A Method for Optimizing the Battery Signal Acquisition Accuracy of a BMS System

Abstract

The present invention relates to the technical field of battery management systems, and specifically discloses a method for optimizing the battery signal acquisition accuracy of a BMS system. By real-time monitoring the voltage, current, and temperature of the battery, the covariance matrices of the process noise and measurement noise are dynamically adjusted to cope with the changes in the battery operating state; according to the changing trends of the charge and discharge rate and temperature, the noise parameters are predicted and corrected to improve the adaptability of the system in extreme environments; by combining data from multiple sensors, an adaptive adjustment mechanism of weighted average and Kalman filter is adopted to enhance the system's evaluation of the signal quality of different sensors and the effective suppression of noise; in response to sudden changes in the battery state, the system can quickly respond by automatically adjusting the filtering algorithm to ensure the precise monitoring of the battery health state. The method of the present invention can improve the stability and accuracy of the battery management system and solve the estimation error problem of traditional methods under extreme working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management systems, and particularly to a method for optimizing the acquisition accuracy of battery signals in a BMS system. Background Art

[0002] With the rapid development of fields such as electric vehicles, energy storage systems, and mobile devices, the battery, as one of the core components, undertakes important tasks of energy storage and supply. The health management, performance optimization, and safety guarantee of the battery have become the focus of research on battery management systems (BMS). Currently, battery management systems generally rely on the real-time monitoring and data processing of key parameters such as battery voltage, current, and temperature to ensure that the battery maintains a safe working state during charging and discharging. However, the battery often faces various extreme working conditions during actual operation, such as rapid charging and discharging, large temperature fluctuations, battery aging, etc. These factors may cause drastic changes in the battery state, posing huge challenges to traditional BMS systems.

[0003] Existing Kalman filters may not converge correctly under extreme conditions (such as rapid charging and discharging of the battery, high or low temperature environments), resulting in inaccurate battery state estimation. When the battery operates in these extreme environments, the noise characteristics of the battery deviate significantly from the assumptions under standard working conditions, causing the noise model of the Kalman filter to fail, and further making the system unable to effectively track the true state of the battery. This non-convergence situation may lead to misjudgment of the charge and discharge state of the battery management system, resulting in serious problems such as overcharging, over-discharging, battery damage, and temperature control failure. Therefore, how to improve the adaptability and convergence of the Kalman filter under extreme environments is a major challenge in current technologies. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for optimizing the acquisition accuracy of battery signals in a BMS system to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for optimizing the acquisition accuracy of battery signals in a BMS system, S1: By real-time monitoring the battery working state including voltage, current, and temperature, dynamically adjust the noise covariance matrix of the Kalman filter;

[0007] S2: According to the real-time changes in the charge and discharge rate and temperature, predict and correct the noise parameters of the Kalman filter to improve the adaptability of the filter to the dynamic changes of noise under extreme conditions;

[0008] S3: Combine data from multiple sensors, and use the weighted average and the adaptive adjustment mechanism of the Kalman filter to enhance the system's evaluation of the signal quality of different sensors and the effective suppression of noise;

[0009] S4: Process the non - linear dynamics in the battery system through an extended Kalman filter to enhance the accurate prediction of non - linear factors such as fast charge - discharge and temperature changes;

[0010] S5: When a sudden change in the battery state is detected, automatically adjust the filtering algorithm to ensure that the system can respond quickly and accurately monitor the battery state in extreme environments.

[0011] As a further solution of the present invention: Dynamically adjusting the noise covariance matrix of the Kalman filter specifically includes:

[0012] Real - time monitor the battery voltage, current and temperature data, and dynamically construct the process noise covariance matrix. The calculation expression is:

[0013] ;

[0014] Among them, and are constant coefficients fitted based on battery characteristics, is the current battery voltage, is the maximum battery voltage, is the battery voltage change rate, represents an adjustment factor established based on the working state of the battery, represents the current - moment current, represents the current - moment temperature, represents the process noise covariance matrix, represents the index of the time step, represents the continuous time interval;

[0015] Dynamically construct the measurement noise covariance matrix. The calculation expression is:

[0016] ;

[0017] Among them, and are constant coefficients fitted according to the sensor performance, represents the adjustment factor of the measurement noise model, represents the measurement noise covariance matrix;

[0018] Dynamically update the covariance matrix of the Kalman filter. The calculation expression is:

[0019] ;

[0020] In the formula, represents the covariance matrix of the Kalman filter.

