Method and device for predicting service life of secondary battery, and energy storage system

Abstract

The invention provides a service life prediction method and device of a secondary battery and an energy storage system, and relates to the technical field of secondary battery management, and the method comprises the steps: obtaining a training data set; parameter identification and battery health state calibration are carried out once every N cycles according to the training data set, a plurality of parameter identification results and a plurality of battery health state test values are obtained, and each parameter identification result corresponds to each battery health state test value; constructing a plurality of simulation models based on the plurality of parameter identification results, wherein each simulation model corresponds to each parameter identification result; acquiring actual voltage data and multiple groups of simulation voltage data of the target secondary battery; and determining a battery health state estimation value of the target secondary battery according to the plurality of battery health state test values, the actual voltage data of the target secondary battery and the plurality of groups of simulation voltage data. According to the method provided by the invention, the accuracy and reliability of predicting the service life of the secondary battery can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of secondary battery management, and in particular to a method, device and energy storage system for predicting the life of a secondary battery. Background Art

[0002] As the application of secondary batteries expands, how to accurately and effectively predict the life of secondary batteries has become a key issue. The predicted life of secondary batteries is not only an important factor in ensuring the safety of secondary batteries, but also an important basis for formulating maintenance strategies for secondary batteries.

[0003] The battery's state of health (SOH) represents the current state of health of the battery. The accuracy of SOH estimation can impact the reliability of battery life prediction, making accurate SOH estimation a top priority for battery management systems. However, SOH estimation methods used in related technologies are inaccurate, impacting the accuracy and reliability of secondary battery life prediction. Summary of the Invention

[0004] The present application provides a method, device and energy storage system for predicting the life of a secondary battery, which helps to improve the accuracy and reliability of the prediction of the life of a secondary battery.

[0005] In a first aspect, the present application provides a method for predicting the life of a secondary battery, comprising: Obtaining a training data set, the training data set including characteristic data of a secondary battery obtained by conducting an aging experiment on the secondary battery under different working conditions; Perform parameter identification and battery health status calibration every N cycles based on the training data set to obtain multiple parameter identification results and multiple battery health status test values. The multiple parameter identification results are used to represent the identification results of the parameters to be identified in the simulation model under different operating conditions and different numbers of cycles. Each parameter identification result corresponds to each battery health status test value. Constructing multiple simulation models based on multiple parameter identification results, each simulation model corresponds to each parameter identification result; Acquire actual voltage data of a target secondary battery and multiple sets of simulated voltage data, where the actual voltage data of the target secondary battery is voltage data obtained when the target secondary battery is actually operated under target operating conditions, and each set of simulated voltage data is voltage data obtained when each simulation model is simulated under target operating conditions; A battery health state estimation value of the target secondary battery is determined based on a plurality of battery health state test values, actual voltage data of the target secondary battery, and a plurality of sets of simulation voltage data.

[0006] In one possible implementation, determining a battery health state estimation value of a target secondary battery based on multiple battery health state test values, actual voltage data of the target secondary battery, and multiple sets of simulated voltage data includes: Comparing the actual voltage data of the target secondary battery with the multiple sets of simulated voltage data to determine a target simulated voltage data set, wherein the target simulated voltage data set is a simulated voltage data set having the smallest deviation from the actual voltage data of the target secondary battery among the multiple sets of simulated voltage data; The estimated battery health state value of the target secondary battery is determined according to the correspondence between the target simulation voltage data group and the battery health state test value. The estimated battery health state value of the target secondary battery is one of the multiple battery health state test values.

[0007] In one possible implementation, determining the estimated battery health state value of the target secondary battery according to the corresponding relationship between the target simulation voltage data group and the battery health state test value includes: Determining a target simulation model according to a target simulation voltage data group, a correspondence between the simulation voltage data and the simulation model; Determining a target parameter identification result according to a target simulation model, a corresponding relationship between the simulation model and the parameter identification result; The estimated battery health state value of the target secondary battery is determined according to the target parameter identification result and the corresponding relationship between the parameter identification result and the battery health state test value.

[0008] In one possible implementation, actual voltage data of a target secondary battery is compared with multiple sets of simulated voltage data to determine a target simulated voltage data set, including: The target simulated voltage data set is determined based on minimizing the root mean square error between the actual voltage data and the simulated voltage data of the target secondary battery.

[0009] In one possible implementation, the characteristic data of the secondary battery includes current data, voltage data, and temperature data under different operating conditions and different numbers of cycles. Parameter identification is performed every N cycles based on a training data set, including: Selecting first current data, first voltage data, and first temperature data at a corresponding number of cycles from characteristic data of the secondary battery according to a preset cycle interval N; The first current data, the first voltage data and the first temperature data are input into a pre-built parameter identification model for parameter identification.

[0010] In one possible implementation, the pre-built parameter identification model is a first P2D model. The construction process of the first P2D model includes: Build an initial P2D model based on PyBaMM and the known battery parameters of the target secondary battery; Based on PyBOP, the parameters to be identified and their value ranges are set in the initial P2D model, and the cost function and optimization algorithm are set to obtain the first P2D model.

[0011] In one possible implementation, the simulation model is a second P2D model, and multiple simulation models are constructed based on multiple parameter identification results, including: A plurality of second P2D models are respectively constructed based on PyBaMM, the plurality of parameter identification results, and the known battery parameters of the target secondary battery.

