Battery physical model performance and life parameter optimization method and system

Abstract

The invention discloses a battery physical model performance and life parameter optimization method and system, and relates to the technical field of batteries. The battery physical model performance and life parameter optimization method comprises the following steps: obtaining aging experiment data, and obtaining single-circle detailed data, a life curve and a specific cycle condition based on the aging experiment data; constructing or optimizing a physical performance model of the battery cell based on the single-circle detailed data; establishing an aging model based on an aging mechanism, and selecting aging identification parameters based on the aging mechanism to realize optimization of the aging model; obtaining a working step based on the specific cycle working condition, and obtaining aging related parameters based on the working step, the optimized physical performance model and the optimized aging model; the life curve is acquired based on energy throughput-capacity data. The traditional cycle number-capacity data is replaced by the energy throughput-capacity data, so that the compatibility is improved.

Description

Technical Field

[0001] This application relates to the technical field of batteries, and particularly to a method and system for optimizing the performance and life parameters of a battery physical model. Background Art

[0002] With the growth of the demand for electric vehicles and renewable energy storage, higher requirements are put forward for the performance and life of lithium-ion batteries.

[0003] However, the existing battery aging tests usually rely on cycle number-capacity data to construct the life curve, which is difficult to accurately reflect the actual usage of the battery under complex working conditions. Summary of the Invention

[0004] To solve the above problems, this application discloses a method for optimizing the performance and life parameters of a battery physical model, which uses energy throughput-capacity data to replace the traditional cycle number-capacity data to improve compatibility; at the same time, a system for optimizing the performance and life parameters of a battery physical model is proposed to implement the method for optimizing the performance and life parameters of a battery physical model in different situations.

[0005] The first technical solution adopted by this application is: providing a method for optimizing the performance and life parameters of a battery physical model, including the following steps:

[0006] Obtain aging experiment data, and based on the aging experiment data, obtain single-cycle detailed data, a life curve, and specific cycling conditions;

[0007] Based on the single-cycle detailed data, construct or optimize the physical performance model of the battery cell;

[0008] Establish an aging model based on the aging mechanism, and select aging identification parameters based on the aging mechanism to optimize the aging model;

[0009] Based on the specific cycling conditions, obtain working steps, and based on the working steps, the optimized physical performance model, and the optimized aging model, obtain aging-related parameters.

[0010] Further, the life curve is obtained based on energy throughput-capacity data, and the energy throughput (ETP) acquisition formula is as follows:

[0011] ETP = ∫0 T I(t)V(t)dt

[0012] T is the total time required for the entire life curve test, I(t) is the current curve, and V(t) is the voltage curve.

[0013] Further, obtaining the single-cycle detailed data includes the following steps:

[0014] Remove the data segments with the standing time greater than the threshold time from the aging experiment data, and perform Fourier transform on the processed aging experiment data to obtain the frequency spectrum;

[0015] Find the wavelength corresponding to the main frequency in the frequency spectrum, and obtain the period based on the wavelength corresponding to the main frequency in the frequency spectrum;

[0016] Extract the corresponding single-cycle detailed data based on the period.

[0017] Further, constructing and optimizing the physical performance model of the battery cell based on the single-cycle detailed data includes the following steps:

[0018] Collect the design parameters of the battery cell, and determine the model parameters to be identified and the corresponding ranges;

[0019] Input the current signal in the single-cycle detailed data into the physical performance model, and the voltage signal is the model identification target; construct the output voltage signal of the model The root mean square error (RMSE) loss function with the experimentally measured voltage signal v, and the root mean square error calculation formula is as follows:

[0020]

[0021] Optimize the physical performance model of the battery cell based on the root mean square error.

[0022] Further, determine the corresponding aging mechanism based on different life parameters; determine the kinetic formula based on the aging mechanism; a single life parameter corresponds to one or more aging mechanisms; the aging mechanism corresponding to a single life parameter can affect other life parameters.

[0023] Further, the specific cycle working conditions determine the working steps based on the extracted single-cycle detailed data, and the working steps include the charge-discharge rate, the standing time, and the charge-discharge time; simulate the battery cell cycling through the working steps to simulate the aging process.

