A soc estimation method for household lithium iron phosphate energy storage system in high temperature environment

Abstract

The application relates to the technical field of energy storage system analysis, and discloses a SOC estimation method for a household lithium iron phosphate energy storage system under a high-temperature environment. The method comprises the following steps: placing a lithium iron phosphate battery in a constant-temperature environment at 35 DEG C, 40 DEG C and 45 DEG C for charging and discharging treatment, collecting open-circuit voltage data to construct a three-dimensional feature data set; performing six-element nonlinear least square fitting on the data set to construct a temperature-coupled state of charge mapping model; establishing a second-order equivalent circuit based on the mapping model, setting parameters as a binary function of the state of charge and temperature, and obtaining a dynamic equivalent circuit model; constructing a six-dimensional extended state vector, processing sensor signals through a Kalman filtering algorithm with adaptive adjustment of a covariance matrix based on a temperature gradient, and obtaining a state of charge value. The application solves the problem of how to accurately estimate the SOC of a household lithium iron phosphate energy storage system under a high-temperature environment, and overcomes the defect of a traditional fixed parameter model that has a decreased precision under a high-temperature working condition.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of energy storage system analysis, and particularly relates to a SOC estimation method for a household lithium iron phosphate energy storage system in a high-temperature environment. BACKGROUND

[0002] As an important part of distributed energy, the accurate estimation of the state of charge (SOC) of a lithium iron phosphate energy storage system is crucial for the safe operation and energy management of the system. Existing SOC estimation methods mainly include the ampere-hour integral method, the open-circuit voltage method, and the Kalman filter method. The ampere-hour integral method calculates the SOC by time-integrating the charging and discharging current, which has the advantage of good real-time performance, but has the problem of cumulative error, and the accuracy decreases significantly over a long period of use. The open-circuit voltage method estimates the SOC by using the correspondence between the terminal voltage of the battery in a static state and the SOC, which has high accuracy but requires a long time to rest to obtain an accurate open-circuit voltage. The Kalman filter method combines the equivalent circuit model of the battery to estimate the state, which can correct the cumulative error online, and is a widely used SOC estimation method at present. Traditional Kalman filter SOC estimation usually uses an equivalent circuit model with fixed parameters, and assumes that the battery characteristics remain unchanged under different working conditions.

[0003] However, the household lithium iron phosphate energy storage system faces complex and variable environmental conditions in actual application, especially in the high-temperature environment in summer, the electrochemical characteristics of the battery will change significantly. The main deficiency of the prior art is that the traditional SOC estimation method does not fully consider the influence of the high-temperature environment on the characteristics of the lithium iron phosphate battery, and the fixed parameter model cannot accurately reflect the dynamic characteristic changes of the battery under different temperature conditions, resulting in a significant decrease in the accuracy of the SOC estimation in the high-temperature environment. Since the household energy storage system usually adopts a sealed structure and relies on natural cooling, in a high-temperature environment above 40℃, the key parameters such as the internal resistance, capacity and open-circuit voltage of the battery will change with temperature, and the existing SOC estimation algorithm lacks an effective temperature compensation mechanism and cannot adapt to such parameter changes, which seriously affects the reliability and efficiency of the energy storage system under high-temperature working conditions. SUMMARY

[0004] The application provides an SOC estimation method for a household lithium iron phosphate energy storage system in a high-temperature environment, which is used to solve the problem of how to accurately estimate the SOC of a household lithium iron phosphate energy storage system in a high-temperature environment, and overcome the defect of the traditional fixed parameter model that the accuracy decreases under high-temperature working conditions.

[0005] In a first aspect, the application provides a SOC estimation method for a household lithium iron phosphate energy storage system in a high-temperature environment, which comprises: placing a lithium iron phosphate battery system in three high-temperature point constant temperature environments for layered charging and discharging treatment, collecting open circuit voltage data at each temperature point to obtain a three-dimensional feature data set; performing six-element nonlinear least squares fitting processing on the three-dimensional feature data set, constructing a multiple fitting function containing interaction terms to obtain a temperature-coupled state of charge mapping model; establishing a second-order equivalent circuit based on the state of charge mapping model, setting the circuit parameters as a binary function of the state of charge and temperature to obtain a parameter-adaptive dynamic equivalent circuit model; and constructing a six-dimensional extended state vector according to the dynamic equivalent circuit model, processing sensor signals through a Kalman filtering algorithm with adaptive adjustment of the covariance matrix based on the temperature gradient to obtain a state of charge value.

[0006] In a second aspect, the application provides a SOC estimation system for a household lithium iron phosphate energy storage system in a high-temperature environment, which comprises:

[0007] A discharging module for placing a lithium iron phosphate battery system in three high-temperature point constant temperature environments for layered charging and discharging treatment, collecting open circuit voltage data at each temperature point to obtain a three-dimensional feature data set;

[0008] A fitting module for performing six-element nonlinear least squares fitting processing on the three-dimensional feature data set, constructing a multiple fitting function containing interaction terms to obtain a temperature-coupled state of charge mapping model;

[0009] A mapping module for establishing a second-order equivalent circuit based on the state of charge mapping model, setting the circuit parameters as a binary function of the state of charge and temperature to obtain a parameter-adaptive dynamic equivalent circuit model;

[0010] A filtering module for constructing a six-dimensional extended state vector according to the dynamic equivalent circuit model, processing sensor signals through a Kalman filtering algorithm with adaptive adjustment of the covariance matrix based on the temperature gradient to obtain a state of charge value.

[0011] In a third aspect, a SOC estimation device for a household lithium iron phosphate energy storage system in a high-temperature environment is provided, which comprises: a memory and at least one processor, the memory having instructions stored therein; the at least one processor calling the instructions in the memory to enable the SOC estimation device for a household lithium iron phosphate energy storage system in a high-temperature environment to perform the above-mentioned SOC estimation method for a household lithium iron phosphate energy storage system in a high-temperature environment.

[0012] In a fourth aspect, a computer readable storage medium is provided, in which instructions are stored, when executed on a computer, cause the computer to perform the SOC estimation method for a household lithium iron phosphate energy storage system in a high temperature environment.

