Method for determining state of charge of battery

Abstract

The invention relates to the field of battery state detection, in particular to a method for determining the state of charge of a battery. The hybrid power pulse data of different temperature intervals are collected and the corresponding state equations are constructed, so that neglect of a single normal temperature / high temperature model to a low-temperature working condition is made up, and the problem of insufficient model precision under low temperature and high internal resistance is solved; secondly, weighting and integrating multiple temperature state equations through temperature related weights, realizing effective fusion of RC models at different temperatures, and fully utilizing battery characteristic parameters at each temperature; secondly, noise matrix optimization is replaced with weight optimization, and the algorithm stability defect caused by noise uncertainty is avoided; and finally, based on the target equivalent state equation, Kalman filtering is performed to estimate the SOC, so that the estimation precision under the full-temperature working condition is ensured, the stability of the algorithm is improved, and the technical problem that the existing scheme cannot give consideration to the precision and stability of the full-temperature range is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of battery state detection, and more specifically to a method for determining the state of charge of a battery. Background Technology

[0002] State of Charge (SOC) estimation is a core function of battery management systems, and its accuracy directly determines the energy utilization efficiency and operational stability of the energy storage system. The accuracy of SOC estimation is highly dependent on the accuracy of the battery model, and the construction of the battery model is easily affected by temperature. The resistance-capacitor (RC) parameters of the battery will vary significantly under different temperature conditions, which will cause the output characteristics of the battery model to deviate from reality and reduce the accuracy of SOC estimation.

[0003] Currently, the industry mainly adopts two technical solutions for battery modeling and SOC estimation: The first solution is to build a single RC model only under normal or high temperature conditions. This solution does not take into account the significant increase in battery internal resistance under low temperature conditions, and the model accuracy drops significantly under low temperature conditions, making it difficult to guarantee the accuracy of SOC estimation; The second solution is to build RC models at different temperatures and use temperature as an input parameter to optimize the noise matrix in the Kalman filter algorithm, thereby improving the accuracy of SOC estimation.

[0004] However, this scheme has obvious drawbacks. On the one hand, the noise itself has strong uncertainty, and it is difficult to ensure the stability of the algorithm by optimizing the noise matrix through temperature. On the other hand, the scheme does not effectively integrate the RC models at different temperatures, and cannot make full use of the battery characteristic parameters under multiple temperature conditions, resulting in a large room for improvement in the model's adaptability and estimation accuracy. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, apparatus, device, and storage medium for determining the state of charge (SOC) of a battery, aiming to solve the technical problems existing in the current battery modeling and SOC estimation schemes: First, some schemes do not fully consider the impact of extreme conditions such as low temperature on the dynamic characteristics of the battery, directly resulting in the model estimation accuracy failing to meet the requirements of practical applications; Second, although other schemes improve the estimation effect by introducing temperature-related parameters or optimizing the noise matrix, they still have defects such as poor model stability, excessive reliance on human experience for noise parameter adjustment, and inability to effectively integrate equivalent circuit models under multiple temperature conditions, making it difficult to balance the accuracy and stability of SOC estimation across the entire temperature range.

[0006] To address the aforementioned issues, this application introduces an adaptive fusion mechanism of multiple temperature equivalent circuit models and a stable state estimation strategy, ultimately achieving high-precision and robust estimation of the battery state of charge under all temperature conditions.

[0007] In a first aspect, embodiments of the present invention provide a method for determining the state of charge of a battery, the method comprising: Collect mixed power pulse data of the battery at different temperatures; Based on the mixed power pulse data at different temperatures, the battery characteristic parameters of the battery at different temperatures are calculated, and the state equations at different temperatures are obtained. By weighting the state equations at different temperatures with temperature-related weights, an equivalent state equation is obtained. Obtain the target weights and substitute them into the equivalent state equation to obtain the target equivalent state equation. Then, perform Kalman filter equation calculation based on the target equivalent state equation to obtain the state of charge of the battery. The target weights are used to represent the proportion of the state equation corresponding to different temperatures in the equivalent state equation.

[0008] Furthermore, based on the mixed power pulse data at different temperatures, the battery characteristic parameters of the battery at different temperatures are calculated to obtain the state equations at different temperatures, including: Based on the mixed power pulse data at different temperatures, the battery characteristic parameters of the battery at different temperatures are calculated; Based on the battery characteristic parameters at different temperatures, an equation framework containing input and output relationships is constructed, and process noise and observation noise terms at different temperatures are added to the equation framework to obtain the initial state equations at the corresponding temperatures. By constructing the correspondence rules between the input, output, and noise in the initial state equation, state equations at different temperatures are obtained.

[0009] Furthermore, before obtaining the target weight, the method also includes: Obtain the actual measured voltage and the battery model predicted voltage corresponding to the battery, and obtain the theoretical weight, the estimated weight and the adjustment intensity corresponding to the current temperature; Based on the actual measured voltage, the battery model predicted voltage, the theoretical weight, the estimated weight at the previous moment, and the adjustment intensity, an objective function is constructed.