[0021] As a further solution of the present invention: The adaptability of the boosting filter to the dynamic change of noise under extreme conditions specifically includes:

[0022] Construct a training set based on historical noise data, build a noise prediction model, and combine machine learning algorithms to predict the current noise parameters, and at the same time correct them by real-time monitoring of the temperature change rate and charge-discharge power change trend of the battery.

[0023] As a further solution of the present invention: The specific process of the correction is as follows:

[0024] Collect historical data of the battery under different working states, including battery voltage, current and temperature;

[0025] Perform denoising and normalization preprocessing on the collected historical data;

[0026] Select a machine learning algorithm to establish a noise prediction model, and the algorithm is a long short-term memory network;

[0027] The input data is the historical state data of the battery;

[0028] The output data is the predicted value of the noise parameter;

[0029] Through the machine learning model, establish a noise prediction formula for predicting the noise parameters at the current moment according to the real-time data of the battery. The noise prediction formula is:

[0030] ;

[0031] Wherein, represents the predicted current process noise parameter, represents the historical battery voltage, represents the historical battery current, represents the historical battery temperature, represents the historical time index, represents the long short-term memory network model;

[0032] By real-time monitoring of the temperature change rate and charge-discharge power change trend of the battery, correct the noise prediction value, and the correction formula is:

[0033] ;

[0034] Wherein, represents the corrected noise parameter, represents the temperature change rate, represents the continuous time interval, represents the charge-discharge power change trend, and represents the correction coefficient.

[0035] As a further solution of the present invention: Combining data from multiple sensors and using an adaptive adjustment mechanism of weighted average and Kalman filter specifically includes:

[0036] Assign weights to the signals of different sensors according to their reliability, where the weights are determined by the sensor signal noise level and signal consistency monitored in real time, and data fusion is achieved by using dynamic weighted average.

[0037] As a further solution of the present invention: Processing the non-linear dynamics in the battery system through an extended Kalman filter specifically includes:

[0038] Estimate the dynamic state variables of the battery, including state of charge, state of health and internal impedance, through a non-linear state transition equation to improve the estimation accuracy under non-linear conditions.

[0039] As a further solution of the present invention: The acquisition process of the non-linear state transition equation is as follows:

[0040] In order to calculate the dynamic state variables of the battery, a non-linear state transition equation is constructed to describe the dynamic behavior of the battery under various working conditions. The specific calculation method is as follows:

[0041] Based on the current, voltage and temperature changes of the battery, calculate the non-linear state transition equation of the state of charge, and the calculation expression is:

[0042] ;

[0043] Wherein, represents the state of charge at the th moment, represents the acquisition moment, represents the state of charge at the previous moment, represents the battery current at the th moment, represents the battery voltage at the th moment, open circuit voltage of the battery calculated based on the current state of charge, represents based on calculated internal impedance of the battery, represents the change amount at each moment;

[0044] Calculate the non-linear state transition equation of the internal impedance, and the calculation expression is:

[0045] ;

[0046] Wherein, represents the initial internal impedance of the battery, represents the State of charge at a moment Denote a preset proportionality coefficient Denote an exponent for controlling the battery internal resistance change rate

[0047] As a further solution of the present invention: when it is detected that the battery state suddenly changes, the filtering algorithm is automatically adjusted, specifically including:

[0048] By setting an error tolerance threshold, when it is detected that the filtering error exceeds the threshold, the Kalman gain coefficient is automatically adjusted to ensure the convergence of the filter and the stability of the system;

[0049] The calculation process of the filtering error is:

[0050] The filtering error is calculated according to the difference between the measured value and the predicted value at the current moment, and the calculation expression is:

[0051] ;

[0052] Wherein, Denote the filtering error Denote the acquisition moment Denote the Actual measured value at the moment Is the predicted value based on the previous state

[0053] As a further solution of the present invention: the acquisition process of the Kalman gain coefficient specifically includes:

[0054] If the filtering error is greater than the error tolerance threshold, the Kalman gain coefficient is adjusted, and the calculation expression is:

[0055] ;

