Parameter updating method of battery circuit model, vehicle and storage medium

Abstract

The embodiment of the invention provides a parameter updating method for a battery circuit model, a vehicle and a storage medium, and the method comprises the steps: obtaining a current state parameter and a current operation condition of a target battery, the current operation condition being used for representing the operation stability degree of the target battery; a battery circuit model corresponding to the target battery is used for predicting and obtaining predicted state parameters of the target battery, and the battery circuit model is used for simulating the electrochemical process of the target battery in the charging and discharging process; based on the current operation condition, determining a parameter adjustment factor of the battery circuit model, the parameter adjustment factor being used for quantifying an influence of the current operation condition on a model parameter corresponding to the battery circuit model; and based on the parameter adjustment factor, the current state parameter and the predicted state parameter, updating the current model parameter of the battery circuit model to obtain a target model parameter of the battery circuit model. According to the invention, the technical problem of low safety of the power battery in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of battery management system technology, and more specifically, to a method for updating parameters of a battery circuit model, a vehicle, and a storage medium. Background Technology

[0002] In the power battery management system, the management technology of the power battery is crucial to ensuring vehicle performance and safety. Traditional battery management systems rely on static parameter models to estimate the battery's state of charge and state of health. However, static parameter models are difficult to adapt to dynamic operating conditions during use, leading to estimation errors. Furthermore, the lag in updating model parameters further exacerbates the uncertainty of battery management, resulting in lower power battery safety in related technologies.

[0003] There is currently no good solution to the above problems. Summary of the Invention

[0004] This application provides a method for updating parameters of a battery circuit model, a vehicle, and a storage medium to at least address the technical problem of low safety of power batteries in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for updating parameters of a battery circuit model is provided, comprising: obtaining current state parameters and current operating conditions of a target battery, wherein the current operating conditions are used to represent the operational stability of the target battery; predicting predicted state parameters of the target battery using a battery circuit model corresponding to the target battery, wherein the battery circuit model is used to simulate the electrochemical process of the target battery during charging and discharging; determining a parameter adjustment factor of the battery circuit model based on the current operating conditions, wherein the parameter adjustment factor is used to quantify the influence of the current operating conditions on the corresponding model parameters of the battery circuit model; and updating the current model parameters of the battery circuit model based on the parameter adjustment factor, the current state parameters, and the predicted state parameters to obtain the target model parameters of the battery circuit model.

[0006] Furthermore, based on the parameter adjustment factor, the current state parameters, and the predicted state parameters, the current model parameters of the battery circuit model are updated to obtain the target model parameters of the battery circuit model, including: constructing state deviation parameters based on the current state parameters and the predicted state parameters; updating the current model parameters using the state deviation parameters to obtain the first updated model parameters; and determining the target model parameters based on the parameter adjustment factor, the current model parameters, and the first updated model parameters.

[0007] Further, based on the parameter adjustment factor, the current model parameters, and the first updated model parameters, the target model parameters are determined, including: adjusting the first initial weights of the current model parameters based on the parameter adjustment factor to obtain the first target weights of the current model parameters; adjusting the second initial weights of the first updated model parameters based on the parameter adjustment factor to obtain the second target weights of the first updated model parameters; determining the first sum of the current model parameters and the first target weights, and determining the second sum of the first updated model parameters and the second target weights; and determining the target model parameters based on the first sum and the second sum.

[0008] Further, based on the first sum and the second sum, the target model parameters are determined, including: based on the first sum and the second sum, the second updated model parameters are determined; the second updated model parameters are adjusted based on the current application scenario of the target battery to obtain the target model parameters, wherein the current application scenario is used to represent the current usage scenario of the target battery.

[0009] Furthermore, the current state parameters and current operating conditions of the target battery are obtained, including: determining the battery current parameters and battery temperature parameters based on the current state parameters; determining the current change rate of the target battery based on the battery current parameters, wherein the current change rate is used to represent the rate of change of current in the target battery; determining the temperature change rate of the target battery based on the battery temperature parameters, wherein the temperature change rate is used to represent the rate of change of cell temperature in the target battery; and determining the current operating conditions based on the current change rate and the temperature change rate.

[0010] Furthermore, based on the rate of change of current and the rate of change of temperature, the current operating condition is determined, including: in response to the rate of change of current being less than or equal to a first threshold and the rate of change of temperature being less than or equal to a second threshold, the current operating condition is determined to be a stable operating condition; in response to the rate of change of current being greater than the first threshold or the rate of change of temperature being greater than the second threshold, the current operating condition is determined to be an abnormal operating condition.

[0011] Furthermore, the current state parameters of the target battery are obtained, including: obtaining the battery voltage parameters, battery current parameters, and battery temperature parameters of the target battery; and determining the current state parameters based on the battery voltage parameters, battery current parameters, and battery temperature parameters.

[0012] Furthermore, based on the current operating conditions, the parameter adjustment factors of the battery circuit model are determined, including: if the current operating conditions are abnormal, outputting a prompt message, wherein the prompt message is used to indicate that the target battery is currently in an abnormal operating condition; if feedback information corresponding to the prompt message is received, the parameter adjustment factors are determined based on the feedback information, wherein the feedback information represents the parameter adjustment information corresponding to the prompt message determined from multiple parameter adjustment factors.

[0013] According to another aspect of the embodiments of this application, a parameter updating device for a battery circuit model is also provided, comprising: an acquisition module, configured to acquire the current state parameters and current operating conditions of a target battery, wherein the current operating conditions represent the operational stability of the target battery; a prediction module, configured to predict the predicted state parameters of the target battery using the battery circuit model corresponding to the target battery, wherein the battery circuit model simulates the electrochemical process of the target battery during charging and discharging; a determination module, configured to determine a parameter adjustment factor of the battery circuit model based on the current operating conditions, wherein the parameter adjustment factor quantifies the influence of the current operating conditions on the corresponding model parameters of the battery circuit model; and an update module, configured to update the current model parameters of the battery circuit model based on the parameter adjustment factor, the current state parameters, and the predicted state parameters to obtain the target model parameters of the battery circuit model.

[0014] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0015] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0019] This application provides a method for updating parameters of a battery circuit model. First, the current state parameters and current operating conditions of the target battery are obtained. Then, using the battery circuit model corresponding to the target battery, the predicted state parameters of the target battery are predicted. Based on the current operating conditions, a parameter adjustment factor for the battery circuit model is determined. Finally, based on the parameter adjustment factor, the current state parameters, and the predicted state parameters, the current model parameters of the battery circuit model are updated to obtain the target model parameters of the battery circuit model. This application first obtains the current state parameters and current operating conditions of the target battery. This information is crucial for understanding the actual working environment of the battery and forms the basis for subsequent parameter updates. Next, using the battery circuit model corresponding to the target battery, the predicted state parameters of the target battery are predicted. Through model prediction, the theoretical performance of the battery under expected operating conditions can be analyzed, providing a theoretical basis for parameter adjustment. Then, based on the current operating conditions, the parameter adjustment factor of the battery circuit model is determined. This process dynamically adjusts the parameter weights in the model through real-time evaluation of the operating conditions, thereby ensuring that the model parameters match the actual operating conditions and reducing the error between the prediction and the actual state. Finally, based on the parameter adjustment factor, current state parameters, and predicted state parameters, the current model parameters of the battery circuit model are updated to obtain the target model parameters. By dynamically adjusting the model parameters using current state data and operating condition information, the model parameters are made to better reflect the current electrochemical characteristics of the battery, thereby improving the prediction accuracy and control decision reliability of the model. This application adopts a dynamic parameter adjustment and real-time operating condition adaptation approach. By updating the parameters of the battery circuit model in real time, it accurately reflects the changes in the electrochemical characteristics of the target battery under various operating conditions, achieving the goal of improving model accuracy. This enables precise monitoring and control of the battery state under complex and changing operating conditions, thus solving the technical problem of low safety of power batteries in related technologies. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 This is a flowchart of a parameter update method for a battery circuit model according to an embodiment of this application;

