A battery life calculation model testing method, apparatus and electronic equipment

Abstract

This application provides a battery life calculation model testing method, apparatus, and electronic device. The method includes: acquiring the current state information and driving pattern information of the vehicle under test; simulating driving scenarios of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test; inputting the vehicle simulation data into a preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model; and determining the test result of the preset battery life calculation model based on the deviation between the battery life prediction index and the expected battery life index corresponding to the vehicle simulation data. The method provided above, by combining the driving pattern information of the vehicle under test and randomly simulating dynamic driving scenarios of the vehicle under test under various operating conditions, ensures the reliability of the model test results.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a battery life calculation model testing method, apparatus and electronic equipment. Background Technology

[0002] As automobiles become increasingly electrified, electric vehicles are becoming more and more common in people's lives, and the lifespan of electric vehicle batteries has always been a topic of great concern. Currently, electric vehicles generally have a battery life calculation model deployed in their battery management system to calculate the battery life based on actual usage conditions. To ensure the accuracy of the model's calculation results, accuracy tests are conducted on the model during the vehicle's factory inspection phase.

[0003] In existing technologies, the accuracy of battery life calculation models is typically verified under static scenarios where key metrics such as vehicle mileage and operating time are determined. However, due to different users' driving habits, there is a random coupling between vehicle mileage and operating time, making it impossible for existing technologies to simulate dynamic driving scenarios and thus failing to guarantee the reliability of test results. Summary of the Invention

[0004] This application provides a battery life calculation model testing method, device, and electronic device to address the shortcomings of existing technologies, such as the inability to simulate dynamic vehicle usage scenarios and thus the inability to guarantee the reliability of test results.

[0005] The first aspect of this application provides a battery life calculation model testing method, including:

[0006] Obtain the current status information and driving pattern information of the vehicle under test;

[0007] Based on the current status information and driving pattern information, the driving scenario of the vehicle under test is simulated to obtain vehicle simulation data corresponding to the vehicle under test;

[0008] The vehicle simulation data is input into a preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model.

[0009] The test results of the preset battery life calculation model are determined based on the deviation between the battery life prediction index and the battery life expectation index corresponding to the vehicle simulation data.

[0010] Optionally, the step of simulating the driving scenario of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test includes:

[0011] Based on the battery power-on and power-off patterns characterized by the current state information and driving pattern information, the battery power-on and power-off scenarios of the vehicle under test are simulated.

[0012] Based on the simulation results of the battery power-on and power-off scenarios, the simulated data of battery life reduction for the vehicle under test are determined.

[0013] Optionally, the step of simulating the driving scenario of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test includes:

[0014] Based on the current status information and driving pattern information representing the mileage increase pattern, the mileage increase scenario of the vehicle under test is simulated.

[0015] Based on the simulation results of the increased mileage scenario, the simulated mileage data corresponding to the vehicle under test is determined.

[0016] Optionally, the step of simulating the driving scenario of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test includes:

[0017] Based on the driving time patterns represented by the current status information and driving pattern information, the sleep and wake-up scenarios of the vehicle under test are simulated.

[0018] Based on the simulation results of the hibernation and wake-up scenarios, the simulation data of the running time corresponding to the vehicle under test is determined.

[0019] Optionally, simulating the sleep and wake-up scenarios of the vehicle under test based on the driving time patterns characterized by the current state information and driving pattern information includes:

[0020] Based on a preset time amplification factor, and according to the driving time pattern represented by the current state information and driving pattern information, the sleep and wake-up scenarios of the vehicle under test are simulated.

[0021] Optional, also includes:

[0022] When the simulation results of the hibernation and wake-up scenarios indicate that the vehicle under test is in a wake-up scenario, the preset battery life calculation model is started, and the step of inputting the vehicle simulation data into the preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model is executed.

[0023] Optional, also includes:

[0024] Based on a preset standard model for calculating battery life, the expected battery life index corresponding to the vehicle simulation data is determined according to the vehicle simulation data of the vehicle under test.

[0025] The second aspect of this application provides a battery life calculation model testing device, comprising:

[0026] The acquisition module is used to acquire the current status information and driving pattern information of the vehicle under test;

[0027] The simulation module is used to simulate the driving scenario of the vehicle under test based on the current state information and driving pattern information, and obtain vehicle simulation data corresponding to the vehicle under test.

[0028] The calculation module is used to input the vehicle simulation data into a preset battery life calculation model, so as to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model.

[0029] The testing module is used to determine the test results of the preset battery life calculation model based on the deviation between the battery life prediction index and the battery life expectation index corresponding to the vehicle simulation data.

