Battery life risk assessment method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a battery life risk assessment method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a test condition; determining target capacity aging data of the to-be-tested battery under the test condition; and comparing the actual capacity aging data of the to-be-tested battery under the test condition with the target capacity aging data, and evaluating the life risk of the to-be-tested battery under the test condition according to a comparison result. By adopting the embodiment of the invention, the service life risk of the battery under different test conditions can be estimated, then the short board test condition with higher risk can be identified, and targeted improvement work can be conveniently carried out on the battery.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery life risk assessment method, device, electronic device and storage medium. Background Art

[0002] Battery life is an important indicator to measure battery quality. How to predict battery life during the battery testing phase has always been a concern of the relevant industry.

[0003] In the related art, the battery test data is used to unilaterally estimate whether the battery life meets the warranty requirements. However, when the battery life does not meet the warranty requirements, the solution cannot identify which test conditions bring about the life risk, making it difficult for researchers to carry out targeted improvements to the battery. Summary of the invention

[0004] The present application provides a battery life risk assessment method, device, electronic device and storage medium, which can estimate the life risk of the battery under different test conditions, and then identify the short board test conditions with higher risks, so as to facilitate targeted improvement work on the battery.

[0005] In a first aspect, an embodiment of the present application provides a battery life risk assessment method, the method comprising:

[0006] Get test conditions;

[0007] Determine target capacity aging data of the battery to be tested under the test conditions;

[0008] The actual capacity aging data of the battery to be tested under the test conditions is compared with the target capacity aging data, and the life risk of the battery to be tested under the test conditions is evaluated according to the comparison result.

[0009] In the above embodiment, according to the comparison result of the actual capacity aging data and the target capacity aging data of the battery to be tested under the same test conditions, the life risk of the battery to be tested under the test conditions can be evaluated, and then according to the life risk of the battery to be tested under different test conditions, the short board test conditions with higher risks can be identified, so as to facilitate targeted improvement work on the battery to be tested. In addition, by evaluating the life risk of the battery to be tested under various test conditions, the time point for predicting the life risk of the battery to be tested can be greatly advanced, without waiting for the end of the long-term test to comprehensively evaluate the life of the battery to be tested based on all test data, which is conducive to saving test resources and time costs.

[0010] In some possible implementations, determining target capacity aging data of the battery to be tested under the test conditions includes:

[0011] The target capacity aging data is obtained according to the test conditions using a life aging model, wherein the life aging model is determined based on operating condition information and warranty life requirements associated with the battery to be tested.

[0012] In the above implementation, a life aging model can be determined according to the operating condition information and warranty life requirements associated with the battery to be tested, and the target capacity aging data of the battery to be tested under different test conditions can be obtained by using the life aging model.

[0013] In some possible implementations, the method for determining the life aging model includes:

[0014] According to the warranty life requirement of the battery to be tested, a target capacity attenuation amount of the battery to be tested when the warranty period is reached is obtained, wherein the target capacity attenuation amount includes a target cycle attenuation amount and a target storage attenuation amount;

[0015] Calculating a first cycle attenuation amount of the battery to be tested when it reaches the warranty period according to the first parameter to be solved, the first operating condition information and the cycle attenuation factor;

[0016] Calculating a first storage attenuation amount of the battery to be tested when it reaches the warranty period according to the second parameter to be solved, the second operating condition information, and the storage attenuation factor;

[0017] The first parameter to be solved and the second parameter to be solved are solved based on the optimization objective to obtain the life aging model, wherein the optimization objective includes: under the constraint that the sum of the first cycle attenuation and the first storage attenuation does not exceed the target capacity attenuation, minimizing the cycle deviation between the first cycle attenuation and the target cycle attenuation, and minimizing the storage deviation between the first storage attenuation and the target storage attenuation.

[0018] In the above embodiment, the target capacity attenuation is decomposed into a target cycle attenuation and a target storage attenuation. An optimization target is constructed based on the deviation between the cycle attenuation predicted by the model and the target cycle attenuation, as well as the deviation between the storage attenuation predicted by the model and the target storage attenuation. The model parameters are solved, and the life aging model thus obtained can more accurately predict the target capacity aging data of the battery under different test conditions.

[0019] In some possible implementations, the target capacity attenuation is equal to the sum of the target cyclic attenuation and the target storage attenuation, and the ratio of the target cyclic attenuation to the target storage attenuation is 1:1 or 2:1.

[0020] In the above implementation, the target capacity attenuation is decomposed into a target cycle attenuation and a target storage attenuation according to a certain allocation ratio, which can be more consistent with the attenuation of the battery to be tested during actual operation.

[0021] In some possible implementations, the first operating condition information includes: ambient temperature and charge and discharge rate, and the cycle attenuation factor is the number of cycles; the second operating condition information includes: ambient temperature and state of charge, and the storage attenuation factor is the storage time.

[0022] In the above implementation, ambient temperature, charge and discharge rate and number of cycles are important factors affecting battery cycle attenuation, and the capacity attenuation during battery cycling can be calculated more accurately using ambient temperature, charge and discharge rate and number of cycles. Ambient temperature, state of charge and storage time are important factors affecting battery storage attenuation, and the capacity attenuation during battery storage can be calculated more accurately using ambient temperature, state of charge and storage time.

[0023] In some possible implementations, solving the first parameter to be solved and the second parameter to be solved based on the optimization objective includes:

[0024] The first parameter to be solved and the second parameter to be solved are solved based on an optimization objective by using a genetic algorithm.

[0025] In the above implementation, the genetic algorithm can solve the multi-objective optimization problem well. By solving the model parameters using the genetic algorithm, the optimal solution of the model parameters can be obtained relatively quickly and accurately.

