A battery state estimation method, system, and medium

Abstract

The application provides a battery state estimation method, system and medium, relates to the technical field of battery management systems, and establishes a second-order RC equivalent circuit model, adopts RLS online identification of ohmic resistance, polarization resistance and polarization capacitance and other parameters with a forgetting factor, constructs a micro state SRCKF channel and a macro parameter SRCKF channel, carries out real-time correction on a SOC influence factor and a current influence factor based on a filtering performance index, adjusts a process noise and a measurement noise covariance in a self-adaptive adjustment process according to a split-channel logic, and simultaneously introduces cross-channel collaborative self-adaption; SOC and terminal voltage estimation values are output from the state channel, dynamic resistance and maximum available capacity are output from the parameter channel, and SOH is calculated and output. The method improves the estimation accuracy and robustness under low SOC and large current working conditions, has good numerical stability, and is suitable for full-life cycle monitoring of power batteries.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery management systems, and particularly relates to a battery state estimation method, system and medium. BACKGROUND

[0002] In the operation process of electric vehicles and energy storage systems, the accurate estimation of key parameters such as battery residual capacity and health state by the battery management system is a core link for ensuring the safe operation of the battery, prolonging the service life and optimizing the system scheduling. However, the electrochemical characteristics of the power battery are complex, and its state and parameters will present strong nonlinearity and time variation with the charging and discharging cycle, temperature change and aging process, which brings challenges to high-precision estimation.

[0003] In the existing battery state estimation method, the extended Kalman filter linearizes the nonlinear system, which is easy to introduce linearization error, especially in the low SOC interval, the estimation accuracy decreases obviously; although the unscented Kalman filter approximates the nonlinear distribution through cubature points, the design of cubature points is easy to cause the numerical instability problem of the covariance matrix. Further, when a single filter channel simultaneously processes state and parameter estimation, it is difficult to adapt to the differentiated characteristics of the rapid change of the state and the slow time variation of the parameters, and it is easy to produce coupling interference and reduce the estimation robustness.

[0004] In order to improve the estimation performance, existing researches attempt to combine RLS with Kalman filter, but there are still problems such as the difficulty in accurately matching the noise covariance of different SOC intervals and current working conditions, the difficulty in dynamically optimizing the key influence factors based on the real-time performance of the filter, and the difficulty in realizing effective decoupling of state and parameter estimation in a single channel. Therefore, how to decouple state and parameter estimation through double filter channels and optimize noise covariance adjustment combined with real-time performance feedback has become a technical bottleneck to be solved. SUMMARY

[0005] Therefore, the application aims to at least solve one problem in the background art by providing a battery state estimation method, system and medium.

[0006] To achieve the above-mentioned purpose, the technical scheme of the application is as follows:

[0007] In a first aspect, the application discloses a battery state estimation method, comprising:

[0008] establishing a second-order RC equivalent circuit model, determining a battery state equation, a measurement equation and a parameter evolution equation, and defining a state vector for representing the polarization voltage and the residual capacity, and defining a parameter vector for representing the dynamic internal resistance and the maximum available capacity;

[0009] Online identification of the polarization resistance and the polarization capacitance of the second-order RC equivalent circuit model based on a recursive least square method with a forgetting factor, to obtain model parameters for subsequent filtering;

[0010] A dual-adaptive square-root cubature Kalman filter framework is constructed, which includes a state square-root cubature Kalman filter channel for battery state estimation and a parameter square-root cubature Kalman filter channel for battery parameter estimation;

[0011] An adaptive factor related to the SOC interval and the current working condition is defined, and the adaptive factor is corrected in real time based on a filtering performance index;

[0012] Based on the corrected adaptive factor, the process noise covariance and the measurement noise covariance of the state square-root cubature Kalman filter channel and the process noise covariance and the measurement noise covariance of the parameter square-root cubature Kalman filter channel are adaptively adjusted respectively, and a cooperative adaptive mechanism of state and parameter is introduced;

[0013] The battery terminal voltage estimation value and the remaining capacity estimation value are output through the state square-root cubature Kalman filter channel, and the dynamic internal resistance estimation value and the maximum available capacity estimation value are output through the parameter square-root cubature Kalman filter channel;

[0014] Based on the maximum available capacity estimation value, the battery health state is calculated, and the estimation results of terminal voltage, remaining capacity, maximum available capacity and health state are output.