[0012] In one possible implementation, the cost function includes a mean square error function, and the optimization algorithm includes an exponential natural evolution strategy algorithm.

[0013] In one possible implementation, the known battery parameters of the target secondary battery include at least electrode thickness and diaphragm thickness; the parameters to be identified include at least positive electrode diffusion rate, negative electrode diffusion rate, electrolyte diffusion rate, positive electrode conductivity, negative electrode conductivity and electrolyte conductivity.

[0014] In one possible implementation, secondary batteries are tested under different operating conditions for aging experiments, including: The test secondary battery is subjected to charge and discharge cycles under different temperature and power operating conditions until the battery health status of the test secondary battery decays to a preset threshold.

[0015] In a second aspect, the present application provides a device for predicting the life of a secondary battery, comprising: A first acquisition module is used to acquire a training data set, where the training data set includes characteristic data of a secondary battery obtained by performing an aging experiment on the secondary battery under different working conditions; The parameter identification and battery health state calibration module is used to perform parameter identification and battery health state calibration every N cycles based on the training data set, obtaining multiple parameter identification results and multiple battery health state test values. The multiple parameter identification results are used to represent the identification results of the parameters to be identified in the simulation model under different operating conditions and different numbers of cycles. Each parameter identification result corresponds to each battery health state test value. A model building module is used to build multiple simulation models based on multiple parameter identification results, each simulation model corresponds to each parameter identification result; a second acquisition module, configured to acquire actual voltage data of a target secondary battery and multiple sets of simulated voltage data, wherein the actual voltage data of the target secondary battery is voltage data obtained by actually operating the target secondary battery under target operating conditions, and each set of simulated voltage data is voltage data obtained by simulating each simulation model under target operating conditions; The determination module is configured to determine a battery health state estimation value of a target secondary battery according to a plurality of battery health state test values, actual voltage data of the target secondary battery, and a plurality of sets of simulation voltage data.

[0016] In a third aspect, the present application provides an energy storage system, including: a BMS, the BMS including a processor and a memory, the memory being used to store a computer program; the processor being used to run the computer program to implement the secondary battery life prediction method as in the first aspect.

[0017] The beneficial effects of this application are: The present application provides a life prediction method, device and energy storage system for a secondary battery, which forms a training data set by collecting characteristic data of the secondary battery obtained by performing aging experiments on the test secondary battery under different working conditions; performing parameter identification and battery health status calibration once every N cycles based on the training data set to obtain multiple parameter identification results and multiple battery health status test values; constructing multiple simulation models based on the multiple parameter identification results; obtaining actual voltage data of the target secondary battery and multiple groups of simulation voltage data, wherein the actual voltage data of the target secondary battery is the voltage data obtained when the target secondary battery is actually operated under the target working conditions, and each group of simulation voltage data is the voltage data obtained when each simulation model is simulated and operated under the target working conditions; and determining an estimated battery health status value of the target secondary battery based on the multiple battery health status test values, the actual voltage data of the target secondary battery and the multiple groups of simulation voltage data. By setting up aging experiments under different working conditions, comprehensive characteristic data of secondary batteries is obtained, fully considering the complex characteristic changes of secondary batteries under different working conditions, and using the characteristic data of secondary batteries for parameter identification, dynamic updating of the parameters to be identified of secondary batteries is achieved, and then a highly applicable simulation model is built, the simulation model's dynamic tracking ability of the aging status of secondary batteries is enhanced, and the accuracy and reliability of secondary battery life prediction are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for predicting the life of a secondary battery provided in an embodiment of the present application; Figure 2 A flow chart of the SOH estimation method provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a secondary battery life prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In the embodiments of this application, unless otherwise specified, the character " / " indicates that the associated objects are in an "or" relationship. For example, A / B can represent A or B. "And / or" describes the relationship between the associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exists simultaneously, or B exists alone.

[0020] It should be pointed out that the words "first", "second", etc. involved in the embodiments of this application are only used for distinguishing description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor can they be understood as indicating or implying order.

[0021] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. In addition, "at least one of the following" or similar expressions refers to any combination of these items, which may include any combination of single or plural items. For example, at least one of A, B, or C can mean: A, B, C, A and B, A and C, B and C, or A, B, and C. Each of A, B, and C can be an element itself or a set containing one or more elements.

[0022] In the embodiments of this application, the terms "exemplary," "in some embodiments," and "in another embodiment" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" in this application should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner.

[0023] In the embodiments of this application, the terms "of," "corresponding," and "relevant" may be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings are consistent. In the embodiments of this application, the terms "communication" and "transmission" may be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings are consistent. For example, "transmission" may include "sending" and / or "receiving," and may be either a noun or a verb.

[0024] In the embodiments of this application, "equal to" can be used in conjunction with "greater than" and is applicable to the technical solution adopted when "greater than" is used, and can also be used in conjunction with "less than" and is applicable to the technical solution adopted when "less than" is used. It should be noted that when "equal to" is used in conjunction with "greater than", it cannot be used in conjunction with "less than"; and when "equal to" is used in conjunction with "less than", it cannot be used in conjunction with "greater than".

[0025] Secondary batteries are prone to aging due to factors such as current, temperature, and internal resistance. During the aging process, the chemical characteristic parameters inside the battery are difficult to measure, which increases the difficulty of predicting battery life.