[0026] In some possible implementations, solving the first parameter to be solved and the second parameter to be solved based on an optimization objective includes:

[0027] An initial population is randomly generated, the initial population includes a preset number of samples, each sample includes a parameter value combination of the first parameter to be solved and the second parameter to be solved, the initial population is used as the current generation population, and the following processing is performed:

[0028] Calculate the fitness of each sample in the current generation population;

[0029] For each sample, calculate the fitness difference between the sample and the corresponding sample of the previous generation, and obtain first gradient information according to the fitness difference, and the maximum fitness difference and the minimum fitness difference among all samples;

[0030] Calculating a decay deviation between the capacity decay corresponding to the sample and the target capacity decay, and obtaining second gradient information according to the decay deviation and a maximum value and a minimum value of the decay deviation in all samples;

[0031] Based on the first gradient information and the second gradient information of the sample, weighting the fitness of the sample to obtain a weighted fitness of the sample;

[0032] According to the weighted fitness of each sample, the current generation population is screened, crossed and mutated to obtain a new population;

[0033] The new population is used as a new current generation population, and the above processing is repeated until the iteration end condition is met, thereby obtaining the optimal parameter values ​​of the first parameter to be solved and the second parameter to be solved.

[0034] In the above implementation, the model parameters are solved by a gradient-weighted genetic algorithm, and the gradient weighting is applied to the fitness calculation in the genetic algorithm. The fitness is weighted using the gradient information of the individual's parents and the gradient information of the contemporary individual, and then the selection operation is performed based on the weighted fitness. This can reduce the possibility of the genetic algorithm falling into a local optimum, and help improve the solution accuracy of the genetic algorithm.

[0035] In a second aspect, an embodiment of the present application provides a battery life risk assessment device, the device comprising:

[0036] An acquisition unit, used for acquiring test conditions;

[0037] A determination unit, used to determine target capacity aging data of the battery to be tested under the test conditions;

[0038] An evaluation unit is used to compare the actual capacity aging data of the battery to be tested under the test conditions with the target capacity aging data, and evaluate the life risk of the battery to be tested under the test conditions according to the comparison result.

[0039] In some possible implementations, the determination unit is specifically used to: utilize a life aging model to obtain the target capacity aging data according to the test conditions, wherein the life aging model is determined based on operating condition information and warranty life requirements associated with the battery to be tested.

[0040] In some possible implementations, the determining unit is further used to determine the life aging model. When determining the life aging model, the determining unit is specifically used to:

[0041] According to the warranty life requirement of the battery to be tested, a target capacity attenuation amount of the battery to be tested when the warranty period is reached is obtained, wherein the target capacity attenuation amount includes a target cycle attenuation amount and a target storage attenuation amount;

[0042] Calculating a first cycle attenuation amount of the battery to be tested when it reaches the warranty period according to the first parameter to be solved, the first operating condition information and the cycle attenuation factor;

[0043] Calculating a first storage attenuation amount of the battery to be tested when it reaches the warranty period according to the second parameter to be solved, the second operating condition information, and the storage attenuation factor;

[0044] The first parameter to be solved and the second parameter to be solved are solved based on the optimization objective to obtain the life aging model, wherein the optimization objective includes: under the constraint that the sum of the first cycle attenuation and the first storage attenuation does not exceed the target capacity attenuation, minimizing the cycle deviation between the first cycle attenuation and the target cycle attenuation, and minimizing the storage deviation between the first storage attenuation and the target storage attenuation.

[0045] In some possible implementations, the target capacity attenuation is equal to the sum of the target cyclic attenuation and the target storage attenuation, and the ratio of the target cyclic attenuation to the target storage attenuation is 1:1 or 2:1.

[0046] In some possible implementations, the first operating condition information includes: ambient temperature and charge and discharge rate, and the cycle attenuation factor is the number of cycles; the second operating condition information includes: ambient temperature and state of charge, and the storage attenuation factor is the storage time.

[0047] In some possible implementations, when the determination unit solves the first parameter to be solved and the second parameter to be solved based on the optimization objective, it is specifically used to: use a genetic algorithm to solve the first parameter to be solved and the second parameter to be solved based on the optimization objective.

[0048] In some possible implementations, when the determining unit uses a genetic algorithm to solve the first parameter to be solved and the second parameter to be solved based on an optimization objective, it is specifically used to:

[0049] An initial population is randomly generated, the initial population includes a preset number of samples, each sample includes a parameter value combination of the first parameter to be solved and the second parameter to be solved, the initial population is used as the current generation population, and the following processing is performed:

[0050] Calculate the fitness of each sample in the current generation population;

[0051] For each sample, calculate the fitness difference between the sample and the corresponding sample of the previous generation, and obtain first gradient information according to the fitness difference, and the maximum fitness difference and the minimum fitness difference among all samples;

[0052] Calculating a decay deviation between the capacity decay corresponding to the sample and the target capacity decay, and obtaining second gradient information according to the decay deviation and a maximum value and a minimum value of the decay deviation in all samples;

[0053] Based on the first gradient information and the second gradient information of the sample, weighting the fitness of the sample to obtain a weighted fitness of the sample;

[0054] According to the weighted fitness of each sample, the current generation population is screened, crossed and mutated to obtain a new population;

[0055] The new population is used as a new current generation population, and the processing is repeated until an iteration end condition is met, thereby obtaining optimal parameter values ​​of the first parameter to be solved and the second parameter to be solved.

[0056] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method in the above-mentioned first aspect and any possible implementation manner thereof when executing the computer program.