[0015] Further, in the second-order RC equivalent circuit model:

[0016] The state vector is U1, U2 and SOC, wherein U1 and U2 are the polarization voltages of the two polarization branches respectively;

[0017] The parameter vector is R0 and Qn, wherein R0 is the dynamic internal resistance and Qn is the maximum available capacity;

[0018] The parameter evolution equation is a slow time-varying process.

[0019] Further, the recursive least square method includes:

[0020] Initialize the to-be-identified parameter vector and the covariance matrix, and set the forgetting factor;

[0021] Based on the terminal voltage measured value and the model predicted value, a regression vector is constructed;

[0022] The recursive least square gain is calculated and the to-be-identified parameter vector is updated;

[0023] The covariance matrix is updated;

[0024] Physical constraints are applied to the parameters to be identified, and smoothing is performed.

[0025] Furthermore, the relationship between open-circuit voltage and SOC in the measurement equation is obtained by fitting the experimental data with a polynomial.

[0026] Furthermore, the filtering performance index is constructed based on the mean, first-order autocorrelation coefficient, and variance of the voltage error, and is used to characterize the stability of the filter and the degree of error convergence.

[0027] Furthermore, the real-time correction of the adaptive factor includes:

[0028] Voltage error, SOC, and current data are stored using a sliding window.

[0029] Calculate the filtering performance in the low SOC range, medium SOC range, high SOC range, and small current range, medium current range, and large current range respectively;

[0030] When the performance in the low SOC range is lower than the preset ratio of the performance in the high SOC range, the adaptive factor used for SOC weight adjustment is increased.

[0031] When the performance in the high current range is lower than the preset ratio of the performance in the low current range, the adaptive factor used for current weight adjustment is increased.

[0032] An adaptive factor for SOC decay control is based on performance gradient adjustment within the SOC range.

[0033] Boundary constraints are then applied to the modified adaptive factor.

[0034] Furthermore, the process noise covariance of the state square root volume Kalman filter channel is adaptively adjusted according to the SOC and current, and the process noise weight is increased in the low SOC range and under high current conditions to compensate for the model uncertainty caused by nonlinearity and polarization effects.

[0035] Furthermore, the measurement noise covariance of the state square root volume Kalman filter channel is adaptively adjusted based on the sliding window variance of the terminal voltage residual to reduce the interference of abnormal measurements on state estimation.

[0036] Furthermore, the process noise covariance of the parameter square root volume Kalman filter channel is adaptively adjusted based on the statistical characteristics of the dynamic internal resistance change and the capacity change, and the measurement noise covariance of the parameter square root volume Kalman filter channel is adaptively adjusted based on the statistical characteristics of the voltage residual during the parameter update stage.

[0037] Furthermore, the cooperative adaptive mechanism for state and parameters includes:

[0038] When the voltage residual variance of the state square-root cubature Kalman filter channel continuously meets a preset abnormal condition, the process noise weight of the parameter square-root cubature Kalman filter channel is increased to promote parameter updating; when the capacity updating amplitude of the parameter square-root cubature Kalman filter channel meets a preset mutation condition, the process noise weight of the state square-root cubature Kalman filter channel is increased to adapt to state estimation requirements.

[0039] Further, the double adaptive square-root cubature Kalman filter channels both update the covariance in a square-root form to maintain the positive definiteness of the covariance matrix and improve numerical stability.

[0040] In a second aspect, the present application discloses a battery state estimation system, comprising:

[0041] A data acquisition module is configured to acquire real-time current and terminal voltage data of the battery.

[0042] A parameter identification module is configured to identify the parameters of the second-order RC equivalent circuit model online based on a recursive least squares method.

[0043] An influence factor correction module is configured to correct the adaptive factors related to the SOC interval and the current working condition in real time based on a filtering performance index.

[0044] A double adaptive noise adjustment module is configured to calculate the process noise covariance and the measurement noise covariance of the state filter channel and the parameter filter channel based on the corrected adaptive factors.

