Battery aging mechanism calculation method, device, equipment, storage medium and program product

Abstract

The application relates to a battery aging mechanism calculation method, device, equipment, storage medium and program product. The method comprises the following steps: collecting working condition data of actual operation of a target vehicle, and predicting the capacity of a battery on the target vehicle according to the working condition data to obtain a first battery capacity of the battery; a lithium iron phosphate battery aging mechanism model and a ternary lithium battery aging mechanism model are used to calculate a first performance parameter of the battery according to the working condition data and experimental data; the experimental data are obtained by performing a battery cycle experiment and a battery calendar aging experiment on the battery; a Kalman filtering model is used to calculate a second performance parameter according to the first performance parameter and the first battery capacity; and the second performance parameter is substituted into the aging mechanism model to perform calculation, and a third performance parameter is calculated. The above method accurately evaluates the performance parameter of the battery aging, and effectively solves the problem that a preset model of an inherent mechanism is disconnected with an actual automobile use scene.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle battery state estimation technology, and in particular to a battery aging mechanism calculation method, device, equipment, storage medium and program product. Background Technology

[0002] With the rapid iteration of new energy vehicle technology, power batteries, as core components, are driving the rapid development of electric vehicles towards longer range and lower energy consumption, becoming a crucial force in the global green transformation of transportation. However, power batteries inevitably face aging issues during long-term charge-discharge cycles. This not only directly affects the vehicle's range, power output stability, and charging speed, but may also trigger battery thermal management risks, thereby shortening the vehicle's lifespan and reducing the user experience. Therefore, accurate and efficient measurement and evaluation of the aging state of power batteries has become a key link in ensuring the safety and reliability of new energy vehicles and a core issue that urgently needs to be addressed in the current new energy vehicle technology field.

[0003] Currently, the aging state of new energy vehicles is mainly analyzed through non-destructive methods to examine the battery aging mechanism and perform simulations based on models of the inherent mechanism. However, while this method is feasible in laboratory simulation environments, it relies on pre-defined models of the inherent mechanism and cannot be applied to the complex operating conditions of real vehicles. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, device, storage medium, and program product for calculating battery aging mechanisms to address the aforementioned technical problems.

[0005] Firstly, this application provides a method for calculating battery aging mechanisms, including:

[0006] Collect actual operating condition data of the target vehicle, and predict the capacity of the battery on the target vehicle based on the operating condition data to obtain the first battery capacity.

[0007] Using the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries, the first performance parameters of the batteries were calculated based on operating condition data and experimental data; the experimental data were obtained by conducting battery cycle tests and battery calendar aging tests.

[0008] The second performance parameter is calculated using the Kalman filter model based on the first performance parameter and the first battery capacity.

[0009] The third performance parameter is obtained by substituting the second performance parameter into the aging mechanism model.

[0010] In one embodiment, a second performance parameter is calculated using a Kalman filter model based on a first performance parameter and a first battery capacity, including:

[0011] Historical experimental data of the battery were obtained, and the Kalman filter model was improved based on the historical experimental data and the first performance parameter to obtain the improved Kalman filter model; the historical experimental data included multiple historical estimation errors and multiple temperature dependence factors.

[0012] The first battery capacity and the first performance parameter are input into the improved Kalman filter model to obtain the second performance parameter.

[0013] In one embodiment, the Kalman filter model is improved based on historical experimental data and a first performance parameter to obtain an improved Kalman filter model, including:

[0014] The correction parameters are determined based on the first performance parameter, multiple historical estimation errors, and multiple temperature-dependent factors;

[0015] The state transition matrix in the Kalman filter model is modified according to the correction parameters to obtain the improved Kalman filter model.

[0016] In one embodiment, correction parameters are determined based on multiple historical estimation errors and multiple temperature-dependent factors, including:

[0017] A weighted moving average is calculated for multiple historical estimation errors to obtain the weighted moving average scalar of historical estimation errors;

[0018] A temperature matrix is ​​constructed based on multiple temperature-dependent factors and the first performance parameter;

[0019] The correction parameters are determined based on the weighted moving average scalar and the temperature matrix.

[0020] In one embodiment, the capacity of the battery in the target vehicle is predicted based on operating condition data to obtain a first battery capacity, including:

[0021] The battery capacity is predicted based on the operating condition data using an ampere-hour calculation model to obtain the second battery capacity;

[0022] The capacity of the second battery is corrected according to the preset temperature correction coefficient to obtain the capacity of the third battery;

[0023] The capacity of the first battery is obtained by recursively calculating the capacity of the third battery using Kalman filter and fuzzy logic.

[0024] In one embodiment, the capacity of the third battery is recursively calculated based on a Kalman filter and fuzzy logic to obtain the capacity of the first battery, including:

[0025] Set the initial values ​​for the target error of the Kalman filter and fuzzy logic;

[0026] Get the maximum and minimum battery capacity;

[0027] Substitute the initial values ​​of the maximum battery capacity, minimum battery capacity, and target error into the fuzzy logic and Kalman filter to perform recursive calculations for the current round, and obtain the predicted battery capacity for the current round.

[0028] The predicted battery capacity is substituted into the battery state estimation equation to calculate the first error, and the first error is used as the new target error. Then, the steps to obtain the maximum and minimum battery capacity are returned.

[0029] In one embodiment, the predicted battery capacity is substituted into the battery state estimation equation to calculate the first error, including:

[0030] The third performance parameter is used as the predicted battery capacity, and the process is repeated to substitute the predicted battery capacity into the battery state estimation equation to calculate the first error, so as to predict the new battery capacity.

[0031] Secondly, this application also provides a battery aging mechanism calculation device, comprising:

[0032] The prediction module is used to collect the actual operating condition data of the target vehicle and predict the capacity of the battery on the target vehicle based on the operating condition data to obtain the first battery capacity.

[0033] The first calculation module is used to calculate the first performance parameters of the battery based on the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries, according to the operating condition data and experimental data. The experimental data is obtained by conducting battery cycle tests and battery calendar aging tests on the battery.

[0034] The second calculation module is used to calculate the second performance parameter based on the first performance parameter and the first battery capacity using a Kalman filter model.

[0035] The third calculation module is used to substitute the second performance parameter into the aging mechanism model for calculation to obtain the third performance parameter.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0037] Collect actual operating condition data of the target vehicle, and predict the capacity of the battery on the target vehicle based on the operating condition data to obtain the first battery capacity.