[0026] In the related art, the life prediction methods for secondary batteries usually adopt relatively simplified empirical models or electrochemical models, but these models have many deficiencies in terms of accuracy and applicability. On the one hand, the battery characteristic data on which the battery life prediction methods in the related art are based are not comprehensive enough, and fail to fully consider the complex characteristic changes of the battery under different operating conditions, resulting in limited accuracy in the estimation of SOH in practical applications, making it difficult to meet the needs of high-precision battery management. On the other hand, the related art has defects in parameter identification, and is unable to update the key parameters of the battery aging process in a timely and accurate manner, resulting in poor dynamic tracking capabilities of the model for the battery state, and making it difficult to adapt to the performance evolution of the battery during long-term cycling. In addition, some methods in the related art are highly dependent on experimental conditions, and parameter identification is performed offline under experimental conditions, which makes the model lack versatility and flexibility, and has poor adaptability to different battery types or operating conditions, limiting its wide application.

[0027] In view of the above problems, an embodiment of the present application proposes a method for predicting the life of a secondary battery, which helps to improve the accuracy and reliability of the prediction of the life of a secondary battery.

[0028] Now combined Figure 1 and Figure 2 The life prediction method of a secondary battery provided in an embodiment of the present application is described.

[0029] Figure 1 The flowchart of the method for predicting the life of a secondary battery provided in an embodiment of the present application specifically includes the following steps: Step S11, obtaining a training data set.

[0030] The training data set includes characteristic data of the secondary battery obtained by conducting aging experiments on the test secondary battery under different operating conditions. In this embodiment, the different common operating conditions of the target secondary battery (i.e., the secondary battery for life prediction) are first determined, and then an aging experiment is designed for the test secondary battery according to the different operating conditions. Different operating conditions include different temperatures and powers, for example, operating condition 1: 25°C, 0.5P, operating condition 2: 25°C, 0.3P, and operating condition 3: 30°C, 0.5P. It is understandable that the operating conditions of the secondary battery may also include factors such as current, humidity, and pressure. Different operating conditions refer to at least one of the factors such as temperature, power, and current being different.

[0031] Optionally, the test secondary battery is subjected to an aging experiment under different operating conditions, including: the test secondary battery is subjected to charge and discharge cycles under operating conditions of different temperatures and powers until the battery health status of the test secondary battery decays to a preset threshold. In this embodiment, multiple test secondary batteries can be selected, and long-cycle aging experiments can be designed based on different operating conditions. Different test secondary batteries are subjected to charge and discharge cycles under different operating conditions until the SOH of the test secondary battery reaches a retirement standard (i.e., a preset threshold). The retirement standard represents the SOH threshold at which the battery can no longer be used due to performance degradation or safety risks. The threshold can be set according to the application field, battery type, and battery management strategy. For example, when the SOH of a test secondary battery is ≤70%, the aging experiment is exited. The secondary battery characteristic data of the test secondary battery in the aging experiment under each operating condition and each cycle is recorded to form a training data set. The characteristic data of the secondary battery includes current data, voltage data, temperature data, charge and discharge capacity, etc. under different operating conditions and different numbers of cycles.

[0032] In this application, comprehensive characteristic data of secondary batteries are obtained by setting up aging experiments under different working conditions, and the complex characteristic changes of secondary batteries under different working conditions are fully considered, which helps to improve the accuracy and reliability of SOH estimation.

[0033] Optionally, in order to ensure the accuracy of the battery life prediction result, a battery of the same model as the target secondary battery may be selected as the test secondary battery.

[0034] Step S12 , performing parameter identification and battery health status calibration every N cycles according to the training data set to obtain multiple parameter identification results and multiple battery health status test values.

[0035] Among them, multiple parameter identification results are used to characterize the identification results of the parameters to be identified of the simulation model under different working conditions and different numbers of cycles. Each parameter identification result corresponds to each battery health status test value, and the identification result refers to the numerical value of the parameter to be identified.

[0036] In some optional embodiments, parameter identification is performed every N cycles based on a training data set, including: selecting first current data, first voltage data, and first temperature data under a corresponding number of cycles from the characteristic data of the secondary battery according to a preset cycle interval N; and inputting the first current data, first voltage data, and first temperature data into a pre-constructed parameter identification model for parameter identification.

[0037] For example, a No. 1 test secondary battery undergoes an aging experiment under working condition 1. After 50 cycles of charge and discharge, the aging experiment is stopped when the preset threshold is reached. The cycle interval N is set to 10, and parameter identification is performed every 10 cycles. In the training data set, the battery characteristic data of the 10th cycle, 20th cycle, 30th cycle, 40th cycle, and 50th cycle under working condition 1 are obtained, including current data, voltage data, and temperature data. The battery characteristic data of these five cycles are input into the pre-built parameter identification model for parameter identification, and the parameter identification results of the 10th cycle, 20th cycle, 30th cycle, 40th cycle, and 50th cycle under working condition 1 are obtained, respectively. That is, 5 parameter identification results are obtained, each of which corresponds to a different number of cycles (10, 20, 30, 40, 50) of working condition 1.

[0038] By performing parameter identification based on the battery characteristic data of different operating conditions and different number of cycles in the training data set, multiple parameter identification results can be obtained. For example, this application designs 5 operating conditions, each of which is cycled 50 times, and parameter identification is performed every 10 times, which means that 25 parameter identification results can be obtained.