[0022] Figure 2 This is an overall architecture diagram of a dynamic lithium battery identification model according to an embodiment of this application;

[0023] Figure 3 This is a flowchart of a variable forgetting factor recursive least squares parameter identification method according to an embodiment of this application;

[0024] Figure 4 This is a flowchart of a scenario-based model output and BMS linkage control according to an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of a parameter updating device for a battery circuit model according to an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to an embodiment of this application, an embodiment of a parameter update method for a battery circuit model is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a method for updating parameters of a battery circuit model. Figure 1 This is a flowchart of a parameter update method for a battery circuit model according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0030] Step S102: Obtain the current state parameters and current operating conditions of the target battery, wherein the current operating conditions are used to represent the degree of operational stability of the target battery.

[0031] The aforementioned target battery refers to a specific battery cell within a Battery Management System (BMS) whose state needs to be monitored and controlled in real time. In vehicle energy storage systems, power battery packs typically consist of multiple battery cells connected in series or parallel, and each battery cell can be considered an independent target battery. The state of the target battery directly affects the performance and safety of the entire battery system. By monitoring the state parameters and operating conditions of the target battery, the BMS can assess the battery's health status, changes in electrochemical characteristics, and potential safety risks, and then adopt corresponding control strategies to ensure the battery operates in optimal condition, extend battery life, and improve overall system performance.

[0032] The aforementioned current state parameters refer to the physical and electrochemical properties of the target battery at a specific moment. These parameters reflect the battery's instantaneous state and are key indicators for evaluating battery performance and health. Current state parameters may include, but are not limited to, terminal voltage, charge / discharge current, temperature, state of charge, state of health, and polarization voltage. Specific current state parameters need to be determined based on actual requirements and the current state of the target battery. By monitoring these state parameters in real time, the battery's health and operating status can be accurately assessed, providing a basis for BMS decision-making and enabling refined battery management and control.

[0033] The aforementioned current operating condition refers to the target battery's current operating environment and mode. The current operating condition reflects the battery's dynamic environment and usage conditions through factors such as charge / discharge rate, temperature, and battery system load. The current operating condition may include, but is not limited to, stable operating conditions and abnormal operating conditions; the specific current operating condition needs to be determined based on actual operating parameters. By identifying and understanding the battery's operating condition, the BMS system can dynamically adjust model parameters, providing control strategies and state estimations that better suit the current operating conditions, thereby improving battery efficiency.

[0034] The methods for obtaining the aforementioned current status parameters and current operating conditions may include, but are not limited to, the following:

[0035] The first method involves sensor data acquisition, utilizing various types of sensors such as voltage sensors, current sensors, and temperature sensors to monitor battery status parameters in real time, including terminal voltage, charging / discharging current, and temperature. These sensors are typically connected to the Battery Management System (BMS), transmitting the data to the BMS processor in real time via signal lines.

[0036] The second method involves acquiring information via communication protocols. Through standard or customized communication protocols, the BMS can obtain battery status parameters and operating condition information from the battery cells or an external vehicle controller. For example, the BMS receives charge / discharge commands from the vehicle controller via the CAN bus to determine the charge / discharge rate of the target battery.

[0037] The third method is model estimation. The battery model running inside the BMS (such as the equivalent circuit model, Richardson model, etc.) can estimate the battery's state of charge (SOC) and state of health (SOH) parameters based on the collected voltage, current, and temperature data.

[0038] The methods for obtaining the above current status parameters and current operating conditions need to be determined according to the actual situation, and are not limited here.

[0039] In one optional embodiment, the current state parameters of the target battery are acquired, including but not limited to terminal voltage, charge / discharge current, temperature, state of charge (SOC), state of health (SOH), and internal resistance. Simultaneously, the system also needs to identify the current operating conditions of the target battery, specifically covering charge / discharge rate, ambient temperature changes, and battery load, to assess the stability of battery operation. Information on the current operating conditions is crucial for dynamically adjusting battery model parameters, improving battery performance, and ensuring safe operation. Based on this data, the BMS can implement more accurate battery state estimation and control strategies.

[0040] Step S104: Using the battery circuit model corresponding to the target battery, the predicted state parameters of the target battery are predicted. The battery circuit model is used to simulate the electrochemical process of the target battery during charging and discharging.

[0041] The aforementioned battery circuit model can refer to a mathematical model designed to simulate the electrochemical behavior and physical characteristics of a battery through a combination of electrical components. The battery circuit model can describe the relationship between voltage, current, temperature changes, and internal reactions during charging and discharging. Types of battery circuit models include, but are not limited to, equivalent circuit models, physical models, and empirical models; the specific model needs to be determined based on actual requirements. The battery circuit model is one of the core components of a battery management system (BMS). It is used to predict battery behavior and assist the BMS in estimating the state of charge (SOC) and state of health (SOH), as well as formulating charging and discharging control strategies. Through the model, the BMS can predict the battery's state parameters under different operating conditions without direct measurement, improving system response speed and control accuracy.

[0042] The aforementioned predicted state parameters refer to the estimated future state of the target battery based on the battery circuit model. These parameters may include, but are not limited to, predicted terminal voltage, predicted state of charge (SOC), predicted state of equilibrium (SOH), and predicted battery temperature. Specific predicted state parameters need to be determined based on actual requirements and prediction conditions. These parameters can help the BMS make decisions in advance, such as adjusting the charging and discharging strategy before the predicted SOC reaches its limit, or activating the cooling system before the predicted battery temperature becomes too high, thereby avoiding overcharging, over-discharging, and thermal runaway, and improving battery safety and lifespan.

[0043] The aforementioned electrochemical processes refer to the chemical reactions occurring inside the battery, including ion migration, electron conduction, and electrochemical polarization between the electrolyte and electrodes. During charging and discharging, lithium ions move back and forth between the positive and negative electrodes, accompanied by the storage and release of charge. A correct understanding and simulation of these electrochemical processes is crucial for constructing accurate battery circuit models.

[0044] In one optional embodiment, a pre-established battery circuit model of the target battery is used to derive predicted state parameters, including predicted terminal voltage, state of charge (SOC), and state of health (SOH). This prediction mechanism, based on the simulation of electrochemical processes using the battery circuit model, enables the battery management system to understand performance changes in advance when the battery is in different charge and discharge states, providing a basis for formulating appropriate control strategies.

[0045] In one optional embodiment, the battery circuit model integrates the battery's physical and electrochemical characteristic parameters, such as ohmic internal resistance, polarization resistance, polarization capacitance, and open-circuit voltage. Based on real-time acquired state parameters of the target battery, such as charge / discharge current, terminal voltage, and temperature, it performs a series of calculations and iterations using preset mathematical relationships. The model first adjusts the internal resistance and capacitance values ​​based on the input current and temperature to reflect changes in the electrochemical process under varying operating conditions. Then, by solving the model's equations, it predicts the battery's terminal voltage, state of charge, and other key performance indicators at the next moment. This process allows the battery management system to pre-assess the target battery's performance under specific charge / discharge conditions, thereby adjusting battery usage strategies and management to ensure the battery operates safely and efficiently.

