vehicle, and is denoted as e_avail_now. The remaining range of the target vehicle is the number of ranges that can be continuously travelled, and is usually displayed on the dashboard or on-vehicle information system of the vehicle, and is denoted as s_avail_pre.
Specifically, the remaining range of the target vehicle is calculated based on the first energy value and the first power consumption value, and the specific calculation process is shown in the expression (1).
S_avail_pre＝E_avail_now/EC_combined (1)
Based on the steps S10 to S16, the method of obtaining the historical usage data of the target vehicle in at least one driving cycle and predicting the working condition based on the historical usage data to obtain the target prediction result is adopted, and the purpose of accurately estimating the remaining range of the vehicle is achieved by calculating the first power consumption value of the target vehicle and determining the remaining range of the target vehicle according to the first energy value and the first power consumption value by using the target prediction result, so that the technical effect of improving the accuracy of estimating the remaining range of the vehicle is achieved, and the technical problem of low accuracy in estimating the remaining range of the vehicle in the related art is solved.
Optionally, in step S12, performing the working condition prediction based on the historical usage data, and obtaining the target prediction result includes:
and S121, carrying out working condition prediction on the historical use data by using a Markov prediction model to obtain a target prediction result.
In step S121 described above, the markov prediction model is capable of predicting a predicted speed value of the target vehicle based on the historical usage data in the driving cycle of the target vehicle.
Specifically, first, a state transition probability matrix is calculated, wherein the probability of the running speed of the target vehicle from the current vehicle speed state to the next vehicle speed state is the state transition probability, and the element of the ith row j of the state transition probability matrix is denoted as P i,j . The travel speed of the target vehicle is dispersed to a limited value by the neighbor method as shown in expression (2). Dividing the running speed of the target vehicle into a plurality of possible states, denoted as U i 。
v s ∈{v 1 ,v 2 ,…,v N }(2)
Where N represents the number of discrete travel speeds of the target vehicle. For example, the running speed of the target vehicle is divided into 100 possible states, the running speed discrete interval of the target vehicle takes a value of 5km/h, and the running speed state of the target vehicle is numbered as U k . The running speed of the target vehicle is controlled by the current vehicle speed state U i Vehicle speed state U to the next moment j The probability of (1) is the state transition probability P i,j That is, the running speed of the target vehicle at the present moment is v i At the next moment, the running speed is v j The probability of (2) is calculated as shown in expression (3).
P i,j ＝P(v(k+1)＝v j |v(k)＝v i ) (3)
Where k represents the current time, then k+1 is the next time to the current time.
Further, P i,j The value can be obtained by a maximum likelihood estimation method as shown in expression (4).
Wherein F is i,j Indicating the running speed of the target vehicle from v i Transfer to v j Number of times F i Indicating the running speed of the target vehicle from v i Total number of transfers, i, j=0, 1, …, N.
And calculating the transition probability and the times from the current running speed to the next running speed of the target vehicle, and combining each state probability value to generate a Markov transition probability matrix P, wherein P is shown in an expression (5).
Next, a markov prediction model is determined with respect to the probability of the target vehicle speed state. In this process, assuming that there are n mutually independent travel speed states of the target vehicle, an initial travel speed state vector of the target vehicle is shown as expression (6).
S(0)＝[S 1 (0),S 2 (0),…,S m (0),…S n (0)] (6)
Wherein S is m (0) The initial probability of the travel speed state of the mth target vehicle is represented. Through the k-step state transition, the probability of the running speed state of the mth target vehicle is S m (k) Therefore, the running speed state vector of the target vehicle after the k-step state transition is shown in expression (7).
S(k)＝[S 1 (k),S 2 (k),…,S m (k),…S n (k)] (7)
Wherein S is m (k) The probability that the target vehicle is in the mth travel speed state at the k time is represented.
From the above, a markov prediction model of the target vehicle running speed state probability is shown in expression (8).
Finally, obtaining a target prediction result, namely a predicted speed value of the target vehicle based on the Markov prediction model and the solution of the state transition matrix, wherein the predicted speed value of the target vehicle is shown as an expression (9).
v(k)＝[(U k -1)+r k ]d (9)
Wherein v (k) represents the running speed of the target vehicle at time k, U k Represents the running speed state of the target vehicle at time k, d represents the running speed state dividing length of the target vehicle, r k Representing random numbers uniformly distributed at time k.