[0013] In the technical solution provided in the application, the lithium iron phosphate battery system is tested for layered charging and discharging at three high temperature points, and three-dimensional feature data sets are constructed by collecting open circuit voltage data at each temperature, thereby solving the problem of insufficient adaptability of the traditional SOC estimation method in a high temperature environment. A complete mapping relationship between temperature and SOC and open circuit voltage is established through systematic high temperature experiments. The three-dimensional feature data sets are subjected to six-element nonlinear least square fitting, a multivariate fitting function containing interaction terms is constructed, and a temperature-coupled state of charge mapping model is obtained. By introducing interaction terms and square terms of the open circuit voltage and the temperature, a more accurate nonlinear mathematical relationship than the traditional linear model is established, and the accuracy of the SOC and open circuit voltage mapping in a high temperature environment is improved. Based on the state of charge mapping model, a second-order equivalent circuit is established, the circuit parameters are set as a bivariate function of the SOC and the temperature, and a parameter-adaptive dynamic equivalent circuit model is constructed. The limitations of the traditional fixed parameter model are broken through, the circuit parameters are dynamically adjusted in real time according to the temperature and the SOC state, and the changes in the electrochemical characteristics in a high temperature environment are accurately reflected. According to the dynamic equivalent circuit model, a six-dimensional extended state vector is constructed, and the Kalman filter algorithm is used to process the sensor signals to obtain the SOC value by adaptively adjusting the covariance matrix according to the temperature gradient.

[0014] The application also uses an intelligent covariance matrix adjustment mechanism to automatically adjust the response characteristics of the filter algorithm according to the temperature change. The temperature gradient adaptive mechanism realizes intelligent optimization of the filter parameters by monitoring the temperature change rate in real time and dynamically correcting the process noise covariance matrix, thereby solving the problem of decreased SOC estimation accuracy in a high temperature environment. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 FIG. 1 is a schematic diagram of an embodiment of the SOC estimation method for a household lithium iron phosphate energy storage system in a high temperature environment in the application;

[0017] Figure 2 FIG. 2 is a schematic diagram of an embodiment of the SOC estimation system for a household lithium iron phosphate energy storage system in a high temperature environment in the application;

[0018] Figure 3 is a structural schematic block diagram of a SOC estimation device of a household lithium iron phosphate energy storage system in a high-temperature environment according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiment of the present application provides a SOC estimation method of a household lithium iron phosphate energy storage system in a high-temperature environment. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present application is described below. Please refer to Figure 1 One embodiment of the SOC estimation method of a household lithium iron phosphate energy storage system in a high-temperature environment according to the embodiment of the present application includes:

[0021] Step S101, place the lithium iron phosphate battery system in three high-temperature point constant temperature environments for layered charging and discharging processing, collect open circuit voltage data at each temperature point, and obtain a three-dimensional feature data set;

[0022] Step S102, perform six-element nonlinear least squares fitting processing on the three-dimensional feature data set, construct a multivariate fitting function containing interaction terms, and obtain a temperature-coupled state of charge mapping model;

[0023] Step S103, establish a second-order equivalent circuit based on the state of charge mapping model, set the circuit parameters as a bivariate function of the state of charge and the temperature, and obtain a parameter-adaptive dynamic equivalent circuit model;

[0024] Step S104, construct a six-dimensional extended state vector according to the dynamic equivalent circuit model, process the sensor signal through a Kalman filter algorithm with adaptive adjustment of the covariance matrix based on the temperature gradient, and obtain the state of charge value.

[0025] It can be understood that the execution subject of the present application can be a SOC estimation system of a household lithium iron phosphate energy storage system in a high-temperature environment, and can also be a terminal or a server, which is not limited here. The embodiment of the present application takes the server as an example for description.

[0026] Specifically, the lithium iron phosphate battery system is placed in a thermostat with precise temperature control function. The thermostat stabilizes the internal temperature at three high-temperature control points of 35°C, 40°C and 45°C respectively through a PID control algorithm, and the temperature control accuracy reaches ±0.5°C. The layered charge and discharge process adopts a step-by-step charge and discharge mode. First, the battery is charged at a 1C rate to 3.65V, and the cutoff current is set to 0.05C. After the charging is completed, the battery is left for 30 minutes to ensure the electrochemical reaction balance. Then, step-by-step discharging is performed, with a discharge depth of 5% DOD each time. After discharging, the battery is left for 10 minutes to measure the open-circuit voltage. This process is repeated 20 times until the battery is completely discharged. The charging process uses the same step-by-step method, starting from a completely discharged state and gradually charging to 100% SOC. The data acquisition system records the open-circuit voltage, current, temperature and timestamp corresponding to each SOC point at a frequency of 1Hz, forming a raw data matrix containing three dimensions of open-circuit voltage, temperature and state of charge. When constructing the three-dimensional feature data set, the open-circuit voltage data of each 21 SOC points at the three temperature points are associated and processed. After filtering and denoising and removing outliers, a complete three-dimensional feature data set containing 63 valid data points is obtained.

[0027] The three-dimensional feature data set is converted into a mathematical model through six-element nonlinear least squares fitting processing. The six-element fitting function includes six characteristic dimensions: the first-order term of open-circuit voltage, the first-order term of temperature, the square term of open-circuit voltage, the square term of temperature, the interaction term of open-circuit voltage and temperature, and the constant term. The least squares method performs multivariate regression analysis on 63 data points by constructing an objective function that minimizes the sum of squares of errors. In the coefficient matrix construction process, the cumulative sum, square sum and cross product sum of each characteristic term are calculated to form a 6x6 symmetric coefficient matrix and a 6x1 constant vector. The linear equation system is solved using the Gaussian elimination method, and the six fitting coefficients are determined through matrix decomposition and back substitution operations. After establishing the temperature-coupled state of charge mapping model, the fitting accuracy is verified through the determination coefficient calculation and residual analysis to ensure that the model can accurately describe the nonlinear relationship between open-circuit voltage, temperature and state of charge.

[0028] A second-order equivalent circuit is constructed based on the SOC mapping model, which contains an ohmic resistance R0 and two sets of RC parallel networks R1C1 and R2C2. Parameter identification is performed through pulse discharge tests, in which a 10-second pulse current is applied at each temperature point, and the voltage response curve is recorded. The ohmic resistance is calculated by the ratio of the voltage jump to the current at the pulse instant, and the polarization parameters are determined by the double-exponential fitting of the voltage recovery curve. The voltage recovery curve is expressed as the voltage equaling the open-circuit voltage minus two exponential decay terms, each containing an initial voltage amplitude and a time constant. The parameter adaptive mechanism constructs all circuit parameters as bivariate polynomial functions of the state of charge and temperature, and determines the function coefficients through multi-point data fitting. The dynamic equivalent circuit model automatically updates the circuit parameters according to the real-time state of charge and temperature values, realizing the dynamic adjustment of the circuit model.

