Methods, apparatus, vehicles and storage media for assessing the driving life of vehicle batteries

Abstract

This application relates to a method, apparatus, vehicle, and storage medium for assessing the driving life of a vehicle battery. The method includes: acquiring the current charging mode, current temperature, and current energy throughput of the battery to be assessed; inputting the current charging mode, current temperature, and current energy throughput into a pre-built driving aging model to obtain the capacity degradation rate of the battery to be assessed, wherein the pre-built driving aging model is trained using the charging mode, charging temperature, and energy throughput of multiple target vehicles distributed in multiple target regions; and assessing the driving life of the battery to be assessed based on the capacity degradation rate to obtain the assessment result of the driving life of the battery to be assessed. This solves the problems of limited empirical models for assessing battery driving life in related technologies, which are often singular and do not consider the impact of user charging habits on battery life, leading to low accuracy in battery driving life assessment, and improves the accuracy of battery driving life prediction.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, vehicle, and storage medium for assessing the driving life of a vehicle battery. Background Technology

[0002] The new energy vehicle industry has developed rapidly in recent years, with power batteries serving as a new power supply system. Life assessment of power vehicles plays a crucial role in the early stages of new model development and provides data support for battery pack health management during vehicle use, thus significantly extending battery lifespan.

[0003] Driving aging assessment is a crucial component of battery pack lifespan assessment. Driving aging refers to the period from when a battery rolls off the production line until its capacity degrades to a certain percentage of its initial capacity, during which it undergoes charging and operating conditions. Long charging times are a major pain point for electric vehicles, and fast charging and super-fast charging are effective methods to address this issue. However, on the other hand, as the charging current increases, it can cause some damage to the structure of the electrode materials, leading to greater heat generation and accelerating the reduction of battery life.

[0004] To assess battery lifespan, commonly used models include empirical models and neural network learning models. However, empirical models are relatively simple and have limitations, leading to discrepancies between predictions and actual usage. Furthermore, different battery cells and vehicle models have varying characteristics, causing the training set of neural networks to often differ from the actual vehicle being assessed. Additionally, the actual lifespan of a battery is heavily influenced by its charging method, and current technologies for assessing battery lifespan do not consider the impact of user charging methods, resulting in inaccurate assessments of the lifespan of electric vehicle battery packs. Summary of the Invention

[0005] This application provides a method, apparatus, vehicle, and storage medium for assessing the driving life of a vehicle battery. It solves the problems of limited empirical models for assessing battery driving life in related technologies, which are often simplistic and do not consider the impact of users' charging habits on battery life, resulting in low accuracy in assessing battery driving life. This improves the accuracy of battery driving life prediction and makes driving life assessment more relevant to real-life usage scenarios.

[0006] The first aspect of this application provides a method for evaluating the driving life of a vehicle battery, comprising the following steps: obtaining the current charging mode, current temperature, and current energy throughput of the battery to be evaluated; inputting the current charging mode, current temperature, and current energy throughput into a pre-built driving aging model to obtain the capacity degradation rate of the battery to be evaluated, wherein the pre-built driving aging model is trained from the charging mode, charging temperature, and energy throughput of multiple target vehicles distributed in multiple target areas; and evaluating the driving life of the battery to be evaluated based on the capacity degradation rate to obtain the evaluation result of the driving life of the battery to be evaluated.

[0007] Optionally, before inputting the current charging method, the current temperature, and the current energy throughput into the pre-built drive aging model, the method further includes: obtaining the charging habit distribution results of users in multiple different regions; obtaining a MAP map of the drive degradation factor of the target battery as a function of energy throughput / temperature / charging method-capacity degradation rate; fusing the charging habit distribution results and the MAP map of the drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate to obtain an energy throughput / temperature-capacity degradation rate MAP map, and obtaining the pre-built drive aging model based on the energy throughput / temperature-capacity degradation rate MAP map.

[0008] Optionally, obtaining the MAP of the drive degradation factor of the target battery as a function of energy throughput / temperature / charging method-capacity degradation rate includes: processing the target battery according to a preset cycle aging test strategy to obtain multiple temperatures and multiple cycle capacity recovery rates of the target battery; and processing the multiple temperatures and the multiple cycle capacity recovery rates based on a preset exponential empirical model, a preset Arrhenius empirical model, and a preset weighting strategy to obtain the MAP of the drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate.