[0039] This application also performs an SOH calibration every N cycles based on the training data set to detect the aging degree of the test secondary battery and provide a reference for the SOH estimation of the subsequent target secondary battery. Optionally, the SOH can be calibrated using the capacity method. For example, the No. 1 test secondary battery undergoes an aging experiment under working condition 1. After the 10th cycle, the ratio of the current maximum available capacity of the No. 1 test secondary battery to the initial rated capacity is calculated to obtain the SOH test value of the 10th cycle of working condition 1. Similarly, the SOH test values ​​of the 20th cycle, 30th cycle, 40th cycle and 50th cycle of working condition 1 can be calculated.

[0040] Taking the above example, this application designs 5 working conditions, each of which is cycled 50 times, and SOH calibration is performed every 10 times, that is, 25 SOH calibration values ​​can be obtained, and each parameter identification result corresponds to each SOH test value.

[0041] Optionally, the loop interval N can be in the range of 10 ≤ N ≤ 20, where N is a positive integer. If N is too small, it can lead to high time costs, high resource consumption, and low efficiency. If N is too large, it can affect the accuracy of parameter identification and SOH calibration. Setting N to an integer between 10 and 20 can achieve a balance between accuracy and efficiency.

[0042] In the present application, characteristic data of secondary batteries with different aging degrees under different working conditions are obtained, and parameter identification is performed using the characteristic data of the secondary batteries, thereby achieving dynamic updating of parameters to be identified of the secondary batteries.

[0043] In some optional embodiments, the pre-built parameter identification model is a first P2D model. The construction process of the first P2D model includes: constructing an initial P2D model based on PyBaMM and the known battery parameters of the target secondary battery; setting the parameters to be identified and their value ranges in the initial P2D model based on PyBOP, and setting a cost function and optimization algorithm to obtain the first P2D model. The input of the first P2D model is the current data, voltage data, and temperature data in the training data set, and the output is the value of the parameters to be identified.

[0044] It should be noted that the P2D model (Pseudo-Two-Dimensional Model) is a battery electrochemical model used to describe the electrochemical reactions, ion transport, and polarization behavior within the battery. The parameters of the P2D model can characterize the material properties of electrodes or electrolytes and can be used to study the battery's state of health. By identifying the model parameters using battery characteristic data (current, voltage, temperature, etc.), parameters that influence battery aging can be determined. PyBaMM (Python Battery Mathematical Modeling Toolkit) is a battery simulation tool for rapid simulation and optimization of electrochemical models. It includes multiple built-in battery models (such as P2D and the SPM single-particle model). PyBOP (Python Battery Optimization Package) is an extension toolkit based on PyBaMM for battery parameter identification and optimization.

[0045] In this application, a first P2D model is established using PyBaMM and PyBOP tools and the known battery parameters of the target secondary battery. The first P2D model is used to perform parameter identification based on the battery characteristic data of the training data set to obtain the parameter identification results of the test battery under different operating conditions and different numbers of cycles, and the parameter changes of the battery under different aging degrees are obtained. The dynamic update of the parameters to be identified of the test secondary battery is realized to improve the dynamic tracking capability of the battery status in the subsequent simulation model constructed using the parameters to be identified, and to improve the accuracy and reliability of SOH estimation.

[0046] In some optional embodiments, the cost function of the first P2D model includes a mean squared error (MSE) function, and the optimization algorithm of the first P2D model includes an exponential natural evolution strategy (XNES) algorithm.

[0047] The cost function, also known as the objective function or loss function, optimizes the model parameters by calculating the degree of deviation between the predicted value and the true value. The optimization goal is to minimize the cost function, that is, minimize the deviation. Optionally, this application uses the voltage data in the training dataset as the cost function of the first P2D model.

[0048] The calculation formula of MSE is as follows:

[0049] in, represents the i-th moment, represents the predicted value, represents the true value, and n represents the total number of data points.

[0050] The parameter update rules of the XNES algorithm are as follows: (1) Mean update:

[0051] (2) Linear transformation matrix update:

[0052] (3) Global step size update:

[0053] in, represents the mean, represents the learning rate of the mean, represents the global step size, represents the linear transformation matrix, represents the component of the natural gradient in the mean direction, represents the learning rate of the covariance, represents the component of the natural gradient in the covariance direction, represents the learning rate of the global step size, represents the matrix exponential, represents the trace of the matrix.

[0054] Optionally, the known battery parameters of the target secondary battery include electrode thickness, diaphragm thickness, solid / liquid phase volume fraction, etc. The known parameters of the target secondary battery refer to set values ​​or nominal values, or parameters that can be obtained by direct measurement; the parameters to be identified include positive electrode diffusion rate, negative electrode diffusion rate, electrolyte diffusion rate, positive electrode conductivity, negative electrode conductivity, electrolyte conductivity, positive electrode reaction activation energy, negative electrode reaction activation energy, positive electrode reaction rate constant and negative electrode reaction rate parameter, etc. The parameters to be identified refer to parameters that cannot be directly measured and need to be identified through experimental data.

[0055] Step S13: construct multiple simulation models based on multiple parameter identification results.