[0046] Step S106: Based on the current operating conditions, determine the parameter adjustment factor of the battery circuit model, wherein the parameter adjustment factor is used to quantify the impact of the current operating conditions on the corresponding model parameters of the battery circuit model.

[0047] The aforementioned parameter adjustment factors can refer to coefficients or functions in the battery circuit model used to quantify the impact of the current operating conditions on the model parameters. These parameter adjustment factors may include, but are not limited to, temperature-related adjustment factors, charge / discharge rate-related adjustment factors, SOC-related adjustment factors, and aging-related adjustment factors. The specific parameter adjustment factors need to be determined based on actual needs. Parameter adjustment factors enable the battery circuit model to flexibly adjust according to different operating conditions, thereby more closely reflecting the battery's behavior in actual use and reducing the gap between model predictions and actual conditions.

[0048] In one optional embodiment, during battery management operation, parameter adjustment factors for the battery circuit model are calculated based on current operating conditions, including but not limited to temperature, charge / discharge rate, and state of charge (SOC). These factors are used to adjust key parameters in the battery circuit model, such as ohmic internal resistance, polarization resistance, and polarization capacitance, to reflect the impact of changes in operating conditions on the battery's electrochemical characteristics. By updating the model parameters in real time, the model can accurately simulate the battery's behavior under current operating conditions, thereby improving the accuracy of battery state prediction.

[0049] Step S108: Based on the parameter adjustment factor, current state parameters, and predicted state parameters, update the current model parameters of the battery circuit model to obtain the target model parameters of the battery circuit model.

[0050] The aforementioned current model parameters refer to a set of parameter values ​​determined by the battery circuit model at a certain moment based on previous historical data and operating condition information. Current model parameters may include, but are not limited to, ohmic internal resistance R0, polarization resistors R1 and R2, polarization capacitors C1 and C2, and open-circuit voltage Uoc, etc. The specific current model parameters need to be determined based on the actual model structure. Current model parameters can serve as the basis for real-time prediction of battery state. Using these parameters, predicted battery state parameters, such as predicted state of charge and predicted terminal voltage, can be calculated, thus providing a basis for battery management strategies.

[0051] The aforementioned target model parameters refer to the latest set of parameter values ​​generated by updating the battery circuit model through parameter adjustment factors after considering the current operating conditions and predicted state parameters. This set of parameters more accurately reflects the actual electrochemical behavior of the battery under the current operating conditions. Target model parameters may include, but are not limited to, ohmic internal resistance, polarization resistance, polarization capacitance, and open-circuit voltage, but their values ​​have been corrected by adjustment factors to better reflect the actual performance of the battery under specific operating conditions. Specific target model parameters need to be determined based on the actual model structure. Target model parameters are used to ensure that the model can adapt to changes in battery characteristics under various operating conditions in real time, providing the battery management system with timely and accurate battery state information.

[0052] In one optional embodiment, the current model parameters in the battery circuit model are first dynamically corrected based on the current state parameters, predicted state parameters, and parameter adjustment factors. This correction covers core components in the model, such as ohmic internal resistance, polarization resistance, polarization capacitance, and open-circuit voltage, ensuring they more accurately reflect the electrochemical characteristics of the battery under specific operating conditions. The updated parameter set becomes the target model parameters, which will be used for real-time assessment of the battery state and adjustment of the battery management system (BMS) control strategy, thereby improving the accuracy and efficiency of battery management. This mechanism, through continuous parameter updates, enables the model to adapt to various changes in battery operation, improving its predictive capabilities and control performance.

[0053] For example, in fast charging applications, when the system detects a sudden jump in charging current from 1C to 3C, it calculates a parameter adjustment factor based on this change in operating condition. At this time, considering that fast charging can lead to enhanced battery polarization, the system adjusts the current model parameters in the battery circuit model—namely, the values ​​of polarization capacitors C1 and C2, and the ohmic internal resistance R0—based on current state parameters (such as temperature and actual charging current) and predicted state parameters (such as predicted voltage response). During this process, the parameter adjustment factor is used to quickly update the current model parameters, obtaining the target model parameters. This allows for a more accurate prediction of the battery's state of charge (SOC) and potential voltage overshoot under fast charging conditions, thereby adjusting the charging strategy to ensure the safety and efficiency of the charging process. The above values ​​are only examples; specific values ​​need to be determined based on actual conditions and are not limited here.

[0054] This application provides a method for updating parameters of a battery circuit model. First, the current state parameters and current operating conditions of the target battery are obtained. Then, using the battery circuit model corresponding to the target battery, the predicted state parameters of the target battery are predicted. Based on the current operating conditions, a parameter adjustment factor for the battery circuit model is determined. Finally, based on the parameter adjustment factor, the current state parameters, and the predicted state parameters, the current model parameters of the battery circuit model are updated to obtain the target model parameters of the battery circuit model. This application first obtains the current state parameters and current operating conditions of the target battery. This information is crucial for understanding the actual working environment of the battery and forms the basis for subsequent parameter updates. Next, using the battery circuit model corresponding to the target battery, the predicted state parameters of the target battery are predicted. Through model prediction, the theoretical performance of the battery under expected operating conditions can be analyzed, providing a theoretical basis for parameter adjustment. Then, based on the current operating conditions, the parameter adjustment factor of the battery circuit model is determined. This process dynamically adjusts the parameter weights in the model through real-time evaluation of the operating conditions, thereby ensuring that the model parameters match the actual operating conditions and reducing the error between the prediction and the actual state. Finally, based on the parameter adjustment factor, current state parameters, and predicted state parameters, the current model parameters of the battery circuit model are updated to obtain the target model parameters. By dynamically adjusting the model parameters using current state data and operating condition information, the model parameters are made to better reflect the current electrochemical characteristics of the battery, thereby improving the prediction accuracy and control decision reliability of the model. This application adopts a dynamic parameter adjustment and real-time operating condition adaptation approach. By updating the parameters of the battery circuit model in real time, it accurately reflects the changes in the electrochemical characteristics of the target battery under various operating conditions, achieving the goal of improving model accuracy. This enables precise monitoring and control of the battery state under complex and changing operating conditions, thus solving the technical problem of low safety of power batteries in related technologies.

[0055] Optionally, based on the parameter adjustment factor, the current state parameters, and the predicted state parameters, the current model parameters of the battery circuit model are updated to obtain the target model parameters of the battery circuit model, including: constructing state deviation parameters based on the current state parameters and the predicted state parameters; updating the current model parameters using the state deviation parameters to obtain the first updated model parameters; and determining the target model parameters based on the parameter adjustment factor, the current model parameters, and the first updated model parameters.

[0056] The aforementioned state deviation parameters refer to the deviation obtained by comparing the current state parameters with the predicted state parameters during battery operation. Specific types of state deviation parameters include voltage deviation, current deviation, and temperature deviation, which reflect the real-time differences between different state parameters. The specific state deviation parameters need to be determined based on the actual situation. State deviation parameters can be used to reveal the difference between battery model predictions and actual performance, providing a data basis for model parameter correction and ensuring that the model can more accurately reflect the battery's real behavior.