[0010] Furthermore, obtaining the target weight includes: Based on the objective function, the minimization direction with weights as optimization variables is determined, and the weight optimization problem to be solved is obtained. The target weight is obtained by solving the weight optimization problem using the recursive least squares method.

[0011] Furthermore, the expression for the objective function is as follows: ; In the formula, J is the objective function. For actual voltage measurement, Battery model predicts voltage. As the current weight, Theoretical weight, , Both are for adjusting intensity. Let be the weight at time i. The weight is the weight at time i-1.

[0012] Furthermore, the step of calculating the state of charge of the battery by performing Kalman filter equations based on the target equivalent state equation includes: Obtain the historical charge state, input parameters, and error covariance matrix of the previous time step; Based on the historical state of charge, the input parameters, and the target equivalent state equation, a state prediction value for the current moment is obtained, and the prior error covariance matrix for the current moment is determined using the error covariance matrix. Calculate the Kalman filter gain based on the prior error covariance matrix and the matrix in the target equivalent state equation; The state is updated based on the Kalman filter gain, the state estimate, and the actual measurement at the current moment to obtain the optimal state estimate. The posterior error covariance matrix at the current moment is determined using the prior error covariance matrix, wherein the posterior error covariance matrix is ​​used as the error covariance matrix at the current moment when determining the state of charge at the next moment. Extract the first element from the optimal state estimate to obtain the battery's state of charge.

[0013] Furthermore, the step of predicting the state estimate at the current moment based on the historical state of charge, the input parameters, and the target equivalent state equation includes: Extract the equivalent matrix from the target equivalent state equation; The estimated value of the historical state at the previous moment is determined based on the historical state of charge. Substituting the product of the equivalent matrix and the historical state estimate, along with the input parameters, into the state equation function, yields the current state estimate.

[0014] Furthermore, the formula for calculating the estimated state value is as follows: ; ; In the formula, The estimated state value at time k-1, For the input parameters at time k-1, The estimated state value at time k. Let be the prior error covariance matrix at time k-1. Let be the prior error covariance matrix at time k. The target equivalent transition matrix, Let be the transpose of the objective equivalent transition matrix. Noise adjustment factor, The temperature at time k is currently... For reference temperature, Let be the process noise, where the process noise is calculated based on the covariance matrix of the process noise. , Let be the covariance matrix of the process noise.

[0015] Furthermore, the formula for calculating the Kalman filter gain is as follows: ; In the formula, For Kalman filter gain, For the previous time (k) The posterior error covariance matrix at time 1, For the target equivalent observation matrix, Let be the transpose of the observation matrix in the target equivalent state equation. To measure the temperature sensitivity coefficient of noise, The current temperature. For reference temperature, The measurement noise is at the reference temperature, where the measurement noise is calculated from the measurement noise covariance matrix. , This is the covariance matrix for measuring noise.

[0016] Furthermore, the step of updating the state based on the Kalman filter gain, the estimated state value, and the actual measurement value at the current moment to obtain the optimal state estimate includes: The observation matrix is ​​extracted from the target equivalent state equation, and the predicted measurement value is obtained by multiplying the observation matrix with the state prediction value. Obtain the measurement deviation between the actual measured value and the predicted measured value; The correction amount is obtained by multiplying the Kalman filter gain by the measurement deviation. The optimal estimate is calculated based on the correction amount and the state estimate.

[0017] Secondly, embodiments of the present invention provide a device for determining the state of charge of a battery, the device comprising: The acquisition module is used to acquire mixed power pulse data of the battery at different temperatures; The calculation module is used to calculate the battery characteristic parameters of the battery at different temperatures based on the mixed power pulse data at different temperatures, and obtain the state equation at different temperatures; The integration module is used to weight and integrate state equations at different temperatures using temperature-related weights to construct equivalent state equations. The processing module is used to obtain the target weights, substitute the target weights into the equivalent state equation to obtain the target equivalent state equation, and perform Kalman filter equation calculation based on the target equivalent state equation to obtain the state of charge of the battery. The target weights are used to represent the proportion of the state equation corresponding to different temperatures in the equivalent state equation.

[0018] Thirdly, embodiments of the present invention provide a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause a computer to perform the method described in the first aspect or any of its corresponding embodiments.