[0056] In this embodiment, the multiple parameter identification results obtained in step S12 are used to obtain the values ​​of the multiple parameters to be identified of the simulation model, and multiple simulation models are constructed, wherein each simulation model corresponds to each parameter identification result.

[0057] In this application, multiple parameter identification results are used to characterize the identification results of the parameters to be identified of the simulation model under different operating conditions and different numbers of cycles. Multiple simulation models constructed based on the multiple parameter identification results characterize the simulation model under different operating conditions and different numbers of cycles. This application uses comprehensive battery data in the training data set to identify the parameters of the simulation model, which helps to comprehensively and accurately reflect the parameter changes during the battery aging process, while enhancing the adaptability of the simulation model to different operating conditions, thereby achieving high-precision and reliable estimation of the SOH of the secondary battery, providing strong support for the efficient management and safe use of secondary batteries.

[0058] It can be understood that each parameter identification result corresponds to each SOH battery health status test data value, each simulation model corresponds to each parameter identification result, and therefore, each simulation model corresponds to each SOH battery health status test data value.

[0059] In some optional embodiments, the simulation model is a second P2D model, and constructing multiple simulation models based on multiple parameter identification results includes: constructing multiple second P2D models based on PyBaMM, the multiple parameter identification results, and known battery parameters of the target secondary battery. The second P2D model receives current data and temperature data as input, and outputs voltage data.

[0060] Similarly, a cost function and an optimization algorithm of the second P2D model may also be set. For example, the cost function of the second P2D model includes a mean squared error (MSE) function, and the optimization algorithm of the second P2D model includes an exponential natural evolution strategy (XNES) algorithm.

[0061] Step S14 , obtaining actual voltage data and multiple sets of simulated voltage data of the target secondary battery.

[0062] The actual voltage data of the target secondary battery is the voltage data obtained when the target secondary battery is actually operated under the target operating conditions (i.e., data showing the actual voltage changing over time). Each set of simulated voltage data is the voltage data obtained when each simulation model is simulated under the target operating conditions (i.e., data showing the simulated voltage changing over time). The target operating conditions include temperature and current information.

[0063] In this embodiment, under the actual operating conditions of the target secondary battery (i.e., the target operating conditions), data on the change in the actual voltage of the target secondary battery over time is obtained and recorded as the actual voltage data of the target secondary battery. Simulations are run on multiple simulation models under the actual operating conditions to obtain data on the change in simulated voltage over time for each model, thereby forming a simulated voltage dataset. The simulated voltage dataset includes multiple sets of simulated voltage data.

[0064] Step S15 , determining a battery health state estimation value of the target secondary battery according to the multiple battery health state test values, the actual voltage data of the target secondary battery, and the multiple sets of simulation voltage data.

[0065] Figure 2 A flow chart of the SOH estimation method provided in the embodiment of the present application is shown as follows: Figure 2 As shown, the target secondary battery is actually operated under the target working conditions to obtain the actual voltage data of the target secondary battery, and multiple simulation models are simulated and run under the target working conditions to obtain a simulation voltage data set. The actual voltage data and the simulation voltage data set of the target secondary battery are input into the SOH estimation model, and the SOH estimation value of the target secondary battery is output.

[0066] In some optional embodiments, the actual voltage data and simulated voltage data set of the target secondary battery are input into the SOH estimation model, and the SOH estimation value of the target secondary battery is output, including: determining the battery health status estimation value of the target secondary battery based on multiple battery health status test values, the actual voltage data of the target secondary battery and multiple sets of simulated voltage data.

[0067] Optionally, determining the estimated battery health state value of the target secondary battery according to the plurality of battery health state test values, the actual voltage data of the target secondary battery, and the plurality of sets of simulation voltage data includes: The actual voltage data of the target secondary battery are compared with multiple sets of simulation voltage data respectively to determine the target simulation voltage data group, which is the simulation voltage data group with the smallest deviation from the actual voltage data of the target secondary battery among the multiple sets of simulation voltage data; the battery health status estimation value of the target secondary battery is determined according to the correspondence between the target simulation voltage data group and the battery health status test value, which is one of the multiple battery health status test values.

[0068] In this embodiment, the actual voltage data of the target secondary battery is compared with multiple sets of simulated voltage data to determine the simulated voltage data set with the smallest deviation from the actual voltage data of the target secondary battery, i.e., to determine the target simulated voltage data set. Then, based on the corresponding relationship between the target simulated voltage data set and the SOH test value, the estimated SOH value of the target secondary battery is determined. The above steps indicate that the voltage variation trend of the target simulated voltage data set over time is most similar to the voltage variation trend of the target secondary battery over time, and the SOH test value corresponding to the target simulated voltage data set can be used as the estimated SOH value of the target secondary battery.

[0069] Optionally, determining the estimated battery health state value of the target secondary battery according to the correspondence between the target simulation voltage data group and the battery health state test value includes: Determining a target simulation model according to a target simulation voltage data group, a correspondence between the simulation voltage data and the simulation model; Determining a target parameter identification result according to a target simulation model, a corresponding relationship between the simulation model and the parameter identification result; The estimated battery health state value of the target secondary battery is determined according to the target parameter identification result and the corresponding relationship between the parameter identification result and the battery health state test value.