[0057] The aforementioned first updated model parameters may refer to the result of preliminary correction of the current model parameters based on the state deviation parameters. The types of the first updated model parameters are the same as those of the current model parameters, including ohmic internal resistance (R0), polarization resistance (R1, R2), polarization capacitance (C1, C2), and open-circuit voltage (Uoc), etc., but the values ​​are closer to the actual performance of the battery after being corrected by the state deviation parameters.

[0058] In one optional embodiment, firstly, state deviation parameters are constructed. By comparing the current state parameters with the predicted state parameters, the estimation deviation of the model is quantified. Then, the current model parameters are updated using the state deviation parameters to obtain the first updated model parameters, achieving real-time correction of the model parameters. Finally, the target model parameters are comprehensively determined by combining the parameter adjustment factor, the current model parameters, and the first updated model parameters, ensuring the rationality and effectiveness of the model parameters, thereby improving the model's accuracy and response speed, and adapting to battery management needs under different operating conditions. This dynamic adjustment mechanism can respond promptly to changes in battery state, reduce model estimation errors, and improve the overall performance and safety of the electric vehicle power battery system.

[0059] Optionally, determining the target model parameters based on the parameter adjustment factor, the current model parameters, and the first updated model parameters includes: adjusting the first initial weights of the current model parameters based on the parameter adjustment factor to obtain the first target weights of the current model parameters; adjusting the second initial weights of the first updated model parameters based on the parameter adjustment factor to obtain the second target weights of the first updated model parameters; determining the first sum of the current model parameters and the first target weights, and determining the second sum of the first updated model parameters and the second target weights; and determining the target model parameters based on the first sum and the second sum.

[0060] The aforementioned first initial weights can refer to the original weights of the model parameters at the current moment when calculating new parameters. The first initial weights can be used to reflect the importance and reliability of the model parameters in historical data, and may be based on preset values ​​or the results of the previous iteration initially.

[0061] The aforementioned first target weight refers to the target weight of the current model parameters in the calculation of new parameters after adjusting the first initial weight through parameter adjustment factors. The adjustment of the target weight is to more accurately reflect the impact of the current operating conditions on the parameters, making the model closer to the actual electrochemical state of the battery.

[0062] The aforementioned second initial weight can refer to the original weights used in the iterative algorithm to calculate the model parameters at the next time step when updating the model parameters.

[0063] The aforementioned second objective weight can refer to the weight of the first updated model parameters after the adjustment factor has been applied. The second objective weight can be used to ensure that the proportion of the first updated model parameters in the new model calculation is more reasonable and more in line with the current dynamic characteristics of the battery.

[0064] The aforementioned first sum can refer to the sum of the current model parameters multiplied by their respective first objective weights.

[0065] The aforementioned second sum can refer to the sum of the products of the first updated model parameters and their respective second objective weights. This second sum can be used to evaluate the weighted total effect of the updated model parameters under new operating conditions, and to guide the dynamic adjustment of the model parameters.

[0066] In one optional embodiment, a parameter adjustment factor is first used to correct the first initial weights of the current model parameters, resulting in a first target weight reflecting its adaptability and stability. Simultaneously, the second initial weights of the first updated model parameters are adjusted to obtain a second target weight reflecting the impact of new data. Subsequently, the sum of the current model parameters and the first target weight, as well as the sum of the first updated model parameters and the second target weight, are calculated. Finally, these two sums are considered together, and the target model parameters are determined through a weighted average or other fusion algorithm. This dynamic weight adjustment mechanism can react quickly to changes in operating conditions, balancing historical parameter information with real-time data updates, ensuring the accuracy and real-time performance of the model parameters.

[0067] In practical applications, this method effectively reduces the lag in model parameter updates, improves the adaptability of the Battery Management System (BMS) under complex operating conditions, and thus enhances the accuracy of lithium battery cell status monitoring and the efficiency of electric vehicle power battery pack management. By progressively fusing new and old parameters, this technical solution avoids model fluctuations caused by parameter mutations, ensuring model stability and reliability, and providing more accurate dynamic model support for electric vehicle battery management. Of course, in other embodiments, the calculation method of the parameter adjustment factor can be adjusted according to different specific application scenarios. For example, a more complex nonlinear function can be used to reflect the impact of operating condition changes on the weights, further improving the dynamic update strategy of model parameters.

[0068] Optionally, determining the target model parameters based on the first sum and the second sum includes: determining the second updated model parameters based on the first sum and the second sum; adjusting the second updated model parameters based on the current application scenario of the target battery to obtain the target model parameters, wherein the current application scenario represents the current usage scenario of the target battery.

[0069] The aforementioned second updated model parameters can refer to model parameters further adjusted based on the first updated model parameters using more comprehensive data analysis or more precise algorithms. These second updated model parameters enable the model to more accurately reflect the electrochemical characteristics of the battery under specific operating conditions, improve the accuracy of SOC and SOH state estimations, reduce errors in charge and discharge control, and thus enhance battery efficiency and safety.

[0070] The aforementioned current application scenario refers to the specific operating environment and usage conditions of the target battery at a certain moment or within a certain time period. Current application scenarios may include, but are not limited to, regular charging and discharging scenarios, low-temperature start-up scenarios, fast charging scenarios, and long-term low-speed driving scenarios. The specific current application scenario needs to be determined based on the actual situation. Identifying and analyzing the current application scenario is crucial for adjusting model parameters and battery management system (BMS) strategies. For example, in a low-temperature start-up scenario, the battery's internal resistance will increase significantly. In this case, the R0 parameter needs to be adjusted to compensate for the effect of temperature on internal resistance and ensure the accuracy of SOC estimation. In a fast charging scenario, the BMS needs to adjust the charging strategy based on the real-time parameters of C1 and C2 to avoid voltage overshoot, protect the battery from damage, and shorten charging time.

[0071] In one optional embodiment, preliminary second updated model parameters are first calculated based on the first and second sums. These parameters can be calculated using the Variable Forgetting Factor Recursive Least Squares (VFF-RLS) method. Subsequently, the second updated model parameters are adjusted according to the current application scenario of the target battery. For example, in a low-temperature start-up scenario, the system introduces a temperature-dependent internal resistance correction factor to adjust the internal resistance model parameters, more accurately reflecting the battery characteristics under low-temperature conditions. In a fast-charging scenario, the system dynamically adjusts the polarization capacitor parameters based on the rate of change of current, ensuring the model can quickly respond to voltage and current changes during high-rate charging and discharging, avoiding voltage overshoot misjudgments. This adjustment process ensures that the model parameters not only track battery state changes in real time but also adjust for specific electrochemical behaviors in different application scenarios, resulting in more accurate target model parameters. Through such scenario-based adaptation, the target model parameters can better reflect the battery's state under specific usage conditions, providing more reliable data support for the battery management system's control strategy, thereby improving battery efficiency and safety.

[0072] Optionally, the current state parameters and current operating conditions of the target battery are obtained, including: determining the battery current parameters and battery temperature parameters based on the current state parameters; determining the current change rate of the target battery based on the battery current parameters, wherein the current change rate is used to represent the rate of change of current in the target battery; determining the temperature change rate of the target battery based on the battery temperature parameters, wherein the temperature change rate is used to represent the rate of change of cell temperature in the target battery; and determining the current operating conditions based on the current change rate and the temperature change rate.