[0045] A double square-root cubature Kalman filter estimation module comprises a state square-root cubature Kalman filter unit and a parameter square-root cubature Kalman filter unit, and is configured to output the terminal voltage and SOC estimation results and the dynamic internal resistance and maximum available capacity estimation results.

[0046] A result output module is configured to calculate the health state based on the maximum available capacity estimation results and output the terminal voltage, SOC, maximum available capacity and health state of the battery.

[0047] Further, the result output module calculates the health state after smoothing the maximum available capacity estimation results.

[0048] In a third aspect, the present application discloses a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the method.

[0049] Compared with the prior art, the battery state estimation method, system and medium have the following advantages:

[0050] (1) The application realizes state and parameter estimation decoupling by constructing microstate SRCKF channel and macro parameter SRCKF channel, reduces coupling interference, and the adaptive logic of channel noise covariance can better match the characteristics of state rapid change and parameter slow change, thereby improving the estimation accuracy of SOC, terminal voltage, internal resistance and capacity;

[0051] (2) The application performs real-time correction on the adaptive factor based on the filtering performance index, and combines the optimization strategy of SOC partition and current partition, so that the noise covariance adjustment is more suitable for complex conditions such as low SOC and large current, and the robustness and stable output capacity in all working conditions are enhanced;

[0052] (3) The two filtering channels in the application update the covariance matrix in the form of square root, and maintain the positive definiteness of the matrix through Cholesky decomposition and QR decomposition, thereby reducing the risk of numerical divergence and improving the numerical stability of the algorithm;

[0053] (4) The RLS and double SRCKF in the application form a closed-loop optimization mechanism, the RLS updates the key parameters of the second-order RC model in real time, and the double SRCKF tracks the changes of internal resistance and capacity in the aging process of the battery, so that the key state estimation such as SOC and SOH is more suitable for the monitoring needs of the whole life cycle of the power battery. BRIEF DESCRIPTION OF DRAWINGS

[0054] The accompanying drawings, which form a part of the present application, are used to provide a further understanding of the present application, and the illustrative embodiments thereof and their descriptions serve to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0055] Figure 1 The method flowchart described in the embodiments of the application;

[0056] Figure 2 The terminal voltage estimation result comparison diagram described in the embodiments of the application;

[0057] Figure 3 The terminal voltage estimation error diagram described in the embodiments of the application;

[0058] Figure 4 The SOC estimation result comparison diagram described in the embodiments of the application;

[0059] Figure 5 The SOC estimation error diagram described in the embodiments of the application;

[0060] Figure 6 The SOH estimation result diagram described in the embodiments of the application. DETAILED DESCRIPTION

[0061] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0062] In the description of the present application, it should be understood that the orientations or positional relationships indicated by the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements indicated must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second" and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" and the like can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0063] In the description of the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected, it can be mechanical connection, or electrical connection, it can be directly connected, or indirectly connected through an intermediate medium, it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0064] The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0065] The present application aims at the problems of low estimation accuracy, poor robustness, insufficient adaptability in all working conditions and the like in the battery state estimation method in the prior art, and provides a battery state estimation method and system based on fusion of double adaptive square root cubature Kalman filtering and RLS, which realizes accurate cooperative estimation of battery SOC, SOH, terminal voltage and key parameters by decoupling state and parameter estimation through double filtering channels, in combination with real-time correction of influence factors and self-adaptive mechanism of channel noise covariance.

[0066] To achieve the above object, the technical scheme adopted by the present application mainly includes the following steps:

[0067] 1. Establish a second-order RC equivalent circuit model: adopt a second-order RC equivalent circuit model to describe the dynamic characteristics of the battery, define a state vector and a parameter vector, and establish a system state equation, a measurement equation and a parameter evolution equation;

[0068] 2. Online parameter identification of RLS: Based on the recursive least squares method with forgetting factor, the key parameters of the second-order RC model are identified online, providing an accurate model foundation for the dual SRCKF;

[0069] 3. Construction of Dual Adaptive SRCKF Framework: Construct two independent channels, a micro (state) SRCKF and a macro (parameter) SRCKF, define adaptive factors related to state and parameters, and adjust the factors in real time based on filtering performance indicators;

[0070] 4. Adaptive adjustment of noise covariance for each channel: To address the differentiated requirements of the two SRCKF channels, adaptive logic for Q and R is designed separately, and a collaborative adaptive mechanism of state and parameters is introduced to dynamically update the noise covariance.