[0030] A third aspect of this application provides an electronic device, comprising: at least one processor and a memory;

[0031] The memory stores computer-executed instructions;

[0032] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method described in the first aspect above and various possible designs of the first aspect.

[0033] The fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method described in the first aspect above and various possible designs of the first aspect.

[0034] The technical solution of this application has the following advantages:

[0035] This application provides a battery life calculation model testing method, apparatus, and electronic device. The method includes: acquiring the current state information and driving pattern information of the vehicle under test; simulating driving scenarios of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test; inputting the vehicle simulation data into a preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model; and determining the test result of the preset battery life calculation model based on the deviation between the battery life prediction index and the expected battery life index corresponding to the vehicle simulation data. The method provided above, by combining the driving pattern information of the vehicle under test and randomly simulating dynamic driving scenarios of the vehicle under test under various operating conditions, ensures the reliability of the model test results. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0037] Figure 1 This is a schematic diagram of the battery life calculation model test system based on the embodiments of this application;

[0038] Figure 2 A schematic flowchart illustrating the battery life calculation model testing method provided in this application embodiment;

[0039] Figure 3 A schematic diagram illustrating the exemplary time amplification principle provided in this application embodiment;

[0040] Figure 4 A schematic diagram of the overall process of the battery life calculation model test method provided in the embodiments of this application;

[0041] Figure 5 This is a schematic diagram of the battery life calculation model testing device provided in the embodiments of this application;

[0042] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0043] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. In the following descriptions of embodiments, "a plurality of" means two or more, unless otherwise explicitly defined.

[0046] In existing technologies, the accuracy of battery life calculation models is typically verified under static scenarios where key indicators such as vehicle mileage and operating time are determined. For the accuracy of the model algorithm, MIL testing is usually sufficient for simulation verification. However, verifying battery lifespan and vehicle mileage involves the coupled logic of the algorithm, and time and vehicle mileage involve a wide range of dimensions, making overall lifespan verification difficult. Existing model testing schemes mainly consist of two parts: one part, during the MIL testing phase, verifies the accuracy of the algorithm and the logic of various battery lifespan degradation combinations; the other part, during the system testing phase, verifies the storage logic of time and charge / discharge data.

[0047] However, due to the different driving habits of different users, there is a random coupling between vehicle mileage and running time, which makes it impossible for existing technologies to simulate dynamic driving scenarios, and thus cannot guarantee the reliability of test results.

[0048] To address the aforementioned issues, the battery life calculation model testing method, apparatus, and electronic equipment provided in this application acquire the current state information and driving pattern information of the vehicle under test; simulate driving scenarios of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test; input the vehicle simulation data into a preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model; and determine the test result of the preset battery life calculation model based on the deviation between the battery life prediction index and the expected battery life index corresponding to the vehicle simulation data. The method provided above, by combining the driving pattern information of the vehicle under test and randomly simulating dynamic driving scenarios of the vehicle under test under various operating conditions, ensures the reliability of the model test results.

[0049] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0050] First, the structure of the battery life calculation model test system on which this application is based will be described:

[0051] The battery life calculation model testing method, apparatus, and electronic equipment provided in this application are suitable for testing the accuracy of the battery life calculation model currently deployed in the vehicle battery management system during the vehicle factory testing phase. Figure 1 The diagram shows the structure of the battery life calculation model testing system based on the embodiments of this application. It mainly includes a vehicle under test, a data acquisition device, and a battery life calculation model testing device. Specifically, the data acquisition device collects the current state information and driving pattern information of the vehicle under test and sends the collected information to the battery life calculation model testing device. Based on the obtained information, the battery life calculation model testing device dynamically simulates the driving scenarios of the vehicle under test, thereby determining the accuracy test results of the preset battery life calculation model deployed on the vehicle under test.

[0052] This application provides a battery life calculation model testing method for testing the accuracy of the battery life calculation model currently deployed in the vehicle's battery management system during the vehicle's factory testing phase. The execution subject of this application embodiment is an electronic device, such as a server, desktop computer, laptop computer, tablet computer, or other electronic devices that can be used to test the battery life calculation model.

[0053] like Figure 2 The diagram shown is a flowchart illustrating the battery life calculation model testing method provided in this application embodiment. The method includes:

[0054] Step 201: Obtain the current status information and driving pattern information of the vehicle under test.

[0055] The current status information includes at least running time, sleep time, current mileage, and current estimated battery life, while the driving pattern information can at least characterize the estimated battery life reduction pattern and mileage increase pattern of the vehicle under test.