[0024] Further, insert a standard capacity test into the battery cell cycling working steps to improve the accuracy of the energy throughput-capacity curve obtained by simulation; construct a loss function based on the root mean square error between the simulation result and the experimental test data.

[0025] The second technical solution adopted by this application is: providing a battery physical model performance and life parameter optimization system, applying the battery physical model performance and life parameter optimization method described in any one of the above, including:

[0026] A parameter acquisition module, acquiring aging experiment data based on the parameter acquisition module;

[0027] A physical performance model construction module, constructing a physical performance model based on the aging experiment data;

[0028] An aging model construction module constructs an aging model based on aging experiment data and realizes the identification of aging model parameters;

[0029] An aging-related parameter determination module determines simulation steps based on the aging model and the physical property model, and loops through the simulation steps to obtain aging-related parameters.

[0030] The third technical solution adopted by this application is: providing an electronic device, the electronic device includes: a memory and a processor coupled to each other, and the processor is configured to execute program instructions stored in the memory to implement the battery physical model performance and life parameter optimization method as described in any one of the above.

[0031] The fourth technical solution adopted by this application is: providing a computer-readable storage medium, the computer-readable storage medium stores program data, and the program data can be executed by a processor to implement the battery physical model performance and life parameter optimization method as described in any one of the above.

[0032] Due to the adoption of the above technical solutions, compared with the prior art, this application has at least one of the following beneficial effects:

[0033] 1. By using energy throughput-capacity data to replace the traditional cycle count-capacity data, the optimization of battery physical model performance and life parameters with better compatibility is achieved.

[0034] 2. By processing the aging experiment data, detailed data for each cycle can be accurately extracted, including voltage and current signals, providing high-quality data support for the subsequent optimization of performance and aging models.

[0035] 3. Corresponding kinetic formulas and their parameter identification methods are provided for different aging mechanisms. This enables a more detailed understanding and simulation of complex aging processes, and helps to more accurately predict battery life.

[0036] 4. Adding a standard capacity test in the long-term working condition simulation ensures the consistency between the results obtained under simulation conditions and the actual experimental results. This method can not only verify the effectiveness of the model, but also obtain a unified cell capacity evaluation standard under different test conditions. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0038] Among them:

[0039] Figure 1 Schematic flowchart of an embodiment of the method for optimizing the performance and life parameters of the battery physical model provided by the present application;

[0040] Figure 2 Schematic flowchart of an embodiment of obtaining detailed single-loop data based on aging experiment data provided by the present application;

[0041] Figure 3 For Figure 1 Schematic flowchart of step S12 in

[0042] Figure 4 Schematic structural diagram of an embodiment of the computer device of the present application;

[0043] Figure 5 Schematic structural diagram of an embodiment of the computer-readable storage medium of the present application. Detailed implementation manners

[0044] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application are shown in the accompanying drawings rather than all the structures. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0045] The terms "first", "second", etc. in the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. 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 may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products, or devices.

[0046] Referring to "embodiment" herein means that a specific feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0047] Traditional cycle - based methods are difficult to adapt to complex actual usage conditions. For example, in application scenarios such as electric vehicles, the working environment and load conditions of the battery are extremely complex and variable, and cannot be simply summarized by a fixed number of cycles; directly using the number of cycles as a reference point may ignore many important detailed information, such as the impact of factors like different charge / discharge rates and temperature changes on battery performance; in order to obtain more accurate results, a large number of experimental tests are often required, which not only consumes time but also increases costs.

[0048] Embodiment 1

[0049] As Figure 1 shown Figure 1 is a schematic flowchart of an embodiment of the method for optimizing the performance and life parameters of the battery physical model provided by this application, including the following steps:

[0050] Step S11: Obtain aging experiment data and collect the operation data of the battery under different conditions based on a series of aging experiments. The operation data includes, but is not limited to, information such as the changes in current and voltage over time. It should be clear that this application does not limit the type of battery. For example, in this embodiment, the battery is a lithium - ion battery, and in other embodiments, the battery type can be arbitrarily selected without any limitation.