[0057] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method in the above-mentioned first aspect and any possible implementation manner thereof is implemented.

[0058] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method in the above-mentioned first aspect and any possible implementation manner thereof.

[0059] The beneficial effects brought about by the second to fifth aspects mentioned above can be referred to the description of the beneficial effects in the first aspect, and will not be repeated here.

[0060] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0062] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and are used together with the specification to illustrate the technical solution of the present application.

[0063] Figure 1 It is a flowchart of a battery life risk assessment method provided in an embodiment of the present application;

[0064] Figure 2 It is a schematic diagram of comparison results between the actual SOH and the target SOH of a battery to be tested at different ambient temperatures and charge and discharge rates provided in an embodiment of the present application;

[0065] Figure 3 It is a flow chart of a method for determining a life aging model provided in an embodiment of the present application;

[0066] Figure 4 is a schematic diagram of a Pareto boundary provided in an embodiment of the present application;

[0067] Figure 5 It is a structural schematic diagram of a battery life risk assessment device provided in an embodiment of the present application;

[0068] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0070] The terms "first", "second", etc. in the embodiments of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0071] It should be understood that in the present application, "at least one (item)" means one or more, "more than one" means two or more, and "at least two (items)" means two or three and more than three. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " can indicate that the previous and next associated objects are in an "or" relationship, which refers to any combination of these items, including any combination of single items (items) or plural items (items). For example, at least one item (item) of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0072] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0073] Battery life is an important indicator to measure battery quality. How to predict battery life during the battery testing phase has always been a concern of the relevant industry.

[0074] In the related art, the battery test data is used to unilaterally infer whether the battery life meets the warranty requirements. However, when the battery life does not meet the warranty requirements, the solution cannot identify which test conditions bring about the life risk, making it difficult for researchers to carry out targeted improvements to the battery. In addition, the solution needs to wait until the long-term test is completed before starting the battery life assessment, which consumes a lot of testing resources and time costs.

[0075] Based on this, the embodiment of the present application provides a battery life risk assessment method, which can estimate the life risk of the battery under different test conditions, and then identify the short board test conditions with higher risks, so as to facilitate targeted improvement work on the battery. In addition, the time point of battery life risk prediction can be greatly advanced, which is conducive to saving test resources and time costs.

[0076] The execution subject of the battery life risk assessment method may be a battery life risk assessment device. For example, the battery life risk assessment method may be executed by a terminal device or a server or other processing device, wherein the terminal device may be, but is not limited to, various personal computers, laptops, tablet computers, smart phones, and portable wearable devices, and the server may be implemented as an independent server or a server cluster composed of multiple servers. In some possible implementations, the battery life risk assessment method may be implemented by a processor calling a computer-readable instruction stored in a memory.

[0077] See also Figure 1 , Figure 1 It is a flow chart of a battery life risk assessment method provided in an embodiment of the present application. The battery life risk assessment method includes the following steps S101 to S103.

[0078] S101, obtaining test conditions.

[0079] The test conditions can be understood as the working condition information of the battery to be tested during the test process. For example, the test conditions may include but are not limited to ambient temperature, charge and discharge rate, state of charge (SOC), number of cycles, and storage time.

[0080] S102, determining target capacity aging data of the battery to be tested under test conditions.

[0081] The target capacity aging data of the battery to be tested under the test conditions can be understood as the capacity aging data corresponding to the battery to be tested meeting the warranty life requirements under the test conditions.

[0082] The warranty life requirement may include but is not limited to the warranty period and the capacity attenuation (or capacity loss) or battery health status (SOH) of the battery when the warranty period expires. Optionally, SOH can be expressed as a percentage of the current capacity to the rated capacity. Capacity aging data may include but is not limited to capacity attenuation or SOH.

[0083] In one possible implementation, the warranty life requirement of the battery to be tested can be decomposed into life requirements under different conditions (such as ambient temperature, charge and discharge rate, SOC). Based on this, the life requirements of the battery to be tested under different test conditions can be directly given during the test phase, thereby obtaining the target capacity aging data of the battery to be tested under different test conditions.

[0084] S103, comparing the actual capacity aging data of the battery to be tested under the test conditions with the target capacity aging data, and evaluating the life risk of the battery to be tested under the test conditions according to the comparison result.

[0085] The actual capacity aging data of the battery to be tested under the test conditions can be understood as the capacity aging data obtained by actually testing the battery to be tested based on the test conditions.

[0086] The actual capacity aging data and the target capacity aging data of the battery to be tested under the same test conditions may be the same or different. By comparing the actual capacity aging data with the target capacity aging data, a comparison result between the actual capacity aging data and the target capacity aging data can be obtained. The comparison result can indicate the size relationship and gap between the actual capacity aging data and the target capacity aging data. Based on the comparison result, the life risk of the battery to be tested under the test conditions can be evaluated.

[0087] In one possible implementation, the life risk can be divided into three levels: high, medium, and low. Based on the comparison results of the actual capacity aging data of the battery to be tested under the same test conditions and the target capacity aging data, the life risk level of the battery to be tested under the test conditions can be evaluated.

[0088] Exemplarily, taking the capacity aging data as SOH as an example, the target capacity aging data is specifically the target SOH, and the actual capacity aging data is specifically the actual SOH. If the actual SOH of the battery to be tested under a certain test condition is greater than or equal to the target SOH, or the actual SOH of the battery to be tested under a certain test condition is less than the target SOH, and the difference between the two is less than the first preset difference, then the life risk level of the battery to be tested under the test condition can be evaluated as low; if the actual SOH of the battery to be tested under a certain test condition is less than the target SOH, and the difference between the two is greater than or equal to the first preset difference and less than the second preset difference, then the life risk level of the battery to be tested under the test condition can be evaluated as medium; if the actual SOH of the battery to be tested under a certain test condition is less than the target SOH, and the difference between the two is greater than or equal to the second preset difference, then the life risk level of the battery to be tested under the test condition can be evaluated as high.