[0056] In the formula, Denote the acquisition moment Denote the Kalman gain coefficient at the moment Denote the error covariance matrix at the previous moment Denote the filtering error at the current moment Denote the adjustment coefficient

[0057] As a further solution of the present invention: for the error covariance matrix, the specific update calculation method is:

[0058] To ensure the stability and accuracy of the filter, the error covariance matrix is updated to reflect the state estimation accuracy at the current moment, and the calculation expression is:

[0059] ;

[0060] In the formula, represents the updated error covariance matrix, represents the Kalman gain coefficient at time represents the observation matrix, represents the identity matrix, represents the error covariance matrix at the previous time, represents the acquisition time.

[0061] Advantages of the present invention:

[0062] (1) By real-time monitoring of the charging and discharging rates and temperature changes of the battery and combining with dynamically adjusting the noise covariance matrix of the Kalman filter, the present invention realizes fine optimization of the battery state estimation. In traditional Kalman filtering methods, the noise model is usually fixed, which leads to the estimation accuracy of the filter being easily interfered by significant errors when facing drastic changes in the battery working state or the influence of the external environment. In contrast, the present invention can dynamically adjust the parameters of the filter according to the actual working conditions of the battery by real-time obtaining the working state parameters of the battery and predicting and correcting the noise parameters based on these real-time data, ensuring that the system can accurately estimate the state of the battery even when the battery is in extreme working conditions such as fast charging and discharging or encountering temperature fluctuations. This method not only improves the robustness of the battery management system but also enables the system to flexibly adapt to different working environments and application scenarios, greatly reducing the estimation errors caused by the fixed traditional noise model, enhancing the adaptive ability and stability of the system. Therefore, the present invention can maintain high-precision battery state monitoring in a changing working environment, ensure the stable operation of the battery management system under various complex working conditions, and effectively extend the service life of the battery.

[0063] (2) By combining the dynamic weighted average with the adaptive adjustment mechanism of the Kalman filter, the present invention realizes the efficient fusion and optimization of data from multiple sensors. Specifically, the system dynamically adjusts the weights of each sensor by real-time monitoring the signal quality of each sensor, based on the noise level and consistency of the signals, so that the sensors with higher reliability account for a larger proportion in data fusion, thereby reducing the interference of noise and errors on the system performance. This mechanism can flexibly adjust the data fusion strategy according to the real-time quality of the sensor signals, ensuring the accuracy and stability of the system under various working conditions. More importantly, when the battery state undergoes a sudden change (such as extreme cases of battery failure or drastic temperature fluctuations), the system can automatically identify this change and quickly adjust the filtering algorithm, optimizing the setting of the Kalman gain, ensuring that the system can still respond quickly and accurately monitor the battery state under emergencies. This mechanism significantly improves the adaptability of the battery management system in a dynamic and complex environment, not only enhancing the robustness of the system, reducing the risks caused by environmental or equipment failures, but also effectively improving the stability and accuracy of the overall system, ensuring the safe and reliable operation of the battery under various extreme working conditions. Brief Description of the Drawings

[0064] The present invention will be further described below with reference to the accompanying drawings.

[0065] Figure 1 is a specific flowchart of the method for optimizing the battery signal acquisition accuracy of a BMS system according to the present invention;

[0066] Figure 2 is a flowchart of the process for dynamically adjusting the noise covariance matrix of the Kalman filter in the present invention. Specific Embodiments

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] Please refer to Figure 1 as shown, the present invention is a method for optimizing the battery signal acquisition accuracy of a BMS system, including the following steps:

[0069] S1: Dynamically adjust the noise covariance matrix of the Kalman filter by real-time monitoring of the battery operating state including voltage, current, and temperature;

[0070] S2: Predict and correct the noise parameters of the Kalman filter according to the real-time changes of the charge and discharge rate and temperature, improving the adaptability of the filter to the dynamic changes of noise under extreme conditions;

[0071] S3: Combine data from multiple sensors, and utilize the adaptive adjustment mechanisms of weighted average and Kalman filter to enhance the system's evaluation of the signal quality of different sensors and effectively suppress noise.

[0072] S4: Process the non-linear dynamics in the battery system through an extended Kalman filter to enhance the accurate prediction of non-linear factors such as fast charging and discharging and temperature changes.