Based on the step S121, the working condition prediction is performed on the historical usage data by using the markov prediction model to obtain a target prediction result, so that the purpose of accurately estimating the remaining range of the vehicle is achieved, and the technical effect of improving the accuracy of estimating the remaining range of the vehicle is achieved.
Optionally, in the step S14, calculating the first power consumption value of the target vehicle using the target prediction result includes:
step S141, determining a second power consumption value based on the target prediction result, wherein the second power consumption value is used for representing the predicted power consumption degree of the target vehicle, and determining a third power consumption value based on the average power consumption information, and the third power consumption value is used for representing the historical power consumption degree of the target vehicle;
in step S141, the second power consumption value is the predicted power consumption level of the target vehicle, and is denoted as ec_after. The third power consumption value is the average power consumption of the previous driving cycle, and is denoted as ec_history.
In step S142, the first power consumption value is determined based on the second power consumption value, the third power consumption value and the preset calibration coefficient.
Specifically, a specific calculation process for determining the first power consumption value based on the second power consumption value, the third power consumption value and the preset calibration coefficient is shown in the expression (10).
EC_combined＝EC_history*η+EC_after*(1-η) (10)
Wherein, eta is calibrated according to the target vehicle performance, and the value range of eta is 0< eta <1.
Determining a second power consumption value based on the target prediction result and a third power consumption value based on the average power consumption information based on the above steps S141 to S142; and determining a first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient, and comprehensively considering the power consumption condition of the target vehicle, thereby further improving the estimation accuracy of the remaining range of the vehicle.
Optionally, in the step S141, determining the second power consumption value based on the target prediction result includes:
step S1411, determining a battery instantaneous power of the target vehicle based on the target prediction result;
specifically, the power of the battery terminal at each time is calculated based on the predicted speed value of the target vehicle, for example, the power of the battery terminal is updated every 0.1 s.
Step S1412, performing integration processing on the battery instantaneous power to obtain a second energy value, where the second energy value is used to represent the total energy of the battery consumed by the target vehicle during the driving process;
step S1413, a second power consumption value is determined using the second energy value and the target travel distance.
Specifically, the total energy of the battery consumed by the target vehicle during running, namely, the second energy value, which is recorded as E_total, can be calculated by integrating the instantaneous power of the battery with time. The target travel distance refers to the total distance traveled by the target vehicle, and can be determined based on table 1. The second electricity consumption value ec_after is calculated based on the second energy value and the target travel distance, as shown in expression (11).
EC_after＝E_total/S_totaln (11)
Determining a battery instantaneous power of the target vehicle based on the target prediction result based on the above steps S1411 to S1413; integrating the instantaneous power of the battery to obtain a second energy value; and determining a second power consumption value by utilizing the second energy value and the target driving distance, and obtaining the total battery energy consumed by the target vehicle in the driving process by integrating the battery instantaneous power, and determining the second power consumption value by combining the target driving distance, so that the remaining endurance mileage of the target vehicle can be estimated more accurately.
Optionally, in the above step S1411, determining the battery instantaneous power of the target vehicle based on the target prediction result includes:
step S1411A, a plurality of attribute parameters corresponding to a target vehicle are acquired;
step S1411B, determining the battery instantaneous power based on the plurality of attribute parameters and the target prediction result.
Specifically, a specific calculation process of determining the instantaneous power of the battery based on the plurality of attribute parameters of the target vehicle and the target prediction result is shown in expression (12).
Where m represents the weight of the target vehicle and g represents the gravitational acceleration, typically 9.8m/s 2 F represents a rolling resistance coefficient, cd represents a windage coefficient, a represents a windage area of the target vehicle, v (i) represents a predicted speed value of the target vehicle at the time i, δ represents a whole-vehicle-amount rotation conversion coefficient of the target vehicle, and a (i) represents an acceleration of the target vehicle at the time i.
Acquiring a plurality of attribute parameters corresponding to the target vehicle based on the steps S1411A to S1411B; the battery instantaneous power is determined based on the attribute parameters and the target prediction result, so that the battery instantaneous power can be calculated more comprehensively, and the accuracy of calculating the residual range of the target vehicle can be improved.