[0089] Among them, the difference between the two can be the difference obtained by subtracting the actual SOH from the target SOH, or it can be the absolute value of the difference obtained by subtracting the target SOH from the actual SOH. The second preset difference and the first preset difference are both positive values, and the second preset difference is greater than the first preset difference. It should be understood that the specific values ​​of the first preset difference and the second preset difference can be set in combination with actual conditions or needs. For example, they can be modified according to different models of battery cells, electric vehicles (EV) or hybrid electric vehicles (HEV). This embodiment of the present application does not limit this. Optionally, the first preset difference is set to 1.5%, and the second preset difference is set to 3%.

[0090] It should be understood that the above-mentioned life risk level division is only an example. In other examples, other division methods may be used to divide life risk into more or fewer levels, and the embodiments of the present application are not limited to this.

[0091] Exemplarily, the life risk assessment results of the tested battery under different test conditions are shown in Table 1 below:

[0092] Table 1

[0093]

[0094]

[0095] The test conditions include ambient temperature, charge and discharge rate, SOC, number of cycles and storage time. For example, the test condition "25℃, 0.5C / 1C, 600 cycles" means: the ambient temperature is 25℃, the charge rate is 0.5C, the discharge rate is 1C, and the number of cycles is 600; for another example, the test condition "35℃, 80SOC, 140 days" means: the ambient temperature is 35℃, the SOC is 80%, and the storage time is 140 days; similarly, the meaning of other test conditions in the above table can be understood.

[0096] From the above table, we can see that 45℃ cycling is a shortcoming with a high life risk for this battery. In addition, storage risk is generally lower than cycling risk. Therefore, battery R&D personnel can intuitively obtain the quantitative gap between the actual SOH and the target SOH, which is convenient for carrying out targeted improvements on the battery. The target SOH in the above table can be directly used as an improvement standard.

[0097] Taking the cycle process as an example, the actual SOH of the battery under test at different ambient temperatures and charge and discharge rates is compared with the target SOH. Figure 2 , Figure 2 : is a schematic diagram of the comparison results between the actual SOH and the target SOH of a battery to be tested at different ambient temperatures and charge and discharge rates provided in an embodiment of the present application, Figure 2 The dots and dashed lines in the figure represent the actual SOH and target SOH under the same test conditions. Figure 2 It can be seen that the actual SOH at different charge and discharge rates at 25°C is close to the target SOH, that is, the test results are close to the requirements, indicating that the battery life risk under low-temperature cycle test conditions is low. However, the actual SOH at different charge and discharge rates at 45°C is lower than the target SOH, that is, the test results are lower than the requirements, indicating that the high-temperature cycle performance of the battery is insufficient and the life risk under high-temperature cycle test conditions is high. In addition, the risks of 0.25C and 0.5C at 45°C are higher than those of 1C.

[0098] In the above embodiment, according to the comparison result of the actual capacity aging data and the target capacity aging data of the battery to be tested under the same test conditions, the life risk of the battery to be tested under the test conditions can be evaluated, and then according to the life risk of the battery to be tested under different test conditions, the short board test conditions with higher risks can be identified, so as to facilitate targeted improvement work on the battery to be tested. In addition, by evaluating the life risk of the battery to be tested under various test conditions, the time point for predicting the life risk of the battery to be tested can be greatly advanced, without waiting for the end of the long-term test to comprehensively evaluate the life of the battery to be tested based on all test data, which is conducive to saving test resources and time costs.

[0099] In a possible implementation, determining the target capacity aging data of the battery to be tested under test conditions may specifically include: using a life aging model to obtain the target capacity aging data according to the test conditions, wherein the life aging model is determined based on operating condition information and warranty life requirements associated with the battery to be tested.

[0100] The life aging model is used to simulate the battery under test based on the test conditions to obtain the target capacity aging data of the battery under the test conditions. Specifically, the test conditions can be used as input items of the life aging model, and the life aging model can be run to obtain the target capacity aging data.

[0101] The operating condition information and warranty life requirements associated with the battery to be tested can be obtained based on the existing operating condition information and warranty life requirements of the sample battery, where the sample battery can be an initial battery of the same model as the battery to be tested, or a battery with a similar design to the battery to be tested. The operating condition information may include but is not limited to total driving time, total driving mileage, ambient temperature, daily driving time, storage ratio, number of cycles, cell discharge capacity, or average charge and discharge current, etc.

[0102] In the above implementation, a life aging model can be determined according to the operating condition information and warranty life requirements associated with the battery to be tested, and the target capacity aging data of the battery to be tested under different test conditions can be obtained by using the life aging model.

[0103] In one possible implementation, see Figure 3 , Figure 3 It is a flow chart of a method for determining a life aging model provided in an embodiment of the present application, including the following steps S301 to S304.

[0104] S301, according to the warranty life requirement of the battery to be tested, obtain the target capacity attenuation of the battery to be tested when the warranty period expires, the target capacity attenuation includes the target cycle attenuation and the target storage attenuation.

[0105] Battery capacity decay may occur during both the cycling process and the storage process. The target capacity decay amount can be decomposed into the target capacity decay amount during the cycling process (recorded as the target cycling decay amount) and the target capacity decay amount during the storage process (recorded as the target storage decay amount).