[0073] S5: When a sudden change in the battery state is detected, automatically adjust the filtering algorithm to ensure that the system can quickly respond and accurately monitor the battery state in extreme environments.

[0074] In S1, by real-time monitoring the battery operating states including voltage, current, and temperature, dynamically adjust the noise covariance matrix of the Kalman filter, specifically including:

[0075] The acquisition of battery voltage is carried out through a voltage sensor in the battery management system (BMS). Specifically, the BMS system is connected in parallel to multiple battery cells, and precise voltage sensors are used to real-time monitor the voltage of each battery cell. The analog signal is converted into a digital signal through an analog-to-digital converter (ADC) for subsequent processing. The sampling frequency is set according to system requirements, ranging from several times to dozens of times per second, to ensure real-time capture of voltage changes under different loads and operating states, guaranteeing the timeliness and accuracy of the data.

[0076] The acquisition of battery current is completed by a current sensor. Common sensor types include Hall effect sensors. When current flows through a shunt resistor, the magnitude of the current causes a voltage drop across the resistor, and the BMS system calculates the current magnitude based on the voltage difference. The Hall effect sensor senses the current intensity by detecting changes in the magnetic field. The acquired current signal is also converted into a digital signal through an analog-to-digital converter (ADC) and synchronized with the voltage acquisition data for processing and analysis by the BMS system.

[0077] The acquisition of battery temperature is completed by a temperature sensor. The BMS system deploys multiple temperature sensors at key parts of the battery pack (such as the positive and negative electrodes of the battery cell) to real-time monitor the temperature changes of the battery. The temperature signal is collected through an analog signal and converted into a digital signal through an analog-to-digital converter (ADC) for processing in the BMS.

[0078] Please refer to Figure 2 as shown, the dynamic adjustment of the noise covariance matrix of the Kalman filter specifically includes:

[0079] Real-time monitor the battery voltage, current, and temperature data, dynamically construct the process noise covariance matrix, and the calculation expression is:

[0080] ;

[0081] Among them, and are constant coefficients fitted based on battery characteristics, is the current battery voltage, is the maximum battery voltage, is the battery voltage change rate, represents an adjustment factor established based on the working state of the battery, represents the current at the current moment, represents the temperature at the current moment, represents the process noise covariance matrix, represents the index of the time step, represents a continuous time interval;

[0082] Dynamically construct the measurement noise covariance matrix, and the calculation expression is:

[0083] ;

[0084] Among them, and are constant coefficients fitted according to the sensor performance, represents the adjustment factor of the measurement noise model, represents the measurement noise covariance matrix;

[0085] Dynamically update the covariance matrix of the Kalman filter, and the calculation expression is:

[0086] ;

[0087] In the formula, represents the covariance matrix of the Kalman filter.

[0088] It should be noted that: by dynamically updating the covariance matrix, the Kalman filter can adaptively adjust the Kalman gain to ensure that the system can still maintain high-precision state estimation under extreme working conditions such as rapid charging and discharging of the battery and temperature changes, solving the estimation error and filter convergence problems caused by the fixed noise model in traditional technologies, and enhancing the robustness and adaptability of the system.

[0089] In S2, according to the real-time changes of the charge and discharge rate and temperature, predict and correct the noise parameters of the Kalman filter to improve the adaptability of the filter to the dynamic changes of noise under extreme conditions, specifically including:

[0090] Construct a training set based on historical noise data, build a noise prediction model, and combine machine learning algorithms to predict the current noise parameters. At the same time, correct them by monitoring the temperature change rate and charge-discharge power change trend of the battery in real time;

[0091] The specific process of the correction is as follows:

[0092] Collect historical data of the battery under different working states, including battery voltage, current, and temperature;

[0093] Perform denoising and normalization preprocessing on the collected historical data;

[0094] Select a machine learning algorithm to establish a noise prediction model. The algorithm is a long short-term memory network;

[0095] The input data is the historical state data of the battery;

[0096] The output data is the predicted value of the noise parameter;

[0097] Through the machine learning model, establish a noise prediction formula for predicting the current noise parameter according to the real-time data of the battery. The noise prediction formula is:

[0098] ;

[0099] Where, represents the predicted current process noise parameter, represents the historical battery voltage, represents the historical battery current, represents the historical battery temperature, represents the historical time index, represents the long short-term memory network model;

[0100] By monitoring the temperature change rate and charge-discharge power change trend of the battery in real time, correct the noise prediction value. The correction formula is:

[0101] ;

[0102] Where, represents the corrected noise parameter, represents the temperature change rate, represents the continuous time interval, represents the charge-discharge power change trend, and represents the correction coefficient.