Optionally, the plurality of attribute parameters includes: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area and whole vehicle rotation conversion coefficient.
Specifically, the mass m of the target vehicle represents the weight of the target vehicle.
The rolling resistance coefficient f of the target vehicle described above is an important parameter describing the rolling resistance to which the target vehicle is subjected when traveling on a flat road surface, and represents a physical quantity representing the ratio of the rolling resistance to which the target vehicle is subjected during traveling to its weight.
The magnitude of the rolling resistance coefficient depends on factors such as the weight of the target vehicle, the type of tire, the tire air pressure, and the roughness of the road surface.
The wind resistance coefficient Cd of the target vehicle refers to the magnitude of the air resistance of the target vehicle during running, and is generally used to describe the aerodynamic performance of the target vehicle during running.
The windward area a of the target vehicle refers to the total area of the target vehicle where the air interacts with the front during running. The windward area is generally used for calculating the air resistance born by the target vehicle when the target vehicle runs at a high speed, and the size of the windward area can influence the air resistance born by the target vehicle when the target vehicle runs at the high speed, so that the fuel economy and the power performance of the target vehicle are influenced.
The overall vehicle-amount rotation conversion coefficient δ of the target vehicle refers to a ratio between a torque required for additional rotation of the target vehicle and a total mass of the target vehicle when the target vehicle rotates during running. The overall vehicle amount rotation conversion coefficient can be used to evaluate the stability and handling performance of the vehicle.
Optionally, the vehicle remaining range estimation method further includes:
step S17, acquiring a battery charge state value and a third energy value of the target vehicle, wherein the third energy value is used for representing available energy of the battery of the target vehicle in a full-charge state;
in the above step S17, the battery state of charge value of the target vehicle refers to the electric energy already stored in the battery reported by the current battery management system (Battery Management System, BMS) in the target vehicle, and is denoted as soc_now. The third energy value refers to the available energy of the battery pack when the target vehicle is fully charged, and is denoted as e_avail_total.
Step S18, determining a first energy value using the battery state of charge value and the third energy value.
Specifically, the first energy value is calculated based on the battery state of charge value and the third energy value, and the specific calculation process is shown in expression (13).
E_avail_now＝E_avail_total*SOC_now/100 (13)
Further, firstly, the user sets a remaining range display mode in the instrument, which is specifically divided into four modes of urban area, suburban area, high speed and comprehensive, and after the user determines the remaining range display mode, the instrument sends the remaining range display mode to the vehicle controller (Vehicle Control Unit, VCU). If the user selects the range display mode which is the same as the last driving cycle, calculating a range display initial value based on the first energy value E_avail_now and the third power consumption value EC_history; if the user selected range display mode is different from the last driving cycle, calculating a range display initial value based on the percentage of the electric energy stored in the battery displayed by the current instrument to the total capacity, wherein the range display initial value is recorded as S_initial.
Specifically, when the endurance mileage display mode selected by the user is the same as the last driving cycle, the third energy value e_avail_total at different temperatures is tested through the test according to the four modes in the early stage, and the obtained data are shown in table 2.
Table 2 third energy values at different temperatures in four modes
| 35℃ | 25℃ | 15℃ | 5℃ | -5℃ | -15℃ | -25℃ | -35 | |
| Urban area | E1 | E2 | E3 | E4 | E5 | E6 | E7 | E8 |
| Suburbs of the city | E9 | E10 | E11 | E12 | E13 | E14 | E15 | E16 |
| Gao Su | E17 | E18 | E19 | E20 | E21 | E22 | E23 | E24 |
| Comprehensive synthesis | E25 | E26 | E27 | E28 | E29 | E30 | E31 | E32 |
And calculating a range display initial value according to the average power consumption EC_history of the last driving cycle recorded by the VCU, wherein the specific calculation process is shown in the expression (14).
S_initial＝E_avail_now/EC_history (14)
Specifically, when the endurance mileage display mode selected by the user is different from the last driving cycle, the whole vehicle electricity consumption corresponding to different temperatures of the target vehicle under the four working conditions is determined through a test or simulation means, and the whole vehicle electricity consumption is recorded as EC, and is specifically shown in Table 3.