[0051] Based on the aging experiment data, obtain single - cycle detailed data, life curves, and specific cycling conditions; single - cycle detailed data refers to the detailed records of signals such as current and voltage in a complete charge - discharge cycle extracted from the aging experiment data; the life curve is a trend graph used to show the change of battery capacity with energy throughput; specific cycling conditions include, but are not limited to, parameters such as charge - discharge rate, rest time, and current change rate.

[0052] Step S12: Construct or optimize the physical performance model of the battery cell based on the single - cycle detailed data; if the battery itself does not have a physical performance model, construct a physical performance model based on the single - cycle detailed data; the steps of constructing a physical performance model based on the single - cycle detailed data are described in detail below:

[0053] First, obtain the battery cell design parameters. The battery cell design parameters include three - dimensional and over - potentials of the positive and negative electrodes, etc.; the battery cell design parameters that are difficult to obtain can also be obtained based on experience or literature data; build a P2D model based on the obtained battery cell design parameters.

[0054] Take the current in the single - cycle detailed data as the input of the P2D model, and make the output voltage of the P2D model the same as the voltage in the single - cycle detailed data by optimizing those predicted values; thus, the construction of the physical performance model is realized.

[0055] The physical performance model can optimize the SOH (State of Health) to improve the accuracy of obtaining the battery capacity; the physical performance model can input current and output voltage; compare the voltage output by the physical performance model with the voltage of the single-cycle detailed data to judge the accuracy of the physical performance model; if the difference between the voltage output by the physical performance model and the voltage of the single-cycle detailed data is too large, the working parameters of the physical performance model can be adjusted to improve the accuracy.

[0056] Step S13: Establish an aging model based on the aging mechanism. The aging mechanism includes SEI (Solid Electrolyte Interface) growth, active material loss, and electrode structure change, etc.; each aging mechanism has its specific kinetic formula and influencing factors; based on the selected aging mechanism and its corresponding kinetic formula, construct an aging model; select aging identification parameters based on the aging mechanism to optimize the aging model. Accurately selecting and optimizing the aging identification parameters can more accurately reflect the actual aging process of the battery, thereby improving the accuracy of battery life prediction; for the multiple aging mechanisms that may exist in different application scenarios, this method can be flexibly adjusted to select the most suitable aging model and parameters according to the specific situation, enhancing the applicable range of the model.

[0057] Step S14: Obtain the working steps based on the specific cycling conditions. Define the specific working steps according to factors such as the charge-discharge rate, rest time, current change, and voltage change in the extracted single-cycle detailed data. For example, a typical working step may include a charging stage at a specific rate, followed by a rest period, then a discharging stage, and finally another rest period.

[0058] Obtain the aging-related parameters based on the working steps, the optimized physical performance model, and the optimized aging model; input the working steps into the optimized aging model for aging simulation; in this embodiment, the physical performance model and the aging model are optimized first and then the working steps are input; and the physical performance model is optimized first. After the physical performance model is optimized, according to the aging data, select the aging mechanism and optimization parameters to optimize the aging model; by combining the specific cycling conditions of actual use for simulation, the aging characteristics of the battery can be predicted more accurately.

[0059] The method for optimizing the battery physical model performance and life parameters in this embodiment includes the following steps: obtain the aging experiment data, and obtain the single-cycle detailed data, life curve, and specific cycling conditions based on the aging experiment data; construct or optimize the physical performance model of the battery cell based on the single-cycle detailed data; establish an aging model based on the aging mechanism, and select aging identification parameters based on the aging mechanism to optimize the aging model; obtain the working steps based on the specific cycling conditions, and obtain the aging-related parameters based on the working steps, the optimized physical performance model, and the optimized aging model; the life curve is obtained based on the energy throughput-capacity data. By using the energy throughput-capacity data to replace the traditional cycle number-capacity data, the compatibility is improved.