[0071] 5. Dual SRCKF Joint Estimation: State of Charge (SOC) and terminal voltage are estimated using the state SRCKF, while internal resistance and maximum available capacity are estimated using the parameter SRCKF.

[0072] 6. SOH Calculation and Output: SOH is calculated based on the estimated maximum available capacity, and the estimated results are smoothed and output in real time.

[0073] Specifically, the establishment of the second-order RC equivalent circuit model is as follows:

[0074] The second-order RC equivalent circuit model includes the ohmic internal resistance. Two RC polarization branches and open-circuit voltage ,in:

[0075] State vector , , These are the polarization voltages of the two RC branches, and SOC is the remaining charge.

[0076] Parameter vector , For dynamic internal resistance, Maximum available capacity;

[0077] Equations of state: , where A is the state transition matrix, B is the input matrix, Ts is the sampling interval, I(k) is the real-time current, and w_state(k) is the state process noise;

[0078] Measurement equation: Where C = [-1, -1, 0], The experimental data were obtained by fitting an 8th-order polynomial. Noise for state measurement;

[0079] Parameter evolution equation: For the parameter process noise, assume the parameter is a slowly time-varying process.

[0080] Wherein, in the RLS online parameter identification, the following is specific:

[0081] The RLS algorithm with forgetting factor is used to identify the parameters of the second-order RC model , and the specific process is as follows:

[0082] 1. Initialize the parameter vector θ=[R1R2C1C2]= and the covariance matrix P=1000·I, and the forgetting factor λ=0.995, , the first / second polarization branch resistance and capacitance;

[0083] 2. Calculate the regression vector based on numerical differentiation , and the partial derivative is calculated by the small perturbation method;

[0084] 3. Calculate the RLS gain matrix: ;

[0085] 4. Update the parameter estimate , wherein is the measured terminal voltage, is the model predicted terminal voltage;

[0086] 5. Update the covariance matrix ;

[0087] Wherein, in the double adaptive SRCKF framework and noise covariance adjustment, the following is specific:

[0088] The double adaptive SRCKF framework includes micro (state) SRCKF and macro (parameter) SRCKF channels, and adjusts the noise covariance through channel adaptive and cross-channel collaborative methods:

[0089] (1) Q, R self-adaptation of micro (state) SRCKF

[0090] The state SRCKF is responsible for SOC and terminal voltage estimation, and the self-adaptation logic of its (process noise) and (measurement noise) is as follows:

[0091] Adaptation: adjust according to the SOC level and current size, and introduce adaptive parameters , , When SOC<0.2, amplify to adapt to the high slope characteristics of OCV-SOC; the larger the current, further amplify to compensate for the polarization effect error,​ The formula for calculation is:

[0092]

[0093] The covariance of the basic process noise in the state channel. For SOC weight adaptive factor, The SOC attenuation adaptive factor. This is the current weight adaptive factor. For real-time charging and discharging current, This is the battery's rated maximum current.

[0094] Adaptive: Based on the sliding window variance adjustment of the voltage residual, the variance σ² is calculated using the voltage residuals of 50 sampling points.

[0095]

[0096] like ,but ;otherwise = Abnormal interference can be avoided by reducing the measurement weight.

[0097] (2) Adaptive Q and R of macroscopic (parameter) SRCKF

[0098] The parameter SRCKF is responsible for estimating internal resistance and capacitance. (Process noise) and The adaptive logic for (measurement noise) is as follows:

[0099] Adaptive: Adjusts based on the variance of the parameter change rate, calculates the variance of the internal resistance change over 10 adjacent sampling points. and variance of capacity change ,

[0100]

[0101]

[0102] For the basic process noise covariance of the parameter channel, The average variance of the rate of change of internal resistance / capacitance. / The variance of the change in internal resistance / capacitance. This serves as a reference variance for parameter stability.