Table 3 power consumption of the whole vehicle at different temperatures in four modes
| 35℃ | 25℃ | 15℃ | 5℃ | -5℃ | -15℃ | -25℃ | -35 | |
| Urban area | EC1 | EC2 | EC3 | EC4 | EC5 | EC6 | EC7 | EC8 |
| Suburbs of the city | EC9 | EC10 | EC11 | EC12 | EC13 | EC14 | EC15 | EC16 |
| Gao Su | EC17 | EC18 | EC19 | EC20 | EC21 | EC22 | EC23 | EC24 |
| Comprehensive synthesis | EC25 | EC26 | EC27 | EC28 | EC39 | EC30 | EC31 | EC32 |
The above tables 2 and 3 can obtain the whole vehicle endurance mileage corresponding to different temperatures under four working conditions, namely the endurance mileage of the target vehicle under the fully charged condition, which is recorded as s_initial, specifically shown in table 4, wherein the calculation process of the whole vehicle endurance mileage is shown as expression (15).
S_initial＝E_avail_total/EC (15)
Table 4 whole vehicle range under four different conditions and at different temperatures
| 35℃ | 25℃ | 15℃ | 5℃ | -5℃ | -15℃ | -25℃ | -35 | |
| Urban area | S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 |
| Suburbs of the city | S9 | S10 | S11 | S12 | S13 | S14 | S15 | S16 |
| Gao Su | S17 | S18 | S19 | S20 | S21 | S22 | S23 | S24 |
| Comprehensive synthesis | S25 | S26 | S27 | S28 | S30 | S31 | S32 | S33 |
Specifically, after the target vehicle is started, according to the remaining range display mode and the ambient temperature selected by the user, a range initial value s_initial of the target vehicle under the condition of full power is determined through a lookup table 4. If the ambient temperature is not in the table 4, determining a range initial value S_initial_1 of the target vehicle under the condition of full power by adopting a linear interpolation method. For example, when the user selects the urban operating mode and the ambient temperature is 10 ℃, the calculation process of s_initial_1 of the target vehicle is shown in expression (16). And determining the initial endurance mileage of the target vehicle based on the percentage of the electric energy stored in the battery displayed by the current meter to the total capacity and S_initial_1, wherein the specific calculation process is shown in an expression (17), and the percentage of the electric energy stored in the battery displayed by the current meter to the total capacity is recorded as SOC_initial.
S_initial_1＝(S3+S4)/2 (16)
S_initial＝(SOC_initial*SOC_initial_1)/100) (17)
Based on the steps S17 to S18, the battery state of charge value and the third energy value of the target vehicle are obtained, and the first energy value is determined by using the battery state of charge value and the third energy value, so that the purpose of accurately estimating the remaining range of the vehicle is achieved, the technical effect of improving the accuracy of estimating the remaining range of the vehicle is achieved, and the technical problem of low accuracy of estimating the remaining range of the vehicle in the related art is solved.
Fig. 2 is a schematic diagram of a vehicle remaining range estimation system according to an embodiment of the present invention, and as shown in fig. 2, the system includes a vehicle navigation system, an air conditioner controller, a BMS, a meter, a cloud server, and a VCU. The vehicle navigation system transmits information such as estimated driving time from a starting point to a terminal point, distance from the starting point to the terminal point, average speed of a vehicle flow in front and the like to the VCU, the air conditioner controller transmits information such as an air conditioner on/off State, average power of an air conditioner system, ambient temperature and the like to the VCU, the BMS transmits information such as battery temperature, a Charge-discharge State (SOC) of the battery, instantaneous Charge-discharge power of the battery and the like to the VCU, the instrument transmits a target vehicle remaining range display mode to the VCU, the cloud server continuously stores driving data of the target vehicle, and transmits historical driving big data to the VCU, and the VCU synthesizes information of all aspects to calculate the target vehicle remaining range.