[0106] In a possible implementation, the target capacity decay is equal to the sum of the target cycle decay and the target storage decay, and the ratio of the target cycle decay to the target storage decay is 1:1 or 2:1.

[0107] Exemplarily, the target capacity attenuation can be decomposed by the following formula:

[0108] SOH_loss total0 =SOH_loss cycle0 +SOH_loss storage0

[0109] Among them, SOH_loss total0 Indicates the target capacity attenuation when the battery to be tested reaches the warranty period; SOH_loss cycle0 Indicates the target cycle attenuation when the battery to be tested reaches the warranty period; SOH_loss storage0 Indicates the target storage attenuation of the battery under test when it reaches the warranty period.

[0110] For example, the warranty life requirement of the battery to be tested is 10 years 70% SOH, that is, the warranty period of the battery to be tested is 10 years, and the target capacity attenuation of the battery to be tested when the warranty period expires is 30%. Assuming that the ratio of the target cycle attenuation to the target storage attenuation is 2:1, the target cycle attenuation of the battery to be tested when the warranty period expires is 20%, and the target storage attenuation of the battery to be tested when the warranty period expires is 10%.

[0111] Based on this, decomposing the target capacity attenuation into target cycle attenuation and target storage attenuation according to a certain distribution ratio can be more in line with the attenuation of the battery to be tested during actual operation.

[0112] S302, calculating a first cycle attenuation amount of the battery to be tested when the warranty period is reached according to the first parameter to be solved, the first operating condition information and the cycle attenuation factor.

[0113] Among them, the first cycle attenuation refers to the capacity attenuation of the battery to be tested during the cycle process predicted by the model, the first parameter to be solved refers to the model parameter associated with the battery cycle process, the first operating condition information refers to the operating condition information associated with the battery cycle attenuation, and the cycle attenuation factor refers to the independent variable factor of the battery cycle attenuation.

[0114] In a possible implementation, the first operating condition information includes the ambient temperature and the charge and discharge rate, and the cycle attenuation factor is the number of cycles.

[0115] Exemplarily, the first cycle attenuation can be calculated by the following formula:

[0116]

[0117] x=STEP*cycle 0

[0118] Among them, SOH_loss cycle represents the attenuation of the first cycle; a 1 、b 1 、c 1 and d 1 represents the first parameter to be solved; T represents the ambient temperature; I represents the charge and discharge rate; x represents the number of cycles; STEP represents the prediction step size; cycle 0 Indicates the number of cycles within one step.

[0119] It can be understood that ambient temperature, charge and discharge rate and number of cycles are important factors affecting battery cycle attenuation. The ambient temperature, charge and discharge rate and number of cycles can be used to more accurately calculate the capacity attenuation during the battery cycle.

[0120] S303, calculating a first storage attenuation amount of the battery to be tested when the warranty period expires according to the second parameter to be solved, the second operating condition information, and the storage attenuation factor.

[0121] Among them, the first storage attenuation refers to the capacity attenuation of the battery to be tested during the storage process predicted by the model, the second parameter to be solved refers to the model parameter associated with the battery storage process, the second operating condition information refers to the operating condition information associated with the battery storage attenuation, and the storage attenuation factor refers to the independent variable factor of the battery storage attenuation.

[0122] In a possible implementation manner, the second operating condition information includes ambient temperature and state of charge, and the storage attenuation factor is storage time.

[0123] Exemplarily, the first storage attenuation can be calculated by the following formula:

[0124]

[0125] x ′ =STEP*storage 0

[0126] Among them, SOH_loss storage represents the first storage attenuation; a 2 、b 2、c 2 and d 2 represents the second parameter to be solved; T represents the ambient temperature; SOC represents the state of charge; x' represents the number of cycles; STEP represents the prediction step size; storage 0 Indicates the storage time within a step.

[0127] It can be understood that ambient temperature, state of charge and storage time are important factors affecting battery storage degradation. The ambient temperature, state of charge and storage time can be used to more accurately calculate the capacity degradation of the battery during storage.

[0128] S304, solving the first parameter to be solved and the second parameter to be solved based on the optimization objective to obtain a life aging model, wherein the optimization objective includes: under the constraint that the sum of the first cycle attenuation and the first storage attenuation does not exceed the target capacity attenuation, minimizing the cycle deviation between the first cycle attenuation and the target cycle attenuation, and minimizing the storage deviation between the first storage attenuation and the target storage attenuation.

[0129] Combined with the above calculation formula, for dynamic working conditions, the ambient temperature (T), state of charge (SOC), and independent variable factor (denoted as y, during the cycle, y = x, during the battery storage process, y = x') in different time steps are input into the model respectively, and the relationship between capacity attenuation and y is obtained as follows:

[0130] F(y)=a*SOH_loss cycle +b*SOH_loss storage

[0131] Wherein, F(y) represents the capacity attenuation; a and b are coefficients; when the battery undergoes a cycle process, a=1, b=0; when the battery undergoes a storage process, a=0, b=1.

[0132] In each STEP step, the change in capacity attenuation F(y) can be in the form of a derivative of F(y), represented by F'(y). The total capacity attenuation of the battery under dynamic conditions can be regarded as a process accumulated over time, and all independent variables have time series characteristics. For example, the total capacity attenuation of the battery under dynamic conditions can be calculated by the following formula:

[0133]

[0134] Among them, Capability loss represents the total capacity attenuation; t k represents the kth time step; N represents the total number of time steps; Indicates t k Capacity decay within a step.