[0060] The life curve is obtained based on the aging experiment data, that is, the life curve is obtained based on the energy throughput-capacity data. The formula for obtaining the energy throughput (ETP) is as follows:

[0061] ETP = ∫0 T I(t)V(t)dt

[0062] T is the time required for the entire life curve test, I(t) is the current curve, and V(t) is the voltage curve. For example, there is the following simplified data set:

[0063] Time (S) Current (A) Voltage (V) 0 5 3.7 1 4.8 3.6 2 4.6 3.5 … … … T 5 3.7

[0064] If you want to obtain the energy throughput ETP of the battery between time 0 - 1s [0,1] :

[0065]

[0066] Repeat the above process until all time points are processed, and then add up all the partial results to obtain the total ETP value.

[0067] Compared with the traditional method based on the number of cycles, using the energy throughput as an index can more accurately reflect the actual usage of the battery under different working conditions; by using the energy throughput as the evaluation criterion, consistent results can be obtained under different test conditions, which is convenient for horizontal comparison of the performance of different batches or types of batteries; the energy throughput takes into account the current and voltage changes during the entire test period, improving the applicability.

[0068] As Figure 2 shown, Figure 2 This is a schematic flowchart of an embodiment for obtaining detailed single-cycle data based on aging experiment data provided by this application, including the following steps:

[0069] Step S111: Remove the data segments in the aging experiment data where the static time is greater than the threshold time, ensuring that the subsequent analysis focuses on the data during the actual charge and discharge process of the battery, rather than the data during the static period; for example, the threshold time is 5 minutes. It should be noted that this application does not limit the specific value of the threshold time.

[0070] Perform Fourier transform on the processed aging experiment data to obtain the spectrum; FFT (Fast Fourier Transform) can convert the time-domain signal into the frequency-domain signal, making it easier to identify the periodic components in the data. Execute FFT to obtain a spectrogram representing different frequencies and their corresponding amplitudes.

[0071] Step S112: Find the wavelength corresponding to the main frequency in the spectrum, and obtain the period based on the wavelength corresponding to the main frequency in the spectrum; find one or more frequency points with the highest energy in the spectrogram. The frequency points represent the frequencies of the main periodicities in the data, and calculate the corresponding wavelength based on the frequency; calculate the period based on the frequency. The formula is:

[0072]

[0073] where T is the period and f is the frequency.

[0074] Step S113: Extract the corresponding detailed single-loop data based on the period; extract the detailed data within each period from the original dataset based on the obtained period to obtain multiple complete detailed single-loop data.

[0075] By removing the data segments of long-term static, the interference of irrelevant information is reduced, and the effectiveness and pertinence of data analysis are improved; the extracted detailed single-loop data provides high-quality input for constructing and optimizing the physical performance model.

[0076] As Figure 3 shown, Figure 3 for Figure 1 the flow schematic diagram of step S12, which includes the following steps:

[0077] Step S121: Collect the design parameters of the battery cell, that is, collect the basic design parameters of the battery cell, such as the length / width / thickness / layer number of the battery cell, the OCV-STO of the positive and negative electrode materials of the battery cell, etc. The basic design parameters cannot be obtained by the optimization algorithm.

[0078] Determine the model parameters to be identified and their corresponding ranges. The model parameters to be identified are usually the physical property parameters of the battery cell materials, such as the diffusion rate of the positive and negative electrode materials relative to lithium ions, the conductivity, the lithium ion diffusion rate of the electrolyte, etc.; and the parameters that cannot be obtained by experiments, such as the interfacial reaction exchange current; the model parameters to be identified correspond to a range. For example, the range of the interfacial reaction exchange current is 5 μA / cm 2 —10 μA / cm 2 ; This application does not limit the values of the specific ranges, which can be adjusted based on actual needs.

[0079] Step S122: Input the current signal in the detailed single-loop data into the physical performance model, and the voltage signal is the model identification target; that is, the detailed single-loop data extracted from the aging experiment is used as the input, especially the current signal among them. This current signal will be used as the input condition of the physical performance model to simulate the response of the battery under actual use conditions. The voltage signal is the main target of model identification, that is, the goal of the model is to predict as accurately as possible the actual output voltage of the battery cell under the given current input condition.