[0038] Using the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries, the first performance parameters of the batteries were calculated based on operating condition data and experimental data; the experimental data were obtained by conducting battery cycle tests and battery calendar aging tests.

[0039] The second performance parameter is calculated using the Kalman filter model based on the first performance parameter and the first battery capacity.

[0040] The third performance parameter is obtained by substituting the second performance parameter into the aging mechanism model.

[0041] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0042] Collect actual operating condition data of the target vehicle, and predict the capacity of the battery on the target vehicle based on the operating condition data to obtain the first battery capacity.

[0043] Using the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries, the first performance parameters of the batteries were calculated based on operating condition data and experimental data; the experimental data were obtained by conducting battery cycle tests and battery calendar aging tests.

[0044] The second performance parameter is calculated using the Kalman filter model based on the first performance parameter and the first battery capacity.

[0045] The third performance parameter is obtained by substituting the second performance parameter into the aging mechanism model.

[0046] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0047] Collect actual operating condition data of the target vehicle, and predict the capacity of the battery on the target vehicle based on the operating condition data to obtain the first battery capacity.

[0048] Using the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries, the first performance parameters of the batteries were calculated based on operating condition data and experimental data; the experimental data were obtained by conducting battery cycle tests and battery calendar aging tests.

[0049] The second performance parameter is calculated using the Kalman filter model based on the first performance parameter and the first battery capacity.

[0050] The third performance parameter is obtained by substituting the second performance parameter into the aging mechanism model.

[0051] The aforementioned battery aging mechanism calculation method, apparatus, equipment, storage medium, and program product obtain the first battery capacity by collecting actual operating condition data of the target vehicle and predicting the battery capacity of the target vehicle based on the operating condition data; calculate the first performance parameter of the battery based on the operating condition data and experimental data using the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries; obtain the experimental data by conducting battery cycle experiments and battery calendar aging experiments on the battery; calculate the second performance parameter based on the first performance parameter and the first battery capacity using the Kalman filter model; and calculate the third performance parameter by substituting the second performance parameter into the aging mechanism model. The above method utilizes the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries, and integrates battery cycle experiments and battery calendar aging experiments to achieve a comprehensive mechanism analysis of battery aging state. Furthermore, by collecting actual vehicle operating condition data and combining it with a Kalman filter model based on the first performance parameter and the first battery capacity, the performance parameters of battery aging are accurately evaluated. This effectively solves the problem of the inherent mechanism's preset model being out of touch with actual vehicle usage scenarios, and provides effective data support for subsequent battery safety diagnosis and life prediction. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is an application environment diagram of the battery aging mechanism calculation method in one embodiment;

[0054] Figure 2 This is one of the flowcharts illustrating the battery aging mechanism calculation method in one embodiment;

[0055] Figure 3 This is a second flowchart illustrating the battery aging mechanism calculation method in one embodiment;

[0056] Figure 4 This is the third flowchart illustrating the battery aging mechanism calculation method in one embodiment;

[0057] Figure 5 This is the fourth flowchart illustrating the battery aging mechanism calculation method in one embodiment;

[0058] Figure 6 This is the fifth flowchart illustrating the battery aging mechanism calculation method in one embodiment;

[0059] Figure 7 This is a flowchart of the battery aging mechanism calculation method in one embodiment, shown as diagram six.

[0060] Figure 8 This is the seventh flowchart illustrating the battery aging mechanism calculation method in one embodiment;

[0061] Figure 9 This is the eighth flowchart illustrating the battery aging mechanism calculation method in one embodiment;

[0062] Figure 10 This is a structural block diagram of a battery aging mechanism calculation device in one embodiment. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0064] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0065] With the rapid iteration of new energy vehicle technology, power batteries, as core components, are driving the rapid development of electric vehicles towards longer range and lower energy consumption, becoming a crucial force in the global green transformation of transportation. However, power batteries inevitably face aging issues during long-term charge-discharge cycles. This not only directly affects the vehicle's range, power output stability, and charging speed, but may also trigger battery thermal management risks, thereby shortening the vehicle's lifespan and reducing the user experience. Therefore, accurate and efficient measurement and evaluation of the aging state of power batteries has become a key link in ensuring the safety and reliability of new energy vehicles and a core issue that urgently needs to be addressed in the current new energy vehicle technology field.

[0066] Currently, the aging state of new energy vehicles is mainly analyzed through non-destructive methods to examine the battery aging mechanism and perform simulations based on models of the inherent mechanism. However, while this method is feasible in laboratory simulation environments, it relies on pre-defined models of the inherent mechanism and cannot be applied to the complex operating conditions of real vehicles.

[0067] In view of the above-mentioned technical problems, this application provides a method for calculating battery aging mechanism. The following embodiments will specifically illustrate the method for calculating battery aging mechanism.

[0068] The battery aging mechanism calculation method provided in this application embodiment can be applied to, for example... Figure 1 The internal structure diagram of the computer device shown can be as follows: Figure 1 As shown.

[0069] The computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for calculating battery aging mechanisms. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display (LCD) or an e-ink display, and the output device can be a touch layer covering the display screen.

[0070] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the battery aging mechanism calculation system applied thereto. The specific calculation system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0071] In one exemplary embodiment, such as Figure 2 As shown, a method for calculating battery aging mechanisms is provided, and this method is applied to... Figure 1 Taking computer devices as an example, the explanation includes:

[0072] S201: Collect the actual operating condition data of the target vehicle, and predict the battery capacity of the target vehicle based on the operating condition data to obtain the first battery capacity.

[0073] Among them, operating condition data refers to the actual operating data of new energy vehicles, including voltage, current, temperature, and charging event data when their state of charge (SOC) is between 30% and 80%.

[0074] In this embodiment, any one of multiple vehicles is selected as the target vehicle. Real-time operating data of the target vehicle is collected via a vehicle terminal or Battery Management System (BMS), including voltage, current, temperature, and charging event data within the 30% to 80% state of charge range. Based on the target vehicle's current state of charge and cumulative charge dissipation, combined with historical charge-discharge cycle records, the initial battery capacity is calculated. Since temperature significantly affects battery capacity—for example, low temperatures reduce lithium-ion battery activity, causing a capacity decrease of approximately 10%—the initial battery capacity is dynamically corrected by analyzing the nonlinear relationship between battery capacity and temperature, resulting in a corrected estimate. Because noise interference (such as sensor errors or environmental fluctuations) exists in the collected operating data, a recursive model (such as a Kalman filter algorithm) is used to iteratively optimize the noise weights. Updates are made through state estimation and covariance, gradually reducing noise interference to obtain a more accurate battery capacity estimate, i.e., the battery's first capacity.