[0103] Adaptive: Adjusts the variance based on the voltage residuals during parameter updates, using a 50-sliding-window residual calculation for variance. ,

[0104]

[0105] VrvarSRCKF, .

[0106] (3) State and parameter collaborative self-adaptation

[0107] To achieve cross-channel optimization, collaborative logic is designed:

[0108] When the voltage residual variance of state SRCKF is continuously 5 sampling points higher than the threshold, it is determined that the parameters are mismatched, and the parameter update is promoted;

[0109]

[0110] For the strengthened parameter process noise covariance, the scaling factor .

[0111] When the of the parameter SRCKF is continuously 5 sampling points higher than the threshold, it is determined that the battery characteristics have suddenly changed, and the scaling factor of is additionally increased by 1.1 times to adapt to the state estimation demand.

[0112]

[0113] For the strengthened state process noise covariance, is the rated capacity of the battery, and the scaling factor .

[0114] (4) Adaptive factor real-time correction

[0115] Define filter performance index

[0116] Filter performance index The mean, first-order autocorrelation coefficient, and variance of the voltage residual are combined to quantify the stability and error convergence degree of the filter, with a value range of [0, 1], and the closer to 1, the better the filter performance.

[0117]

[0118] The mean of the voltage residual, is the normalization coefficient of the mean sub-index, is the first-order autocorrelation coefficient of the voltage residual, is the normalization coefficient of the autocorrelation coefficient sub-index, is the reference threshold of the variance sub-index, Variance of voltage residual.

[0119] Adaptive factor is corrected based on the index:

[0120] (SOC weight factor) correction: adapt to the nonlinearity of low SOC interval

[0121] The core role is to amplify the process noise covariance of low SOC interval , compensate for the nonlinearity error caused by high slope of OCV-SOC curve. The correction logic is based on "comparison of low SOC interval performance and high SOC interval performance", if the low SOC performance is insufficient, increase .

[0122] (1) SOC interval division and partition performance calculation

[0123] First, divide the SOC data in the window into 3 intervals, and calculate the filtering performance of each interval:

[0124]

[0125] , , is the data set of low / middle / high SOC interval, , is the filtering performance of low / high SOC interval.

[0126] (2) Correction formula

[0127] When the low SOC interval performance is lower than 80% (preset proportion) of the high SOC interval performance, increase by gradient descent method; otherwise, keep it unchanged:

[0128]

[0129] is the corrected SOC weight factor at the kth moment, is the historical value at the k-1th moment, is the learning rate, =0.01, is the preset proportion threshold of performance comparison, =0.8.

[0130] 2. (Current weight factor) correction: compensate for the polarization effect of large current

[0131] The core role is to amplify the process noise covariance of large current working condition , to compensate the model error caused by the polarization effect aggravation. The correction logic is based on the comparison between the large-current-interval performance and the small-current-interval performance. If the large-current performance is insufficient, the .

[0132] (1) Current-interval division and partition performance calculation

[0133] First, the current data in the window is divided into three intervals according to the absolute value (to avoid the influence of charge and discharge signs), and the filtering performance of each interval is calculated respectively:

[0134]

[0135] , , is the data set of the small / medium / large current interval, is the rated current, , is the filtering performance of the small / large current interval.

[0136] (2) Correction formula

[0137] When the large-current-interval performance is lower than 80% (preset proportion) of the small-current-interval performance, the is increased by gradient descent method; otherwise, it remains unchanged:

[0138]

[0139] is the current weight factor after correction at the kth moment, is the historical value at the (k-1)th moment, is the learning rate, = 0.02, is the preset proportion threshold of performance comparison, = 0.8.

[0140] (SOC decay factor) correction: balance the SOC-interval performance gradient

[0141] The core role of is to regulate the decay speed of as the SOC rises: The larger is, the faster the decay is as the SOC rises; The smaller is, the slower the decay is. The correction logic is based on the performance gradient between SOC intervals to avoid performance mutation in the low-to-medium and medium-to-high SOC intervals.