[0073] The aforementioned battery current parameters refer to the current intensity values ​​monitored in real time during battery charging and discharging; it is a dynamically changing signal. Battery current parameters may include, but are not limited to, peak current, average current, and instantaneous current. Specific battery current parameters need to be determined based on actual calculation requirements. Current parameters reflect the battery's charging and discharging rate and are key indicators for determining whether a battery is fast-charging, normally charging / discharging, or deeply discharged.

[0074] The battery temperature parameters mentioned above refer to the temperature during battery operation, encompassing the temperature distribution from the cell surface to its interior. Battery temperature directly affects the battery's electrochemical reaction rate, internal resistance, and polarization state, thus impacting battery performance and safety.

[0075] The aforementioned rate of change of current refers to the speed at which the current in a target battery changes over time, typically expressed as a percentage change in current per unit time. Rapid current changes indicate high power demand or sudden loads, which is crucial real-time operating information for battery management systems.

[0076] The temperature change rate mentioned above refers to the magnitude of the battery temperature change per unit time, expressed in degrees Celsius per minute (°C / min). For example, if the temperature rises rapidly from room temperature of 25°C to 35°C within 10 minutes, the temperature change rate is 1°C / min. The temperature change rate guides the battery's thermal management system to take appropriate measures, such as activating the cooling system or adding insulation. The temperature change rate described above is only an example; the specific temperature change rate needs to be determined based on the actual battery temperature changes.

[0077] In one optional embodiment, firstly, the battery current parameter (I) and battery temperature parameter (T) are determined based on the current state parameters. Then, by calculating the ratio of the difference in battery current parameter between adjacent sampling points to the current value at the previous sampling point, the current change rate (ΔI / I) of the target battery is determined. This indicator directly reflects the current fluctuations during battery charging and discharging, and is crucial for identifying whether the battery is in a stable charging, discharging, or fast charging state. Next, based on the difference in battery temperature parameter between adjacent sampling points, the temperature change rate (ΔT / T) of the target battery is determined. This parameter helps distinguish whether the battery is in a temperature stable state or experiencing temperature increases or decreases, which is extremely important for accurately assessing battery performance and safety status under different temperature conditions. Finally, by comprehensively analyzing the current change rate and temperature change rate, the current operating condition of the battery can be accurately determined, such as whether the condition is stable or abnormal, providing an accurate operating condition reference for subsequent parameter identification and model improvement. This process not only improves the accuracy of operating condition identification but also enables rapid responses to the characteristics of the battery under different conditions, effectively reducing the lag of model parameters and ensuring the timeliness and reliability of the battery management system (BMS) strategy. In other embodiments, different algorithms can be considered to estimate the rates of change of current and temperature, such as quadratic difference or exponentially weighted moving average. This can improve the accuracy of the rate of change calculation and make the operating condition identification more sensitive, but it may also increase computational complexity, requiring a trade-off between algorithm performance and computational resources. Furthermore, in addition to current and temperature, factors such as voltage change rate can be incorporated into the operating condition judgment criteria to further refine the monitoring of battery status. However, this will place higher demands on the configuration of the data acquisition system, potentially involving additional hardware investment and software optimization. Overall, determining battery operating conditions by accurately calculating the rates of change of current and temperature can significantly improve the accuracy and real-time performance of model identification, providing strong support for refined management and safety early warning of battery status.

[0078] Optionally, the current operating condition is determined based on the rate of change of current and the rate of change of temperature, including: determining the current operating condition as a stable operating condition in response to the rate of change of current being less than or equal to a first threshold and the rate of change of temperature being less than or equal to a second threshold; and determining the current operating condition as an abnormal operating condition in response to the rate of change of current being greater than the first threshold or the rate of change of temperature being greater than the second threshold.

[0079] The aforementioned first threshold can refer to the upper limit of the ratio of battery current change to its previous current. This first threshold is used to determine whether current fluctuations are within an acceptable range. The first threshold may vary depending on the application scenario and operating environment, and is typically set around 5%. In electric vehicle power systems, considering conditions such as fast charging and rapid acceleration, the threshold may need to be adjusted appropriately. For example, in normal charging and discharging scenarios, the first threshold might be set to 3%; in fast charging or high-power discharging scenarios, the first threshold might be increased to 10%. The specific first threshold needs to be determined based on the actual situation. The first threshold for the rate of change of current is mainly used to identify whether the battery is in a stable charging and discharging condition or facing a sudden change in the charge / discharge rate. Current changes below the first threshold indicate that the battery is in a relatively stable operating state, in which case a higher forgetting factor should be used to ensure the stability of the model parameters; while current changes above the first threshold indicate a sudden change in operating conditions, requiring rapid updates to the model parameters to adapt to real-time changes in battery characteristics, in which case the forgetting factor should be lowered to a lower value.

[0080] The aforementioned second threshold can refer to the upper limit of the ratio of battery temperature change to its previous temperature, used to determine whether temperature fluctuations are within a predictable range. In electric vehicle applications, considering battery usage in different regions and seasons, the second threshold may be set at 2°C / min. For example, under normal temperature conditions, the second threshold may be as low as 1°C / min; under extreme temperature conditions, such as low-temperature start-up or high-temperature fast charging, the second threshold may be relaxed to 5°C / min to ensure rapid response to temperature changes under these conditions. The second threshold needs to be determined based on the battery type and usage environment. The second threshold for temperature change rate can be used to identify whether the battery is in a temperature-stable operating state or facing rapid temperature changes (such as sudden changes in ambient temperature or internal thermal runaway of the battery). Temperature changes below the second threshold mean that the temperature is in a relatively stable state, and model parameters can be updated slowly to maintain stability; while temperature changes above the second threshold mean that parameters need to be updated rapidly to adapt to the impact of temperature changes on battery performance.

[0081] The aforementioned stable operating conditions refer to a state where the battery experiences minimal fluctuations in current and temperature during charging and discharging, satisfying a current change rate ≤ a first threshold and a temperature change rate ≤ a second threshold. Under these conditions, the battery's electrochemical characteristics are relatively stable, and the model parameters exhibit minimal changes. Using a higher forgetting factor under stable operating conditions can reduce the frequency of parameter updates, lower computational complexity, and simultaneously ensure the long-term stability and reliability of the model parameters, avoiding misjudgments caused by unnecessary parameter fluctuations.

[0082] The aforementioned abnormal operating conditions refer to a state where the battery experiences significant changes in current or temperature during charging and discharging, specifically, a current change rate exceeding a first threshold or a temperature change rate exceeding a second threshold. Under these conditions, the battery's electrochemical characteristics may change rapidly, requiring quick updates to model parameters to match these changes. In abnormal operating conditions, model parameter updates need to be more agile. Using a lower forgetting factor can accelerate parameter learning, enabling the model to adapt to new operating states more quickly, reducing estimation errors, and improving the response speed and safety of the battery management system.