[0135] It can be understood that for the battery cycle process, the total cycle attenuation of the battery under test when it reaches the warranty period can be calculated by the above formula, that is, the first cycle attenuation, recorded as F(1). For the battery storage process, the total storage attenuation of the battery under test when it reaches the warranty period can be calculated by the above formula, that is, the first storage attenuation, recorded as F(2).

[0136] Exemplarily, the following multi-objective optimization problem can be constructed:

[0137] Find a 1 ,b 1 ,c 1 ,d 1 ,a 2 ,b 2 ,c 2 ,d 2

[0138] Min|F(1)-SOH_loss cycle0 |

[0139] Min|F(2)-SOH_loss storage0 |

[0140] st|F(1)+F(2)|≤|SOH_loss cycle0 +SOH_loss storage0 |

[0141] Among them, a 1 , b 1 、c 1 ,d 1 、a 2 , b 2 、c 2 and d 2 represents the model parameters to be solved; min() function represents the objective function; st() represents the constraint condition. That is, when the sum of F(1) and F(2) does not exceed SOH_loss total0 Under the constraints of , solve the model parameter combination so that F(1) and SOH_loss cycle0 The deviation is the smallest, and F(2) and SOH_loss storage0 The deviation is minimal.

[0142] In the above embodiment, the target capacity attenuation is decomposed into a target cycle attenuation and a target storage attenuation. An optimization target is constructed based on the deviation between the cycle attenuation predicted by the model and the target cycle attenuation, as well as the deviation between the storage attenuation predicted by the model and the target storage attenuation. The model parameters are solved, and the life aging model thus obtained can more accurately predict the target capacity aging data of the battery under different test conditions.

[0143] In a possible implementation, the first parameter to be solved and the second parameter to be solved are solved based on the optimization target, which may be specifically: using a genetic algorithm to solve the first parameter to be solved and the second parameter to be solved based on the optimization target.

[0144] The above multi-objective optimization problem is a high-dimensional nonlinear multi-objective optimization problem. At the same time, the independent variables and parameters such as ambient temperature, SOC, and charge and discharge rate have time series characteristics, and can be solved by genetic algorithms.

[0145] Genetic algorithm is a sample-based random optimization method that iterates by simulating the genetic process of biology. The core process of genetic algorithm can include: determining the objective function, generating a set of initial samples and performing fitness evaluation; individuals with better fitness are selected to enter the next generation and undergo a series of bionic treatments: screening, crossover and mutation; the whole process is repeated until the results converge or the iteration end condition (or cutoff condition) is triggered.

[0146] Among them, excellent individual preservation means that the individuals with the best fitness are directly retained in the next generation of samples. Crossover means generating new variables based on the information of two variables, such as linear interpolation. Mutation means randomly changing the value of the sample, which is controlled by the mutation probability function.

[0147] Based on this, genetic algorithms can better solve multi-objective optimization problems. By using genetic algorithms to solve model parameters, the optimal solution of model parameters can be obtained relatively quickly and accurately.

[0148] In some possible cases, genetic algorithms have the problem of falling into local optimality. The initial population generation method of the genetic algorithm will cause individuals with a large number of individuals to have an advantage in the early stage of the algorithm. For example, when the initial population occupies the majority in a certain range, the proportion of the next generation of samples in this range will also be greater. In the early stage of the algorithm, even if the fitness of a small number of samples is significantly better than that of the rest of the individuals, these samples will not have a prominent chance to be screened; the operation of preserving excellent individuals accelerates the algorithm on the one hand, and on the other hand, it also makes the algorithm converge to the preserved individuals faster; the two-point crossover operation requires fewer steps than the single-point crossover, but it will also increase the possibility of the algorithm falling into local optimality. In order to reduce the possibility of falling into local optimality, the model parameters can be solved by the gradient weighted genetic algorithm.

[0149] In a possible implementation, a genetic algorithm is used to solve the first parameter to be solved and the second parameter to be solved based on an optimization objective, which may specifically include: randomly generating an initial population, the initial population including a preset number of samples, each sample including a parameter value combination of the first parameter to be solved and the second parameter to be solved, taking the initial population as the current generation population, and performing the following processing: calculating the fitness of each sample in the current generation population; for each sample, calculating the fitness difference between the sample and the corresponding sample of the previous generation, and obtaining the first gradient information according to the fitness difference, and the maximum fitness difference and the minimum fitness difference among all samples; Calculate the attenuation deviation between the capacity attenuation corresponding to the sample and the target capacity attenuation, and obtain the second gradient information according to the attenuation deviation and the maximum and minimum attenuation deviations in all samples; weight the fitness of the sample based on the first gradient information and the second gradient information of the sample to obtain the weighted fitness of the sample; perform screening, crossover and mutation operations on the current generation population according to the weighted fitness of each sample to obtain a new population; use the new population as the new current generation population, repeat the above process until the iteration end condition is met, and obtain the optimal parameter values ​​of the first parameter to be solved and the second parameter to be solved.

[0150] Exemplarily, the weighted fitness of a sample can be calculated by the following formula:

[0151]

[0152]

[0153] Among them, Fitness represents the weighted fitness of a sample; Fitness0 represents the weighted fitness of the sample (or original fitness); k 1 and k 2 represents the weighting factor, k 1 and k 2 The sum is 1 and they are all positive values; Δf represents the fitness difference between the sample and the corresponding sample of the previous generation, referred to as the fitness difference of the sample; Δf max Indicates the maximum value of the fitness difference of all samples, recorded as the maximum fitness difference; Δf min Represents the minimum value of the fitness difference of all samples, recorded as the minimum fitness difference; represents the first gradient information; delta represents the attenuation deviation between the capacity attenuation corresponding to the sample and the target capacity attenuation, referred to as the attenuation deviation of the sample. Since the optimization problem is a multi-objective optimization problem, the algorithm needs to approach the target cyclic attenuation and the target storage attenuation at the same time. Therefore, the cyclic attenuation deviation and the storage attenuation deviation are considered in the expression of delta at the same time; delta maxIndicates the maximum value of the attenuation deviation of all samples, recorded as the maximum attenuation deviation; delta min Represents the minimum value of the attenuation deviation of all samples, recorded as the minimum attenuation deviation; Represents the second gradient information.