[0142] (1) SOC-interval performance gradient calculation

[0143] First, calculate the performance difference (gradient) in the low → medium and medium → high SOC ranges to reflect the smoothness of performance changes with SOC:

[0144]

[0145]

[0146] This represents the performance gradient in the low to medium SOC range. This represents the performance gradient in the medium to high SOC range. This represents the filtering performance in the SOC range.

[0147] (2) Correction formula

[0148] Adjust according to the anomalies of the two gradients respectively. :

[0149]

[0150] The SOC decay factor is corrected at time k. =0.005 is The learning rate =0.2 is the upper limit threshold for the low → medium SOC gradient. =-0.1 is the lower limit threshold for the medium to high SOC gradient.

[0151] 4. Modified boundary constraints: to prevent factor runaway.

[0152] The corrected adaptive factor must be forced to meet the boundary range between physical meaning and engineering reality to prevent the factor from becoming too large / too small due to abnormal data, thereby causing problems. abnormal: ∈[0.1,2.0]、 ∈[0.05,0.5]、 ∈[0.1,1.0]. If the corrected factor exceeds the boundary, the corresponding boundary value is taken as the final result, for example... When the revised value is 2.1, it should be forcibly set to 2.0. This constraint prevents the factor from increasing abnormally. Excessive magnification or abnormal reduction leads to Insufficient adaptation ensures the stability of noise covariance adjustment.

[0153] Specifically, the joint estimation process using two SRCKFs is as follows:

[0154] State SRCKF estimation

[0155] 1. Initialization phase

[0156]

[0157] State channel initial estimate vector, , is the initial estimate of the capacitor voltage for both polarization branches, is the initial estimate of the SOC, is the initial covariance square root matrix, is the initial state covariance matrix.

[0158] 2. Adaptive noise covariance calculation

[0159] Process noise covariance of the state channel and measurement noise covariance Adaptive adjustment based on SOC, current and voltage residuals:

[0160]

[0161] is the base process noise covariance of the state channel, is the SOC weight adaptive factor, is the SOC decay adaptive factor, is the current weight adaptive factor, is the real-time charge and discharge current, is the battery rated maximum current, is the battery rated maximum current, is the voltage residual sliding window variance.

[0162] 3. Volume point generation

[0163]

[0164]

[0165] is the jthstate volume point, is the standard volume point base vector, is the state vector dimension; is the identity matrix.

[0166] 4. Time update

[0167] (1) Volume point state transfer

[0168]

[0169] The state transition matrix A and the input matrix B are:

[0170]

[0171] Here is the state transition matrix. For the input matrix, The sampling time interval, For battery coulomb efficiency, This is the maximum usable capacity of the battery. / For the first / second polarization branch resistance, / For the first / second polarization branch capacitors.

[0172] (2) Prior state estimation

[0173]

[0174] These are prior state estimates. The weights are the state volume points.

[0175] (3) Covariance Square Root Update

[0176]

[0177]

[0178] Let be the prior square root of covariance matrix. The volume point matrix after time update. Let be the square root matrix of the process noise covariance.

[0179] 5. Measurement Update

[0180] (1) Observation volume point transfer

[0181]

[0182] Where the observation matrix Open circuit voltage It is an 8th-order polynomial:

[0183]

[0184] For the observed predicted value of the j-th volume point, The prior SOC value of the j-th volume point For ohmic internal resistance, These are the polynomial fitting coefficients, obtained through experimental calibration.

[0185] (2) Observation prediction and innovation calculation

[0186]

[0187]

[0188] is the prior prediction of terminal voltage, is the voltage innovation, is the measured value of terminal voltage at time k+1.

[0189] (3) Covariance and Kalman gain

[0190]

[0191]

[0192]

[0193] is the square root matrix of observation covariance, is the observation volume point matrix, is the square root of measurement noise covariance, is the state-observation mutual covariance matrix, is the state channel Kalman gain, is the transpose inverse matrix.

[0194] (4) State and covariance update

[0195]

[0196] is the optimal state estimate, and the estimated result is output. is the posterior covariance square root matrix.

[0197] Parameter SRCKF estimation

[0198] 1. Initialization phase

[0199]

[0200] is the initial covariance square root matrix of parameters.