[0103] It should be noted that: the noise prediction model can intelligently adjust the noise prediction value according to the actual working state of the battery, improve the adaptability to the dynamic change of the battery noise under different working conditions, and correct the noise prediction value by real-time monitoring the temperature change rate and the charging and discharging power change trend of the battery, so as to enhance the stability and accuracy of the system under extreme working conditions.

[0104] In S3, by combining data from multiple sensors and using the adaptive adjustment mechanism of weighted average and Kalman filter, the system's evaluation of the signal quality of different sensors and the effective suppression of noise are enhanced, specifically including:

[0105] Weights are assigned to the signals of different sensors according to their reliability, where the weights are determined by the sensor signal noise level and signal consistency monitored in real time, and dynamic weighted average is used to achieve data fusion.

[0106] It should be noted that: the system first monitors the noise level of each sensor signal through a noise detection algorithm, and the sensor with lower signal noise will obtain a higher weight; at the same time, by analyzing the consistency between the signals of each sensor, if multiple sensor signals show high consistency, these signals will also be given a higher weight. Based on these weights, the data of multiple sensors are fused by dynamic weighted average, ensuring that the final fusion result reflects the reliability of each sensor, minimizing the influence of noise and error, and improving the accuracy and stability of data fusion. This dynamic weighting mechanism enables the system to flexibly adjust the data acquisition strategy according to the actual performance of the sensors in different working environments, thereby improving the reliability and accuracy of the overall system.

[0107] In S4, the extended Kalman filter is used to process the non-linear dynamics in the battery system, enhancing the accurate prediction of non-linear factors such as fast charging and discharging and temperature change, specifically including:

[0108] The dynamic state variables of the battery, including state of charge, state of health and internal impedance, are estimated through a non-linear state transition equation to improve the estimation accuracy under non-linear conditions.

[0109] The acquisition process of the non-linear state transition equation is as follows:

[0110] In order to calculate the dynamic state variables of the battery, a non-linear state transition equation is constructed to describe the dynamic behavior of the battery under various working states, and the specific calculation method is as follows:

[0111] Based on the current, voltage and temperature changes of the battery, the non-linear state transition equation of the state of charge is calculated, and the calculation expression is:

[0112] ;

[0113] Where, represents the state of charge at the moment, represents the acquisition moment, represents the state of charge at the previous moment, represents the battery current at the moment, represents the battery voltage at the moment, open-circuit voltage of the battery calculated based on the current state of charge, represents based on calculated internal impedance of the battery, represents the change amount at each moment;

[0114] The non-linear state transition equation for calculating the internal impedance, the calculation expression is:

[0115] ;

[0116] wherein, represents the initial internal impedance of the battery, represents the state of charge at the moment, represents a preset proportionality coefficient, represents the exponent for controlling the change rate of the battery internal resistance.

[0117] It should be noted that: by constructing a non-linear state transition equation, the dynamic behavior of the battery under various working conditions can be accurately described, especially the battery state change under the influence of non-linear factors such as rapid charge and discharge and temperature change. The non-linear state transition equation can not only effectively improve the prediction accuracy of the battery health state and battery performance, but also maintain a high-precision battery state estimation under extreme working conditions, enhance the stability and robustness of the system, solve the estimation error caused by ignoring non-linear factors in traditional methods, and improve the overall performance of the battery management system.

[0118] In S5, when a sudden change in the battery state is detected, the filtering algorithm is automatically adjusted to ensure that the system can quickly respond and accurately monitor the battery state in an extreme environment, specifically including:

[0119] By setting an error tolerance threshold, when the filtering error exceeds the threshold, the Kalman gain coefficient is automatically adjusted to ensure the convergence of the filter and the stability of the system;

[0120] The calculation process of the filtering error is:

[0121] The filtering error is calculated according to the difference between the measured value and the predicted value at the current moment, and the calculation expression is:

[0122] ;

[0123] Among them, represents the filtering error, represents the acquisition time, represents the actual measurement value at the time, and

[0124] is the predicted value based on the previous state.