[0080] Construct the model output voltage signal The root mean square error (RMSE) loss function of the voltage signal v measured experimentally, and the calculation formula of the root mean square error is as follows:

[0081]

[0082] where v t represents the voltage signal measured in the aging experiment, represents the voltage signal predicted by the model, and T is the period of the detailed data of a single turn.

[0083] In this embodiment, the RMSE of the voltage signal v measured in the aging experiment t and the voltage signal predicted by the model are used to construct the loss function; in other embodiments, the MAE (mean absolute error) of the voltage signal v measured in the aging experiment t and the voltage signal predicted by the model can be used to construct the loss function, and no specific limitation is imposed on this.

[0084] Step S123: Optimize the physical performance model of the battery cell based on the root mean square error; use an optimization algorithm (such as the particle swarm algorithm, genetic algorithm, LBFGS - B, or Bayesian optimization, etc.) to optimize the model, aiming to minimize the RMSE loss function; continuously adjust the model parameters until a set of parameter combinations that minimize the RMSE is found, so as to achieve the best fit of the model.

[0085] Determine the corresponding kinetic formula based on different aging mechanisms; determine the life parameters based on the kinetic formula; a single life parameter corresponds to a single or multiple reaction kinetic formulas.

[0086] The aging mechanism load of the battery cell, different aging mechanisms correspond to different identification parameters, which increases the difficulty of parameter identification; for example, the life parameters include the growth of SEI (solid electrolyte interface), and the aging mechanisms for SEI growth include interstitial diffusion limitation and reaction rate limitation; different aging mechanisms correspond to different kinetic formulas that determine the SEI growth rate; the aging identification parameter corresponding to the interstitial diffusion limitation aging mechanism is the Li - ion interstitial diffusion rate, and the kinetic formula is as follows:

[0087]

[0088] where j SEI is the SEI exchange current density, F is the Faraday constant, D Li,i is the diffusion rate of Li - ions in the SEI, c Li,i,0 is the concentration of Li - ions in the negative electrode, L SEI is the SEI film thickness, R is the gas constant, and T is the temperature.

[0089] The aging mechanism is reaction rate limitation. The aging identification parameters corresponding to it are the basic rate of SEI formation, the activation energy of SEI reaction, and the SEI film resistance. The kinetic formula is as follows:

[0090]

[0091] Among them, R film is the resistivity of the SEI film, U SEI is the potential difference of the SEI formation reaction equilibrium potential relative to Li, k 0,SEI is the basic rate of the SEI reaction, a is the reaction area, a c,SEI is the activation energy of the SEI reaction.

[0092] It should be clear that in this embodiment, the life parameters include SEI growth, and different aging mechanisms correspond to different life parameters; a single aging mechanism can correspond to multiple reaction kinetics; in other embodiments, different life parameters and corresponding aging mechanisms can be selected, which are not limited here; for example, the life parameters also include lithium plating and active material loss; the aging mechanisms corresponding to lithium plating include reversible lithium plating reaction, irreversible lithium plating reaction, and partially reversible lithium plating reaction; the aging mechanisms corresponding to active material loss include particle rupture, which can be divided into kinetics based on reaction and kinetics based on crack propagation.

[0093] The aging mechanism corresponding to a single life parameter can affect other life parameters. For example, SEI growth will affect the overpotential in the kinetic formula of the lithium plating reaction, directly affecting whether the lithium plating reaction occurs.

[0094] The lithium-ion aging mechanism is divided into three categories, including SEI growth, lithium deposition (lithium plating), and loss of positive and negative active materials. Each of these three mechanisms has its own different kinetic formulas, and the three mechanisms will be coupled with each other. The following describes the different kinetic formulas and coupling relationships corresponding to different aging mechanisms:

[0095] The kinetic formulas of SEI growth include: kinetics based on SEI formation reaction, SEI formation reaction kinetics with EC in the electrolyte, solvent diffusion kinetics, lithium interstitial diffusion kinetics, and electron tunneling kinetics. Among them, different kinetic formulas will have some identical life parameters. For example, the kinetics based on SEI formation reaction and the SEI formation reaction kinetics with EC in the electrolyte both have the SEI reaction kinetic constant and the SEI film resistance; while the life parameter of solvent diffusion kinetics is the diffusion coefficient of the solvent in the SEI layer, and the life parameter of lithium interstitial diffusion kinetics is the diffusion coefficient of lithium in the SEI layer, etc.