[0201] 2. Adaptive noise covariance calculation

[0202]

[0203] is the basic process noise covariance of the parameter channel, is the average variance of internal resistance / capacitance change rate, / is the variance of the internal resistance / capacity change amount, is the variance of the parameter stable reference, is the variance of the parameter update voltage residual.

[0204] 3. Volume point generation

[0205]

[0206] is the jth parameter volume point, is the parameter vector dimension, is the parameter standard volume point basis vector.

[0207] 4. Time update

[0208] (1) Volume point parameter transfer

[0209]

[0210] (2) Prior parameter estimation

[0211]

[0212] (3) Covariance square root update

[0213]

[0214] 5. Measurement update

[0215] (1) Observation volume point transfer

[0216]

[0217] is the observation prediction value of the parameter volume point, is the measurement equation parameter Jacobian matrix.

[0218] (2) Observation prediction and innovation

[0219]

[0220]

[0221] is the parameter channel innovation.

[0222] (3) Kalman gain and parameter update

[0223]

[0224] is the parameter channel Kalman gain, For optimal parameter estimation.

[0225] Among them, in the SOH calculation and result output, the specific as follows:

[0226] 1. Calculation :

[0227]

[0228] For the rated capacity of the battery;

[0229] 2. Output results: real-time output , , and , store factor correction history and filter performance data.

[0230] Three kinds of algorithm improved A-DSRCKF-RLS (the improved algorithm of the invention patent), A-DSRCKF and A-DSRCKF-RLS results are compared, and the average absolute error (MAE) and root mean square error (RMSE) of the terminal voltage and SOC are compared as follows:

[0231]

[0232] On the other hand, the system architecture corresponding to the scheme includes the following modules:

[0233] 1. Data acquisition module: high-precision current sensor (precision ≤0.05%) and voltage sensor (precision ≤0.1%) are used to collect real-time current and terminal voltage data of the battery at a sampling interval of 0.1s. The data is transmitted to the subsequent module after filtering and pretreatment;

[0234] 2. Parameter identification module: based on RLS algorithm, receiving the original data of the data acquisition module and the error feedback of the double SRCKF estimation module, identifying the parameters of the second-order RC model online, and outputting to the double SRCKF estimation module;

[0235] 3. Influence factor correction module: store filter error data, calculate filter performance index, based on the performance difference of partition (SOC partition, current partition) real-time correction , and other adaptive factors, output to the double adaptive noise adjustment module;

[0236] 4. Double adaptive noise adjustment module: receiving the corrected adaptive factor, real-time SOC and current data, calculating , according to the adaptive logic of the channel, converting to square root form through Cholesky decomposition, and outputting to the double SRCKF estimation module;

[0237] 5. Double SRCKF estimation module: including state estimation SRCKF unit and parameter estimation SRCKF unit, respectively receiving model parameters of parameter identification module and covariance data of noise adjustment module, completing state and parameter estimation, and outputting to result fusion output module;

[0238] 6. Result fusion output module: smoothing the capacity estimation value, calculating SOH, outputting the estimation result through CAN bus or serial port, supporting LCD screen visual display, and reserving data uploading interface.

[0239] The above merely describes preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A battery state estimation method, characterized in that, include: A second-order RC equivalent circuit model is established to determine the battery state equation, measurement equation and parameter evolution equation. A state vector is defined to characterize the polarization voltage and remaining charge, and a parameter vector is defined to characterize the dynamic internal resistance and maximum usable capacity. The polarization resistance and polarization capacitance of the second-order RC equivalent circuit model are identified online using the recursive least squares method with a forgetting factor to obtain model parameters for subsequent filtering. A dual adaptive square root capacitive Kalman filter framework is constructed, which includes a state square root capacitive Kalman filter channel for battery state estimation and a parameter square root capacitive Kalman filter channel for battery parameter estimation. Define adaptive factors related to SOC range and current conditions, and correct the adaptive factors in real time based on filter performance indicators; Based on the corrected adaptive factor, the process noise covariance and measurement noise covariance of the state square root volume Kalman filter channel and the process noise covariance and measurement noise covariance of the parameter square root volume Kalman filter channel are respectively adaptively adjusted for each channel, and a collaborative adaptive mechanism of state and parameter is introduced. The battery terminal voltage estimate and remaining power estimate are output through the state square root volume Kalman filter channel, and the dynamic internal resistance estimate and maximum available capacity estimate are output through the parameter square root volume Kalman filter channel. The battery health status is calculated based on the estimated maximum available capacity, and the estimated results of terminal voltage, remaining charge, maximum available capacity and health status are output. The cooperative adaptive mechanism of state and parameters includes: When the voltage residual variance of the state square root volume Kalman filter channel continuously meets the preset abnormal conditions, the process noise weight of the parameter square root volume Kalman filter channel is increased to promote parameter updates. When the capacity update magnitude of the parameter square root volume Kalman filter channel meets the preset abrupt change condition, the process noise weight of the state square root volume Kalman filter channel is increased to adapt to the state estimation requirements.