[0125] ;

[0126] In the formula, represents the acquisition time, represents the Kalman gain coefficient at the time, represents the error covariance matrix at the previous time, represents the filtering error at the current time,

[0127]

[0128] To ensure the stability and accuracy of the filter, update the error covariance matrix to reflect the state estimation accuracy at the current time. The calculation expression is: ;

[0129] In the formula, represents the updated error covariance matrix, represents the Kalman gain coefficient at the time, represents the observation matrix, represents the error covariance matrix at the previous time, represents the acquisition time.

[0130] Working principle of the present invention: The voltage, current, and temperature of the battery are monitored in real time, and the noise covariance matrix of the Kalman filter is dynamically adjusted based on this data to ensure that the filter can adapt to the noise levels under different battery states, thereby improving the estimation accuracy. On this basis, the present invention further introduces real-time data according to the charge and discharge rate and temperature changes to predict and correct the noise parameters of the Kalman filter, thereby enhancing its adaptability to the dynamic changes of noise under extreme conditions. Through the dynamic weighted average method, combining data from multiple sensors, the system assigns weights according to the noise levels and consistencies of each sensor signal, and uses an adaptive adjustment mechanism to fuse the data to ensure that the system can effectively suppress noise and reduce errors under multi-sensor input. For the non-linear dynamic behavior in the battery system, the present invention processes the non-linear state transition equation of the battery through the extended Kalman filter to accurately predict the state of charge, state of health, and internal impedance of the battery, especially under the influence of non-linear factors such as rapid charge and discharge and temperature changes, maintaining high-precision estimation and further optimizing the stability and robustness of the system.

[0131] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0132] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0133] It should be understood that the term "and / or" in this text is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, and the specific meaning can be understood by referring to the context before and after.

[0134] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0135] The above has described in detail one embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for optimizing the battery signal acquisition accuracy of a BMS system, characterized in that, It includes the following steps: S1: By real-time monitoring of the battery operating state including voltage, current, and temperature, dynamically adjust the noise covariance matrix of the Kalman filter, specifically including: Real-time monitor the battery voltage, current, and temperature data, dynamically construct the process noise covariance matrix, and the calculation expression is: ； Among them, and are constant coefficients fitted based on battery characteristics, is the current battery voltage, is the maximum battery voltage, is the battery voltage change rate, represents an adjustment factor established based on the working state of the battery, represents the current at the current moment, represents the temperature at the current moment, represents the process noise covariance matrix, represents the index of the time step, represents a continuous time interval; Dynamically construct the measurement noise covariance matrix, and the calculation expression is: ； wherein, and are constant coefficients fitted according to the sensor performance, represents the adjustment factor of the measurement noise model, represents the measurement noise covariance matrix; Dynamically update the covariance matrix of the Kalman filter, and the calculation expression is: ； In the formula, represents the covariance matrix of the Kalman filter; S2: According to the real-time changes of the charge-discharge rate and temperature, predict and correct the noise parameters of the Kalman filter to enhance the adaptability of the filter to the dynamic changes of noise under extreme conditions; S3: Combine data from multiple sensors, and use the weighted average and the adaptive adjustment mechanism of the Kalman filter to enhance the system's evaluation of the signal quality of different sensors and the effective suppression of noise; S4: Process the non-linear dynamics in the battery system through the extended Kalman filter to enhance the accurate prediction of non-linear factors such as fast charge-discharge and temperature changes; S5: When a sudden change in the battery state is detected, automatically adjust the filtering algorithm to ensure that the system can quickly respond and accurately monitor the battery state under extreme environments.

2. The method for optimizing the battery signal acquisition accuracy of a BMS system according to claim 1, wherein, The enhancement of the adaptability of the filter to the dynamic changes of noise under extreme conditions specifically includes: Construct a training set based on historical noise data, construct a noise prediction model, and combine machine learning algorithms to predict the current noise parameters, and at the same time correct them by real-time monitoring of the battery temperature change rate and charge-discharge power change trend.