FIG. 3 is a flow chart of yet another vehicle remaining range estimation method according to one embodiment of the present invention; as shown in fig. 3, the method comprises the steps of:
step S301, acquiring historical usage data of a target vehicle in at least one driving cycle;
Step S302, working condition prediction is carried out on historical use data by using a Markov prediction model, and a target prediction result is obtained;
step S303, a plurality of attribute parameters corresponding to the target vehicle are obtained;
step S304, determining the instantaneous power of the battery based on a plurality of attribute parameters and target prediction results;
step S305, performing integral processing on the instantaneous power of the battery to obtain a second energy value;
step S306, determining a second power consumption value by using the second energy value and the target driving distance, and determining a third power consumption value based on the average power consumption information;
step S307, determining a third power consumption value based on the average power consumption information;
step S308, determining a first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient;
step S309, obtaining a battery state of charge value and a third energy value of the target vehicle;
step S310, determining a first energy value by using the battery state of charge value and the third energy value;
step S311, determining the remaining range of the target vehicle according to the first energy value and the first power consumption value.
According to the vehicle remaining range estimation method, the historical use data of the target vehicle in at least one driving cycle is acquired, and the working condition prediction is carried out based on the historical use data, so that the target prediction result is obtained, the first power consumption value of the target vehicle is calculated by using the target prediction result, and the remaining range of the target vehicle is determined according to the first energy value and the first power consumption value, so that the purpose of accurately estimating the remaining range of the vehicle is achieved, the technical effect of improving the accuracy of vehicle remaining range estimation is achieved, and the technical problem that the accuracy of estimating the remaining range of the vehicle in the related art is low is solved.
The following describes the workflow of the vehicle remaining range estimation method in detail by way of example:
firstly, a user sets a remaining range display mode in an instrument, and the remaining range display mode is specifically divided into four modes of urban area, suburban area, high speed and comprehensive, and the instrument sends the remaining range display mode to a VCU after the user determines the remaining range display mode.
And secondly, determining a display initial value of the remaining range according to the information such as the ambient temperature, the battery pack temperature and the like, and marking the initial value as S_initial. If the user selected range display mode is the same as the last driving cycle, calculating S_initial based on E_avail_now and EC_history; if the user selected range display mode is not the same as the last driving cycle, S_initial is calculated based on the percentage of the total capacity of the stored electrical energy in the battery currently displayed by the meter.
Specifically, when the endurance mileage display mode selected by the user is the same as the last driving cycle, the e_avail_total at different temperatures is tested through the test according to the four modes in the early stage, and the obtained data are shown in table 2. And calculating a range display initial value according to the average power consumption EC_history of the last driving cycle recorded by the VCU, wherein the specific calculation process is shown in the expression (14).
Specifically, when the endurance mileage display mode selected by the user is different from the last driving cycle, EC corresponding to different temperatures of the target vehicle under the four working conditions is determined through a test or simulation means, as shown in table 3. From the above tables 2 and 3, s_initial corresponding to different temperatures under four working conditions can be obtained, specifically as shown in table 4, where the calculation process of the whole vehicle endurance mileage is shown in expression (15). After the target vehicle is started, determining a range initial value S_initial of the target vehicle under the condition of full power through a lookup table 4 according to the residual range display mode selected by the user and the environment temperature. If the ambient temperature is not in the table 4, determining a range initial value S_initial_1 of the target vehicle under the condition of full power by adopting a linear interpolation method. For example, when the user selects the urban operating mode and the ambient temperature is 10 ℃, the calculation process of s_initial_1 of the target vehicle is shown in expression (16). And determining the initial range of the target vehicle based on the percentage of the electric energy stored in the battery displayed by the current instrument to the total capacity and S_initial_1, wherein the specific calculation process is shown in an expression (17).
And then, carrying out working condition prediction on the historical use data by using a Markov prediction model to obtain a target prediction result. Specifically, the vehicle navigation system records and stores a start position and an end position of the target vehicle corresponding to each driving cycle, the VCU records and stores an average speed, a total distance traveled and an average acceleration of the target vehicle in each driving cycle, and the air conditioner controller stores external average environmental temperature data in each driving cycle. The above stored historical usage data of the target vehicle is shown in table 1. After each driving cycle, the vehicle navigation system, the air conditioner controller and the VCU upload the historical use data to the cloud server for storage. Calculating a state transition probability matrix using the data in table 1, as shown in expressions (2) to (5); determining a Markov prediction model concerning a target vehicle speed state probability as shown in expressions (6) - (8); and obtaining a target prediction result, namely a predicted speed value of the target vehicle, based on the Markov prediction model and the solution of the state transition matrix, as shown in an expression (9).