[0020] This application collects mixed power pulse data from different temperature ranges and constructs corresponding state equations, compensating for the neglect of low-temperature conditions by single room temperature / high temperature models and solving the problem of insufficient model accuracy under low temperature and high internal resistance. Secondly, by integrating multiple temperature state equations through temperature-related weighting, the effective fusion of RC models at different temperatures is achieved, making full use of battery characteristic parameters at each temperature. Then, weight optimization replaces noise matrix optimization, avoiding the algorithm stability defects caused by noise uncertainty. Finally, Kalman filtering is used to estimate SOC based on the target equivalent state equation, which not only ensures the estimation accuracy under all temperature conditions but also improves the algorithm stability, effectively solving the technical pain point that existing solutions cannot simultaneously achieve accuracy and stability across the entire temperature range. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1This is a flowchart illustrating a method for determining the state of charge of a battery according to some embodiments of the present invention. Figure 2 This is a flowchart illustrating another method for determining the state of charge of a battery according to some embodiments of the present invention. Figure 3 This is a flowchart illustrating another method for determining the state of charge of a battery according to some embodiments of the present invention. Figure 4 This is a structural block diagram of a battery state of charge determination device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] According to an embodiment of the present invention, a method for determining the state of charge of a battery 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.

[0025] This embodiment provides a method for determining the state of charge of a battery. Figure 1 This is a flowchart of a method for determining the state of charge of a battery according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Collect mixed power pulse data of the battery at different temperatures.

[0026] In this embodiment, the battery is first placed in a temperature-controlled test environment, and multiple temperature points covering actual application scenarios (such as -20℃, 0℃, 25℃, 45℃, etc.) are selected. For each temperature point, the battery is first allowed to stand until the temperature stabilizes, and then a specific pulse current sequence (including high-power discharge, charging and resting stages) is applied to the battery according to the Hybrid Power Pulse Characteristics (HPPC) test standard. The battery terminal voltage, current and time data of each pulse stage are recorded simultaneously. This operation is repeated to complete the test of all target temperature points, and finally the voltage-current-time data of the corresponding pulse conditions at different temperatures are obtained.

[0027] Step S102: Based on the mixed power pulse data at different temperatures, calculate the battery characteristic parameters at different temperatures and obtain the state equations at different temperatures.

[0028] In this embodiment of the application, based on mixed power pulse data at different temperatures, the battery characteristic parameters of the battery at different temperatures are calculated to obtain the state equations at different temperatures, including: Step A1: Calculate the battery characteristic parameters at different temperatures based on the mixed power pulse data at different temperatures.

[0029] Specifically, based on HPPC data at different temperatures, for each temperature point, the ohmic internal resistance of the battery (the ratio of the voltage difference before and after the pulse to the current) is calculated using the voltage change and current variation during the pulse phase. Then, by using the voltage recovery curve during the resting phase and combining the fitting method of the RC equivalent circuit model, the RC parameters such as polarization resistance and polarization capacitance at that temperature are obtained. Finally, the battery characteristic parameters (including internal resistance, RC parameters, etc.) corresponding to each temperature are determined.

[0030] Step A2: Based on the battery characteristic parameters at different temperatures, construct an equation framework that includes the input and output relationships, and add the process noise and observation noise terms at different temperatures to the equation framework to obtain the initial state equation at the corresponding temperature.

[0031] Specifically, based on the battery characteristic parameters at each temperature, an "input-state-output" equation framework is constructed: with battery current as input, battery state (such as state of charge and polarization voltage) as internal variables, and terminal voltage as output, the basic logic of state transition is formed; then, process noise (characterizing model uncertainty) and observation noise (characterizing measurement error) at that temperature are added to the framework to obtain an initial state equation that includes input, state, output, and noise.

[0032] Step A3: Construct the correspondence rules between input, output and noise in the initial state equation to obtain the state equations at different temperatures.

[0033] Specifically, the state equations are as follows: (1) In the formula, For the first temperature points Input at any time For the first temperature points Input at any time For the first temperature points Output at any moment For the first temperature points Time-based process noise, For the first temperature points Observational noise at any given moment; Step S103: The state equations at different temperatures are weighted and integrated using temperature-related weights to obtain the equivalent state equations.

[0034] In the embodiments of this application, the core matrices corresponding to the state equations at different temperatures are first defined (i.e., the state transition matrix A, control matrix B, and observation matrix C in the state equations at each temperature). These matrices are constructed based on the battery characteristic parameters at each temperature.

[0035] Temperature-related weights were then introduced. ,in Let be the weight coefficient for the i-th temperature point, and it must satisfy the sum of all weights ∑ The constraint is set to 1; then, for each type of matrix, the equivalent matrix is ​​calculated using a weighted summation of corresponding elements. (2) Finally, by substituting these equivalent matrices into the framework of the state equation and replacing the corresponding matrices at different temperatures, we can obtain an equivalent state equation applicable to multiple temperature scenarios. This equation can adapt to the battery characteristics at different actual temperatures through weights. The resulting equivalent state equation is as follows: (3) It should be noted that by integrating the state equations under multiple temperatures through weighting, the differences in battery characteristics at different temperatures are preserved, and the equivalent state equations can adaptively match the actual temperature conditions. This avoids the problem of decreased accuracy of a single temperature model in temperature fluctuation scenarios. At the same time, the weight constraints ensure the rationality and stability of the model, and improve the reliability of battery state estimation in complex temperature environments.