[0075] S202 uses the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries to calculate the first performance parameters of the battery based on operating condition data and experimental data.

[0076] The aging mechanism models for lithium iron phosphate batteries include a solid electrolyte interphase (SEI) growth sub-model, an active material loss sub-model, and a lithium plating sub-model; the aging mechanism models for ternary lithium batteries include an SEI film growth sub-model, an active material loss sub-model, a lithium plating model, and a transition metal dissolution sub-model. Experimental data were obtained through battery cycle experiments and battery calendar aging experiments. Battery cycle experiments simulate battery aging during charge-discharge cycles, revealing the aging behavior of the battery under charge-discharge stress, such as the SEI film growth rate or the fracture cycle of the active material. Battery calendar aging simulates battery aging during static storage, such as the self-discharge of a vehicle when idle. The first performance parameter includes the capacity loss caused by SEI film growth. Volume loss due to loss of active substances Capacity loss due to lithium plating Capacity loss due to transition metal dissolution SEI film thickening reaction rate constant Rate constant of active substance exfoliation reaction lithium plating reaction rate constant and the rate constant of transition metal dissolution reaction .

[0077] In this embodiment of the application, to analyze the aging behavior of automotive batteries, battery cycle tests and battery calendar aging tests are conducted on any brand-new lithium iron phosphate batteries and brand-new ternary lithium batteries, respectively. The aging data and experimental data obtained from the experiments are integrated and used as inputs for the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries, respectively. These two aging mechanism models are each composed of multiple different aging sub-models. The aging mechanism model for lithium iron phosphate batteries is composed of a sub-model of SEI film growth, an active material loss sub-model, and a lithium plating sub-model. These models analyze the causes of aging from different physical or chemical processes, such as the formation and growth of the SEI film on the battery electrode surface, the reduction of active material, and lithium ion deposition. Based on physical or chemical principles, corresponding mathematical formulas are constructed to calculate the capacity loss caused by each aging factor. The aging mechanism model for ternary lithium batteries, on the other hand, is derived from a combination of the SEI film growth sub-model, active material loss sub-model, lithium plating sub-model, and transition metal dissolution sub-model. In addition to considering the aging factors in the aforementioned sub-models, the ternary lithium battery aging mechanism model also considers the unique aging factor of transition metal dissolution in ternary lithium batteries. Computer equipment uses comprehensive calculations based on these sub-models to obtain the battery's primary performance parameters.

[0078] For the SEI film growth sub-model, this model is constructed based on diffusion and reduction kinetics and the Arrhenius equation. It is used to quantitatively calculate the capacity loss caused by the continuous growth of the SEI film during battery cycling, denoted as... In this model, the SEI film growth rate is described by the side reaction current, as shown in equation (1):

[0079] (1);

[0080] in, This indicates the magnitude of the time-varying SEI film growth side reaction current; This represents the rate constant for the SEI film thickening reaction; This represents the activation energy of the SEI film thickening reaction; Indicates reaction time; Represents the ideal gas constant; Indicates the SEI membrane structure factor; This indicates the battery temperature.

[0081] in, From relation (2), we get:

[0082] (2);

[0083] in, Indicates the number of electrons reduced in the SEI film thickening reaction; Denotes Faraday's constant; Represents the ideal gas constant; Indicates battery temperature; Indicates the dynamic overpotential; Indicates the open-circuit potential of the negative terminal; This indicates the open-circuit potential of the side reaction during SEI film thickening.

[0084] Therefore, by integrating the change of the secondary reaction current over time, a computer device can construct a SEI membrane growth sub-model, which is expressed or constructed using equation (3):

[0085] (3);

[0086] in, This represents the rate constant for the SEI film thickening reaction; Indicates the SEI membrane structure factor; It is obtained from relation (2); Indicates reaction time; This represents the activation energy of the SEI film thickening reaction; Represents the ideal gas constant; This indicates the battery temperature.

[0087] As can be seen from equation (3), the main factors affecting the growth of SEI film are temperature, current and SOC.

[0088] For the active material loss sub-model, this model is constructed based on stress accumulation fracture, Basquin's power law fatigue equation, and Miner's rule, and is used to quantitatively calculate the active material (such as the cathode) during battery cycling. Capacity loss caused by structural damage to the negative electrode graphite is denoted as In this model, the rate of loss of active substances is described by the side reaction current, as shown in equation (4):

[0089] (4);

[0090] in, Indicates the magnitude of the time-varying side reaction current during the exfoliation of active substances; This represents the rate constant of the active substance exfoliation reaction; and These represent the maximum and minimum static stress values, respectively. This represents the stress at the maximum yield strength. This represents the slope of the Basconian power-law fatigue equation; This represents the radius of the spherical particle.

[0091] Therefore, by integrating the change of the side reaction current over time, a computer device can construct a sub-model of active substance loss, which can be expressed or constructed through relation (5):

[0092] (5);

[0093] in, This indicates the capacity loss caused by the shedding of active material.

[0094] As can be seen from equation (5), the main influencing factor on the exfoliation of active substances is SOC.

[0095] For the lithium plating model, this model is based on charge transfer kinetics and is used to quantitatively calculate the capacity loss caused by lithium plating during battery cycling, denoted as In this model, the lithium plating rate is described by the side reaction current, as shown in equation (6):

[0096] (6);

[0097] in, Indicates the time-varying magnitude of the lithium plating side reaction current; This represents the rate constant for the lithium plating reaction; Indicates the specific surface area of ​​active particles; This represents the volume fraction of solid particles in the negative electrode. The thickness of the negative electrode sheet is represented; the function Φ represents the relationship between the overpotential and the lithium plating current density. Indicates the dynamic overpotential; Indicates the open-circuit potential of the negative terminal; Indicates the internal resistance of the SEI film; This indicates the current flowing through the battery; This indicates the lithium plating trigger potential.