[0009] Optionally, obtaining the user's charging habit distribution results includes: obtaining a public dataset, wherein the public dataset consists of charging methods of multiple target vehicles distributed in multiple target areas; and removing data in the public dataset that does not meet the preset charging habits based on a preset elimination strategy to obtain the charging habit distribution results.

[0010] Optionally, the fusion processing of the charging habit distribution result and the MAP map of energy throughput / temperature / charging method-capacity degradation rate includes: fusing the charging habit distribution result and the MAP map of energy throughput / temperature / charging method-capacity degradation rate based on a preset fusion formula, wherein the preset fusion formula is:

[0011] C loss(T，E)=∑C loss(T，E)C *W c ;

[0012] Among them, W C For the percentage of charging methods, C loss(T，E)C C represents the energy throughput / temperature-capacity degradation under the preset charging mode. loss(T，E) The weighted fusion is calculated as energy throughput / temperature-capacity decay.

[0013] A second aspect of this application provides a device for evaluating the driving life of a vehicle battery, comprising: an acquisition module for acquiring the current charging mode, current temperature, and current energy throughput of the battery to be evaluated; an input module for inputting the current charging mode, current temperature, and current energy throughput into a pre-built driving aging model to obtain the capacity degradation rate of the battery to be evaluated, wherein the pre-built driving aging model is trained from the charging mode, charging temperature, and energy throughput of multiple target vehicles distributed in multiple target areas; and an evaluation module for evaluating the driving life of the battery to be evaluated based on the capacity degradation rate to obtain an evaluation result of the driving life of the battery to be evaluated.

[0014] Optionally, before inputting the current charging method, the current temperature, and the current energy throughput into the pre-built drive aging model, the input module is further configured to: obtain the charging habit distribution results of users in multiple different regions; obtain a MAP diagram of the drive degradation factor of the target battery as a function of energy throughput / temperature / charging method-capacity degradation rate; fuse the charging habit distribution results and the MAP diagram of the drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate to obtain an energy throughput / temperature-capacity degradation rate MAP diagram, and obtain the pre-built drive aging model based on the energy throughput / temperature-capacity degradation rate MAP diagram.

[0015] Optionally, the input module for obtaining the target battery's drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate (MAP) is further configured to: process the target battery according to a preset cycle aging test strategy to obtain multiple temperatures and multiple cycle capacity recovery rates of the target battery; and process the multiple temperatures and multiple cycle capacity recovery rates based on a preset exponential empirical model, a preset Arrhenius empirical model, and a preset weighting strategy to obtain the drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate (MAP).

[0016] Optionally, the input module is further configured to: obtain a public dataset, wherein the public dataset comprises charging methods of multiple target vehicles distributed in multiple target areas; and, based on a preset elimination strategy, eliminate data in the public dataset that does not meet preset charging habits to obtain the charging habit distribution results.

[0017] Optionally, in the process of fusing the charging habit distribution result and the MAP map of energy throughput / temperature / charging method-capacity degradation rate, the input module is further configured to: fuse the charging habit distribution result and the MAP map of energy throughput / temperature / charging method-capacity degradation rate based on a preset fusion formula, wherein the preset fusion formula is:

[0018] C loss(T，E) =∑C loss(T，E)C *W C ;

[0019] Among them, W C For the percentage of charging methods, C loss(T，E)C C represents the energy throughput / temperature-capacity degradation under the preset charging mode. loss(T，E) The weighted fusion is calculated as energy throughput / temperature-capacity decay.

[0020] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle battery drive life assessment method as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the vehicle battery drive life assessment method as described in the above embodiments.

[0022] This application inputs the current charging method, current temperature, and current energy throughput into a pre-built drive aging model to obtain the capacity degradation rate of the battery to be evaluated. Based on the capacity degradation rate, the drive life of the battery to be evaluated is assessed to obtain the evaluation result of the drive life of the battery to be evaluated. This solves the problems of limited empirical models for evaluating battery drive life in related technologies, which are often singular and do not consider the impact of user charging habits on battery life, leading to low accuracy in battery drive life assessment. It improves the accuracy of battery drive life prediction and makes drive life assessment more closely reflect real-life usage scenarios.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0025] Figure 1 This is a flowchart of a method for evaluating the driving life of a vehicle battery according to an embodiment of this application;

[0026] Figure 2 This is a schematic diagram illustrating the distribution of user charging habits according to one embodiment of this application;

[0027] Figure 3 This is a schematic diagram of the capacity degradation rate based on the energy throughput / temperature according to one embodiment of this application;