[0036] Step S104: Obtain the target weights and substitute them into the equivalent state equation to obtain the target equivalent state equation. Perform Kalman filtering based on the target equivalent state equation to obtain the state of charge of the battery. The target weights are used to represent the proportion of the state equation corresponding to different temperatures in the equivalent state equation.

[0037] In this embodiment of the application, before obtaining the target weight, the method further includes: obtaining the actual measured voltage and the battery model predicted voltage corresponding to the battery, and obtaining the theoretical weight, the estimated weight and the adjustment intensity corresponding to the current temperature; and constructing the target function based on the actual measured voltage, the battery model predicted voltage, the theoretical weight, the estimated weight and the adjustment intensity of the previous time step.

[0038] Specifically, during the actual operation of the battery, the voltage at the battery terminal is collected in real time by a voltage sensor to obtain the actual measured voltage. Simultaneously, the existing battery model (based on state equations at different temperatures) is invoked, and parameters such as the battery current at the current moment are input to calculate the predicted voltage of the battery model. .

[0039] Next, based on the actual temperature of the battery, and combined with the weighted-temperature correlation model obtained through pre-fitting temperature characteristic experiments, the theoretical weights corresponding to that temperature are determined. (That is, the ideal state equation weight allocation at this temperature); then, retrieve the estimated weights from the previous calculation cycle obtained from the system's historical data cache. .

[0040] Based on the robustness requirements of the battery model and the fluctuation characteristics of actual operating conditions, two adjustment intensity parameters, λ and μ, are preset. λ is used to control the degree of fit between the weights and the theoretical values, and μ is used to control the time smoothness of the weights.

[0041] Finally, calculate the actual measured voltage. With model predicted voltage The difference and the square Its function is to make the prediction results as close as possible to the actual battery state, thereby reducing voltage prediction errors; then, the current weight ω and the theoretical weight of the current temperature are calculated. The norm squared, multiplied by the adjustment intensity λ, i.e. This is used to constrain the weights to not deviate from the theoretically reasonable range at that temperature, avoiding a disconnect between weight allocation and temperature characteristics; then, the norm square of the current weight ω and the estimated weight at the previous moment is calculated and multiplied by the adjustment intensity μ, i.e. This is used to ensure smoother changes in weights over time, avoiding drastic fluctuations or jumps. Finally, these three parts are added together to obtain the objective function, as follows: (4) In the formula, J is the objective function. For actual voltage measurement, Battery model predicts voltage. As the current weight, Theoretical weight, , Both are for adjusting intensity. Let be the weight at time i. The weight is the weight at time i-1.

[0042] It should be noted that by integrating three types of constraints—minimizing voltage prediction error, ensuring that weights align with theoretical temperature values, and smoothing weight changes over time—the battery model prediction results are closely aligned with actual operating conditions. Furthermore, the allocation of temperature weights conforms to battery characteristics, while avoiding model instability caused by drastic weight fluctuations. Ultimately, this results in more reasonable target weights obtained through subsequent optimization, effectively improving the adaptability and estimation accuracy of the battery state equation under various temperature scenarios, and enhancing the reliability of the battery management system under complex operating conditions.

[0043] In this embodiment of the application, obtaining the target weight includes: determining the minimization direction with the weight as the optimization variable based on the objective function, thereby obtaining the weight optimization problem to be solved; and solving the weight optimization problem by recursive least squares method to obtain the target weight.

[0044] Specifically, the weighting coefficients corresponding to different temperatures are determined as the unique optimization variables; then, the optimization objective is defined as "minimizing the objective function J", that is, minimizing the combined result of voltage prediction error, the deviation of the weights from the theoretical values, and the time fluctuation of the weights; at the same time, the constraint condition ∑ of the weights is combined. =1, integrating these elements into a weight optimization problem with "weight ω as the variable, minJ as the objective, and the sum of weights being 1 as the constraint".

[0045] Next, initialize the initial estimated values ​​of the weights (the theoretical weights corresponding to the current temperature can be selected). As an initial value, the initial value of the covariance matrix of the recursive least squares method is also set; then, based on the actual measured voltage and model predicted voltage at the current moment, the gradient information of the objective function with respect to the weights is calculated and transformed into the observation vector and parameter matrix of the recursive least squares method; then, according to the iterative formula of the recursive least squares method, combined with the weight estimate and covariance matrix of the previous moment, the weight estimate at the current moment is updated to obtain the weight estimate; during this process, it is necessary to continuously verify whether the weights satisfy ∑ If the constraint of =1 is not met, normalization adjustment is performed; repeat the above iterative process until the change in weight is less than the preset convergence threshold, and the weight estimate obtained at this time is the final target weight.

[0046] Finally, first, retrieve the state matrices previously constructed based on different temperatures (i.e., the state transition matrix A, control matrix B, and observation matrix C corresponding to each temperature), then substitute the obtained target weights into the weighted calculation formula of the equivalent matrix, and perform weighted summation of corresponding elements of the same type of matrix to obtain the target equivalent matrix. Finally, these calculated target equivalent matrices are substituted into the framework of the state equations, replacing the corresponding matrices under the original multi-temperature conditions, to obtain the target equivalent state equations adapted to the current working conditions.