[0056] Step 202: Based on the current status information and driving pattern information, simulate the driving scenario of the vehicle under test to obtain the vehicle simulation data corresponding to the vehicle under test.

[0057] Specifically, based on the current status and driving pattern information of the vehicle under test, the mileage change and estimated battery life change of the vehicle under test over a period of time can be simulated, that is, the driving scenario of the vehicle under test can be simulated to obtain the vehicle simulation data corresponding to the vehicle under test. Among them, the vehicle simulation data includes at least the mileage simulation data and the estimated battery life simulation data of the vehicle under test.

[0058] Step 203: Input the vehicle simulation data into the preset battery life calculation model, and calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model.

[0059] It should be noted that the preset battery life calculation model is deployed on the battery management system (BMS) of the vehicle under test.

[0060] Specifically, after obtaining vehicle simulation data, an automated script can be used to send the vehicle simulation data to the BMS to set the SOH (State of Health) atomic value. Based on this preset battery life calculation model, the battery life prediction index corresponding to the vehicle simulation data is calculated according to the set SOH atomic value. The battery life prediction index can specifically be the battery SOH index.

[0061] Step 204: Determine the test results of the preset battery life calculation model based on the deviation between the battery life prediction index and the battery life expectation index corresponding to the vehicle simulation data.

[0062] Specifically, in one embodiment, the expected battery life index corresponding to the vehicle simulation data can be determined based on a preset battery life calculation standard model and the vehicle simulation data corresponding to the vehicle under test.

[0063] Specifically, the battery life prediction index obtained by the preset battery life calculation model can be judged based on the relationship between the deviation between the battery life prediction index and the expected battery life index corresponding to the vehicle simulation data and the preset deviation threshold, thereby obtaining the accuracy test results of the preset battery life calculation model.

[0064] When the accuracy test result of the preset battery life calculation model is abnormal, an alarm message can be generated to prompt optimization of the preset battery life calculation model.

[0065] Based on the above embodiments, as an implementable approach, in one embodiment, the driving scenario of the vehicle under test is simulated according to the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test, including:

[0066] Step 2021: Based on the battery power-on and power-off patterns represented by the current status information and driving pattern information, simulate the battery power-on and power-off scenarios of the vehicle under test.

[0067] Step 2022: Based on the simulation results of battery power-on and power-off scenarios, determine the simulated data of battery life loss for the vehicle under test.

[0068] The battery life reduction simulation data includes estimated battery life simulation data.

[0069] It should be noted that the battery SOH index is obtained by coupling three characteristic factors: estimated battery life, battery time life, and battery mileage life. Among them, the estimated battery life is determined based on the battery's internal characteristics and is independent of the mileage and operating time of the vehicle under test.

[0070] Specifically, the simulated battery life degradation data for the vehicle under test can be determined using the formula SOHf = SOHfo – Cnt / Time. Here, SOHf represents the estimated battery life, SOHfo is the previously calculated SOHf value, Cnt is the number of battery power cycles (represented by the battery power cycle pattern), and Time represents the 1% degradation of SOHf per Time power cycle. The Time value can be determined based on the battery power cycle pattern. This algorithm can simulate the reduction in battery life with the number of power cycles. For example, with Time = 10, the estimated battery life degradation is 1% after every 10 simulated power cycles. By changing Time, the battery life degradation rate can be customized to improve the efficiency of the simulation test.

[0071] Specifically, in one embodiment, the mileage increase scenario of the vehicle under test can be simulated based on the mileage increase pattern characterized by the current state information and driving pattern information; and the mileage simulation data corresponding to the vehicle under test can be determined based on the mileage increase scenario simulation results.

[0072] Specifically, the simulated mileage data for the vehicle under test can be determined using the formula S = So + (So % a) - b. Here, So is the previously calculated mileage S, and S is the mileage increase. By setting the values ​​of a and b, a mileage increase rate and fluctuation range that match the mileage increase pattern are defined. For example, if the initial So is 500km, a = 489, and b = 245, after continuous iterative calculations, the mileage increase fluctuates between 250km and 730km each time, with an overall average increase of 493km.

[0073] Specifically, in one embodiment, the sleep and wake-up scenarios of the vehicle under test can be simulated based on the driving time patterns represented by the current state information and driving pattern information; and the simulation data of the running time of the vehicle under test can be determined based on the simulation results of the sleep and wake-up scenarios.

[0074] Among them, vehicle sleep corresponds to the parked state, and vehicle wake-up corresponds to the vehicle start state.