[0070] In this application, each set of simulation voltage data corresponds to a simulation model, each simulation model corresponds to a parameter identification result, and each parameter identification result corresponds to an SOH test value. Therefore, the correspondence between the parameters can be used to obtain the SOH test value corresponding to the target simulation voltage data group, and the SOH estimated value of the target secondary battery can be determined.

[0071] In some optional embodiments, comparing the actual voltage data of the target secondary battery with the multiple sets of simulated voltage data to determine the target simulated voltage data set includes: The target simulated voltage data set is determined based on minimizing the root mean square error between the actual voltage data and the simulated voltage data of the target secondary battery.

[0072] Specifically, the root mean square error between the target voltage data and the plurality of sets of simulation voltage data is calculated to determine the target simulation voltage data set, wherein the root mean square error between the target simulation voltage data set and the target voltage data is the smallest.

[0073] The formula for calculating the root mean square is:

[0074] in, represents the i-th moment, represents the predicted value, represents the true value, and n represents the total number of data points.

[0075] In this embodiment, by calculating the root mean square error between the target voltage data and multiple groups of simulation voltage data, the simulation voltage data group with the smallest root mean square error is determined. The trend of voltage change over time of this simulation voltage data group is closest to the trend of voltage change over time of the target secondary battery. The SOH test value corresponding to this simulation voltage data group can be used as the SOH estimation value of the target secondary battery.

[0076] In other embodiments, methods such as mean absolute error (MAE), maximum absolute error (Max Error), and coefficient of determination (R²) may be used to determine the simulated voltage data set with the smallest deviation from the actual voltage data of the target secondary battery.

[0077] The life prediction method of a secondary battery provided in the present application is to form a training data set by forming characteristic data of the secondary battery obtained by performing aging experiments on the test secondary battery under different working conditions; performing parameter identification and battery health status calibration once every N cycles according to the training data set to obtain multiple parameter identification results and multiple battery health status test values; constructing multiple simulation models based on the multiple parameter identification results; obtaining actual voltage data of the target secondary battery and multiple groups of simulation voltage data, wherein the actual voltage data of the target secondary battery is the voltage data obtained when the target secondary battery is actually operated under the target working conditions, and each group of simulation voltage data is the voltage data obtained when each simulation model is simulated and operated under the target working conditions; and determining the battery health status estimation value of the target secondary battery based on the multiple battery health status test values, the actual voltage data of the target secondary battery and the multiple groups of simulation voltage data. By setting up aging experiments under different working conditions, comprehensive characteristic data of secondary batteries is obtained, fully considering the complex characteristic changes of secondary batteries under different working conditions, and using the characteristic data of secondary batteries for parameter identification, dynamic updating of the parameters to be identified of secondary batteries is achieved, and then a highly applicable simulation model is built, the simulation model's dynamic tracking ability of the aging status of secondary batteries is enhanced, and the accuracy and reliability of secondary battery life prediction are improved.

[0078] In some optional embodiments, the method provided in the present application further includes: formulating a maintenance strategy for the target secondary battery according to the estimated SOH value of the target secondary battery.

[0079] Optionally, when the estimated SOH is greater than 90%, the SOH is tested every three months and the number of charge and discharge cycles is recorded; or When 80% < estimated SOH ≤ 90%, check SOH once a month and optimize the charging and discharging strategy; When 70%<SOH estimate ≤80%, check SOH every 2 weeks and implement equalization charging.

[0080] When the estimated SOH value is ≤70%, stop the machine for inspection, perform safety diagnosis (such as internal resistance and lithium plating detection), and prepare to replace the battery.

[0081] This application uses the estimated SOH value to formulate a battery maintenance strategy, which can provide early warning, ensure the safety of the battery and system, and reduce maintenance time and costs.

[0082] Based on the same idea, the embodiment of the present application also provides a device for predicting the life of a secondary battery, such as Figure 3 This is a schematic diagram of the structure of a secondary battery life prediction device provided in an embodiment of the present application. The secondary battery life prediction device 30 mainly includes: A first acquisition module 31 is configured to acquire a training data set, wherein the training data set includes characteristic data of a secondary battery obtained by performing an aging experiment on the secondary battery under different operating conditions; a parameter identification and battery health state calibration module 32 for performing parameter identification and battery health state calibration every N cycles based on the training data set, obtaining multiple parameter identification results and multiple battery health state test values. The multiple parameter identification results are used to represent the identification results of the parameters to be identified of the simulation model under different operating conditions and different numbers of cycles, and each parameter identification result corresponds to each battery health state test value; A model building module 33 is used to build multiple simulation models based on multiple parameter identification results, each simulation model corresponding to each parameter identification result; A second acquisition module 34 is configured to acquire actual voltage data of a target secondary battery and multiple sets of simulated voltage data, wherein the actual voltage data of the target secondary battery is voltage data obtained when the target secondary battery is actually operated under target operating conditions, and each set of simulated voltage data is voltage data obtained when each simulation model is simulated under target operating conditions; The determination module 35 is configured to determine a battery health state estimation value of the target secondary battery according to a plurality of battery health state test values, actual voltage data of the target secondary battery, and a plurality of sets of simulation voltage data.

[0083] In one possible implementation, the determination module 35 may also be configured to: Comparing the actual voltage data of the target secondary battery with the multiple sets of simulated voltage data to determine a target simulated voltage data set, wherein the target simulated voltage data set is a simulated voltage data set having the smallest deviation from the actual voltage data of the target secondary battery among the multiple sets of simulated voltage data; The estimated battery health state value of the target secondary battery is determined according to the correspondence between the target simulation voltage data group and the battery health state test value. The estimated battery health state value of the target secondary battery is one of the multiple battery health state test values.