[0028] Figure 4 A flowchart of a method for evaluating the driving life of a vehicle battery according to an embodiment of this application;

[0029] Figure 5 A block diagram of a vehicle battery drive life assessment device according to an embodiment of this application;

[0030] Figure 6 This is a structural schematic diagram of a vehicle according to an embodiment of this application. Detailed Implementation

[0031] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0032] The following description, with reference to the accompanying drawings, outlines a method, apparatus, vehicle, and storage medium for assessing the driving life of a vehicle battery according to embodiments of this application. Addressing the limitations of existing empirical models for assessing battery driving life, as mentioned in the background section, and their failure to consider the impact of user charging habits on battery life, resulting in low accuracy in driving life assessment, this application provides a method for assessing the driving life of a vehicle battery. In this method, the current charging method, current temperature, and current energy throughput are input into a pre-built driving aging model to obtain the capacity degradation rate of the battery to be assessed. The driving life of the battery is then assessed based on this capacity degradation rate to obtain the assessment result. This method solves the problems of existing empirical models for assessing battery driving life, such as their limitations and failure to consider the impact of user charging habits on battery life, leading to low accuracy in driving life assessment. It improves the accuracy of battery driving life prediction, making driving life assessment more relevant to real-life usage scenarios.

[0033] Specifically, Figure 1 This is a flowchart illustrating a method for evaluating the driving life of a vehicle battery, as provided in an embodiment of this application.

[0034] like Figure 1 As shown, the method for assessing the driving life of the vehicle battery includes the following steps:

[0035] In step S101, the current charging mode, current temperature, and current energy throughput of the battery to be evaluated are obtained.

[0036] Among them, the current charging methods are 1C and fast charging. In this embodiment, the current temperature of the battery to be evaluated is detected by the temperature detection method of related technologies, and the current energy throughput of the battery to be evaluated is detected by the battery throughput detection method.

[0037] In step S102, the current charging method, current temperature, and current energy throughput are input into the pre-built drive aging model to obtain the capacity degradation rate of the battery to be evaluated. The pre-built drive aging model is trained by the charging method, charging temperature, and energy throughput of multiple target vehicles distributed in multiple target areas.

[0038] Understandably, the input values ​​of the pre-built driving aging model are the current charging method, current temperature, and current energy throughput, and the output value is the capacity degradation rate of the battery to be evaluated.

[0039] Optionally, in some embodiments, before inputting the current charging method, current temperature, and current energy throughput into the pre-built drive aging model, the method further includes: obtaining the charging habit distribution results of users in multiple different regions; obtaining a MAP map of the drive degradation factor of the target battery as a function of energy throughput / temperature / charging method-capacity degradation rate; fusing the charging habit distribution results and the MAP map of the drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate to obtain an energy throughput / temperature-capacity degradation rate MAP map, and obtaining the pre-built drive aging model based on the energy throughput / temperature-capacity degradation rate MAP map.

[0040] Optionally, in some embodiments, obtaining the user's charging habit distribution results includes: obtaining a public dataset, wherein the public dataset consists of the charging methods of multiple target vehicles distributed in multiple target areas; and removing data in the public dataset that does not meet the preset charging habits based on a preset elimination strategy to obtain the charging habit distribution results.

[0041] Specifically, this application embodiment first obtains information on vehicles that have been operating normally for more than 3 years on a big data platform, requiring more than 5,000 vehicles, distributed across more than 80% of cities nationwide. It then statistically analyzes the vehicle charging methods, removing abnormal and useless data, and statistically analyzes the distribution of user charging habits on an annual basis to obtain the distribution of user charging habits, such as... Figure 2 As shown.

[0042] Furthermore, in some embodiments, obtaining the target battery's drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate (MAP) includes: processing the target battery according to a preset cycle aging test strategy to obtain multiple temperatures and multiple cycle capacity recovery rates of the target battery; and processing the multiple temperatures and multiple cycle capacity recovery rates based on a preset exponential empirical model, a preset Arrhenius empirical model, and a preset weighting strategy to obtain the drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate (MAP).