[0083] In one optional embodiment, when the rate of change of current is less than or equal to a first threshold and the rate of change of temperature is less than or equal to a second threshold, the current operating condition is determined to be a stable operating condition. This logic helps maintain the stability and continuity of model parameters, avoiding unnecessary parameter fluctuations when the operating condition is relatively stable, thus ensuring the accuracy and reliability of the model. Conversely, when the rate of change of current is greater than the first threshold or the rate of change of temperature is greater than the second threshold, the current operating condition is determined to be an abnormal operating condition. At this time, the parameter identification mechanism will be automatically adjusted to improve the response speed to sudden changes, ensuring that the model can reflect the electrochemical characteristics of the battery under extreme conditions in a timely manner, avoiding control strategy failure caused by parameter update lag, and enhancing the safety and adaptability of the system. In the above steps, the thresholds for determining the rate of change of current and the rate of change of temperature can be individually adjusted according to the battery type, usage environment, and specific application scenario, further improving the adaptability and performance of the model, and ensuring high-precision identification and management of lithium battery status under various complex operating conditions.

[0084] Optionally, the current state parameters of the target battery are obtained, including: obtaining the battery voltage parameters, battery current parameters, and battery temperature parameters of the target battery; and determining the current state parameters based on the battery voltage parameters, battery current parameters, and battery temperature parameters.

[0085] The battery voltage parameters mentioned above refer to the voltage at both ends of the battery, i.e., the battery's output voltage. Battery voltage parameters may include, but are not limited to, terminal voltage, open-circuit voltage, polarization voltage, and individual cell voltage. The specific battery voltage parameters need to be determined according to actual needs. Battery voltage parameters can be used to reflect the potential difference of the internal electrochemical reaction of the battery and are important indicators for judging the battery's state of charge (SOC), state of health (SOH), and overall performance.

[0086] The battery current parameters mentioned above refer to the magnitude of the current flowing through the battery. Battery current parameters may include, but are not limited to, charging / discharging current, instantaneous current, average current, and peak current. Specific battery current parameters need to be determined based on actual requirements. The magnitude and direction of the current can be used to reflect the battery's charging / discharging rate and state, thereby determining the battery's state of charge.

[0087] The battery temperature parameters mentioned above refer to the battery's temperature during operation. These parameters may include, but are not limited to, battery surface temperature, average battery pack temperature, internal battery cell temperature, and battery temperature difference. Specific battery temperature parameters need to be determined based on actual requirements. Based on these parameters, the battery pack's cooling or heating system can be adjusted to ensure the battery operates within a suitable temperature range.

[0088] In one optional embodiment, the battery voltage, current, and temperature parameters of the target battery are acquired in real time; and the current state parameters of the battery are determined by fusing and analyzing these parameters. This scheme uses high-precision sensors to synchronously and frequently acquire the voltage, current, and temperature of the lithium battery, ensuring the accuracy and timeliness of the data.

[0089] Optionally, based on the current operating conditions, the parameter adjustment factor of the battery circuit model is determined, including: if the current operating conditions are abnormal, outputting a prompt message, wherein the prompt message is used to indicate that the target battery is currently in an abnormal operating condition; if feedback information corresponding to the prompt message is received, determining the parameter adjustment factor based on the feedback information, wherein the feedback information represents the parameter adjustment information corresponding to the prompt message determined from multiple parameter adjustment factors.

[0090] The aforementioned warning message refers to a signal issued when the target battery is detected to be operating under abnormal conditions. This warning message may include, but is not limited to, over-temperature warnings, overcharge / over-discharge warnings, abnormal internal resistance warnings, and abnormal voltage warnings; the specific warning message must be determined based on the actual abnormal situation. The warning message can be used to notify the operator or control system that the target battery's operating state has deviated from the normal range.

[0091] The aforementioned feedback information refers to the actions or parameter adjustments the system decides to take after receiving a prompt, through further analysis or manual intervention. It is a response mechanism that, based on the content of the prompt information, formulates specific solutions or adjustment strategies to restore the battery to normal operating condition or minimize the impact of abnormal operating conditions on the battery.

[0092] In one optional embodiment, when an abnormal operating condition is detected, such as a sudden temperature drop exceeding 5°C or a current change rate exceeding 5%, a prompt message is output, explicitly indicating that the target battery is facing an abnormal operating condition. If feedback information related to the prompt message is received, the parameter adjustment factor will be adjusted based on the feedback information. The determination of the parameter adjustment factor is based on a deep understanding of the abnormal operating condition. For example, under conditions of rapid temperature changes or sudden changes in charge / discharge rates, a more suitable internal resistance or polarization capacitance value is calculated based on the feedback information to match the current operating environment. This mechanism ensures dynamic updates of model parameters, maintaining the accuracy and real-time performance of the battery model even under extreme conditions. This improves the battery management system's assessment and control of the battery state, reduces the risk of overcharging, over-discharging, or thermal runaway, and enhances battery life and safety. The values ​​in the above process are for illustrative purposes only; specific values ​​need to be determined based on actual conditions.

[0093] In one alternative embodiment, Figure 2 This is an overall architecture diagram of a dynamic lithium battery model according to an embodiment of this application, such as... Figure 2As shown, the architecture mainly consists of six key parts: First, the multi-dimensional data acquisition module is responsible for collecting real-time voltage, current, and temperature information of the lithium battery, as well as the equalization voltage data between cells. The acquisition accuracy and frequency of this part are designed to exceed industry standards to ensure data quality. Next is the abnormal data processing module, which uses algorithms such as Kalman filtering to eliminate noise and interference in the data, while handling data loss caused by potential sensor failures, ensuring data continuity and reliability. The adaptive parameter identification module is the core of the entire system. It uses the variable forgetting factor recursive least squares (VFF-RLS) method to continuously update the key parameters of the lithium battery model to adapt to different operating conditions. The scenario-based adjustment module adjusts the model parameters based on the current operating scenario to ensure model accuracy under specific conditions (such as low-temperature start-up and high-speed fast charging). The BMS control linkage module enables the model parameters to interact with the battery management system in real time, adjusting the battery management and control strategies, such as equalization strategies and charge / discharge rate control, based on the model output. Finally, multiple individual cells in the power battery pack are managed efficiently and accurately through this model. The external control command input interface allows the vehicle controller or other systems to communicate with this model system and input control commands. The model parameter / status output interface is responsible for feeding back the model's parameters and battery status (such as SOC, SOH) to the display terminal or other relevant systems.

[0094] Figure 3 This is a flowchart of a variable forgetting factor recursive least squares parameter identification method according to an embodiment of this application, as follows: Figure 3 As shown, the process begins with data input, which includes preprocessed voltage U, current I, and temperature T data. The operating condition judgment module then analyzes this data to determine if the current operating condition is stable. If the operating condition is stable, the forgetting factor assignment node sets the forgetting factor between 0.95 and 0.99 to retain more historical data information; conversely, if a sudden change in operating condition is detected, the forgetting factor is set to a lower range of 0.90 to 0.94 to track parameter changes more quickly. The observation matrix construction is responsible for generating the observation matrix H(k) used for parameter estimation. Parameter recursive calculation updates the battery model parameters in real time using a recursive formula. Parameter boundary verification checks whether the updated parameters are within the physical boundaries to ensure parameter rationality. Valid parameter output outputs the verified parameters to the scenario-based adjustment module. If parameters exceed the boundaries, parameter correction uses the valid parameters from the previous time step to prevent errors caused by unreasonable parameters. The values ​​in the above steps are for illustrative purposes only; specific values ​​need to be determined based on actual conditions.