[0029] A six-dimensional extended state vector is constructed, which contains the state of charge, ohmic resistance, first polarization voltage, first polarization capacitance, second polarization voltage, and second polarization capacitance. The Kalman filtering algorithm adopts a prediction-correction recursive structure, in which the prediction step calculates the prior state estimate according to the state transition equation, and the correction step updates the posterior state estimate using the voltage measurement. The temperature gradient adaptive adjustment mechanism monitors the temperature change rate, and when the temperature gradient exceeds a preset threshold, the diagonal elements of the process noise covariance matrix are increased by a proportional factor, enhancing the tracking ability of the filter to temperature changes. The Jacobian matrix calculates the partial derivatives of each state variable with respect to the observed voltage, and the Kalman gain matrix is calculated based on the prediction error covariance and the measurement noise covariance. Sensor signal preprocessing includes low-pass filtering and outlier detection, and the filtered battery terminal voltage, charge and discharge current, and environmental temperature data are input into the Kalman filtering algorithm for iterative calculation.

[0030] In a specific embodiment, the process of step S101 can specifically include the following steps:

[0031] The lithium iron phosphate battery system is placed in an incubator for temperature control processing, and three high-temperature control points of thirty-five degrees Celsius, forty degrees Celsius, and forty-five degrees Celsius are set to obtain a stable layered temperature test environment;

[0032] The lithium iron phosphate battery system in the layered temperature test environment is subjected to constant-current constant-voltage charging to the rated voltage, and then subjected to step-by-step discharge cycling at a fixed rate of five percent depth of discharge. The open-circuit voltage is measured after each discharge for ten minutes, and the open-circuit voltage sequence corresponding to twenty-one states of charge at each temperature point is obtained.

[0033] The open-circuit voltage sequence and the corresponding state of charge values and temperature values are associated in triplets, and the measured data is filtered and denoised and outliers are identified and removed to obtain an effective voltage data set after preprocessing;

[0034] The matrix construction process arranges the effective voltage data set into a matrix form according to the three dimensions of open circuit voltage, temperature and state of charge. The data of the three temperature points are combined and reorganized to obtain a complete three-dimensional feature data set containing sixty-three data points.

[0035] Specifically, the temperature control process realizes accurate temperature regulation through the temperature sensor and heating system built-in the thermostat. The temperature sensor adopts platinum resistance PT100, and the measurement accuracy reaches ±0.1℃. The heating system stabilizes the temperature in the box at three set values of 35℃, 40℃ and 45℃ respectively through the PID control algorithm. The layered temperature test environment refers to independent charge and discharge tests under different temperature conditions. The test environment at each temperature point is independent of each other, and the temperature stability is maintained within the range of ±0.5℃. The step-by-step discharge cycle process adopts a fixed 5% DOD discharge depth, i.e. the discharge amount is 5% of the rated capacity of the battery each time. The discharge starts from 100% SOC and gradually discharges to 0% SOC, a total of 20 discharge cycles plus the initial full charge state, obtaining 21 SOC measurement points. During the constant current and constant voltage charging process, the constant current stage charges at 1C rate until the terminal voltage reaches 3.65V, and then enters the constant voltage charging mode. When the charging current drops to 0.05C, the charging is stopped. The purpose of standing for ten minutes is to eliminate the polarization effect in the charging and discharging process, so that the internal electrochemical reaction of the battery reaches a balanced state. At this time, the measured terminal voltage is the open circuit voltage. The open circuit voltage sequence contains the voltage value corresponding to each SOC point, and forms a voltage-time sequence data in chronological order.

[0036] The triplet association process combines the three parameters of open circuit voltage, state of charge and temperature into a data triplet. Each triplet represents the open circuit voltage value under specific temperature and SOC conditions. The filtering and denoising process adopts a moving average filtering algorithm to smooth the continuously collected voltage data. The filter window length is set to 5 sampling points. The abnormal value identification adopts the 3σ criterion. The mean and standard deviation of the voltage data are calculated. The data points exceeding the range of ±3 times the standard deviation of the mean are marked as abnormal values and excluded. The effective voltage data set is a high-quality data set retained after filtering and abnormal value elimination.

[0037] The matrix construction process arranges the effective voltage data set into a matrix form according to the three dimensions of open circuit voltage, temperature and state of charge. The matrix's rows represent different data samples, and the columns represent different feature dimensions. The data merging and reorganization process uniformly arranges each of the 21 data points at the three temperature points to form a 63-row 3-column data matrix, each row containing a complete voltage-temperature-SOC data combination. The three-dimensional feature data set is the standardized data structure finally formed, containing complete SOC-OCV mapping information under all temperature conditions.

[0038] In a specific embodiment, the process of performing step S102 can specifically include the following steps:

[0039] The three-dimensional feature data set is processed by feature decomposition in six dimensions of open circuit voltage first order term, temperature first order term, open circuit voltage square term, temperature square term, open circuit voltage and temperature interaction term, constant term, to obtain a six-element feature vector matrix;

[0040] The six-element feature vector matrix is processed by coefficient matrix construction to calculate the cumulative sum, square sum and cross product sum of each feature term, to construct a six-order symmetric coefficient matrix and a constant vector, and to obtain a least square linear equation set;

[0041] The linear equation set is input into a matrix solver for Gaussian elimination method solving processing, and six fitting coefficients are calculated through matrix decomposition and back substitution operations to obtain a parameter vector of a multiple fitting function, wherein the multiple fitting function is as follows:

[0042]

[0043] Wherein SOC is the state of charge, V OCV is the open circuit voltage, T is the temperature value, a0, a1, a2, a3, a4, a5 are six fitting coefficients to be solved.

[0044] Based on the parameter vector, a nonlinear mapping function of the state of charge with respect to the open circuit voltage and the temperature is constructed, and the fitting result is processed by decision coefficient calculation and residual analysis to obtain a temperature-coupled state of charge mapping model for accuracy verification.