[0084] In one possible implementation, determining the estimated battery health state value of the target secondary battery according to the corresponding relationship between the target simulation voltage data group and the battery health state test value includes: Determining a target simulation model according to a target simulation voltage data group, a correspondence between the simulation voltage data and the simulation model; Determining a target parameter identification result according to a target simulation model, a corresponding relationship between the simulation model and the parameter identification result; The estimated battery health state value of the target secondary battery is determined according to the target parameter identification result and the corresponding relationship between the parameter identification result and the battery health state test value.

[0085] In one possible implementation, actual voltage data of a target secondary battery is compared with multiple sets of simulated voltage data to determine a target simulated voltage data set, including: The target simulated voltage data set is determined based on minimizing the root mean square error between the actual voltage data and the simulated voltage data of the target secondary battery.

[0086] In one possible implementation, the characteristic data of the secondary battery includes current data, voltage data, and temperature data under different operating conditions and different numbers of cycles. The parameter identification and battery health status calibration module 32 may also be used to: Selecting first current data, first voltage data, and first temperature data at a corresponding number of cycles from characteristic data of the secondary battery according to a preset cycle interval N; The first current data, the first voltage data and the first temperature data are input into a pre-built parameter identification model for parameter identification.

[0087] In one possible implementation, the pre-built parameter identification model is a first P2D model. The construction process of the first P2D model includes: Build an initial P2D model based on PyBaMM and the known battery parameters of the target secondary battery; Based on PyBOP, the parameters to be identified and their value ranges are set in the initial P2D model, and the cost function and optimization algorithm are set to obtain the first P2D model.

[0088] In one possible implementation, the simulation model is a second P2D model, and multiple simulation models are constructed based on multiple parameter identification results, including: A plurality of second P2D models are respectively constructed based on PyBaMM, the plurality of parameter identification results, and the known battery parameters of the target secondary battery.

[0089] In one possible implementation, the cost function includes a mean square error function, and the optimization algorithm includes an exponential natural evolution strategy algorithm.

[0090] In one possible implementation, the known battery parameters of the target secondary battery include at least electrode thickness and diaphragm thickness; the parameters to be identified include at least positive electrode diffusion rate, negative electrode diffusion rate, electrolyte diffusion rate, positive electrode conductivity, negative electrode conductivity and electrolyte conductivity.

[0091] In one possible implementation, secondary batteries are tested under different operating conditions for aging experiments, including: The test secondary battery is subjected to charge and discharge cycles under different temperature and power operating conditions until the battery health status of the test secondary battery decays to a preset threshold.

[0092] Figure 3 The secondary battery life prediction device 30 provided in the illustrated embodiment can be used to implement the technical solution of the method embodiment shown in this application. Its implementation principle and technical effects can be further referred to the relevant description in the method embodiment.

[0093] It should be understood that the above Figure 3 The division of the various modules of the secondary battery life prediction device 30 shown is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software calling through processing elements; they can also all be implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the first acquisition module can be a separately established processing element, or it can be integrated in a chip of an electronic device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be implemented independently. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0094] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more microprocessors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, these modules may be integrated together to implement a system-on-a-chip (SOC).

[0095] In each of the above embodiments, the processor involved may include, for example, a CPU, a DSP, a microcontroller, or a digital signal processor, and may also include a GPU, an embedded neural network processor (NPU), and an image signal processor (ISP). The processor may also include necessary hardware accelerators or logic processing hardware circuits, such as an ASIC, or one or more integrated circuits for controlling the execution of the program of the technical solution of this application. In addition, the processor may have the function of operating one or more software programs, and the software programs may be stored in a storage medium.

[0096] An embodiment of the present application further provides an energy storage system, including: a BMS (Battery Management System), the BMS including a processor and a memory, the memory being used to store a computer program; the processor being used to run the computer program to implement the secondary battery life prediction method provided in the embodiment shown in the present application.

[0097] The above description is merely a specific embodiment of the present application. Any person skilled in the art may easily conceive of variations or substitutions within the technical scope disclosed in this application, and such variations or substitutions shall be within the scope of protection of this application. The scope of protection of this application shall be subject to the scope of protection of the claims.

Claims

1. A method for predicting the life of a secondary battery, characterized in that: The method comprises: Acquiring a training data set, wherein the training data set includes characteristic data of a secondary battery obtained by performing an aging experiment on the test secondary battery under different operating conditions; Performing parameter identification and battery health status calibration once every N cycles according to the training data set to obtain multiple parameter identification results and multiple battery health status test values, wherein the multiple parameter identification results are used to represent identification results of the parameters to be identified of the simulation model under different operating conditions and different numbers of cycles, and each parameter identification result corresponds to each battery health status test value; constructing a plurality of simulation models based on the plurality of parameter identification results, each simulation model corresponding to each parameter identification result; Acquire actual voltage data of a target secondary battery and multiple sets of simulated voltage data, wherein the actual voltage data of the target secondary battery is voltage data obtained by actually operating the target secondary battery under target operating conditions, and each set of simulated voltage data is voltage data obtained by simulating the operation of each simulation model under the target operating conditions; A battery health state estimation value of the target secondary battery is determined according to the plurality of battery health state test values, actual voltage data of the target secondary battery, and the plurality of sets of simulation voltage data.