[0043] Specifically, in this embodiment, each battery group is first subjected to 3 standard cycles, and the last capacity is recorded as capacity C0. According to the preset cycle aging test strategy, the battery is adjusted to the corresponding temperature and subjected to cycle tests at the corresponding rate. 3 standard cycles are performed every 100 cycles, and the last capacity is recorded as the corresponding Ci (i is the i-th 100th cycle). Ci / C0 is used to obtain the cycle capacity recovery rate of the battery, as shown in Table 1:

[0044] Table 1

[0045] 1. Stand still for 30 minutes at 1.25℃±2℃, then discharge at 1C to the cutoff voltage; 2.25℃±2℃, stand still for 30 minutes, then charge at 1 / 3C to the cutoff voltage; 3. Allow to stand at 3.25℃±2℃ for 30 minutes, then discharge at 1C to the cutoff voltage and record the discharge capacity.

[0046] Furthermore, in this embodiment of the application, multiple temperatures and multiple cycle capacity recovery rates of the battery are obtained according to a preset cycle aging test strategy. A preset exponential empirical model is used to affect the energy throughput, a preset Arrhenius empirical model is used to affect the temperature, and a preset weighting strategy is used to affect the charging method. Thus, the driving degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate factor can be obtained.

[0047] Wherein, the Arrhenius dynamics expression is:

[0048]

[0049] Among them, Q LOSS To drive the capacity loss, B is the pre-exponential factor, which is treated as a constant during fitting, and E... a The activation energy is treated as a constant during fitting, R is the molar gas constant, T is the absolute temperature, E is the driving energy throughput, and z is a dimensionless number, also treated as a constant during fitting.

[0050] Optionally, in some embodiments, fusing the charging habit distribution results and the MAP plot as a function of energy throughput / temperature / charging method-capacity degradation rate includes: fusing the charging habit distribution results and the MAP plot as a function of energy throughput / temperature / charging method-capacity degradation rate based on a preset fusion formula, wherein the preset fusion formula is:

[0051] C loss(T，E) =∑C loss(T，E)C *W C ;

[0052] Among them, W C For the percentage of charging methods, C loss(T，E)C C represents the energy throughput / temperature-capacity degradation under the preset charging mode. loss(T，E) The weighted fusion is calculated as energy throughput / temperature-capacity decay.

[0053] It should be understood that, based on the charging habit distribution results, this application embodiment predicts the subsequent charging mode distribution and performs weighted processing on the MAP of drive attenuation factor as a function of energy throughput / temperature / charging mode-capacity degradation rate. The weight of each charging mode is taken from its proportion, and the influence of charging mode can be integrated into a MAP as a function of energy throughput / temperature-capacity degradation rate, such as... Figure 3 As shown.

[0054] Furthermore, in this embodiment, a drive aging model is established using the MTLAB software Simulink based on the charging method, charging temperature, and energy throughput of multiple target vehicles distributed in multiple target areas. The MAP diagram of energy throughput / temperature-capacity degradation rate is parameterized into a 2D Lookup Table module. By inputting the battery pack temperature and energy throughput, the capacity degradation rate at the corresponding temperature and energy throughput can be obtained by looking up the table. The cumulative value of these values ​​is the capacity degradation amount of the drive life.

[0055] In summary, as Figure 4 As shown, this embodiment of the application formulates a preset test plan for the cyclic aging of battery cells, removes abnormal data, uses a preset exponential empirical model, a preset Arrhenius empirical model and a preset weighting strategy to obtain the cell degradation factor, obtains vehicle charging information connected to a big data platform, removes abnormal data, extracts user habits to obtain the distribution of charging methods, performs weighting processing based on the distribution of charging methods to obtain the energy throughput / temperature-capacity degradation rate, and establishes a drive aging model through MTLAB software to evaluate the drive life of the battery.

[0056] In step S103, the driving life of the battery to be evaluated is evaluated based on the capacity degradation rate to obtain the evaluation result of the driving life of the battery to be evaluated.

[0057] It is understood that, in the embodiments of this application, the capacity degradation rate at the corresponding temperature and energy throughput is obtained by looking up a table, and the battery driving life is evaluated to obtain the evaluation result of the battery driving life to be evaluated.

[0058] For example, in this embodiment of the application, four groups of batteries from the same batch after formation were selected for testing. The Design of Experiments (DOE) was used, with temperature and SOC as two factors. The temperature had two levels: 25°C and 45°C. The charging methods had two levels: 1C and fast charging. Three parallel cells were used in each group of experiments to ensure the validity of the test data. The remaining capacity of the batteries after degradation was measured, as shown in Table 2.