[0045] Specifically, the feature decomposition processing converts the three-dimensional feature data set containing 63 data points into six independent feature dimensions, the open circuit voltage first order term directly extracts the original voltage value, the temperature first order term extracts the corresponding temperature value, the open circuit voltage square term is obtained by squaring each voltage value, the temperature square term is also obtained by squaring the temperature value, the open circuit voltage and temperature interaction term is calculated by multiplying the voltage value and the corresponding temperature value, and the constant term is a value of 1 set for all data points. The six-element feature vector matrix is a 63x6 matrix, each row represents six feature values of a data sample, and each column represents all data points of a feature type.

[0046] The coefficient matrix construction processing is based on the mathematical principle of least square method, and needs to calculate all combinations between the six feature terms. The fitting function is set as:

[0047]

[0048] Wherein SOC is the state of charge, V OCVfor open circuit voltage, T is temperature value, a0, a1, a2, a3, a4, a5 are six fitting coefficients to be solved. The objective of the least square method is to minimize the sum of distances from the data points on the fitting line to the line, and the error square sum expression is:

[0049]

[0050] Wherein m represents the number of temperature points, n represents the number of SOC measurement points at each temperature point, i is the temperature serial number, and j is the SOC serial number.

[0051] The partial derivatives of each coefficient are obtained and set to 0 to obtain a linear equation group. For example:

[0052]

[0053] The constant vector element is calculated as:

[0054]

[0055] The Gaussian elimination method solves the processing by gradually eliminating the elements of the coefficient matrix to convert it into an upper triangular matrix form. First, the element in the first row and the first column is taken as the main element, and all elements below the first column are eliminated to zero. Then, the element in the second row and the second column is taken as the main element to eliminate the elements below the second column, and so on until an upper triangular matrix is formed. Back substitution operation starts from the last equation and gradually solves each unknown parameter from front to back, finally obtaining the parameter vector.

[0056] The nonlinear mapping function is constructed based on the obtained parameter vector, and the temperature-coupled state of charge mapping model can calculate the corresponding SOC value according to any open circuit voltage and temperature value. The determination coefficient and residual analysis are used to verify the fitting accuracy and ensure the accuracy of the model in high temperature environment.

[0057] In a specific embodiment, the process of performing step S103 can specifically include the following steps:

[0058] The linear equation group composed of the six-order symmetric coefficient matrix and the constant vector is subjected to the main element selection processing, and the element below the first column is eliminated by taking the element in the first row and the first column as the main element, to obtain the matrix form after partial elimination of the first column;

[0059] Based on the matrix form after partial elimination of the first column, the main element elimination processing is performed on the second column to the sixth column in turn, and the elements in the lower triangular part of the coefficient matrix are gradually eliminated to zero through row transformation operation, to obtain the upper triangular matrix and the corresponding modified constant vector;

[0060] The upper triangular matrix and the modified constant vector are subjected to back substitution solving processing, and the fitting coefficient values are calculated step by step from the last equation to the front, and the values of the six fitting coefficients are obtained by recursive solving through the method of substituting the previous equation.

[0061] The numerical results of the six fitting coefficients are used to construct a parameter vector, which is arranged in the order of constant term, open-circuit voltage first-order term, temperature first-order term, open-circuit voltage square term, temperature square term, and interaction term, to obtain a complete parameter vector of the multivariate fitting function.

[0062] Specifically, the pivot selection process is the core data processing step of the Gaussian elimination method. The element in the first row and the first column of the six-order symmetric coefficient matrix is taken as the pivot, and the numerical value of the element is the total number of all data points, i.e., 63. The elimination operation is performed by linear combination of the first row and other rows to eliminate the five elements below the first column one by one. Specifically, the second row is subtracted from the first row multiplied by the ratio of the first column element of the second row to the first column element of the first row. The third row to the sixth row use the same elimination operation. After the partial elimination of the first column, the matrix form is that all the elements below the first column are zero, and the numerical values of the elements in other columns change accordingly, and the corresponding elements of the constant vector are also adjusted. The pivot elimination process of the second column to the sixth column is performed in the same logic. The second column is eliminated by taking the second column element of the second row as the pivot to eliminate the second column elements of the third row to the sixth row. The third column is eliminated by taking the third column element of the third row as the pivot to process the fourth row to the sixth row. In this way, the sixth column is eliminated. The row transformation operation includes addition and subtraction operations and multiplication operations between rows. Each elimination needs to perform the same row transformation operation on the coefficient matrix and the constant vector to ensure the equivalence of the equation set. The upper triangular matrix refers to the matrix form in which all elements below the main diagonal are zero. The modified constant vector is the new constant vector obtained after all row transformation operations. The back substitution solving process starts from the last row of the upper triangular matrix. The sixth equation only contains the sixth unknown number, and the sixth fitting coefficient is obtained by dividing the sixth element of the constant vector by the element in the sixth row and the sixth column of the coefficient matrix. The calculation of the fifth fitting coefficient needs to substitute the sixth coefficient obtained into the fifth equation, subtract the product of the sixth coefficient and the corresponding coefficient from the fifth element of the constant vector, and then divide by the coefficient in the fifth row and the fifth column. The recursive solving calculates each fitting coefficient in the order from back to front. Each calculation needs to substitute the obtained coefficient into the current equation for elimination operation. The numerical results of the six fitting coefficients correspond to the coefficients of the fitting function, including the constant term coefficient, the open-circuit voltage first-order term coefficient, the temperature first-order term coefficient, the open-circuit voltage square term coefficient, the temperature square term coefficient, and the interaction term coefficient.

[0063] The parameter vector construction arranges the six fitting coefficients in a column vector in a specific order, which is strictly in accordance with the arrangement order of the terms in the fitting function, ensuring the correct correspondence between the parameter vector and the fitting function. The complete parameter vector contains all the coefficient information required to construct the temperature-coupled state-of-charge mapping model, and each coefficient corresponds to a specific term in the fitting function. Through these coefficient values, the mathematical relationship between the open-circuit voltage, temperature, and state of charge is determined.