[0098] Therefore, by integrating the change of the side reaction current over time, a lithium plating model can be constructed using computer equipment. This model can be expressed or constructed using equation (7):

[0099] (7);

[0100] in, This indicates capacity loss due to lithium plating.

[0101] As can be seen from equation (7), the main influencing factors of lithium plating are SOC, temperature and current.

[0102] For the transition metal dissolution sub-model, this model is based on the Arrhenius equation and is used to quantitatively calculate the capacity loss caused by transition metal dissolution during battery cycling, denoted as... In this model, the dissolution rate of the transition metal is described by the side reaction current, as shown in equation (8):

[0103] (8);

[0104] in, This indicates the magnitude of the time-varying current in the transition metal dissolution side reaction at the positive electrode; This represents the rate constant for the dissolution reaction of the transition metal at the positive electrode. This represents the activation energy of the transition metal dissolution reaction at the positive electrode; Indicates positive electrode potential; Represents the step function; This represents the voltage correction slope term; This indicates the voltage correction bias term; Represents the ideal gas constant; This indicates the battery temperature.

[0105] Therefore, by integrating the change of the side reaction current over time, a transition metal dissolution sub-model can be constructed using computer equipment. This transition metal dissolution sub-model can be expressed or constructed using relation (9):

[0106] (9);

[0107] in, This indicates the capacity loss caused by the dissolution of transition metals.

[0108] As can be seen from equation (9), the main influencing factors of transition metal dissolution are SOC and temperature.

[0109] Therefore, the computer device can obtain the battery capacity decay percentage based on equations (1) to (9). and remaining effective battery capacity The percentage of battery capacity decay can be expressed by equation (10), which is shown below:

[0110] (10);

[0111] in, This indicates the capacity loss caused by SEI film growth; This indicates the capacity loss caused by the loss of active material; This indicates capacity loss due to lithium plating; This indicates the capacity loss caused by the dissolution of transition metals.

[0112] The remaining effective capacity of the battery can be expressed by equation (11), which is shown below:

[0113] (11);

[0114] in, Indicates the battery's rated capacity; This indicates the percentage of battery capacity degradation.

[0115] S203 uses a Kalman filter model to calculate the second performance parameter based on the first performance parameter and the first battery capacity.

[0116] The second performance parameter includes the SEI film thickening reaction rate constant after correction by the Kalman filter model. Rate constant of active substance exfoliation reaction lithium plating reaction rate constant and the rate constant of transition metal dissolution reaction .

[0117] In this embodiment, to eliminate errors in the aging mechanism model, the computer device modifies the Kalman filter model. Specifically, the computer device combines historical estimation errors and temperature dependence factors to construct a new state transition matrix, thereby obtaining the modified Kalman filter model. Subsequently, the computer device inputs the first performance parameter and the first battery capacity as inputs to the modified Kalman filter model. The modified Kalman filter model can address the SEI film thickening reaction rate constant in the first performance parameter. Rate constant of active substance exfoliation reaction lithium plating reaction rate constant and the rate constant of transition metal dissolution reaction The correction was then performed. The final corrected SEI film thickening reaction rate constant was obtained. Rate constant of active substance exfoliation reaction lithium plating reaction rate constant and the rate constant of transition metal dissolution reaction These four modified constants together constitute the second performance parameter.

[0118] S204, substitute the second performance parameter into the aging mechanism model for calculation, and obtain the third performance parameter.

[0119] The third performance parameter refers to the capacity loss caused by the growth of the new SEI film, calculated by substituting the second performance parameter into the aging mechanism model. Volume loss due to loss of active substances Capacity loss due to lithium plating Capacity loss due to transition metal dissolution .

[0120] In this embodiment of the application, the second performance parameter (SEI film thickening reaction rate constant) obtained through the modified Kalman filter model is used. Rate constant of active substance exfoliation reaction lithium plating reaction rate constant and the rate constant of transition metal dissolution reaction The capacity loss caused by the growth of the new SEI film was calculated by re-introducing the components into the aging mechanism models (lithium iron phosphate battery aging mechanism model and ternary lithium battery aging mechanism model). Volume loss due to loss of active substances Capacity loss due to lithium plating Capacity loss due to transition metal dissolution And according to , , as well as The calculation results provide users with suggestions regarding safety issues, for example, scenario one: when a car travels 20,000 kilometers, An increase exceeding 0.5% can alert users to potential battery safety issues caused by lithium metal. Based on this, users can be advised to: avoid fast charging at low temperatures using currents far exceeding the battery's rated capacity, limit extreme acceleration (use economy driving mode), and avoid deep discharge (below 10% state of charge, especially in cold weather). Scenario 2: When the car travels 20,000 kilometers... An increase exceeding 0.15% can alert users to potential battery safety issues caused by the degradation and dissolution of the positive electrode material into the electrolyte. Based on this, users can be advised to avoid high-temperature operations (charging or driving, etc.) and heat exposure. Scenario 3: When the car travels 20,000 kilometers... An increase of more than 0.2% or An increase of more than 0.01% can alert users to potential battery safety issues caused by accelerated battery aging. Based on this issue, users can be advised to: avoid storing the battery at a high state of charge (below 80%) and charge it to above 80% state of charge; avoid deep discharge to below 20% state of charge; and manage battery temperature to ensure the battery operates within a reasonable temperature range.

[0121] The aforementioned battery aging mechanism calculation method collects actual operating condition data of the target vehicle and predicts the battery capacity based on this data to obtain the first battery capacity. Then, using aging mechanism models for lithium iron phosphate and ternary lithium batteries, it calculates the first performance parameter of the battery based on the operating condition data and experimental data. Experimental data is obtained through battery cycle experiments and battery calendar aging experiments. A Kalman filter model is used to calculate the second performance parameter based on the first performance parameter and the first battery capacity. Finally, the second performance parameter is substituted into the aging mechanism model to calculate the third performance parameter. This method utilizes aging mechanism models for lithium iron phosphate and ternary lithium batteries, and integrates battery cycle experiments and battery calendar aging experiments to achieve a comprehensive mechanistic analysis of battery aging. Furthermore, by collecting actual operating condition data of the vehicle and combining it with the Kalman filter model based on the first performance parameter and the first battery capacity, it accurately evaluates the battery aging performance parameters, effectively solving the problem of the disconnect between the pre-set model of the inherent mechanism and actual automotive usage scenarios. This provides effective data support for subsequent battery safety diagnosis and lifespan prediction.