2. The battery state estimation method according to claim 1, characterized in that, In the second-order RC equivalent circuit model: The state vectors are U1, U2 and SOC, where U1 and U2 are the polarization voltages of the two polarization branches, respectively. The parameter vector is R0 and Qn, where R0 is the dynamic internal resistance and Qn is the maximum available capacity; The parameter evolution equation is a slowly time-varying process of parameters.

3. The battery state estimation method according to claim 1, characterized in that, The recursive least squares method includes: Initialize the parameter vector to be identified and the covariance matrix, and set the forgetting factor; A regression vector is constructed based on the measured terminal voltage values ​​and the model prediction values. Calculate the recursive least squares gain and update the vector of parameters to be identified; Update the covariance matrix; Physical constraints are applied to the parameters to be identified, and smoothing is performed.

4. The battery state estimation method according to claim 1, characterized in that, The relationship between open-circuit voltage and SOC in the measurement equation was obtained by fitting experimental data with a polynomial.

5. The battery state estimation method according to claim 1, characterized in that, The filtering performance metrics are constructed based on the mean, first-order autocorrelation coefficient, and variance of the voltage error, and are used to characterize the stability of the filter and the degree of error convergence.

6. The battery state estimation method according to claim 1, characterized in that, Real-time correction of the adaptive factor includes: Voltage error, SOC, and current data are stored using a sliding window. Calculate the filtering performance in the low SOC range, medium SOC range, high SOC range, and small current range, medium current range, and large current range respectively; When the performance in the low SOC range is lower than the preset ratio of the performance in the high SOC range, the adaptive factor used for SOC weight adjustment is increased. When the performance in the high current range is lower than the preset ratio of the performance in the low current range, the adaptive factor used for current weight adjustment is increased. An adaptive factor for SOC decay control is based on performance gradient adjustment within the SOC range. Boundary constraints are then applied to the modified adaptive factor.

7. The battery state estimation method according to claim 1, characterized in that, The process noise covariance of the parameter square root volume Kalman filter channel is adaptively adjusted based on the statistical characteristics of the dynamic internal resistance change and the capacity change, and the measurement noise covariance of the parameter square root volume Kalman filter channel is adaptively adjusted based on the statistical characteristics of the voltage residual during the parameter update stage.

8. A battery state estimation system, based on the battery state estimation method according to any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect real-time battery current and terminal voltage data. The parameter identification module is used to identify the parameters of the second-order RC equivalent circuit model online based on the recursive least squares method. The influence factor correction module is used to correct adaptive factors related to SOC range and current conditions in real time based on filter performance indicators. The dual adaptive noise adjustment module is used to calculate the process noise covariance and measurement noise covariance of the state filter channel and the parameter filter channel respectively based on the corrected adaptive factor. The dual square root capacitive Kalman filter estimation module includes a state square root capacitive Kalman filter unit and a parameter square root capacitive Kalman filter unit, which are used for output voltage and SOC estimation results, as well as dynamic internal resistance and maximum available capacity estimation results, respectively. The results output module is used to calculate the health status based on the maximum available capacity estimation results and output the battery terminal voltage, SOC, maximum available capacity and health status.

9. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing a battery state estimation method as described in any one of claims 1 to 7.

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