[0047] In this embodiment of the application, Kalman filtering is performed based on the target equivalent state equation to obtain the state of charge of the battery, including: Step B1: Obtain the historical charge state, input parameters, and error covariance matrix of the previous time step.

[0048] Specifically, the system extracts the state information from the previous time step (k-1) from the historical data cache of the battery management system, including the historical state of charge (SOC), polarization voltages U1 and U2; and simultaneously obtains the system input parameters from the previous time step, i.e., the battery operating current at that time. In addition, it is necessary to retrieve the updated posterior error covariance matrix obtained at the previous time step. This matrix reflects the error level of the state estimation at the previous time step.

[0049] Step B2 involves making predictions based on historical state of charge, input parameters, and the target equivalent state equation to obtain the current state estimate, and using the error covariance matrix to determine the prior error covariance matrix for the current time.

[0050] Specifically, the current state estimate is obtained by predicting the state based on the historical state of charge, input parameters, and the target equivalent state equation. This includes: extracting the equivalent matrix from the target equivalent state equation; determining the historical state estimate of the previous moment based on the historical state of charge; and substituting the product of the equivalent matrix and the historical state estimate, along with the input parameters, into the state equation function to obtain the current state estimate.

[0051] The specific formula is as follows: (5) (6) In the formula, The estimated state value at time k-1, For the input parameters at time k-1, The estimated state value at time k. Let be the prior error covariance matrix at time k-1. Let be the prior error covariance matrix at time k. The target equivalent transition matrix, Let be the transpose of the objective equivalent transition matrix. Noise adjustment factor, The temperature at time k is currently... For reference temperature, Let be the process noise, where the process noise is calculated from the covariance matrix of the process noise.

[0052] Based on formulas (5) and (6), the prediction process consists of two parts: one part uses the estimated value from the previous time step, i.e., time step k-1. The input at time k-1 is the sum of the inputs at the previous time. To the current moment The second part of the estimation is based on the error covariance matrix at time k-1. Predict the prior error covariance matrix at time k+1. .

[0053] It should be noted that, combining the equivalent state transition matrix in the target equivalent state equation and the process noise covariance matrix for temperature adaptation, the error covariance matrix from the previous time step is used in the formula. Estimate the prior error covariance matrix at the current time. This matrix reflects the error range of the state prediction. Step B3: Calculate the Kalman filter gain based on the prior error covariance matrix and the matrix in the target equivalent state equation.

[0054] Specifically, first, the equivalent observation matrix and the temperature-adapted measurement noise covariance matrix are retrieved from the target equivalent state equation. Then, the prior error covariance matrix, equivalent observation matrix, and measurement noise covariance matrix are substituted into the Kalman filter gain formula, and the Kalman filter gain at the current time is calculated through matrix operations. This gain serves to balance the reliability of the state prediction and the actual measurement, providing correction weights for subsequent state updates.

[0055] The specific formula is as follows: (7) In the formula, For Kalman filter gain, For the previous time (k) The posterior error covariance matrix at time 1, For the target equivalent observation matrix, Let be the transpose of the observation matrix in the target equivalent state equation. To measure the temperature sensitivity coefficient of noise, The current temperature. For reference temperature, Let be the measurement noise at the reference temperature, where the measurement noise is calculated from the measurement noise covariance matrix.

[0056] It should be noted that the calculation of the Kalman filter gain is dynamically affected by both temperature conditions and prior errors: when the actual temperature deviates significantly from the reference temperature, the temperature sensitivity coefficient of the measurement noise will amplify the weight of the measurement noise term, thereby adjusting the gain; and fluctuations in the prior error covariance matrix will also synchronously change the correction strength of the gain. Based on the dynamic adjustment mechanism, it is precisely to ensure that the gain can adapt to the changes in the reliability of the "state prediction" and "actual measurement" under different operating conditions, so as to ensure the accuracy and stability of subsequent state updates.

[0057] Furthermore, in formulas (6) and (7), the covariance matrix of the process noise and the covariance matrix of the measurement noise can be obtained from the following formulas: (8) (9) and These are the process noise covariance matrix and the measurement noise covariance matrix, respectively. and These represent process and observation noise at the reference temperature, respectively. The reference temperature is 25℃. and The sensitivity coefficient has a value range of (0.01~0.05 / ℃).