[0064] In a specific embodiment, the process of performing step S104 can specifically include the following steps:

[0065] Based on the state-of-charge mapping model, a second-order equivalent circuit topology containing ohmic resistance, first polarization resistance, second polarization resistance, first polarization capacitance, and second polarization capacitance is constructed, obtaining a five-parameter equivalent circuit architecture;

[0066] Pulse discharge tests are performed on the five-parameter equivalent circuit architecture at each temperature point. The ohmic resistance value is calculated through the voltage jump variable and the current ratio, and the polarization parameters are calculated through double exponential fitting of the voltage recovery curve, obtaining a circuit parameter database under each working condition;

[0067] The ohmic resistance, polarization resistance, and polarization capacitance in the circuit parameter database are respectively constructed as bivariate polynomial functions of state of charge and temperature. The function coefficients are determined through multi-point fitting, obtaining a parameter function set of temperature-state-of-charge coupling;

[0068] Based on the parameter function set, a dynamic adjustment mechanism is established for real-time updating of circuit parameters with state of charge and temperature, converting the static equivalent circuit into a dynamic circuit model with variable parameters, obtaining a dynamic equivalent circuit model with adaptive parameters.

[0069] Specifically, the second-order equivalent circuit topology is constructed based on the open-circuit voltage function of the state-of-charge mapping model, and the internal electrochemical process of the battery is abstracted as a combination of five basic circuit elements. The ohmic internal resistance R0 represents the pure resistive loss inside the battery, the first polarization resistance R1 and the first polarization capacitance C1 form the first RC parallel network to simulate the fast polarization process of the battery, and the second polarization resistance R2 and the second polarization capacitance C2 form the second RC parallel network to simulate the slow polarization process of the battery. The five-parameter equivalent circuit architecture connects the open-circuit voltage source and the ohmic internal resistance in series, and then connects the two RC parallel networks in series, forming a complete circuit topology relationship, and each parameter corresponds to a specific physical phenomenon inside the battery. The pulse discharge test identifies the circuit parameters by applying a fixed current transient load to the battery, and the test process includes a pulse phase and a recovery phase. The voltage jump variable is calculated by measuring the jump amplitude of the battery terminal voltage at the discharge moment, and the ohmic internal resistance value is equal to the voltage jump variable divided by the pulse current. The voltage recovery curve records the trajectory of the battery terminal voltage changing with time after the pulse stops, and the curve reflects the discharge process of the two RC networks. The double exponential fitting algorithm fits the voltage recovery curve into the superposition form of two exponential decay functions, the first exponential term corresponds to the fast polarization process, and the second exponential term corresponds to the slow polarization process. The time constant and initial voltage amplitude of each RC network are obtained by fitting, and then the polarization resistance and polarization capacitance values are calculated. The circuit parameter database under each working condition contains all parameter values under different temperature and SOC conditions, forming a corresponding relationship table of parameters and working conditions.

[0070] The binary polynomial function constructs each circuit parameter as a polynomial form of SOC and temperature, and the function usually includes a linear term, a quadratic term and an interaction term. The multi-point fitting process uses the least squares method to determine the coefficients of the polynomial, and solves the optimal coefficient combination by minimizing the sum of squares of fitting errors. The parameter function set coupled with temperature and state-of-charge includes five independent binary functions, each function corresponding to a circuit parameter, and the function coefficients are obtained by regression analysis of the data points of the corresponding parameter under all working conditions. The establishment of the parameter function enables the real-time calculation of the circuit parameters according to the current SOC and temperature values.

[0071] The dynamic adjustment mechanism calls the corresponding parameter function to calculate the circuit parameter values under the current working condition by monitoring the SOC and temperature states of the battery in real time. The dynamic circuit model with variable parameters converts the originally fixed circuit parameters into variables that change with the state, and each parameter in the circuit equation becomes a function of SOC and temperature. The dynamic equivalent circuit model with adaptive parameters automatically updates the internal parameters according to the real-time state of the battery, ensuring that the circuit model always accurately reflects the current characteristics of the battery.

[0072] In a specific embodiment, the process of performing step S105 can specifically include the following steps:

[0073] According to the dynamic equivalent circuit model, a six-dimensional extended state vector containing the state of charge, ohmic internal resistance, first polarization voltage, first polarization capacitance, second polarization voltage and second polarization capacitance is constructed, a state transition equation and a voltage observation equation are established, and a Kalman filter state space model is obtained;

[0074] The real-time temperature change rate is input into the process noise covariance matrix for dynamic adjustment processing, and when the temperature gradient exceeds the preset threshold, the diagonal elements of the covariance matrix are increased by a proportional factor to obtain a temperature-sensitive adaptive covariance adjustment mechanism;

[0075] Based on the adaptive covariance adjustment mechanism, a Kalman filter prediction update algorithm is constructed, the Kalman gain is calculated through the Jacobian matrix, and the state vector and the covariance matrix are recursively updated to obtain a temperature-compensated Kalman filter algorithm;

[0076] The battery terminal voltage, charging and discharging current and environmental temperature sensor signals are input into the temperature-compensated Kalman filter algorithm for prediction correction iterative calculation processing, the state of charge information is extracted through the first component of the state vector, and the real-time state of charge value in the high-temperature environment is obtained.

[0077] Specifically, the six-dimensional extended state vector constructs the key state variables of the dynamic equivalent circuit model into a vector form, the first component is the state of charge SOC, the second component is the ohmic internal resistance R0, the third component is the first polarization voltage U1, the fourth component is the first polarization capacitance C1, the fifth component is the second polarization voltage U2, and the sixth component is the second polarization capacitance C2. The state transition equation describes the evolution rule of the state vector within a time step, the state of charge is updated by current integration, the ohmic internal resistance and the polarization capacitance are adjusted according to the temperature function, and the polarization voltage is recursively calculated by a first-order differential equation. The voltage observation equation establishes a mathematical relationship between the state vector and the measurable terminal voltage, the terminal voltage is equal to the open-circuit voltage minus the ohmic internal resistance voltage drop and the sum of the two polarization voltages, and the open-circuit voltage is calculated by the state of charge mapping model. The Kalman filter state space model contains four core elements: state transition matrix, observation matrix, process noise covariance matrix and measurement noise covariance matrix, which converts the nonlinear battery model into a linearized state space form. The temperature change rate is calculated by the difference between the current temperature and the temperature at the previous time divided by the time interval, reflecting the instantaneous change speed of the temperature. The dynamic adjustment of the process noise covariance matrix is based on the size of the temperature change rate, and the adjustment mechanism is triggered when the temperature gradient exceeds the preset threshold. The proportion factor is determined according to the size of the temperature change rate, the more intense the temperature change, the larger the proportion factor, and the more obvious the increase of the diagonal elements of the covariance matrix. The diagonal elements of the covariance matrix represent the uncertainty of each state variable, increasing the diagonal elements means increasing the distrust of the model prediction, forcing the filter to rely more on the measurement value for state correction. The temperature-sensitive adaptive covariance adjustment mechanism dynamically adjusts the response characteristics of the filter according to the change of the temperature environment, and enhances the tracking ability of the filter when the temperature changes rapidly.