[0095] Figure 4 This is a scenario-based model output and BMS linkage control flowchart according to an embodiment of this application, such as... Figure 4 As shown, Figure 4This section describes the process of scenario-based model output and its linkage control with the Battery Management System (BMS). Scenario identification uses real-time battery current and temperature to determine the current operating scenario. If it's a normal charge / discharge scenario, the system switches to the normal charge / discharge scenario branch, focusing on improving SOC accuracy. If a low-temperature start-up scenario is detected, it switches to the low-temperature start-up scenario branch, specifically adjusting the model to compensate for changes in internal resistance as temperature decreases. When the system determines it's in a fast-charging scenario, it switches to the fast-charging scenario branch, dynamically correcting polarization capacitors C1 and C2 to accelerate polarization response and reduce voltage overshoot. The model improvement parameter output node sends the scenario-adjusted parameters to the BMS. The BMS command receiving node receives control requests from the BMS, such as equalization and charge / discharge rate adjustments. The control strategy generation node formulates specific control strategies based on model parameters and BMS commands, such as equalization current magnitude and charge / discharge rate adjustments. The command output to BMS node is responsible for returning the generated control strategy commands to the BMS for real-time control. The fault warning and judgment node monitors parameter changes. When a parameter deviation from its initial value exceeds 15%, the system will send an abnormal warning signal to the BMS via the fault code output node, allowing for timely measures to prevent battery failure. The values ​​in the above steps are for illustrative purposes only; specific values ​​need to be determined based on actual conditions.

[0096] In one alternative embodiment, a parameter update method for a battery circuit model includes five key steps:

[0097] Step 1: System Hardware Setup. The dynamic recognition system hardware of this application consists of a data acquisition module, a processor module, and a BMS interaction module, with the specific configuration as follows:

[0098] Data acquisition module: High-precision voltage, current and temperature sensors are selected. It communicates synchronously with the processor through the Serial Peripheral Interface (SPI) bus, and the sampling frequency is set to 1kHz. For multi-cell scenarios, each cell is equipped with an independent voltage and temperature sensor to ensure consistent data acquisition.

[0099] Processor module: It adopts STMicroelectronics STM32H750 microprocessor (400MHz), which has high-speed floating-point operation capability, can run VFF-RLS algorithm in real time (operation time <500μs / time) and scenario-based improvement logic, and meet the real-time requirements of BMS.

[0100] BMS Interaction Module: Communicates with the power battery pack BMS via the Controller Area Network Flexible Data Rate (CAR) bus. The communication rate is 500kbps and the transmission cycle is 10ms. This enables bidirectional interaction between model parameters (R0, R1, R2, C1, C2, Uoc), state estimation results (SOC, SOH), and BMS control commands.

[0101] Step 2, Data Acquisition and Preprocessing Process: Data acquisition begins: After the system is powered on, the sensor initializes and enters real-time acquisition mode, transmitting voltage, current, and temperature data to the processor cache every 1ms. The cache uses a circular queue design with a capacity of 1000 groups to ensure no data loss.

[0102] Abnormal data handling: Data acquisition start-up: After the system is powered on, the sensor initializes and enters real-time acquisition mode, transmitting voltage, current and temperature data to the processor cache every 1ms; the cache adopts a circular queue design with a capacity of 1000 groups to ensure that no data is lost.

[0103] Abnormal data processing: Kalman filtering is used for data noise reduction. The filtering is achieved by "first predicting the current state based on historical data, and then correcting the error by combining the actual measurement value". For data missing in three consecutive sampling points, linear interpolation is used to complete the data to ensure data continuity.

[0104] Step 3, Adaptive Parameter Identification Implementation. Initial Parameter Calibration: When the system is first started, the lithium battery is calibrated at room temperature (25℃) and 1C charge / discharge rate to obtain initial parameters, including initial internal resistance R0_init, initial polarization resistances R1_init and R2_init, initial polarization capacitances C1_init and C2_init, and initial open-circuit voltage Uoc_init, which are used as initial values ​​for parameter identification.

[0105] VFF-RLS algorithm execution: The processor performs parameter identification every 10ms, with the following steps: reading preprocessed voltage U(k), current I(k), and temperature T(k) data; determining operating condition stability; and calculating the rate of change of current. ,in, The rate of change of current, The magnitude of the current at the current moment. The magnitude of the current and the rate of temperature change at the previous moment. ,in, For the rate of temperature change, The current temperature. If the temperature was at the previous moment, then... <5% and If the temperature is <2℃, the operating condition is considered stable, with a forgetting factor λ=0.98; otherwise, the operating condition is considered to have abruptly changed, with λ=0.92. An observation matrix H(k) is constructed, combining the current current with historical polarization data; new parameters are calculated according to the core parameter update formula. Update the gain matrix and covariance; parameter boundary verification: if the identification result exceeds the preset physical boundary (e.g., R0 > 50mΩ or R0 < 5mΩ), use the parameters from the previous time step. Replacement ensures parameter validity.

[0106] Step 4: Scenario-based improvements and integration with BMS.

[0107] Scene recognition: The processor automatically identifies the application scenario based on the current current ratio and temperature data: Regular charging and discharging scenario: 0.5C≤I≤2C and 10℃≤T≤45℃; Low temperature start-up scenario: T<-10℃ and I≥1C (start-up current); Fast charging scenario: I≥3C and 20%≤SOC≤80%.

[0108] Scenario-based correction: Low-temperature scenario: Apply correction formula based on temperature T. ,in, To correct the internal resistance, adjust the internal resistance R0 and polarization resistors R1 and R2; for fast charging scenarios: calculate the current change ΔI and adjust it according to the correction factor. , ( The corrected polarization capacitor. (The polarization capacitors before correction) are adjusted to accelerate the polarization response by adjusting polarization capacitors C1 and C2.

[0109] BMS linkage control: Equalization control: Calculates the voltage difference between cells every 500ms. ( Let i be the voltage of the i-th cell. (meaning average voltage), if If the voltage is >10mV and the internal resistance deviation is >10%, activate the equalization circuit and adjust the current to 50mA until... <5mV; Rate control: Real-time calculation of polarization voltage ( Polarization voltage, , (for polarization resistance), if >100mV, reduce the upper limit of charge / discharge rate by 30%, until... <80mV recovery; Fault warning: Daily statistical model parameter change rate If Δθ > 15%, a fault code (such as abnormal increase in internal resistance) is sent to the BMS to prompt maintenance personnel to check.

[0110] Step 5: Multi-cell model adaptation.

[0111] For multi-cell series connection scenarios in power battery packs, a strategy of "single-cell dynamic model + overall pack consistency correction" is adopted:

[0112] Establish an independent dynamic model for each battery cell and execute the above data acquisition and parameter identification process;

[0113] Total State of Charge (SOC) Calculation: Using a weighted average method, based on the identified capacity of each cell. , ( For the capacity of a certain battery, The formula for calculating the SOC of a battery pack (for the state of health of a specific battery) is as follows: , ( This represents the state of charge of the entire battery pack. (This refers to the state of charge of a battery, taking into account differences in cell capacity.)

[0114] Overall health status assessment: using the worst-performing cell (SOH_i = current capacity / rated capacity × 100%) is used as the entire SOH_pack, and the worst-case cell number is sent to the BMS for targeted maintenance. The values ​​in the above steps are for illustrative purposes only; the specific values ​​need to be determined based on the actual situation and are not limited here.

[0115] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0116] According to an embodiment of this application, a parameter updating device for a battery circuit model is provided. It should be noted that this device can be used to execute the parameter updating method for the battery circuit model described above. The specific implementation method and preferred application scenarios are the same as those in the above embodiment, and will not be repeated here.