[0058] and The size of the EKF determines the level of confidence between model predictions and actual measurements. The larger the value, the more EKF trusts the test results. A larger EKF value indicates greater confidence in the model's predicted values. In battery systems, temperature changes directly impact the accuracy and reliability of model measurements. Increased temperature leads to higher chemical reaction rates, greater parameter fluctuations, and more complex model predictions. The voltage should be increased; if the temperature is too low, the voltage curve will be flat, and measurement noise will have a greater impact, which may affect measurement accuracy. It should be bigger, therefore and It should have a functional relationship with temperature T, if it remains constant. and, Therefore, in the high / low temperature region, EKF either converges too slowly or oscillates and diverges; as can be seen from formulas (6) and (7), the measurement with increasing temperature leads to increased noise in the process. To prevent the model from being inaccurate, the response needs to be increased. Otherwise, the Kalman gain in formula (7) will increase, leading to over-reliance on noise measurements; temperature changes will cause fluctuations in model parameters, resulting in inaccurate model predictions, therefore, it is appropriate to increase the Kalman gain. It can compensate for the growth of model error. Step B4 involves updating the state based on the Kalman filter gain, the state estimate, and the actual measured value at the current moment to obtain the optimal state estimate. It also involves determining the posterior error covariance matrix at the current moment using the prior error covariance matrix, where the posterior error covariance matrix is ​​used as the error covariance matrix at the current moment when determining the state of charge at the next moment.

[0059] Specifically, the state is updated based on the Kalman filter gain, the state prediction, and the actual measurement at the current moment to obtain the optimal state estimate. This includes: extracting the observation matrix from the target equivalent state equation and multiplying the observation matrix with the state prediction to obtain the predicted measurement; obtaining the measurement deviation between the actual measurement and the predicted measurement; multiplying the Kalman filter gain with the measurement deviation to obtain the correction; and calculating the optimal prediction based on the correction and the state prediction.

[0060] Understandably, the first step is to extract the equivalent observation matrix from the constructed target equivalent state equation to describe the mapping relationship between the state and the measurement output. Subsequently, the estimated state value at the current moment obtained from the EKF prediction process is retrieved. Finally, through matrix multiplication, the equivalent observation matrix is ​​obtained. Compared with the state prediction Multiplying them yields the model-based predicted measurement. .

[0061] The battery management system uses voltage sensors to collect the actual terminal voltage of the battery in real time, obtaining the actual measured value. The predicted measurement values ​​were then retrieved. The difference between the actual measured value and the predicted measured value is calculated by subtraction, which yields the measurement deviation. This deviation reflects the degree of deviation between the current model prediction and the actual state of the battery, and is the core basis for subsequent correction of the state estimate. That is, the larger the deviation, the more obvious the difference between the model prediction and the actual state.

[0062] The Kalman filter gain obtained during the calibration process is retrieved; then the measurement deviation is retrieved; and the Kalman filter gain is multiplied by the measurement deviation using matrix multiplication to obtain the state correction. The magnitude of this correction is determined by both the gain and the deviation: if the gain is large (indicating higher reliability of the actual measurement value), the deviation has a more significant impact on the correction; if the deviation is large, the magnitude of the correction will also increase accordingly.

[0063] Obtain the state prediction value obtained during the prediction process And the state correction amount; then, through addition, the state correction amount is added to the state prediction amount, that is, through the formula: (10) The optimal state estimate at the current moment is calculated. By integrating the state information predicted by the model with the deviation information measured in reality, the model's prediction of battery state changes is preserved, and the prediction error of the model is corrected by the actual measurement deviation. The final optimal state estimate can more accurately reflect the current true state of the battery.

[0064] Step B5: Extract the first element from the optimal state estimate to obtain the battery's state of charge.

[0065] Specifically, the vector structure for determining the optimal state estimate at the current moment is based on a second-order RC model, where the state vector is defined as follows: the first element of the vector corresponds to the state of charge. Therefore, the optimal state estimate can be directly derived from the vector. Extracting the first element yields the estimated state of charge (SOC) of the battery at the current moment. This estimate is the result of optimization through prediction, correction, and update processes, taking into account both model predictions and actual measurements, and accurately reflects the battery's current SOC.

[0066] It should be noted that this application relies on the target equivalent state equation for Kalman filtering, which is obtained by weighting and integrating the state equations at different temperatures using temperature-related weights. This fully incorporates the RC parameter characteristics under low-temperature conditions, solving the problem of insufficient SOC estimation accuracy caused by neglecting the high internal resistance characteristics at low temperatures in traditional single room temperature / high temperature models. Secondly, the filtering process uses the target equivalent state equation as its core, completing state prediction through historical charge states and input parameters, without needing to optimize the noise matrix using temperature parameters, thus avoiding the poor algorithm stability caused by the uncertainty of the noise itself. Then, the iterative transfer of the prior error covariance matrix and the posterior error covariance matrix constructs a closed-loop error correction mechanism, ensuring the continuity and reliability of state estimation and further improving the estimation accuracy across the entire temperature range. Finally, by extracting the first element of the optimal estimate to output the SOC, a dual guarantee of SOC estimation accuracy and stability across the entire temperature range is achieved.