[0075] Specifically, in a wake-up scenario, the simulated running time of the vehicle under test can be determined using the following formula: T = To + (To % c) - d. Here, To is the previously calculated running time T. By setting the values ​​of c and d, the length and fluctuation range of each running time can be set to match the vehicle's driving time pattern. For example, if To = 15, c = 11, and d = 5, after continuous iterative calculations, the running time will fluctuate between 6 and 14, with an average running time of 10. Further, after waiting for time T, the vehicle under test will enter a sleep state.

[0076] Specifically, in one embodiment, in order to improve the efficiency of vehicle operating condition simulation and obtain simulation data of the vehicle under test over a long life cycle in a short time, a time magnification factor Bt can be set. According to the preset time magnification factor, the driving time pattern represented by the current state information and driving pattern information is used to simulate the sleep and wake-up scenarios of the vehicle under test.

[0077] For example, such as Figure 3 The diagram shown is an exemplary time amplification principle provided in the embodiment of this application. A crystal oscillator circuit generates a correct clock source. This time is amplified by a factor of Bt through secondary calculation and then transmitted to the SOH module (preset battery life calculation model). This achieves the effect that the time obtained by the SOH is faster than the actual correct clock, thereby accelerating the simulation of the entire life cycle driving scenario of the vehicle under test battery.

[0078] Furthermore, after the vehicle under test enters a sleep state, the script needs to calculate the sleep time t for this round. The formula for calculating the sleep time t is: t = to + (to % e) - f. Here, to is the previously calculated sleep time t. By setting the values ​​of e and f, the length and fluctuation range of each sleep time can be set to match the driving time pattern. For example, if to = 15, e = 11, and f = 5, after continuous iterative calculation, the running time will fluctuate between 6 and 14 seconds, with an average running time of 10 seconds. This time, combined with the time amplification factor Bt, can quickly simulate the sleep time of the vehicle under test. After calculating the sleep time t, the script will wait to wake up based on t.

[0079] Specifically, in one embodiment, since the BMS of the vehicle under test will be started in the vehicle wake-up state, in order to ensure that the vehicle simulation results are consistent with the actual operation of the vehicle, when the simulation results of the hibernation and wake-up scenarios indicate that the vehicle under test is in the wake-up scenario, a preset battery life calculation model can be started, and the steps of inputting the vehicle simulation data into the preset battery life calculation model can be performed to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model.

[0080] For example, such as Figure 4The diagram shows the overall flow of the battery life calculation model test method provided in this application embodiment. With the vehicle under test in a wake-up state (i.e., the BMS is in a wake-up state), gray-box test parameters such as the time amplification factor Bt, the estimated initial value of SOHf, the initial value of running time T, and the initial value of sleep time t are set. Then, the estimated SOHf and mileage S are calculated. The estimated SOHf, mileage S, and initial running time T are used as inputs to the preset battery life calculation model to obtain the corresponding SOH value (battery life prediction index). Then, the vehicle running time is calculated. When the actual running time of the vehicle under test reaches the calculated vehicle running time, the vehicle under test will enter a sleep state, i.e., the BMS will enter normal sleep mode. Then, the vehicle sleep time is calculated. When the actual sleep time of the vehicle under test reaches the calculated vehicle sleep time, the BMS is woken up to begin a new round of simulation testing.

[0081] The battery life calculation model testing method provided in this application obtains the current state information and driving pattern information of the vehicle under test; simulates the driving scenarios of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test; inputs the vehicle simulation data into a preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model; and determines the test result of the preset battery life calculation model based on the deviation between the battery life prediction index and the expected battery life index corresponding to the vehicle simulation data. The method provided above, by combining the driving pattern information of the vehicle under test, randomly simulates the dynamic driving scenarios of the vehicle under test under various operating conditions, ensuring the reliability of the model test results. Furthermore, this application embodiment uses gray-box testing to modify and amplify software parameters, solving the difficulty of large-scale cross-dimensionality throughout the entire life cycle. Moreover, through automated scripts, time and mileage information are randomly calculated, and then this information is fed back to the preset battery life calculation model in the BMS to simulate the coupling verification of SOH calculation atoms under various dynamic scenarios, while improving the accuracy of the coupling verification results.

[0082] This application provides a battery life calculation model testing device for executing the battery life calculation model testing method provided in the above embodiments.

[0083] like Figure 5 The diagram shown is a structural schematic of the battery life calculation model testing device provided in an embodiment of this application. The battery life calculation model testing device 50 includes: an acquisition module 501, a simulation module 502, a calculation module 503, and a testing module 504.