[0096] The kinetic formulas of lithium deposition are divided into three categories: 1. Reversible, 2. Irreversible, 3. Partially reversible. All three of these formulas require the life parameter of the lithium deposition reaction rate constant, and the kinetic formula of partially reversible also includes the life parameter of the lithium deposition ratio.

[0097] The loss of active material (LAM) includes reaction-driven and stress-driven components, both of which have different lifetime parameters. The reaction-driven component is the ratio of SEI generation to LAM, and the stress-driven component is the yield stress of the positive and negative electrode materials and two empirical coefficients.

[0098] It should be clear that these three major types of mechanisms will be coupled with each other (the coupling method sometimes depends on the choice of kinetic formula). Among them, the growth of SEI will lead to an increase in film resistance, making it easier to deposit lithium, and the SEI reaction will result in the loss of active material. Lithium deposition and SEI will cause the porosity of the positive and negative electrodes of the battery cell to become smaller, which will affect these three major types of mechanisms indirectly. When stress-driven kinetics is selected for LAM, the newly generated surface due to particle fragmentation will lead to the generation of SEI.

[0099] In this embodiment, the identification of lifetime parameters uses the Optuna library in Python to automatically select the aging mechanism based on the lifetime curve and identify the corresponding parameters. The Optuna library can not only perform conventional parameter identification but also perform categorical parameter identification, and set different conventional parameter identifications according to different categorical parameters.

[0100] The Optuna library is an open-source hyperparameter optimization tool library that enables parallelization and allows multiple optimization tasks to be carried out simultaneously, improving the calculation efficiency. Optuna splits each optimization task into independent trial tasks through a distributed Trial design, allowing multiple computing nodes to conduct their respective trials simultaneously. This mechanism is mainly reflected in the following aspects: The parallel mechanism of Optuna is described in detail below:

[0101] Trial allocation: Optuna can generate trial tasks through different sampling strategies such as random sampling and Bayesian optimization and dynamically allocate them to different computing nodes.

[0102] Asynchronous update: In a distributed environment, Optuna supports an asynchronous update strategy, which means that the trial results are immediately returned after the calculation of each node is completed without waiting for other nodes. This can avoid wasting computing resources between nodes and make full use of the computing power of the cluster.

[0103] Easy to expand: The parallel framework of Optuna can easily interface with various parallel computing environments (such as multithreading, multiprocessing, or distributed clusters) without making too many changes to the underlying code, facilitating rapid deployment on different hardware platforms.

[0104] Using the Optuna library for aging parameter optimization can not only improve the efficiency and accuracy of parameter tuning but also provide a flexible and powerful toolset to address complex optimization challenges.

[0105] The specific cycling conditions determine the working steps based on the extracted detailed data of a single cycle. The working steps include charge-discharge rate, rest time, and charge-discharge time. The charge-discharge time is the charging time and discharging time in a single working step. The cycling working steps of the simulated battery cells are used to simulate the aging process. The determined working steps are input into the battery model to simulate the operation mode of the battery cells under actual use conditions, and the working steps are repeatedly executed multiple times to simulate the long-term aging process. During the simulation process, key performance indicators such as capacity fade and internal resistance increase are monitored to evaluate the impact of different working conditions on the battery life.

[0106] Standard capacity tests are inserted into the cycling working steps of the simulated battery cells to improve the accuracy of the energy throughput-capacity curve obtained from the simulation; standard capacity tests are inserted to calibrate the simulation results, improve the accuracy of the simulation results, and ensure consistent battery cell capacity evaluation criteria under different conditions. In this embodiment, the standard capacity test is carried out at an ambient temperature of 25 °C with a constant current and constant voltage charge-discharge cycle of 0.33C for 2-3 full cycles, and the capacity of the battery cell is taken as the average value of the discharge charge. In other embodiments, the test conditions of the standard capacity test can be modified as required, and no limitation is made thereto.