[0067] This embodiment also provides a device for determining the state of charge of a battery. This device is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0068] This embodiment provides a device for determining the state of charge of a battery, such as... Figure 4 As shown, it includes: Acquisition module 401 is used to acquire mixed power pulse data of the battery at different temperatures; The calculation module 402 is used to calculate the battery characteristic parameters of the battery at different temperatures based on the mixed power pulse data at different temperatures, and obtain the state equation at different temperatures; Integration module 403 is used to weight and integrate state equations at different temperatures using temperature-related weights to construct equivalent state equations; The processing module 404 is used to obtain the target weights, substitute the target weights into the equivalent state equation to obtain the target equivalent state equation, and perform Kalman filtering based on the target equivalent state equation to obtain the state of charge of the battery. The target weights are used to represent the proportion of the state equation corresponding to different temperatures in the equivalent state equation.

[0069] In this embodiment of the application, the calculation module 402 is used to calculate the battery characteristic parameters of the battery at different temperatures based on the mixed power pulse data at different temperatures; construct an equation framework containing input and output relationships based on the battery characteristic parameters at different temperatures, and supplement the process noise and observation noise terms at different temperatures into the equation framework to obtain the initial state equation at the corresponding temperature; construct the correspondence rules between input, output and noise in the initial state equation to obtain the state equation at different temperatures.

[0070] In this embodiment of the application, the device further includes: a construction module, used to obtain the actual measured voltage and the battery model predicted voltage corresponding to the battery, and to obtain the theoretical weight, the estimated weight and the adjustment intensity corresponding to the current temperature; and to construct an objective function based on the actual measured voltage, the battery model predicted voltage, the theoretical weight, the estimated weight and the adjustment intensity of the previous time. In this embodiment of the application, the processing module 404 is used to determine the minimization direction with weights as optimization variables based on the objective function, thereby obtaining the weight optimization problem to be solved; and to solve the weight optimization problem by recursive least squares method to obtain the target weights.

[0071] In this embodiment, the expression of the objective function is as follows: ; In the formula, J is the objective function. For actual voltage measurement, Battery model predicts voltage. As the current weight, Theoretical weight, , Both are for adjusting intensity. Let be the weight at time i. The weight is the weight at time i-1.

[0072] In this embodiment, the processing module 404 is used to acquire the historical state of charge, input parameters, and error covariance matrix of the previous moment; to predict the state estimate of the current moment based on the historical state of charge, input parameters, and target equivalent state equation, and to determine the prior error covariance matrix of the current moment using the error covariance matrix; to calculate the Kalman filter gain based on the prior error covariance matrix and the matrix in the target equivalent state equation; to update the state based on the Kalman filter gain, the state estimate, and the actual measurement value of the current moment, to obtain the optimal state estimate; and to determine the posterior error covariance matrix of the current moment using the prior error covariance matrix, wherein the posterior error covariance matrix is ​​used as the error covariance matrix of the current moment when determining the state of charge at the next moment; and to extract the first element from the optimal state estimate to obtain the state of charge of the battery.

[0073] In this embodiment of the application, the processing module 404 is used to extract the equivalent matrix from the target equivalent state equation; determine the historical state estimate of the previous moment based on the historical charge state; and substitute the product between the equivalent matrix and the historical state estimate, as well as the input parameters, into the state equation function to obtain the state estimate of the current moment.

[0074] In this embodiment of the application, the formula for calculating the state prediction value is as follows: ; ; In the formula, The estimated state value at time k-1, For the input parameters at time k-1, The estimated state value at time k. Let be the prior error covariance matrix at time k-1. Let be the prior error covariance matrix at time k. The target equivalent transition matrix, Let be the transpose of the objective equivalent transition matrix. Noise adjustment factor, The temperature at time k is currently... For reference temperature, Let be the process noise, where the process noise is calculated based on the covariance matrix of the process noise. , Let be the covariance matrix of the process noise.

[0075] In this embodiment, the formula for calculating the Kalman filter gain is as follows: ; In the formula, For Kalman filter gain, For the previous time (k) The posterior error covariance matrix at time 1, For the target equivalent observation matrix, Let be the transpose of the observation matrix in the target equivalent state equation. To measure the temperature sensitivity coefficient of noise, The current temperature. For reference temperature, The measurement noise is at the reference temperature, where the measurement noise is calculated from the measurement noise covariance matrix. , This is the covariance matrix for measuring noise.

[0076] In this embodiment, the processing module 404 is used to extract the observation matrix from the target equivalent state equation, and multiply the observation matrix with the state prediction value to obtain the predicted measurement value; obtain the measurement deviation between the actual measurement value and the predicted measurement value; multiply the Kalman filter gain with the measurement deviation to obtain the correction amount; and calculate the optimal prediction value based on the correction amount and the state prediction value.

[0077] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system).