[0078] The Kalman filter prediction update algorithm contains two stages of prediction step and update step, the prediction step calculates the prior state estimate and the prior covariance matrix by the state transition equation. The Jacobian matrix is the partial derivative matrix of the observation equation with respect to each component of the state vector, since the observation equation contains a nonlinear open-circuit voltage function, the partial derivative needs to be calculated by numerical differentiation or analytical differentiation. The Kalman gain matrix is calculated according to the prior covariance matrix, the Jacobian matrix and the measurement noise covariance matrix, which represents the weight allocation of the credibility of the predicted value and the measured value. The recursive update process allocates the prediction error to each state variable through the Kalman gain, updates the posterior state estimate and the covariance matrix, and forms the optimal state estimate at the current time. The temperature-compensated Kalman filter algorithm increases the temperature adaptability adjustment function on the basis of the standard Kalman filter, and automatically adjusts the filter parameters according to the temperature change.

[0079] The sensor signal preprocessing includes data acquisition, filtering and denoising, unit conversion and other operations. The battery terminal voltage is measured by a voltage sensor, the charging and discharging current is obtained by a current sensor, and the ambient temperature is monitored by a temperature sensor. The prediction correction iterative calculation process is executed in a fixed time interval, and a complete prediction-update process is performed in each calculation period. The first component of the state vector is extracted by array index operation to obtain the state of charge value, which is the SOC estimation result of the battery at the current time. The real-time state of charge value output frequency in high temperature environment is consistent with the execution frequency of the filtering algorithm, forming a continuous SOC estimation sequence.

[0080] In a specific embodiment, the process of performing step S106 can specifically include the following steps:

[0081] The battery terminal voltage, charging and discharging current, and ambient temperature sensor signals are subjected to data preprocessing, high-frequency noise and abnormal abrupt values are filtered out by a low-pass filter, and a filtered sensor data set is obtained;

[0082] Based on the sensor data set and the current state vector, the prior state prediction value is calculated, the time update recursive operation is performed through the state transition equation, and the dynamic equivalent circuit model parameters are updated according to the current temperature value, to obtain the state prediction result and the parameter update result;

[0083] The state prediction result is input into the observation equation to calculate the voltage prediction value, the innovation sequence is constructed by the difference between the measured voltage and the predicted voltage, the Jacobian matrix and the Kalman gain matrix are calculated, and the filtering gain and the innovation vector are obtained;

[0084] Based on the filtering gain and the innovation vector, the prior state prediction value is corrected, the posterior state vector and the covariance matrix are calculated through weighted update, the state of charge value is extracted from the first component of the state vector, and the state of charge value at the current time is obtained.

[0085] Specifically, data preprocessing ensures the quality and reliability of input data through synchronous acquisition and filtering of multi-sensor signals. The battery terminal voltage sensor acquires real-time voltage signals between the positive and negative electrodes of the battery, the charge and discharge current sensor measures the instantaneous current value flowing through the battery, and the environmental temperature sensor monitors the temperature changes around the battery. The low-pass filter uses a digital filtering algorithm to process the original sensor signals in the frequency domain. The cutoff frequency of the filter is determined based on the dynamic response characteristics of the battery system and is usually set in the range of several hertz to several tens of hertz. High-frequency noise mainly comes from circuit switches, electromagnetic interference, and sensor quantization noise. These high-frequency components are filtered out by the frequency selection characteristics of the low-pass filter. The abnormal mutation value detection uses a sliding window algorithm to calculate the deviation of the current data point from the historical data points. When the deviation exceeds the preset threshold, the data point is marked as an abnormal value and is replaced by interpolation processing. The filtered sensor data set contains denoised voltage, current, and temperature signals, which have good signal-to-noise ratio and continuity characteristics. The prior state prediction value calculation is based on the prediction stage of Kalman filtering, which uses the posterior state estimation of the previous time as the initial condition for the current time. The state transition equation describes the evolution law of each component of the state vector in the time dimension. The state of charge is updated by current integration, and the calculation formula is current SOC equal to the SOC of the previous time minus the current integration quantity divided by the battery capacity in the time interval. The time update recursive operation uses numerical integration methods to convert the differential equation of continuous time into the difference equation form of discrete time. The dynamic equivalent circuit model parameter update calls the pre-established parameter function according to the current temperature value to recalculate the numerical values of the ohmic resistance, polarization resistance, and polarization capacitance, which directly affect the voltage response characteristics of the battery. The polarization voltage state is updated by a first-order differential equation, and its evolution law follows the charge and discharge characteristics of the RC network. The state prediction result is the prior estimation of the six-dimensional state vector at the current time, and the parameter update result is the latest numerical value of each parameter in the equivalent circuit model.

[0086] The voltage prediction value calculation substitutes the state prediction result into the observation equation, and the observation equation establishes a mathematical relationship between the state vector and the observable. According to the equivalent circuit model, the terminal voltage is equal to the open circuit voltage minus the sum of the ohmic internal resistance voltage drop and the two polarization voltages, and the open circuit voltage is calculated by a mapping function of the state of charge and the temperature. The innovation sequence construction is calculated by the difference between the measured voltage and the predicted voltage, reflecting the accuracy of the model prediction and the reliability of the measurement information. The Jacobian matrix is the partial derivative matrix of the observation equation to each component of the state vector, and since the observation equation contains a nonlinear open circuit voltage function, the partial derivative values of each component need to be calculated by a numerical differentiation method. The Kalman gain matrix is obtained by matrix operation according to the prior covariance matrix, the Jacobian matrix and the measurement noise covariance matrix, and its numerical size determines the weight distribution ratio of the prediction information and the measurement information. The filtering gain is the Kalman gain matrix, and the innovation vector is the measurement residual vector, and both of them determine the direction and amplitude of the state correction.