[0059] Table 2

[0060]

[0061] The vehicle battery driving life assessment method proposed in this application involves inputting the current charging method, current temperature, and current energy throughput into a pre-built driving aging model to obtain the capacity degradation rate of the battery to be assessed. Based on this capacity degradation rate, the driving life of the battery is then assessed to obtain the assessment result. This method solves the problems of limited empirical models for assessing battery driving life in related technologies, which often fail to consider the impact of user charging habits on battery life, leading to low accuracy in driving life assessment. It improves the accuracy of battery driving life prediction and makes driving life assessment more relevant to real-life usage scenarios.

[0062] Next, with reference to the accompanying drawings, a vehicle battery drive life assessment device according to an embodiment of this application is described.

[0063] Figure 5 This is a block diagram of a vehicle battery drive life assessment device according to an embodiment of this application.

[0064] like Figure 5 As shown, the vehicle battery drive life assessment device 10 includes: an acquisition module 100, an input module 200, and an assessment module 300.

[0065] The module includes an acquisition module 100 for acquiring the current charging method, current temperature, and current energy throughput of the battery to be evaluated; an input module 200 for inputting the current charging method, current temperature, and current energy throughput into a pre-built drive aging model to obtain the capacity degradation rate of the battery to be evaluated, wherein the pre-built drive aging model is trained using the charging method, charging temperature, and energy throughput of multiple target vehicles distributed in multiple target regions; and an evaluation module 300 for evaluating the drive life of the battery to be evaluated based on the capacity degradation rate to obtain the evaluation result of the drive life of the battery to be evaluated.

[0066] Optionally, before inputting the current charging method, current temperature, and current energy throughput into the pre-built drive aging model, the input module 200 is further configured to: obtain the charging habit distribution results of users in multiple different regions; obtain the MAP map of the drive degradation factor of the target battery as a function of energy throughput / temperature / charging method-capacity degradation rate; fuse and process the charging habit distribution results and the MAP map of the drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate to obtain the MAP map of energy throughput / temperature-capacity degradation rate, and obtain the pre-built drive aging model based on the MAP map of energy throughput / temperature-capacity degradation rate.

[0067] Optionally, the target battery's drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate (MAP) is obtained and input into module 200. This is further used to: process the target battery according to a preset cycle aging test strategy to obtain multiple temperatures and multiple cycle capacity recovery rates of the target battery; and process the multiple temperatures and multiple cycle capacity recovery rates based on a preset exponential empirical model, a preset Arrhenius empirical model, and a preset weighting strategy to obtain the drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate (MAP).

[0068] Optionally, the input module 200 is further configured to: obtain a public dataset, wherein the public dataset consists of the charging methods of multiple target vehicles distributed in multiple target areas; and, based on a preset elimination strategy, eliminate data in the public dataset that do not meet the preset charging habits to obtain the charging habit distribution results.

[0069] Optionally, the input module 200, which integrates the charging habit distribution results and the MAP plot of energy throughput / temperature / charging method-capacity degradation rate, is further configured to: integrate the charging habit distribution results and the MAP plot of energy throughput / temperature / charging method-capacity degradation rate based on a preset fusion formula, wherein the preset fusion formula is:

[0070] C loss(T，E) =∑C loss(T，E)C *W C ;

[0071] Among them, W C For the percentage of charging methods, C loss(T，E)C C represents the energy throughput / temperature-capacity degradation under the preset charging mode. loss(T，E) The weighted fusion is calculated as energy throughput / temperature-capacity decay.

[0072] It should be noted that the explanation of the aforementioned embodiment of the vehicle battery drive life assessment method also applies to the vehicle battery drive life assessment device of this embodiment, and will not be repeated here.

[0073] The vehicle battery drive life assessment device proposed in this application inputs the current charging method, current temperature, and current energy throughput into a pre-built drive aging model to obtain the capacity degradation rate of the battery to be assessed. Based on the capacity degradation rate, the drive life of the battery to be assessed is evaluated to obtain the assessment result. This solves the problems of limited empirical models for assessing battery drive life in related technologies, which do not consider the impact of user charging habits on battery life, resulting in low accuracy in battery drive life assessment. It improves the accuracy of battery drive life prediction and makes drive life assessment more relevant to real-life usage scenarios.

[0074] Figure 6 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0075] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0076] When the processor 602 executes the program, it implements the vehicle battery drive life assessment method provided in the above embodiments.

[0077] Furthermore, the vehicle also includes:

[0078] Communication interface 603 is used for communication between memory 601 and processor 602.

[0079] The memory 601 is used to store computer programs that can run on the processor 602.

[0080] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0081] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0082] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0083] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0084] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for assessing the driving life of a vehicle battery.