[0122] In an exemplary embodiment, the "calculation of the second performance parameter based on the first performance parameter and the first battery capacity using the Kalman filter model" in S203 above is as follows: Figure 3 As shown, it includes:

[0123] S301: Obtain historical experimental data of the battery, and improve the Kalman filter model based on the historical experimental data and the first performance parameter to obtain the improved Kalman filter model.

[0124] The historical experimental data includes multiple historical estimation errors and multiple temperature-dependent factors.

[0125] In this embodiment, the computer device acquires and analyzes historical battery data through a battery management system. Based on the predicted battery capacity and remaining effective battery capacity, multiple historical estimation errors are obtained. Within a preset time period, the computer device assigns different weights to these historical estimation errors, thereby calculating a weighted moving average scalar. Simultaneously, by analyzing the impact of temperature on battery aging, the computer device constructs multiple identity matrices with temperature-dependent factors for different performance parameters. Subsequently, the computer device combines the weighted moving average scalar of the historical estimation errors with the temperature-dependent factor matrix to obtain a new state transition matrix. This new state transition matrix is ​​then used to improve the Kalman filter model, ultimately resulting in an improved Kalman filter model.

[0126] S302, the first battery capacity and the first performance parameter are input into the improved Kalman filter model to obtain the second performance parameter.

[0127] The first battery capacity refers to the battery capacity obtained after correcting the initial capacity of the battery and reducing noise interference using a recursive model.

[0128] In this embodiment, the computer device inputs the first battery capacity and the first performance parameter into the improved Kalman filter model. The initial parameters of the aging mechanism model are used as the prior state for the prediction step of the improved Kalman filter model. Then, the first battery capacity is used as the observed value for the observation step of the improved Kalman filter model, and a new state transition matrix is ​​used to complete the state transition. Finally, the first performance parameter is used as the predicted value for the observation model of the improved Kalman filter model. Thus, the computer device completes the closed-loop process from prediction to update of the improved Kalman filter model, thereby obtaining the second performance parameter.

[0129] In an exemplary embodiment, the phrase "improving the Kalman filter model based on historical experimental data and the first performance parameter to obtain an improved Kalman filter model" in S301 above is as follows: Figure 4 As shown, it includes:

[0130] S401 determines the correction parameters based on the first performance parameter, multiple historical estimation errors, and multiple temperature-dependent factors.

[0131] The correction parameters include target historical estimation error data obtained from multiple historical estimation errors, and temperature-related parameters obtained from the first performance parameter and multiple temperature-dependent factors.

[0132] In this embodiment, the computer device calculates multiple historical estimation errors based on the predicted battery capacity and the remaining effective battery capacity. Then, a weighted average is performed on the most recent historical estimation errors to obtain a weighted moving average scalar of the historical estimation errors, which can be obtained from parameters. The computer equipment effectively filters out random fluctuations or noise in historical error data by averaging multiple historical errors. Then, the computer equipment analyzes the effect of temperature on battery capacity; for example, high temperatures accelerate the growth rate of the SEI film, while low temperatures accelerate lithium deposition, combined with the SEI film thickening reaction rate constant in the first performance parameter. Rate constant of active substance exfoliation reaction lithium plating reaction rate constant and the rate constant of transition metal dissolution reaction Multiple temperature-dependent factor matrices for different performance parameters were constructed, which can be derived from the parameters. To express.

[0133] S402, the state transition matrix in the Kalman filter model is modified according to the correction parameters to obtain the improved Kalman filter model.

[0134] In this embodiment of the application, the computer device uses a temperature-dependent factor matrix. Weighted moving average scalar of historical estimation errors Perform matrix multiplication to construct a new state transition matrix. Computer devices use a new state transition matrix. The Kalman filter model was improved, resulting in the improved Kalman filter model.

[0135] First, the computer device uses the initial parameters of the aging mechanism model as the prior state of the prediction step of the improved Kalman filter model. The prior state of the prediction step can be represented by relation (12), which is shown below:

[0136] (12);

[0137] in, Indicates according to the first Information at time, predicting the first State estimate at time 1; This represents the rate constant for the SEI film thickening reaction; This represents the rate constant of the active substance exfoliation reaction; This represents the rate constant for the lithium plating reaction; This represents the rate constant for the dissolution reaction of transition metals.

[0138] Then, the computer device uses the first battery capacity as the observation value of the improved Kalman filter model, which can be represented by the relation (13), as shown below:

[0139] (13);

[0140] in, This indicates the initial battery capacity.

[0141] Subsequently, the computer device will generate a new state transition matrix. And the previous moment The result of the multiplication can be represented by relation (14), which is shown below:

[0142] (14);

[0143] in, This represents the new state transition matrix. This represents the state vector for state estimation.

[0144] Finally, the computer equipment uses the results of the aging mechanism model as the predicted values ​​of the observation model of the improved Kalman filter model. The residuals are then compared with the actual observations and can be expressed by the relation (15), which is shown below:

[0145] (15);

[0146] in, Indicates according to the first The moment and all prior information, for the first The predictions and estimates made at any given time; This indicates the result of the aging mechanism model, i.e., the cause of battery aging.

[0147] In an exemplary embodiment, the "determine correction parameters based on multiple historical estimation errors and multiple temperature-dependent factors" in S401 above, such as Figure 5 As shown, it includes:

[0148] S501 calculates a weighted moving average of multiple historical estimation errors to obtain a weighted moving average scalar of the historical estimation errors.

[0149] In this embodiment, the computer device obtains multiple historical estimation error data based on multiple historical data, and processes the multiple historical estimation error data using a weighted moving average to obtain a weighted moving average scalar of the historical estimation error. This involves assigning different weights to multiple historical estimation error data points within a preset time period, with the most recent data receiving the highest weight. Weighted moving average scalar It can be represented by relation (16), which is shown below:

[0150] (16);

[0151] Wherein, the numerator represents the actual value for each period ( (Class) and corresponding predicted values ​​for each period ( The error value obtained by subtracting the two (classes) is calculated according to the following rules: The weights are calculated according to the pattern of ""; the denominator is the sum of the weight values. and Equivalent, representing a weighted moving average scalar; This is the window size, used to control the range of data referenced when processing historical error data. For example: there is 10 days of estimation error data (... , The daily error is assumed to be a random number, as shown in Table 1:

[0152] Table 1

[0153]

[0154] Assuming window =5, calculate the weighted moving average of the most recent 5 days, with weights as shown in Table 2:

[0155] Table 2

[0156]

[0157] Calculate the weighted sum: ;

[0158] Calculate the weighted sum: ;

[0159] Calculate the weighted moving average: .