2. The method for predicting the life of a secondary battery according to claim 1, wherein: The determining, based on the plurality of battery health state test values, the actual voltage data of the target secondary battery, and the plurality of sets of simulation voltage data, a battery health state estimation value of the target secondary battery includes: Comparing the actual voltage data of the target secondary battery with the multiple sets of simulated voltage data respectively to determine a target simulated voltage data set, wherein the target simulated voltage data set is the simulated voltage data set having the smallest deviation from the actual voltage data of the target secondary battery among the multiple sets of simulated voltage data; The estimated battery health state value of the target secondary battery is determined according to the correspondence between the target simulation voltage data group and the battery health state test value, and the estimated battery health state value of the target secondary battery is one of the multiple battery health state test values.

3. The method for predicting the life of a secondary battery according to claim 2, wherein: The determining the estimated battery health state value of the target secondary battery according to the correspondence between the target simulation voltage data group and the battery health state test value includes: Determining a target simulation model according to the target simulation voltage data group, the corresponding relationship between the simulation voltage data and the simulation model; Determining a target parameter identification result according to the target simulation model, the corresponding relationship between the simulation model and the parameter identification result; The estimated battery health state value of the target secondary battery is determined according to the target parameter identification result, the corresponding relationship between the parameter identification result and the battery health state test value.

4. The method for predicting the life of a secondary battery according to claim 2 or 3, wherein: The step of comparing the actual voltage data of the target secondary battery with the plurality of sets of simulated voltage data to determine a target simulated voltage data set includes: The target simulated voltage data group is determined according to minimizing the root mean square error between the actual voltage data and the simulated voltage data of the target secondary battery.

5. The method for predicting the life of a secondary battery according to claim 1, wherein: The characteristic data of the secondary battery includes current data, voltage data, and temperature data under different operating conditions and different numbers of cycles. The parameter identification is performed every N cycles based on the training data set, including: Selecting first current data, first voltage data, and first temperature data at a corresponding number of cycles from the characteristic data of the secondary battery according to a preset cycle interval N; The first current data, the first voltage data, and the first temperature data are input into a pre-built parameter identification model for parameter identification.

6. The method for predicting the life of a secondary battery according to claim 5, wherein: The pre-built parameter identification model is a first P2D model, and the construction process of the first P2D model includes: Building an initial P2D model based on PyBaMM and known battery parameters of the target secondary battery; The first P2D model is obtained by setting parameters to be identified and value ranges of the parameters to be identified in the initial P2D model based on PyBOP, and setting a cost function and an optimization algorithm.

7. The method for predicting the life of a secondary battery according to claim 1, wherein: The simulation model is a second P2D model, and the constructing of multiple simulation models based on the multiple parameter identification results includes: A plurality of second P2D models are respectively constructed based on PyBaMM, the plurality of parameter identification results, and the known battery parameters of the target secondary battery.

8. The method for predicting the life of a secondary battery according to claim 6, wherein: The cost function includes a mean square error function, and the optimization algorithm includes an exponential natural evolution strategy algorithm.

9. The method for predicting the life of a secondary battery according to claim 6, wherein: The known battery parameters of the target secondary battery include at least electrode thickness and diaphragm thickness; the parameters to be identified include at least positive electrode diffusivity, negative electrode diffusivity, electrolyte diffusivity, positive electrode conductivity, negative electrode conductivity and electrolyte conductivity.

10. The method for predicting the life of a secondary battery according to claim 1, wherein: The test secondary battery is subjected to aging experiments under different working conditions, including: The test secondary battery is subjected to charge and discharge cycles under different temperature and power operating conditions until the battery health state of the test secondary battery decays to a preset threshold.

11. A device for predicting the life of a secondary battery, characterized in that: include: A first acquisition module is configured to acquire a training data set, wherein the training data set includes characteristic data of a secondary battery obtained by performing an aging experiment on the secondary battery under different operating conditions; a parameter identification and battery health state calibration module, configured to perform parameter identification and battery health state calibration every N cycles based on the training data set, to obtain a plurality of parameter identification results and a plurality of battery health state test values, wherein the plurality of parameter identification results are used to represent the identification results of the parameters to be identified of the simulation model under different operating conditions and different numbers of cycles, and each parameter identification result corresponds to each battery health state test value; A model building module is used to build multiple simulation models based on the multiple parameter identification results, each simulation model corresponds to each parameter identification result; a second acquisition module, configured to acquire actual voltage data of a target secondary battery and multiple sets of simulated voltage data, wherein the actual voltage data of the target secondary battery is voltage data obtained by actually operating the target secondary battery under target operating conditions, and each set of simulated voltage data is voltage data obtained by simulating the operation of each simulation model under the target operating conditions; A determination module is configured to determine a battery health state estimation value of the target secondary battery according to the multiple battery health state test values, the actual voltage data of the target secondary battery, and the multiple sets of simulation voltage data.

12. An energy storage system, characterized in that: include: A BMS, the BMS comprising a processor and a memory, the memory being used to store a computer program; the processor being used to run the computer program to implement the secondary battery life prediction method according to any one of claims 1 to 10.

Images