[0154] In the above gradient weighted calculation, Fitness0 can represent the performance of the algorithm in the current generation of samples. Can represent the gradient information of the previous generation of samples, It can represent the gradient information of the algorithm to the next generation. The above three items reflect the optimization direction of the genetic algorithm in the past, present and future, which helps to obtain the global optimal solution.

[0155] In the above implementation, the model parameters are solved by a gradient-weighted genetic algorithm, and the gradient weighting is applied to the fitness calculation in the genetic algorithm. The fitness is weighted using the gradient information of the individual's parents and the gradient information of the contemporary individual, and then the selection operation is performed based on the weighted fitness. This can reduce the possibility of the genetic algorithm falling into a local optimum, and help improve the solution accuracy of the genetic algorithm.

[0156] In an example, the warranty life requirement of a certain battery model is 10 years 70% SOH, the target cycle attenuation is 20%, the target storage attenuation is 10%, the number of genetic algorithm samples is set to 200, the maximum number of iterations is 200, the initial population is randomly generated, the mutation method is non-uniform mutation, and the gradient weighting factor k is 1 and k 2 Both are 0.5. By simulating on the MATLAB / Simulink platform, multiple sets of solutions that meet the conditions can be obtained. The curve formed by the solution set is called the Pareto front, which represents the game relationship between multiple optimization objectives.

[0157] See also Figure 4 , Figure 4 This is a schematic diagram of a Pareto boundary provided by an embodiment of the present application. Among them, objective1 (in %) represents the optimization objective of the cycle attenuation deviation, and objective2 (in %) represents the optimization objective of the cycle attenuation deviation. It can be seen from the figure that the maximum deviation of the capacity attenuation is about 0.02% of the battery capacity, about 60% of the sample deviations are less than 0.01%, and the minimum deviation can reach 10 -5 This result shows that after the fitness function is gradient-weighted, the algorithm operation result has a high solution accuracy.

[0158] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0159] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0160] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a battery life risk assessment device provided in an embodiment of the present application. The battery life risk assessment device 500 includes:

[0161] An acquisition unit 501 is used to acquire test conditions;

[0162] A determination unit 502, used to determine target capacity aging data of the battery to be tested under test conditions;

[0163] The evaluation unit 503 is used to compare the actual capacity aging data of the battery to be tested under the test conditions with the target capacity aging data, and evaluate the life risk of the battery to be tested under the test conditions according to the comparison result.

[0164] In some possible implementations, the determination unit 502 is specifically used to: obtain target capacity aging data according to test conditions using a life aging model, wherein the life aging model is determined based on operating condition information and warranty life requirements associated with the battery to be tested.

[0165] In some possible implementations, the determination unit 502 is also used to determine a life aging model. When determining the life aging model, the determination unit 502 is specifically used to: obtain a target capacity decay amount when the battery to be tested reaches the warranty period according to the warranty life requirement of the battery to be tested, wherein the target capacity decay amount includes a target cycle decay amount and a target storage decay amount; calculate the first cycle decay amount of the battery to be tested when it reaches the warranty period according to the first parameter to be solved, the first operating condition information, and the cycle decay factor; calculate the first storage decay amount of the battery to be tested when it reaches the warranty period according to the second parameter to be solved, the second operating condition information, and the storage decay factor; solve the first parameter to be solved and the second parameter to be solved based on the optimization objective to obtain the life aging model, wherein the optimization objective includes: under the constraint that the sum of the first cycle decay amount and the first storage decay amount does not exceed the target capacity decay amount, minimize the cycle deviation between the first cycle decay amount and the target cycle decay amount, and minimize the storage deviation between the first storage decay amount and the target storage decay amount.

[0166] In some possible implementations, the target capacity decay is equal to the sum of the target cycle decay and the target storage decay, and the ratio of the target cycle decay to the target storage decay is 1:1 or 2:1.

[0167] In some possible implementations, the first operating condition information includes: ambient temperature and charge and discharge rate, and the cycle attenuation factor is the number of cycles; the second operating condition information includes: ambient temperature and state of charge, and the storage attenuation factor is the storage time.

[0168] In some possible implementations, when the determination unit 502 solves the first parameter to be solved and the second parameter to be solved based on the optimization objective, it is specifically configured to: use a genetic algorithm to solve the first parameter to be solved and the second parameter to be solved based on the optimization objective.

[0169] In some possible implementations, when the determination unit 502 uses a genetic algorithm to solve the first parameter to be solved and the second parameter to be solved based on the optimization objective, it is specifically used to: randomly generate an initial population, the initial population includes a preset number of samples, each sample includes a parameter value combination of the first parameter to be solved and the second parameter to be solved, and use the initial population as the current generation population to perform the following processing: calculate the fitness of each sample in the current generation population; for each sample, calculate the fitness difference between the sample and the corresponding sample of the previous generation, and obtain the first generation according to the fitness difference, and the maximum fitness difference and the minimum fitness difference among all samples. The method comprises the following steps: calculating the attenuation deviation between the capacity attenuation corresponding to the sample and the target capacity attenuation, and obtaining the second gradient information according to the attenuation deviation and the maximum and minimum attenuation deviations in all samples; weighting the fitness of the sample based on the first gradient information and the second gradient information of the sample to obtain the weighted fitness of the sample; screening, crossover and mutation operations are performed on the current generation population according to the weighted fitness of each sample to obtain a new population; taking the new population as the new current generation population, repeating the above processing until the iteration end condition is met, and obtaining the optimal parameter values ​​of the first parameter to be solved and the second parameter to be solved.