[0087] The correction processing fuses the prediction information and the measurement information by weighted average, and the weighting coefficient is determined by the Kalman gain matrix. The posterior state vector calculation adopts the form of the prior state prediction value plus the product of the Kalman gain and the innovation vector, and this linear combination ensures the optimality of the state estimation. The covariance matrix update reflects the change of the state estimation uncertainty, and the recursive calculation is carried out by the Joseph stabilization formula for numerical stability. The state vector first component extraction directly obtains the estimated value of the state of charge by array index operation, and this value represents the percentage of the remaining power of the battery at the current time. The state of charge value at the current time is output as the final result of the SOC estimation, and its accuracy and real-time performance directly affect the operation efficiency of the energy storage system.

[0088] The SOC estimation method of the household lithium iron phosphate energy storage system in the high temperature environment in the embodiment of the application is described above, and the SOC estimation system of the household lithium iron phosphate energy storage system in the high temperature environment in the embodiment of the application is described below. Please refer to Figure 2 An embodiment of the SOC estimation system of the household lithium iron phosphate energy storage system in the high temperature environment in the embodiment of the application includes:

[0089] The discharge module 201 is configured to place the lithium iron phosphate battery system in three high temperature point constant temperature environments for hierarchical charging and discharging processing, collect open circuit voltage data at each temperature point, and obtain a three-dimensional feature data set.

[0090] The fitting module 202 is configured to perform six-element nonlinear least squares fitting processing on the three-dimensional feature data set, construct a multivariate fitting function containing interaction terms, and obtain a temperature-coupled state of charge mapping model.

[0091] The mapping module 203 is configured to establish a second-order equivalent circuit based on the state of charge mapping model, set circuit parameters as a binary function of the state of charge and temperature, and obtain a dynamic equivalent circuit model with adaptive parameters.

[0092] The filtering module 204 is configured to construct a six-dimensional extended state vector according to the dynamic equivalent circuit model, process a sensor signal by using a Kalman filtering algorithm with adaptive adjustment of a covariance matrix according to a temperature gradient, and obtain a state of charge value.

[0093] The above Figure 2 The SOC estimation system of the household lithium iron phosphate energy storage system under a high-temperature environment in the embodiment is described in detail from the perspective of a modular functional entity, and the SOC estimation device of the household lithium iron phosphate energy storage system under a high-temperature environment in the embodiment is described in detail from the perspective of hardware processing.

[0094] Referring to Figure 3 The SOC estimation device of the household lithium iron phosphate energy storage system under a high-temperature environment in the embodiment can be a server, and the internal structure of the SOC estimation device of the household lithium iron phosphate energy storage system under a high-temperature environment can be as shown in Figure 3 The SOC estimation device of the household lithium iron phosphate energy storage system under a high-temperature environment includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer is configured to provide computing and control capabilities. The memory of the SOC estimation device of the household lithium iron phosphate energy storage system under a high-temperature environment includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The database of the SOC estimation device of the household lithium iron phosphate energy storage system under a high-temperature environment is configured to store corresponding data in the embodiment. The network interface of the SOC estimation device of the household lithium iron phosphate energy storage system under a high-temperature environment is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the above method.

[0095] Those skilled in the art can understand Figure 3 The structure shown in the above

[0096] The application further provides a computer readable storage medium, which can be a nonvolatile computer readable storage medium or a volatile computer readable storage medium, and the computer readable storage medium stores instructions, and the instructions make a computer execute the steps of the SOC estimation method for the household lithium iron phosphate energy storage system in a high-temperature environment when the instructions are run on the computer.

[0097] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described here.

[0098] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the application or the whole or part of the technical solutions that make essential contributions to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for making a high-temperature environment household lithium iron phosphate energy storage system SOC estimation device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0099] The above embodiments are only used to illustrate the technical solutions of the application, rather than limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A method for estimating the State of Charge (SOC) of a residential lithium iron phosphate energy storage system under high-temperature conditions, characterized in that, The method includes: The lithium iron phosphate battery system was subjected to stratified charge and discharge processing in three constant temperature environments at high temperatures. Open circuit voltage data at each temperature point were collected to obtain a three-dimensional feature dataset. The three-dimensional feature dataset is subjected to a six-variable nonlinear least squares fitting process to construct a multivariate fitting function containing interaction terms, thereby obtaining a temperature-coupled state-of-charge mapping model. A second-order equivalent circuit is established based on the state-of-charge mapping model. The circuit parameters are set as a bivariate function of state of charge and temperature to obtain a dynamic equivalent circuit model with adaptive parameters. A six-dimensional extended state vector is constructed based on the dynamic equivalent circuit model. The sensor signal is processed by a Kalman filter algorithm that adaptively adjusts the covariance matrix by temperature gradient to obtain the state of charge value.

2. The SOC estimation method for a residential lithium iron phosphate energy storage system under high temperature conditions according to claim 1, characterized in that, The lithium iron phosphate battery system is subjected to stratified charge-discharge processing in three constant-temperature environments at three high temperatures. Open-circuit voltage data at each temperature point are collected to obtain a three-dimensional feature dataset, including: The lithium iron phosphate battery system was placed in a constant temperature chamber for temperature control. Three high temperature control points of 35 degrees Celsius, 40 degrees Celsius and 45 degrees Celsius were set to obtain a stable stratified temperature test environment. After the lithium iron phosphate battery system in the stratified temperature test environment is charged to the rated voltage by constant current and constant voltage, it is subjected to step discharge cycle treatment at a fixed rate and a discharge depth of 5%. After each discharge, the open circuit voltage is measured after resting for 10 minutes to obtain the open circuit voltage sequence corresponding to the 21 states of charge at each temperature point. The open-circuit voltage sequence is associated with the corresponding state of charge and temperature values ​​using a triplet. The measurement data is then filtered for noise reduction and outlier identification and removal to obtain a pre-processed effective voltage data set. Based on the effective voltage data set, a matrix was constructed according to three dimensions: open circuit voltage, temperature, and state of charge. The data from the three temperature points were merged and recombined to obtain a complete three-dimensional feature dataset containing sixty-three data points.