[0107] A loss function is constructed based on the root mean square error between the simulation results and the experimental test data. The construction process of the loss function is described in detail below:

[0108] Interpolate the energy throughput-capacity during the standard capacity test of the experimental data, and also add the corresponding standard capacity test during the long-term working condition simulation to make the cycling conditions of the simulated battery cells as consistent as possible with those of the experimental battery cells and obtain the correct capacity value. Interpolate the energy throughput-capacity curve of the simulated battery cells during the standard capacity test in the same way, compare it with the interpolated curve obtained from the experiment, and calculate its root mean square error for constructing the loss function.

[0109] Embodiment 2

[0110] This embodiment provides a system for optimizing the performance and life parameters of a battery physical model, which is used to implement the method for optimizing the performance and life parameters of the battery physical model provided in Embodiment 1, including:

[0111] A parameter acquisition module, which acquires aging experimental data based on the parameter acquisition module.

[0112] An aging model construction module, which constructs an aging model based on the aging experimental data and realizes the identification of the aging model parameters.

[0113] A physical performance model construction module, which constructs a physical performance model based on the aging experimental data.

[0114] An aging-related parameter determination module, which determines the simulation working steps based on the aging model and the physical performance model, and cycles the simulation working steps to obtain aging-related parameters.

[0115] In a preferred embodiment, the parameter acquisition module includes:

[0116] A single - loop detailed data acquisition module, configured to extract single - loop detailed data according to the aging experiment data.

[0117] A life - curve acquisition module, configured to extract the energy throughput - capacity data life - curve according to the aging experiment data.

[0118] A cycle condition acquisition module, configured to extract specific cycle conditions according to the aging experiment data.

[0119] In a preferred embodiment, the aging model construction module includes:

[0120] A life - parameter determination module, configured to receive the energy throughput - capacity data curve and determine the life parameters.

[0121] An aging identification parameter determination module, which selects an aging mechanism based on the determined life parameters and obtains the aging identification parameters corresponding to the aging mechanism.

[0122] In a preferred embodiment, the physical property model construction module includes:

[0123] A design - parameter acquisition module, configured to acquire the design parameters of the battery.

[0124] An optimization - parameter determination module, configured to determine the parameters to be optimized according to the design parameters of the battery.

[0125] A loss - function module, configured to receive the current signal input of the single - loop detailed data and output a predicted voltage signal, and calculate the root - mean - square error between the predicted voltage signal and the experimental voltage signal.

[0126] A correction module, which acquires the root - mean - square error between the predicted voltage signal and the experimental voltage signal and determines whether to correct the loss function; if the root - mean - square error is greater than the error threshold, the loss function is corrected; if the root - mean - square error is less than the error threshold, the loss function is not corrected.

[0127] In a preferred embodiment, the aging - related parameter determination module includes:

[0128] A working - step determination module, which acquires the working steps based on the specific cycle conditions and the period of the single - loop detailed data.

[0129] A simulation - test module, which performs a simulation test based on the cycle working steps, the optimized physical property model, and the optimized aging model to obtain aging - related parameters.

[0130] A cycle - number determination module, which determines the number of cycles of the working steps in the simulation test.

[0131] Standard capacity test module, insert standard capacity test in simulation test; adjust test conditions and test frequencies of standard capacity test based on the standard capacity test module.

[0132] Embodiment 3

[0133] This embodiment provides a computer device corresponding to the method for optimizing the performance and life parameters of the battery physical model provided in Embodiment 1. As Figure 4 shown, Figure 4 is a schematic structural diagram of an embodiment of the computer device of the present application. The computer device includes a memory and a processor. Among them, the memory and the processor are coupled to each other. The memory stores program data, and the processor is used to execute the program data to implement the steps of any embodiment of the above method for optimizing the performance and life parameters of the battery physical model.

[0134] In this embodiment, the processor can also be called a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0135] Embodiment 4

[0136] For the method of the above embodiment, it can be implemented in the form of a computer program. Therefore, the present application proposes a computer-readable storage medium. Please refer to Figure 5 , Figure 5 is a schematic structural diagram of an embodiment of the computer-readable storage medium of the present application. The computer-readable storage medium stores program data that can be run by the processor. The program data can be executed by the processor to implement the steps of any embodiment of the above method for optimizing the performance and life parameters of the battery physical model.