[0117] Figure 5 This is a schematic diagram of a parameter updating device for a battery circuit model according to an embodiment of this application, such as... Figure 5 As shown, the device includes: an acquisition module 502, a prediction module 504, a determination module 506, and an update module 508.

[0118] The acquisition module 502 is used to acquire the current state parameters and current operating conditions of the target battery, wherein the current operating conditions represent the operational stability of the target battery; the prediction module 504 is used to predict the predicted state parameters of the target battery using the battery circuit model corresponding to the target battery, wherein the battery circuit model is used to simulate the electrochemical process of the target battery during charging and discharging; the determination module 506 is used to determine the parameter adjustment factor of the battery circuit model based on the current operating conditions, wherein the parameter adjustment factor is used to quantify the impact of the current operating conditions on the corresponding model parameters of the battery circuit model; the update module 508 is used to update the current model parameters of the battery circuit model based on the parameter adjustment factor, the current state parameters, and the predicted state parameters to obtain the target model parameters of the battery circuit model.

[0119] Optionally, the update module is used to construct state deviation parameters based on the current state parameters and the predicted state parameters; update the current model parameters using the state deviation parameters to obtain the first updated model parameters; and determine the target model parameters based on the parameter adjustment factor, the current model parameters, and the first updated model parameters.

[0120] Optionally, the update module is further configured to adjust the first initial weight of the current model parameters based on the parameter adjustment factor to obtain the first target weight of the current model parameters; adjust the second initial weight of the first updated model parameters based on the parameter adjustment factor to obtain the second target weight of the first updated model parameters; determine the first sum of the current model parameters and the first target weight, and determine the second sum of the first updated model parameters and the second target weight; and determine the target model parameters based on the first sum and the second sum.

[0121] Optionally, the update module is also used to determine the second update model parameters based on the first sum and the second sum; and to adjust the second update model parameters based on the current application scenario of the target battery to obtain the target model parameters, wherein the current application scenario is used to represent the current usage scenario of the target battery.

[0122] Optionally, the acquisition module is used to determine the battery current parameters and battery temperature parameters based on the current state parameters; determine the current change rate of the target battery based on the battery current parameters, wherein the current change rate is used to represent the rate of change of current in the target battery; determine the temperature change rate of the target battery based on the battery temperature parameters, wherein the temperature change rate is used to represent the rate of change of cell temperature in the target battery; and determine the current operating condition based on the current change rate and the temperature change rate.

[0123] Optionally, the acquisition module is further configured to determine the current operating condition as a stable operating condition in response to the current change rate being less than or equal to a first threshold and the temperature change rate being less than or equal to a second threshold; and to determine the current operating condition as an abnormal operating condition in response to the current change rate being greater than the first threshold or the temperature change rate being greater than the second threshold.

[0124] Optionally, the acquisition module is also used to acquire the battery voltage parameters, battery current parameters, and battery temperature parameters of the target battery; and to determine the current state parameters based on the battery voltage parameters, battery current parameters, and battery temperature parameters.

[0125] Optionally, the determining module is used to output a prompt message if the current operating condition is an abnormal operating condition, wherein the prompt message is used to indicate that the target battery is currently in an abnormal operating condition; if feedback information corresponding to the prompt message is received, a parameter adjustment factor is determined based on the feedback information, wherein the feedback information represents the parameter adjustment information corresponding to the prompt message determined from multiple parameter adjustment factors.

[0126] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0127] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0128] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0129] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0130] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0131] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0135] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0136] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for updating parameters of a battery circuit model, characterized in that, include: Obtain the current state parameters and current operating conditions of the target battery, wherein the current operating conditions are used to represent the operational stability of the target battery; Using the battery circuit model corresponding to the target battery, the predicted state parameters of the target battery are predicted, wherein the battery circuit model is used to simulate the electrochemical process of the target battery during charging and discharging. Based on the current operating conditions, a parameter adjustment factor for the battery circuit model is determined, wherein the parameter adjustment factor is used to quantify the impact of the current operating conditions on the corresponding model parameters of the battery circuit model. Based on the parameter adjustment factor, the current state parameter, and the predicted state parameter, the current model parameter of the battery circuit model is updated to obtain the target model parameter of the battery circuit model.

2. The method according to claim 1, characterized in that, Based on the parameter adjustment factor, the current state parameters, and the predicted state parameters, the current model parameters of the battery circuit model are updated to obtain the target model parameters of the battery circuit model, including: Based on the current state parameters and the predicted state parameters, a state deviation parameter is constructed; The current model parameters are updated using the state deviation parameters to obtain the first updated model parameters; The target model parameters are determined based on the parameter adjustment factor, the current model parameters, and the first updated model parameters.

3. The method according to claim 2, characterized in that, Based on the parameter adjustment factor, the current model parameters, and the first updated model parameters, the target model parameters are determined, including: The first initial weights of the current model parameters are adjusted based on the parameter adjustment factor to obtain the first target weights of the current model parameters; The second initial weights of the first updated model parameters are adjusted based on the parameter adjustment factor to obtain the second target weights of the first updated model parameters; Determine a first sum of the current model parameters and the first target weights, and determine a second sum of the first updated model parameters and the second target weights; The target model parameters are determined based on the first sum and the second sum.

4. The method according to claim 3, characterized in that, Determining the target model parameters based on the first sum and the second sum includes: Based on the first sum and the second sum, determine the second updated model parameters; The second updated model parameters are adjusted based on the current application scenario of the target battery to obtain the target model parameters, wherein the current application scenario represents the current usage scenario of the target battery.

5. The method according to claim 1, characterized in that, Obtain the current state parameters and current operating conditions of the target battery, including: Based on the current state parameters, determine the battery current parameters and battery temperature parameters; Based on the battery current parameters, the current change rate of the target battery is determined, wherein the current change rate is used to represent the rate of change of current in the target battery; Based on the battery temperature parameters, the temperature change rate of the target battery is determined, wherein the temperature change rate is used to represent the rate of change of cell temperature in the target battery; The current operating condition is determined based on the current change rate and the temperature change rate.

6. The method according to claim 5, characterized in that, Determining the current operating condition based on the current change rate and the temperature change rate includes: In response to the current change rate being less than or equal to a first threshold and the temperature change rate being less than or equal to a second threshold, the current operating condition is determined to be a stable operating condition. In response to the current change rate being greater than the first threshold, or the temperature change rate being greater than the second threshold, the current operating condition is determined to be an abnormal operating condition.

7. The method according to any one of claims 1-6, characterized in that, Obtain the current state parameters of the target battery, including: Obtain the battery voltage parameters, battery current parameters, and battery temperature parameters of the target battery; The current state parameters are determined based on the battery voltage parameters, the battery current parameters, and the battery temperature parameters.

8. The method according to any one of claims 1-6, characterized in that, Based on the current operating conditions, the parameter adjustment factors of the battery circuit model are determined, including: If the current operating condition is an abnormal operating condition, a prompt message is output, wherein the prompt message is used to indicate that the target battery is currently in the abnormal operating condition; If feedback information corresponding to the prompt information is received, the parameter adjustment factor is determined based on the feedback information, wherein the feedback information represents the parameter adjustment information corresponding to the prompt information determined from multiple parameter adjustment factors.

9. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 7.

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