[0160] S502 constructs a temperature matrix based on multiple temperature-dependent factors and the first performance parameter.

[0161] Among them, the temperature dependence factor represents the effect of temperature determined experimentally.

[0162] In this embodiment, since battery aging is affected by temperature, a temperature-dependent factor is introduced to modify the Kalman filter model.

[0163] Temperature matrix It can be represented by relation (17), which is shown below:

[0164] (17);

[0165] in, express The corresponding weight value; express The corresponding weight value; express The corresponding weight value; express The corresponding weight value.

[0166] For example, when When the angle is 10, we can obtain the following from equation (18): =6.8, The corresponding values ​​are given a larger weight because the lithium plating reaction is more likely to occur at low temperatures, as shown in equation (18) below:

[0167] (18);

[0168] when When the angle is 50 degrees, we can obtain the following from equation (19): =8.5, The corresponding components are given a greater weight because the probability of transition metal dissolution occurring at high temperatures is relatively high. The relationship (19) is shown below:

[0169] (19).

[0170] S503 determines the correction parameters based on the weighted moving average scalar and the temperature matrix.

[0171] In this embodiment of the application, the computer device can obtain the correction parameters according to matrix multiplication. The correction parameters can be represented by the relation (20), which is shown below:

[0172]

[0173] (20);

[0174] in, This represents the rate constant for the SEI film thickening reaction; This represents the rate constant of the active substance exfoliation reaction; This represents the rate constant for the lithium plating reaction; This represents the rate constant for the dissolution reaction of transition metals; This represents the weighted moving average scalar; express The corresponding weight value; express The corresponding weight value; express The corresponding weight value; express The corresponding weight value.

[0175] In an exemplary embodiment, the phrase "predicting the capacity of the battery on the target vehicle based on operating condition data to obtain the first battery capacity" in S201 above is as follows: Figure 6 As shown, it includes:

[0176] S601 uses an ampere-hour calculation model to predict the battery capacity based on operating condition data to obtain the second battery capacity.

[0177] In this embodiment of the application, the computer device initially predicts the battery capacity to obtain a second battery capacity, that is, it uses the ampere-hour calculation model to predict the battery capacity based on the collected operating condition data. The prediction result is represented by the relation (21), which is shown below:

[0178] (twenty one);

[0179] in, Indicates the capacity of the second battery; Indicates the cumulative charge; Indicates the state of charge of the battery; Indicates the SOC corresponding to the end of charging; Indicates the SOC corresponding to the start of charging; Indicates the time when charging ends; Indicates the start time of charging; Indicates Coulomb efficiency; This represents the current at time t.

[0180] S602, the second battery capacity is corrected according to the preset temperature correction coefficient to obtain the third battery capacity.

[0181] The temperature correction factor can be preset based on the conversion relationship between temperature and capacity in the laboratory.

[0182] In this embodiment, since temperature affects the chemical reaction rate of the battery (e.g., the lower the temperature, the faster the capacity decays), the computer device can obtain a preset temperature correction coefficient based on the conversion relationship between temperature and capacity. Subsequently, the computer device corrects the second battery capacity using the preset temperature correction coefficient to obtain the third battery capacity, which can be represented by equation (22), as shown below:

[0183] (twenty two);

[0184] Wherein, CAP represents the second battery capacity; This represents the temperature correction factor.

[0185] S603 recursively calculates the capacity of the third battery based on the Kalman filter and fuzzy logic to obtain the capacity of the first battery.

[0186] In this embodiment of the application, after obtaining the third battery capacity, the computer device uses a Kalman filter and fuzzy logic to iteratively optimize the noise weights contained in the third battery capacity in order to eliminate noise interference in the collected actual operating data, thereby obtaining the first battery capacity.

[0187] In an exemplary embodiment, the "recursive calculation of the third battery capacity based on the Kalman filter and fuzzy logic to obtain the first battery capacity" in S603 above is as follows: Figure 7 As shown, it includes:

[0188] S701 sets the initial value of the target error for the Kalman filter and fuzzy logic.

[0189] In this embodiment of the application, before iteratively optimizing the noise weights included in the third battery capacity, the computer device needs to set an initial value for the target error in advance, which can be determined by parameters. The target error can be a related error or uncertainty parameter of battery capacity, such as sensor noise or the estimation deviation of battery capacity caused by fluctuations in the operation of the vehicle. The initial value is determined according to the actual situation.

[0190] S702, obtain the maximum and minimum battery capacity.

[0191] In this embodiment of the application, the computer device obtains the battery's maximum state of charge, i.e., the maximum battery capacity, through the battery management system, which can be determined by parameters. This can be represented; it also allows us to obtain the minimum state of charge of the battery, i.e., the minimum battery capacity, which can be obtained from parameters. To express.

[0192] S703 substitutes the initial values ​​of the maximum battery capacity, minimum battery capacity, and target error into the fuzzy logic and Kalman filter to perform recursive calculations for the current round, thus obtaining the predicted battery capacity for the current round.

[0193] In this embodiment of the application, the computer device will have the maximum battery capacity. Minimum battery capacity and the initial value of the target error As input parameters for fuzzy logic, fuzzy logic uses preset fuzzy rules to fuzzify input parameters (such as the range of SOC, etc.). The range of values ​​(of which) is transformed into a clear output, denoted as . For example: adding SOC close to and If the value is large, then Take the maximum value. Substituting into the noise weight optimization formula, the noise weight optimization formula is expressed by relation (23), which is shown below:

[0194] (twenty three);

[0195] in, Indicates the base observation noise weight; This represents the noise weighting factor output by fuzzy logic; This represents the optimized noise weight.

[0196] The computer equipment inputs the optimized noise weights into the Kalman filter to perform recursive calculations for the current round. The main process is as follows:

[0197] First, the computer device performs state prediction, i.e., prior estimation, which is represented by relation (24), as shown below:

[0198] (twenty four);

[0199] in, It indicates the state at the previous moment.