[0085] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0086] Furthermore, the terms "first" and "second" 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. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0087] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0089] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0090] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0091] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0092] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating the driving life of a vehicle battery, characterized in that, Includes the following steps: Obtain the current charging method, current temperature, and current energy throughput of the battery to be evaluated; The current charging method, current temperature, and current energy throughput are input into a pre-built drive aging model to obtain the capacity degradation rate of the battery to be evaluated. The pre-built drive aging model is trained using the charging methods, charging temperatures, and energy throughput of multiple target vehicles distributed across multiple target regions. The driving life of the battery to be evaluated is assessed based on the capacity degradation rate to obtain the evaluation result of the driving life of the battery to be evaluated. Before inputting the current charging method, the current temperature, and the current energy throughput into the pre-built drive aging model, the method further includes: Obtain the distribution results of charging habits of users in multiple different regions; Obtain the target battery's drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate MAP; The charging habit distribution results and the drive attenuation factor as a function of energy throughput / temperature / charging method-capacity degradation rate MAP are fused to obtain the energy throughput / temperature-capacity degradation rate MAP, and the pre-constructed drive aging model is obtained based on the energy throughput / temperature-capacity degradation rate MAP.

2. The method according to claim 1, characterized in that, The acquisition of the target battery's drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate MAP includes: The target battery is processed according to a preset cycle aging test strategy to obtain multiple temperatures and multiple cycle capacity recovery rates of the target battery. Based on a preset exponential empirical model, a preset Arrhenius empirical model, and a preset weighting strategy, the multiple temperatures and the multiple cycle capacity recovery rates are processed to obtain a MAP diagram of the driving decay factor as a function of energy throughput / temperature / charging method-capacity decay rate.

3. The method according to claim 1, characterized in that, The acquisition of user charging habit distribution results includes: Obtain a public dataset, wherein the public dataset comprises charging methods for multiple target vehicles distributed across multiple target regions; Based on a preset elimination strategy, data in the public dataset that do not meet the preset charging habits are eliminated to obtain the charging habit distribution results.

4. The method according to claim 1, characterized in that, The fusion processing of the charging habit distribution results and the MAP plot of energy throughput / temperature / charging method-capacity degradation rate includes: Based on a preset fusion formula, the charging habit distribution result and the MAP plot of energy throughput / temperature / charging method-capacity degradation rate are fused and processed, wherein the preset fusion formula is: ； in, The percentage of charging methods. Energy throughput / temperature-capacity degradation under preset charging mode. The weighted fusion is calculated as energy throughput / temperature-capacity decay.

5. A device for evaluating the driving life of a vehicle battery, characterized in that, include: The acquisition module is used to acquire the current charging method, current temperature, and current energy throughput of the battery to be evaluated. An input module is used to input the current charging method, the current temperature, and the current energy throughput into a pre-built drive aging model to obtain the capacity degradation rate of the battery to be evaluated. The pre-built drive aging model is trained using the charging methods, charging temperatures, and energy throughput of multiple target vehicles distributed across multiple target regions. An evaluation module is used to evaluate the driving life of the battery to be evaluated based on the capacity degradation rate, and obtain the evaluation result of the driving life of the battery to be evaluated. Before inputting the current charging method, the current temperature, and the current energy throughput into the pre-built drive aging model, the input module is further configured to: Obtain the distribution results of charging habits of users in multiple different regions; Obtain the target battery's drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate MAP; The charging habit distribution results and the drive attenuation factor as a function of energy throughput / temperature / charging method-capacity degradation rate MAP are fused to obtain the energy throughput / temperature-capacity degradation rate MAP, and the pre-constructed drive aging model is obtained based on the energy throughput / temperature-capacity degradation rate MAP.

6. The apparatus according to claim 5, characterized in that, The input module for obtaining the target battery's drive degradation factor as a function of energy throughput / temperature / charging method-capacity degradation rate MAP is further used for: The target battery is processed according to a preset cycle aging test strategy to obtain multiple temperatures and multiple cycle capacity recovery rates of the target battery. Based on a preset exponential empirical model, a preset Arrhenius empirical model, and a preset weighting strategy, the multiple temperatures and the multiple cycle capacity recovery rates are processed to obtain a MAP diagram of the driving decay factor as a function of energy throughput / temperature / charging method-capacity decay rate.

7. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for evaluating the drive life of a vehicle battery as described in any one of claims 1-4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for evaluating the driving life of a vehicle battery as described in any one of claims 1-4.

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