3. The method for optimizing the battery signal acquisition accuracy of a BMS system according to claim 2, characterized in that, The specific process of the correction is: Collect historical data of the battery under different operating states, including battery voltage, current, and temperature; Perform denoising and normalization preprocessing on the collected historical data; Select a machine learning algorithm to establish a noise prediction model, and the algorithm is a long short-term memory network; The input data is the battery historical state data; The output data is the predicted value of the noise parameters; Through the machine learning model, establish a noise prediction formula for predicting the noise parameters at the current moment according to the real-time data of the battery, and the noise prediction formula is: ； Among them, represents the predicted current process noise parameter, represents the historical battery voltage, represents the historical battery current, represents the historical battery temperature, represents the historical time index, represents the long short-term memory network model; By real-time monitoring of the battery temperature change rate and charge-discharge power change trend, correct the noise prediction value, and the correction formula is: ； Among them, represents the corrected noise parameter, represents the temperature change rate, represents the continuous time interval, represents the charging and discharging power change trend, and represents the correction coefficient.

4. The method for optimizing the battery signal acquisition accuracy of a BMS system according to claim 1, characterized in that The combination of data from multiple sensors, using the weighted average and the adaptive adjustment mechanism of the Kalman filter, specifically includes: Assign weights to the signals of different sensors according to their reliability, where the weights are determined by the real-time monitored sensor signal noise level and signal consistency, and adopt dynamic weighted average to achieve data fusion.

5. The method for optimizing the battery signal acquisition accuracy of a BMS system according to claim 1, characterized in that, The processing of the non-linear dynamics in the battery system through the extended Kalman filter specifically includes: Estimate the dynamic state variables of the battery, including state of charge, state of health, and internal impedance, through the non-linear state transition equation to improve the estimation accuracy under non-linear conditions.

6. The method for optimizing the battery signal acquisition accuracy of a BMS system according to claim 5, characterized in that The acquisition process of the non-linear state transition equation is: To calculate the dynamic state variables of the battery, construct a non-linear state transition equation to describe the dynamic behavior of the battery under various operating states, and the specific calculation method is as follows: Based on the current, voltage, and temperature changes of the battery, calculate the non-linear state transition equation of the state of charge, and the calculation expression is: ； Among them, represents the state of charge at the moment, represents the acquisition moment, represents the state of charge at the previous moment, represents the battery current at the moment, represents the battery voltage at the moment, open-circuit voltage of the battery calculated based on the current state of charge, represents based on the calculated internal impedance of the battery, represents the change amount at each moment; The non-linear state transition equation for calculating the internal impedance, and the calculation expression is as follows: ； Among them, represents the initial internal impedance of the battery, represents the state of charge at the represents a preset proportionality coefficient, represents the exponent for controlling the rate of change of the battery internal resistance.

7. The method for optimizing the battery signal acquisition accuracy of a BMS system according to claim 1, wherein When it is detected that the battery state has a mutation, the filtering algorithm is automatically adjusted, which specifically includes: By setting an error tolerance threshold, when it is detected that the filtering error exceeds the threshold, the Kalman gain coefficient is automatically adjusted to ensure the convergence of the filter and the stability of the system; The calculation process of the filtering error is as follows: The filtering error is calculated based on the difference between the measured value and the predicted value at the current moment, and the calculation expression is: ； Among them, represents the filtering error, represents the acquisition time, represents the actual measured value at the time, which is based on the predicted value of the previous state.

8. A method for optimizing the battery signal acquisition accuracy of a BMS system according to claim 7, characterized in that, The process of obtaining the Kalman gain coefficient specifically includes: If the filtering error is greater than the error tolerance threshold, the Kalman gain coefficient is adjusted, and the calculation expression is: ； In the formula, represents the acquisition time, represents the Kalman gain coefficient at the time, represents the error covariance matrix at the previous time, represents the adjustment coefficient.

9. The method for optimizing the battery signal acquisition accuracy of a BMS system according to claim 8, characterized in that, For the error covariance matrix, the specific update calculation method is: To ensure the stability and accuracy of the filter, the error covariance matrix is updated to reflect the state estimation accuracy at the current moment, and the calculation expression is: ； wherein, represents the updated error covariance matrix, represents the Kalman gain coefficient at the observation matrix, represents the identity matrix, represents the error covariance matrix at the previous moment, represents the acquisition moment.

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