[0200] Then, the computer device performs a time update of the error covariance, which is represented by relation (25), as shown below:

[0201] (25);

[0202] in, This represents the prior error covariance; This represents the posterior error covariance of the previous time step; This represents the process noise covariance.

[0203] Then, the computer device performs Kalman gain calculation, which is represented by relation (26), as shown below:

[0204] (26);

[0205] in, It is the observation noise covariance.

[0206] Then, the computer device performs a state update, i.e., a posterior estimate, which is represented by relation (27), as shown below:

[0207] (27);

[0208] in, Indicates the first The observation value at each time point.

[0209] Finally, the computer device performs a measurement update of the error covariance, which is represented by relation (28), as shown below:

[0210] (28);

[0211] in, This represents the posterior error covariance.

[0212] Therefore, the computer device can obtain the predicted battery capacity for the current round, which is the value obtained after the state update. It can also be expressed as .

[0213] S704, substitute the predicted battery capacity into the battery state estimation equation to calculate the first error, and use the first error as the new target error, then return to execute the steps to obtain the maximum battery capacity and the minimum battery capacity.

[0214] In this embodiment of the application, the computer device will predict the battery capacity based on fuzzy logic and Kalman filtering. Substituting into the battery estimation equation for iteration, the battery estimation equation is expressed by relation (29), which is shown below:

[0215] (29);

[0216] in, Indicates the first error; Indicates the first Reference battery capacity at any given time; Indicates the first The predicted battery capacity at time t, i.e., the output of the Kalman filter. .

[0217] After obtaining the first error through relation (29), the first error is then used as the new target error, and the steps of obtaining the maximum and minimum battery capacity are returned to be executed, and the iteration is repeated.

[0218] In one exemplary embodiment, Figure 7 The specific implementation of S704 "substituting the predicted battery capacity into the battery state estimation equation to calculate the first error" in the scheme described in the embodiment is as follows: Figure 8 As shown, the process includes: using a third performance parameter as the predicted battery capacity, and returning to perform the step of substituting the predicted battery capacity into the battery state estimation equation to calculate the first error, so as to predict the new battery capacity.

[0219] In this embodiment, the computer device re-introduces the different aging performance parameters of the battery (as shown in the above relation (12)) obtained by updating the new state transition matrix in the improved Kalman filter into the aging mechanism model to predict the new battery capacity. .

[0220] Computer equipment uses aging mechanism models to assess battery capacity. Perform estimation and define new state-space variables. At this point, the state equation can be represented by relation (30), which is shown below:

[0221] (30);

[0222] in, This indicates an estimated future capacity of the battery; For the transformation function, it represents the parameters after the update. Down, Capacity estimation results for each battery cycle; This is process noise.

[0223] At this point, the new observation equation can be expressed by relation (31), which is shown below:

[0224] (31);

[0225] in, H represents the predicted battery capacity. H is the observed Jacobian matrix. To observe noise.

[0226] Finally, using the predicted battery capacity as new input, the process returns to the step of substituting the predicted battery capacity into the battery state estimation equation to calculate the first error, in order to predict the new battery capacity. .

[0227] In summary, based on all the above embodiments, a method for calculating battery aging mechanisms is also provided, such as... Figure 9 As shown, the method includes:

[0228] S801 collects actual operating condition data of the target vehicle;

[0229] S802, execute S803-S809 to obtain the first battery capacity, execute S810 to obtain the first performance parameter of the battery, and use the first battery capacity and the first performance parameter of the battery as input to the improved Kalman filter model;

[0230] S803 uses an ampere-hour calculation model to predict the battery capacity based on operating condition data to obtain the second battery capacity;

[0231] S804, the second battery capacity is corrected according to the preset temperature correction coefficient to obtain the third battery capacity;

[0232] S805 sets the initial value of the target error for the Kalman filter and fuzzy logic;

[0233] S806, obtain the maximum and minimum battery capacity;

[0234] S807 substitutes the initial values ​​of the maximum battery capacity, minimum battery capacity, and target error into the fuzzy logic and Kalman filter to perform recursive calculations for the current round, thus obtaining the predicted battery capacity for the current round.

[0235] S808, Substitute the predicted battery capacity into the battery state estimation equation to calculate the first error, and use the first error as the new target error, then return to execute S806;

[0236] S809, obtain the first battery capacity;

[0237] S810 uses the aging mechanism model of lithium iron phosphate battery and ternary lithium battery to calculate the first performance parameter of the battery based on working condition data and experimental data.

[0238] S811: Obtain historical experimental data of the battery, and improve the Kalman filter model based on the historical experimental data and the first performance parameter to obtain the improved Kalman filter model.

[0239] S812 calculates a weighted moving average of multiple historical estimation errors to obtain a weighted moving average scalar of historical estimation errors;

[0240] S813 constructs a temperature matrix based on multiple temperature-dependent factors and the first performance parameter;

[0241] S814, determine the correction parameters based on the weighted moving average scalar and the temperature matrix;

[0242] S815, the state transition matrix in the Kalman filter model is corrected according to the correction parameters to obtain the improved Kalman filter model;

[0243] S816 inputs the first battery capacity and the first performance parameter into the improved Kalman filter model to obtain the second performance parameter;

[0244] S817, substitute the second performance parameter into the aging mechanism model for calculation, and obtain the third performance parameter;

[0245] S818, take the third performance parameter as the predicted battery capacity, and return to execute S808;

[0246] S819 predicts new battery capacity.

[0247] The methods described in each of the above steps have been described in the foregoing embodiments. For details, please refer to the foregoing descriptions. They will not be repeated here.

[0248] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0249] Based on the same inventive concept, this application also provides a battery aging mechanism calculation device for implementing the battery aging mechanism calculation method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more battery aging mechanism calculation device embodiments provided below can be found in the limitations of the battery aging mechanism calculation method described above, and will not be repeated here.

[0250] In one exemplary embodiment, such as Figure 10 As shown, a battery aging mechanism calculation device is provided, comprising:

[0251] The prediction module 11 is used to collect the actual operating condition data of the target vehicle and predict the capacity of the battery on the target vehicle based on the operating condition data to obtain the first battery capacity.

[0252] The first calculation module 12 is used to calculate the first performance parameters of the battery based on the aging mechanism model of lithium iron phosphate battery and ternary lithium battery, according to the working condition data and experimental data. The experimental data is obtained by conducting battery cycle experiments and battery calendar aging experiments on the battery.