Finally, a first power consumption value ec_combined of the target vehicle is calculated using the target prediction result, and a remaining range s_avail_pre of the target vehicle is determined according to the first energy value e_avail_now and the first power consumption value, as shown in expression (1). In the process, calculating the power of a battery end at each moment according to a predicted speed value of a target vehicle, performing integral processing on the battery instantaneous power to obtain a second energy value E_total, and determining a second power consumption value EC_after by using the E_total and a target driving distance, wherein the calculation of the battery instantaneous power of the target vehicle is shown in an expression (11); determining EC_combined based on the second power consumption value EC_after, the third power consumption value EC_history and a preset calibration coefficient, wherein the specific calculation process is shown in an expression (10); the battery state of charge value soc_now and the third energy value e_avail_total of the target vehicle are acquired, and e_avail_now is determined based on the soc_now and the e_avail_total, as shown in expression (13).
Based on the steps S301 to S310, the method of obtaining the historical usage data of the target vehicle in at least one driving cycle and predicting the working condition based on the historical usage data to obtain the target prediction result is adopted, and the first power consumption value of the target vehicle is calculated by using the target prediction result and the remaining range of the target vehicle is determined according to the first energy value and the first power consumption value, so that the purpose of accurately estimating the remaining range of the vehicle is achieved, the technical effect of improving the accuracy of estimating the remaining range of the vehicle is achieved, and the technical problem that the accuracy of estimating the remaining range of the vehicle in the related art is low is solved.
Fig. 4 is a block diagram illustrating a structure of a vehicle remaining range estimation apparatus according to one embodiment of the present invention; as shown in fig. 4, the apparatus includes:
an obtaining module 401, configured to obtain historical usage data of a target vehicle in at least one driving cycle, where the historical usage data includes vehicle navigation information, vehicle driving information, environmental temperature information, and average power consumption information corresponding to the at least one driving cycle;
the prediction module 402 is configured to perform working condition prediction based on the historical usage data to obtain a target prediction result, where the target prediction result is used to determine the battery instantaneous power of the target vehicle;
A determining module 403, configured to determine a remaining range of the target vehicle according to the target prediction result.
Optionally, the prediction module 402 is further configured to: and carrying out working condition prediction on the historical use data by using a Markov prediction model to obtain a target prediction result.
Optionally, the determining module 403 is further configured to: determining a second power consumption value based on the target prediction result, wherein the second power consumption value is used for representing the predicted power consumption degree of the target vehicle, and determining a third power consumption value based on the average power consumption information, and the third power consumption value is used for representing the historical power consumption degree of the target vehicle; and determining the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient.
Optionally, the determining module 403 is further configured to: determining a battery instantaneous power of the target vehicle based on the target prediction result; integrating the battery instantaneous power to obtain a second energy value, wherein the second energy value is used for representing the total energy of the battery consumed by the target vehicle in the driving process; a second power consumption value is determined using the second energy value and the target travel distance.
Optionally, the obtaining module 401 is further configured to: acquiring a plurality of attribute parameters corresponding to a target vehicle; the determining module 403 is further configured to: a battery instantaneous power is determined based on the plurality of attribute parameters and the target prediction result.
Optionally, the plurality of attribute parameters includes: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area and whole vehicle rotation conversion coefficient.
Optionally, the obtaining module 401 is further configured to: acquiring a battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used for representing available battery energy of the target vehicle in a full-charge state; the determining module 403 is further configured to: the first energy value is determined using the battery state of charge value and the third energy value.
It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; alternatively, the above modules may be located in different processors in any combination.
According to an embodiment of the present invention, there is also provided a non-volatile storage medium in which a computer program is stored, wherein the computer program is configured to execute the vehicle remaining range estimation method in any one of the above-described claims when run.
Alternatively, in the present embodiment, the above-described storage medium may be configured to store a computer program for performing the steps of:
S1, acquiring historical use data of a target vehicle in at least one driving cycle, wherein the historical use data comprises vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information corresponding to the at least one driving cycle;
s2, working condition prediction is carried out based on historical use data, and a target prediction result is obtained;
s3, calculating a first power consumption value of the target vehicle by using the target prediction result, wherein the first power consumption value is used for representing the comprehensive power consumption degree of the target vehicle;
and S4, determining the remaining range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used for representing the available energy of the battery of the target vehicle in the current state.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
According to one embodiment of the present invention, there is also provided an electronic device including a memory, in which a computer program is stored, and a processor configured to run the computer program to perform the vehicle remaining range estimation method of any one of the above.
Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s1, acquiring historical use data of a target vehicle in at least one driving cycle, wherein the historical use data comprises vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information corresponding to the at least one driving cycle;
s2, working condition prediction is carried out based on historical use data, and a target prediction result is obtained;
s3, calculating a first power consumption value of the target vehicle by using the target prediction result, wherein the first power consumption value is used for representing the comprehensive power consumption degree of the target vehicle;
and S4, determining the remaining range of the target vehicle according to the first energy value and the first power consumption value, wherein the first energy value is used for representing the available energy of the battery of the target vehicle in the current state.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments and optional implementations, and this embodiment is not described herein.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. The method for estimating the remaining range of the vehicle is characterized by comprising the following steps of:
acquiring historical use data of a target vehicle in at least one driving cycle, wherein the historical use data comprises vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information corresponding to the at least one driving cycle;
carrying out working condition prediction based on the historical use data to obtain a target prediction result;
calculating a first power consumption value of the target vehicle by using the target prediction result, wherein the first power consumption value is used for representing the comprehensive power consumption degree of the target vehicle;
and determining the remaining endurance mileage of the target vehicle according to a first energy value and the first power consumption value, wherein the first energy value is used for representing the available energy of a battery of the target vehicle in the current state.
2. The vehicle remaining range estimation method according to claim 1, wherein performing a condition prediction based on the historical usage data, obtaining the target prediction result includes:
and carrying out working condition prediction on the historical use data by using a Markov prediction model to obtain the target prediction result.
3. The vehicle remaining range estimation method according to claim 1, characterized in that calculating the first power consumption value of the target vehicle using the target prediction result includes:
determining a second power consumption value based on the target prediction result and determining a third power consumption value based on the average power consumption information, wherein the second power consumption value is used for representing the predicted power consumption degree of the target vehicle, and the third power consumption value is used for representing the historical power consumption degree of the target vehicle;
and determining the first power consumption value based on the second power consumption value, the third power consumption value and a preset calibration coefficient.
4. The vehicle remaining range estimation method according to claim 3, characterized in that determining the second power consumption value based on the target prediction result includes:
determining a battery instantaneous power of the target vehicle based on the target prediction result;
integrating the battery instantaneous power to obtain a second energy value, wherein the second energy value is used for representing the total energy of the battery consumed by the target vehicle in the running process;
and determining the second power consumption value by using the second energy value and the target driving distance.
5. The vehicle remaining range estimation method of claim 4, wherein determining the battery instantaneous power of the target vehicle based on the target prediction result comprises:
acquiring a plurality of attribute parameters corresponding to the target vehicle;
the battery instantaneous power is determined based on the plurality of attribute parameters and the target prediction result.
6. The vehicle remaining range estimation method according to claim 5, wherein the plurality of attribute parameters include: vehicle mass, rolling resistance coefficient, wind resistance coefficient, frontal area and whole vehicle rotation conversion coefficient.
7. The vehicle remaining range estimation method according to claim 1, characterized in that the method further comprises:
acquiring a battery state of charge value and a third energy value of the target vehicle, wherein the third energy value is used for representing available battery energy of the target vehicle in a full-charge state;
the first energy value is determined using the battery state of charge value and the third energy value.
8. A vehicle remaining range estimation device, characterized by comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical use data of a target vehicle in at least one driving cycle, wherein the historical use data comprises vehicle navigation information, vehicle driving information, environment temperature information and average power consumption information corresponding to the at least one driving cycle;
The prediction module is used for predicting working conditions based on the historical use data to obtain a target prediction result, wherein the target prediction result is used for determining the battery instantaneous power of the target vehicle;
and the determining module is used for determining the remaining endurance mileage of the target vehicle according to the target prediction result.
9. A non-volatile storage medium, wherein a computer program is stored in the storage medium, wherein the computer program is arranged to perform the vehicle range estimation method of any one of claims 1 to 7 when run.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the vehicle range estimation method of any one of claims 1 to 7.
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