3. The SOC estimation method for a residential lithium iron phosphate energy storage system under high temperature conditions according to claim 1, characterized in that, The process of performing a six-variable nonlinear least squares fitting on the three-dimensional feature dataset to construct a multivariate fitting function including interaction terms, and obtaining a temperature-coupled state-of-charge mapping model, includes: The three-dimensional feature dataset is decomposed into six dimensions: first-order open-circuit voltage, first-order temperature, squared open-circuit voltage, squared temperature, interaction between open-circuit voltage and temperature, and constant, to obtain a six-element feature vector matrix. The coefficient matrix is ​​constructed by processing the six-element eigenvector matrix, and the cumulative sum, sum of squares and sum of cross products of each eigenterm are calculated to construct a sixth-order symmetric coefficient matrix and a constant vector, thereby obtaining a system of least squares linear equations. The linear equations are input into a matrix solver for Gaussian elimination. Six fitting coefficients are calculated through matrix decomposition and back substitution to obtain the parameter vector of the multivariate fitting function, which is shown below: ； Where SOC is the state of charge, V OCV Let be the open-circuit voltage, T be the temperature value, and a0, a1, a2, a3, a4, and a5 be the six fitting coefficients to be determined. Based on the parameter vector, a nonlinear mapping function of the state of charge with respect to open-circuit voltage and temperature is constructed. The coefficient of determination and residual analysis are performed on the fitting results to obtain a temperature-coupled state of charge mapping model with verified accuracy.

4. The SOC estimation method for a residential lithium iron phosphate energy storage system under high temperature conditions according to claim 3, characterized in that, The linear equation system is input into a matrix solver for Gaussian elimination. Six fitting coefficients are calculated through matrix decomposition and back substitution to obtain the parameter vector of the multivariate fitting function, including: The linear equation system composed of the sixth-order symmetric coefficient matrix and constant vector is subjected to principal component selection processing. The elements in the first row and first column are used as the principal component to perform elimination operations on the elements below the first column, resulting in the matrix form after partial elimination in the first column. Based on the matrix form after partial elimination in the first column, the second to sixth columns are sequentially subjected to pivot elimination. Through row transformation operations, the lower triangular elements of the coefficient matrix are gradually eliminated to zero, resulting in the upper triangular matrix and the corresponding correction constant vector. The upper triangular matrix and the correction constant vector are solved by back substitution. Starting from the last equation, the values ​​of each fitting coefficient are calculated step by step backward. The solution is obtained by substituting into the previous equation to obtain the numerical results of the six fitting coefficients. Based on the numerical results of the six fitting coefficients, a parameter vector is constructed. The parameter vector is then arranged in the order of constant term, first-order open-circuit voltage term, first-order temperature term, squared open-circuit voltage term, squared temperature term, and interaction term to obtain the complete parameter vector of the multivariate fitting function.

5. The SOC estimation method for a residential lithium iron phosphate energy storage system under high temperature conditions according to claim 1, characterized in that, The step of establishing a second-order equivalent circuit based on the state-of-charge mapping model, setting the circuit parameters as a bivariate function of state of charge and temperature, and obtaining a parameter-adaptive dynamic equivalent circuit model includes: Based on the state-of-charge mapping model, a second-order equivalent circuit topology containing ohmic internal resistance, first polarization resistor, second polarization resistor, first polarization capacitor, and second polarization capacitor is constructed to obtain a five-parameter equivalent circuit architecture. The five-parameter equivalent circuit architecture was subjected to pulse discharge tests at various temperature points. The ohmic internal resistance was calculated by voltage jump and current ratio, and the polarization parameters were calculated by double exponential fitting of voltage recovery curve to obtain a circuit parameter database for each operating condition. The ohmic internal resistance, polarization resistance, and polarization capacitance in the circuit parameter database are respectively constructed as bivariate polynomial functions of charge state and temperature. The coefficients of each function are determined by multi-point fitting to obtain the set of parameter functions coupled with temperature and charge state. Based on the set of parameter functions, a dynamic adjustment mechanism is established to update circuit parameters in real time with the state of charge and temperature, transforming the static equivalent circuit into a dynamic circuit model with variable parameters, thus obtaining a dynamic equivalent circuit model with adaptive parameters.

6. The SOC estimation method for a residential lithium iron phosphate energy storage system under high temperature conditions according to claim 1, characterized in that, The step of constructing a six-dimensional extended state vector based on the dynamic equivalent circuit model, and processing the sensor signal using a Kalman filter algorithm that adaptively adjusts the covariance matrix by temperature gradient to obtain the state of charge value includes: Based on the dynamic equivalent circuit model, a six-dimensional extended state vector containing the state of charge, ohmic internal resistance, first polarization voltage, first polarization capacitance, second polarization voltage, and second polarization capacitance is constructed. State transition equations and voltage observation equations are established to obtain the Kalman filter state space model. The real-time temperature change rate is input into the process noise covariance matrix for dynamic adjustment. When the temperature gradient exceeds a preset threshold, the diagonal elements of the covariance matrix are increased by a proportional factor to obtain a temperature-sensitive adaptive covariance adjustment mechanism. Based on the aforementioned adaptive covariance adjustment mechanism, a Kalman filter prediction update algorithm is constructed. The Kalman gain is calculated using the Jacobian matrix, and the state vector and covariance matrix are recursively updated to obtain a temperature-compensated Kalman filter algorithm. The battery terminal voltage, charging and discharging current, and ambient temperature sensor signals are input into the temperature-compensated Kalman filter algorithm for prediction, correction, and iterative calculation. The state of charge information is extracted through the first component of the state vector to obtain the real-time state of charge value under high temperature conditions.

7. The SOC estimation method for a residential lithium iron phosphate energy storage system under high temperature conditions according to claim 6, characterized in that, The step of inputting battery terminal voltage, charging / discharging current, and ambient temperature sensor signals into the temperature-compensated Kalman filter algorithm for prediction, correction, and iterative calculation includes: The battery terminal voltage, charging and discharging current, and ambient temperature sensor signals are preprocessed, and high-frequency noise and abnormal abrupt values ​​are filtered out by a low-pass filter to obtain the filtered sensor data set. Based on the sensor data set and the current state vector, the prior state prediction value is calculated, and the time update recursive operation is performed through the state transition equation. At the same time, the parameters of the dynamic equivalent circuit model are updated according to the current temperature value to obtain the state prediction result and the parameter update result. The state prediction results are input into the observation equation to calculate the voltage prediction value. An innovation sequence is constructed by the difference between the measured voltage and the predicted voltage. The Jacobian matrix and Kalman gain matrix are calculated to obtain the filter gain and the innovation vector. The prior state prediction value is corrected based on the filter gain and the innovation vector. The posterior state vector and covariance matrix are calculated by weighted update. The state of charge value is extracted from the first component of the state vector to obtain the state of charge value at the current time.

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