[0084] The system comprises the following modules: an acquisition module for acquiring the current status and driving pattern information of the vehicle under test; a simulation module for simulating the driving scenarios of the vehicle under test based on the current status and driving pattern information to obtain vehicle simulation data; a calculation module for inputting the vehicle simulation data into a preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data; and a testing module for determining the test results of the preset battery life calculation model based on the deviation between the battery life prediction index and the expected battery life index corresponding to the vehicle simulation data.

[0085] Regarding the battery life calculation model test device in this embodiment, the specific way in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0086] The battery life calculation model testing device provided in this application embodiment is used to execute the battery life calculation model testing method provided in the above embodiment. Its implementation method and principle are the same, and will not be described again.

[0087] This application provides an electronic device for executing the battery life calculation model test method provided in the above embodiments.

[0088] like Figure 6 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application. The electronic device 60 includes at least one processor 61 and a memory 62.

[0089] The memory stores computer-executable instructions; at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to execute the battery life calculation model test method provided in the above embodiment.

[0090] The present application provides an electronic device for executing the battery life calculation model test method provided in the above embodiments. Its implementation method and principle are the same, and will not be described again.

[0091] This application provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the battery life calculation model test method provided in any of the above embodiments.

[0092] The storage medium containing computer-executable instructions in this application embodiment can be used to store the computer-executable instructions of the battery life calculation model test method provided in the foregoing embodiments. Its implementation method and principle are the same, and will not be described again.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0096] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A battery life calculation model testing method, characterized in that, include: Obtain the current status information and driving pattern information of the vehicle under test; Based on the current status information and driving pattern information, the driving scenario of the vehicle under test is simulated to obtain the vehicle simulation data corresponding to the vehicle under test; The vehicle simulation data is input into a preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model. The test results of the preset battery life calculation model are determined based on the deviation between the battery life prediction index and the battery life expectation index corresponding to the vehicle simulation data.

2. The method according to claim 1, characterized in that, The step of simulating the driving scenario of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test includes: Based on the battery power-on and power-off patterns characterized by the current state information and driving pattern information, the battery power-on and power-off scenarios of the vehicle under test are simulated. Based on the simulation results of the battery power-on and power-off scenarios, the simulated data of battery life reduction for the vehicle under test are determined.

3. The method according to claim 1, characterized in that, The step of simulating the driving scenario of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test includes: Based on the current status information and driving pattern information representing the mileage increase pattern, the mileage increase scenario of the vehicle under test is simulated. Based on the simulation results of the increased mileage scenario, the simulated mileage data corresponding to the vehicle under test is determined.

4. The method according to claim 1, characterized in that, The step of simulating the driving scenario of the vehicle under test based on the current state information and driving pattern information to obtain vehicle simulation data corresponding to the vehicle under test includes: Based on the driving time patterns represented by the current status information and driving pattern information, the sleep and wake-up scenarios of the vehicle under test are simulated. Based on the simulation results of the hibernation and wake-up scenarios, the simulation data of the running time corresponding to the vehicle under test is determined.

5. The method according to claim 4, characterized in that, The step of simulating the sleep and wake-up scenarios of the vehicle under test based on the driving time pattern characterized by the current state information and driving pattern information includes: Based on a preset time amplification factor, and according to the driving time pattern represented by the current state information and driving pattern information, the sleep and wake-up scenarios of the vehicle under test are simulated.

6. The method according to claim 4, characterized in that, Also includes: When the simulation results of the hibernation and wake-up scenarios indicate that the vehicle under test is in a wake-up scenario, the preset battery life calculation model is started, and the step of inputting the vehicle simulation data into the preset battery life calculation model to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model is executed.

7. The method according to claim 1, characterized in that, Also includes: Based on a preset standard model for calculating battery life, the expected battery life index corresponding to the vehicle simulation data is determined according to the vehicle simulation data of the vehicle under test.

8. A battery life calculation model testing device, characterized in that, include: The acquisition module is used to acquire the current status information and driving pattern information of the vehicle under test; The simulation module is used to simulate the driving scenario of the vehicle under test based on the current state information and driving pattern information, and obtain vehicle simulation data corresponding to the vehicle under test. The calculation module is used to input the vehicle simulation data into a preset battery life calculation model, so as to calculate the battery life prediction index corresponding to the vehicle simulation data based on the preset battery life calculation model. The testing module is used to determine the test results of the preset battery life calculation model based on the deviation between the battery life prediction index and the battery life expectation index corresponding to the vehicle simulation data.

9. An electronic device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the method as described in any one of claims 1 to 7.

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