[0078] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0079] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0080] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0081] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0082] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0083] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0084] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for determining the state of charge of a battery, characterized in that, The method includes: Collect mixed power pulse data of the battery at different temperatures; Based on the mixed power pulse data at different temperatures, the battery characteristic parameters of the battery at different temperatures are calculated, and the state equations at different temperatures are obtained. By weighting the state equations at different temperatures with temperature-related weights, an equivalent state equation is obtained. Obtain the target weights and substitute them into the equivalent state equation to obtain the target equivalent state equation. Then, perform Kalman filter equation calculation based on the target equivalent state equation to obtain the state of charge of the battery. The target weights are used to represent the proportion of the state equation corresponding to different temperatures in the equivalent state equation.

2. The method according to claim 1, characterized in that, The method, based on hybrid power pulse data at different temperatures, calculates the battery characteristic parameters at different temperatures, obtaining the state equations at different temperatures, including: Based on the mixed power pulse data at different temperatures, the battery characteristic parameters of the battery at different temperatures are calculated; Based on the battery characteristic parameters at different temperatures, an equation framework containing input and output relationships is constructed, and process noise and observation noise terms at different temperatures are added to the equation framework to obtain the initial state equations at the corresponding temperatures. By constructing the correspondence rules between the input, output, and noise in the initial state equation, state equations at different temperatures are obtained.

3. The method according to claim 1, characterized in that, Before obtaining the target weight, the method further includes: Obtain the actual measured voltage and the battery model predicted voltage corresponding to the battery, and obtain the theoretical weight, the estimated weight and the adjustment intensity corresponding to the current temperature; Based on the actual measured voltage, the battery model predicted voltage, the theoretical weights, the estimated weights from the previous moment, and the adjustment intensity, an objective function is constructed.

4. The method according to claim 3, characterized in that, The acquisition of the target weight includes: Based on the objective function, the minimization direction with weights as optimization variables is determined, and the solution to be solved is obtained. The problem of weight optimization in solving the problem; The target weight is obtained by solving the weight optimization problem using the recursive least squares method.

5. The method according to claim 3, characterized in that, The expression for the objective function is as follows: ； In the formula, J is the objective function. For actual voltage measurement, Battery model predicts voltage. As the current weight, Theoretical weight, , Both are for adjusting intensity. Let be the weight at time i. The weight is the weight at time i-1.

6. The method according to claim 1, characterized in that, The process of calculating the state of charge of the battery by performing Kalman filtering equations based on the target equivalent state equation includes: Obtain the historical charge state, input parameters, and error covariance matrix of the previous time step; Based on the historical state of charge, the input parameters, and the target equivalent state equation, a state prediction value for the current moment is obtained, and the prior error covariance matrix for the current moment is determined using the error covariance matrix. Calculate the Kalman filter gain based on the prior error covariance matrix and the matrix in the target equivalent state equation; The state is updated based on the Kalman filter gain, the state estimate, and the actual measurement at the current moment to obtain the optimal state estimate. The posterior error covariance matrix at the current moment is determined using the prior error covariance matrix, wherein the posterior error covariance matrix is ​​used as the error covariance matrix at the current moment when determining the state of charge at the next moment. Extract the first element from the optimal state estimate to obtain the battery's state of charge.

7. The method according to claim 6, characterized in that, The step of predicting the state estimate at the current moment based on the historical state of charge, the input parameters, and the target equivalent state equation includes: Extract the equivalent matrix from the target equivalent state equation; The estimated value of the historical state at the previous moment is determined based on the historical state of charge. Substituting the product of the equivalent matrix and the historical state estimate, along with the input parameters, into the state equation function, yields the current state estimate.

8. The method according to claim 6, characterized in that, The formula for calculating the estimated state value is as follows: ； ； In the formula, The estimated state value at time k-1, For the input parameters at time k-1, The estimated state value at time k. Let be the prior error covariance matrix at time k-1. Let be the prior error covariance matrix at time k. The target equivalent transition matrix, Let be the transpose of the objective equivalent transition matrix. Noise adjustment factor, The temperature at time k is currently... For reference temperature, Let be the process noise, where the process noise is calculated based on the covariance matrix of the process noise. , Let be the covariance matrix of the process noise.

9. The method according to claim 6, characterized in that, The formula for calculating the Kalman filter gain is as follows: ； In the formula, For Kalman filter gain, For the previous time (k) The posterior error covariance matrix at time 1, For the target equivalent observation matrix, Let be the transpose of the observation matrix in the target equivalent state equation. To measure the temperature sensitivity coefficient of noise, The current temperature. For reference temperature, The measurement noise is at the reference temperature, where the measurement noise is calculated from the measurement noise covariance matrix. , This is the covariance matrix for measuring noise.

10. The method according to claim 6, characterized in that, The process of updating the state based on the Kalman filter gain, the estimated state value, and the actual measurement value at the current moment to obtain the optimal state estimate includes: The observation matrix is ​​extracted from the target equivalent state equation, and the predicted measurement value is obtained by multiplying the observation matrix with the state prediction value. Obtain the measurement deviation between the actual measured value and the predicted measured value; The correction amount is obtained by multiplying the Kalman filter gain by the measurement deviation. The optimal estimate is calculated based on the correction amount and the state estimate.

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