[0170] For the specific definition of the battery life risk assessment device, please refer to the definition of the battery life risk assessment method above, which will not be repeated here. Each unit in the above-mentioned battery life risk assessment device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned units.

[0171] See also Figure 6 , Figure 66 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 600 includes a memory 601 and a processor 602. Optionally, the electronic device 600 also includes a communication interface 603 and a bus 604. The memory 601, the processor 602 and the communication interface 603 are connected to each other through the bus 604. The memory 601 stores a computer program, and the processor 602 is used to execute the computer program stored in the memory 601 to implement the methods in the above-mentioned method embodiments.

[0172] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the methods in the above-mentioned method embodiments are implemented.

[0173] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in the above-mentioned method embodiments is implemented.

[0174] It should be understood that the memory / readable storage medium in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory may be a hard disk drive (HDD), a solid-state drive (SSD), a read-only memory (ROM) or a flash memory, etc. The volatile memory may be a random access memory (RAM) or an external cache memory. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0175] The processor in the embodiments of the present application may be a combination of one or more processing modules such as a central processing unit (CPU), a graphics processing unit (GPU) or a microprocessor (MPU), and may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0176] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0177] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A battery life risk assessment method, characterized in that: The method comprises: Get test conditions; Determine target capacity aging data of the battery to be tested under the test conditions; The actual capacity aging data of the battery to be tested under the test conditions is compared with the target capacity aging data, and the life risk of the battery to be tested under the test conditions is evaluated according to the comparison result.

2. The method according to claim 1, characterized in that Determining target capacity aging data of the battery to be tested under the test conditions includes: The target capacity aging data is obtained according to the test conditions using a life aging model, wherein the life aging model is determined based on operating condition information and warranty life requirements associated with the battery to be tested.

3. The method according to claim 2, characterized in that The method for determining the life aging model includes: According to the warranty life requirement of the battery to be tested, a target capacity attenuation amount of the battery to be tested when the warranty period is reached is obtained, wherein the target capacity attenuation amount includes a target cycle attenuation amount and a target storage attenuation amount; Calculating a first cycle attenuation amount of the battery to be tested when it reaches the warranty period according to the first parameter to be solved, the first operating condition information and the cycle attenuation factor; Calculating a first storage attenuation amount of the battery to be tested when it reaches the warranty period according to the second parameter to be solved, the second operating condition information, and the storage attenuation factor; The first parameter to be solved and the second parameter to be solved are solved based on the optimization objective to obtain the life aging model, wherein the optimization objective includes: under the constraint that the sum of the first cycle attenuation and the first storage attenuation does not exceed the target capacity attenuation, minimizing the cycle deviation between the first cycle attenuation and the target cycle attenuation, and minimizing the storage deviation between the first storage attenuation and the target storage attenuation.

4. The method according to claim 3, characterized in that The target capacity attenuation is equal to the sum of the target cycle attenuation and the target storage attenuation, and the ratio of the target cycle attenuation to the target storage attenuation is 1:1 or 2:

1.

5. The method according to claim 3, characterized in that: The first operating condition information includes: ambient temperature and charge and discharge rate, and the cycle attenuation factor is the number of cycles; the second operating condition information includes: ambient temperature and state of charge, and the storage attenuation factor is the storage time.

6. The method according to any one of claims 3 to 5, characterized in that The solving the first parameter to be solved and the second parameter to be solved based on the optimization objective includes: The first parameter to be solved and the second parameter to be solved are solved based on an optimization objective by using a genetic algorithm.

7. The method according to claim 6, characterized in that The using a genetic algorithm to solve the first parameter to be solved and the second parameter to be solved based on an optimization objective includes: An initial population is randomly generated, the initial population includes a preset number of samples, each sample includes a parameter value combination of the first parameter to be solved and the second parameter to be solved, the initial population is used as the current generation population, and the following processing is performed: Calculate the fitness of each sample in the current generation population; For each sample, calculate the fitness difference between the sample and the corresponding sample of the previous generation, and obtain first gradient information according to the fitness difference, and the maximum fitness difference and the minimum fitness difference among all samples; Calculating a decay deviation between the capacity decay corresponding to the sample and the target capacity decay, and obtaining second gradient information according to the decay deviation and a maximum value and a minimum value of the decay deviation in all samples; Based on the first gradient information and the second gradient information of the sample, weighting the fitness of the sample to obtain a weighted fitness of the sample; According to the weighted fitness of each sample, the current generation population is screened, crossed and mutated to obtain a new population; The new population is used as a new current generation population, and the processing is repeated until an iteration end condition is met, thereby obtaining optimal parameter values ​​of the first parameter to be solved and the second parameter to be solved.

8. A battery life risk assessment device, characterized in that: The device comprises: An acquisition unit, used for acquiring test conditions; A determination unit, used to determine target capacity aging data of the battery to be tested under the test conditions; An evaluation unit is used to compare the actual capacity aging data of the battery to be tested under the test conditions with the target capacity aging data, and evaluate the life risk of the battery to be tested under the test conditions according to the comparison result.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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