[0137] The computer-readable storage medium of this embodiment can be a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc, etc., which can store program data. Or it can also be a server storing the program data. The server can send the stored program data to other devices for running, or it can also run the stored program data by itself.

[0138] In several embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0139] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0141] The above is only the embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for optimizing battery physical model performance and life parameters, characterized in that: The steps include: Acquire aging test data, and acquire single-cycle detailed data, life curve and specific cycle conditions based on the aging test data; Building or optimizing a physical performance model of the battery cell based on the single-cycle detailed data; Establishing an aging model based on the aging mechanism, and selecting aging identification parameters based on the aging mechanism to achieve optimization of the aging model; A working step is obtained based on the specific cycle working condition, and aging-related parameters are obtained based on the working step, the optimized physical property model and the optimized aging model.

2. The battery physical model performance and life parameter optimization method according to claim 1, characterized in that: The life curve is obtained based on energy throughput-capacity data, and the energy throughput (ETP) is obtained by the following formula: T is the time required for the entire life curve test, I(t) is the current curve, and V(t) is the voltage curve.

3. The battery physical model performance and life parameter optimization method according to claim 1, characterized in that: Acquiring the single lap detailed data includes the following steps: The data segments in the aging test data where the static time is greater than the threshold time are removed, and the processed aging test data are subjected to Fourier transform to obtain the frequency spectrum; Finding the wavelength corresponding to the main frequency in the spectrum, and obtaining the period based on the wavelength corresponding to the main frequency in the spectrum; Corresponding single-lap detailed data is extracted based on the period.

4. The battery physical model performance and life parameter optimization method according to claim 1, characterized in that: Optimizing the physical performance model of the battery cell based on the single-cycle detailed data includes the following steps: Collect the design parameters of the battery cell and determine the performance model parameters that need to be identified and their corresponding ranges; The current signal in the single-cycle detailed data is input into the physical performance model, and the voltage signal is the model identification target; the model output voltage signal is constructed; Compared with the root mean square error (RMSE) loss function of the experimentally measured voltage signal v, the root mean square error calculation formula is as follows: The physical performance model of the battery cell is optimized based on the root mean square error.

5. The method for optimizing battery physical model performance and life parameters according to claim 4, characterized in that: The corresponding aging mechanism is determined based on different life parameters; the kinetic formula is determined based on the aging mechanism; a single life parameter corresponds to a single or multiple aging mechanisms; the aging mechanism corresponding to a single life parameter may affect other life parameters.

6. The battery physical model performance and life parameter optimization method according to claim 1, characterized in that: The specific cycle condition determines the working steps based on the extracted single-cycle detailed data, and the working steps include the charge and discharge rate, the rest time and the charge and discharge time; the battery cell is simulated to cycle the working steps to simulate the aging process.

7. The method for optimizing battery physical model performance and life parameters according to claim 6, characterized in that: The standard capacity test is inserted into the simulated battery cell cycle step to improve the accuracy of the energy throughput-capacity curve obtained by simulation; the loss function is constructed based on the root mean square error between the simulation results and the experimental test data.

8. A battery physical model performance and life parameter optimization system, using the battery physical model performance and life parameter optimization method according to any one of claims 1 to 7, characterized in that: include: A parameter acquisition module, based on which aging experiment data is acquired; Physical performance model building module, which builds physical performance models based on aging experimental data; Aging model building module, which builds an aging model based on aging experimental data and realizes the identification of aging model parameters; The aging-related parameter determination module determines the simulation steps based on the aging model and the physical performance model, and cycles the simulation steps to obtain the aging-related parameters.

9. An electronic device, characterized in that: The electronic device comprises: a memory and a processor coupled to each other, wherein the processor is used to execute program instructions stored in the memory to implement the battery physical model performance and life parameter optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program data, and the program data can be executed by a processor to implement the battery physical model performance and life parameter optimization method as described in any one of claims 1-7.

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