[0253] The second calculation module 13 is used to calculate the second performance parameter based on the first performance parameter and the first battery capacity using a Kalman filter model.

[0254] The third calculation module 14 is used to substitute the second performance parameter into the aging mechanism model for calculation to obtain the third performance parameter.

[0255] The modules in the aforementioned battery aging mechanism calculation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0256] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described... Figures 2 to 9 The method described in the examples.

[0257] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program implementing the above-described functionality when executed by a processor. Figures 2 to 9 The method described in the examples.

[0258] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described functionality. Figures 2 to 9 The method described in the examples.

[0259] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0260] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0261] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for calculating battery aging mechanisms, characterized in that, The method includes: Collect actual operating condition data of the target vehicle, and predict the capacity of the battery on the target vehicle based on the operating condition data to obtain the first battery capacity of the battery. Using the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries, the first performance parameters of the battery are calculated based on the operating condition data and experimental data. The experimental data are obtained by conducting battery cycle tests and battery calendar aging tests on the battery. The first performance parameters include capacity loss caused by SEI film growth, capacity loss caused by active material loss, capacity loss caused by lithium plating, capacity loss caused by transition metal dissolution, SEI film thickening reaction rate constant, active material peeling reaction rate constant, lithium plating reaction rate constant, and transition metal dissolution reaction rate constant. The second performance parameter is calculated using the Kalman filter model based on the first performance parameter and the first battery capacity; the second performance parameter includes the SEI film thickening reaction rate constant, the active material peeling reaction rate constant, the lithium plating reaction rate constant, and the transition metal dissolution reaction rate constant after being corrected by the Kalman filter model. The second performance parameter is substituted into the aging mechanism model for calculation to obtain the third performance parameter; and the predicted capacity of the battery is determined based on the third performance parameter; the third performance parameter includes the capacity loss caused by the growth of the new SEI film, the capacity loss caused by the loss of active material, the capacity loss caused by lithium plating, and the capacity loss caused by the dissolution of transition metals, all calculated based on the second performance parameter substituted into the aging mechanism model.

2. The method according to claim 1, characterized in that, The calculation of the second performance parameter using the Kalman filter model based on the first performance parameter and the first battery capacity includes: Historical experimental data of the battery is obtained, and the Kalman filter model is improved based on the historical experimental data and the first performance parameter to obtain an improved Kalman filter model; the historical experimental data includes multiple historical estimation errors and multiple temperature-dependent factors. The first battery capacity and the first performance parameter are input into the improved Kalman filter model to obtain the second performance parameter.

3. The method according to claim 2, characterized in that, The step of improving the Kalman filter model based on the historical experimental data and the first performance parameter to obtain the improved Kalman filter model includes: The correction parameters are determined based on the first performance parameter, the plurality of historical estimation errors, and the plurality of temperature dependence factors; The state transition matrix in the Kalman filter model is modified according to the modification parameters to obtain the improved Kalman filter model.

4. The method according to claim 3, characterized in that, The step of determining the correction parameters based on the multiple historical estimation errors and the multiple temperature dependence factors includes: A weighted moving average is calculated on the multiple historical estimation errors to obtain the weighted moving average scalar of the historical estimation errors; Construct a temperature matrix based on the multiple temperature-dependent factors and the first performance parameter; The correction parameters are determined based on the weighted moving average scalar and the temperature matrix.

5. The method according to any one of claims 1 to 4, characterized in that, The step of predicting the capacity of the battery in the target vehicle based on the operating condition data to obtain the first battery capacity includes: The capacity of the battery is predicted based on the operating condition data using an ampere-hour calculation model to obtain the second battery capacity; The second battery capacity is corrected according to a preset temperature correction coefficient to obtain the third battery capacity; The capacity of the third battery is recursively calculated using Kalman filters and fuzzy logic to obtain the capacity of the first battery.

6. The method according to claim 5, characterized in that, The recursive calculation of the third battery capacity based on Kalman filter and fuzzy logic to obtain the first battery capacity includes: Set the initial values ​​for the target error of the Kalman filter and fuzzy logic; Get the maximum and minimum battery capacity; The maximum battery capacity, the minimum battery capacity, and the initial value of the target error are substituted into the fuzzy logic and the Kalman filter to perform recursive calculations for the current round, thereby obtaining the predicted battery capacity for the current round. The predicted battery capacity is substituted into the battery state estimation equation to calculate the first error, and the first error is used as the new target error. Then, the process returns to the step of obtaining the maximum and minimum battery capacity.

7. The method according to claim 6, characterized in that, The method further includes: The predicted capacity of the battery is determined based on the third performance parameter, and the process returns to the step of substituting the predicted capacity into the battery state estimation equation to calculate the first error, so as to predict the new battery capacity.

8. A battery aging mechanism calculation device, characterized in that, The device includes: The prediction module is used to collect the actual operating condition data of the target vehicle and predict the capacity of the battery on the target vehicle based on the operating condition data to obtain the first battery capacity of the battery. The first calculation module is used to calculate the first performance parameters of the battery based on the operating condition data and experimental data using the aging mechanism models of lithium iron phosphate batteries and ternary lithium batteries. The experimental data is obtained by conducting battery cycle tests and battery calendar aging tests on the battery. The first performance parameters include capacity loss caused by SEI film growth, capacity loss caused by active material loss, capacity loss caused by lithium plating, capacity loss caused by transition metal dissolution, SEI film thickening reaction rate constant, active material peeling reaction rate constant, lithium plating reaction rate constant, and transition metal dissolution reaction rate constant. The second calculation module is used to calculate the second performance parameters based on the first performance parameters and the first battery capacity using a Kalman filter model. The second performance parameters include the SEI film thickening reaction rate constant, the active material peeling reaction rate constant, the lithium plating reaction rate constant, and the transition metal dissolution reaction rate constant after being corrected by the Kalman filter model. The third calculation module is used to substitute the second performance parameter into the aging mechanism model for calculation to obtain the third performance parameter; and to determine the predicted capacity of the battery based on the third performance parameter; the third performance parameter includes the capacity loss caused by the growth of the new SEI film, the capacity loss caused by the loss of active material, the capacity loss caused by lithium plating, and the capacity loss caused by the dissolution of transition metals, calculated by substituting the second performance parameter into the aging mechanism model.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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