Adaptive linear modeling and state of charge estimation of power battery considering internal and external information

Abstract

The application relates to a power battery adaptive linear modeling and state of charge estimation method considering internal and external information of a battery, and belongs to the field of automobile power lithium battery modeling and SOC estimation. oc The method comprises the following steps: collecting dynamic data of the power battery, pre-processing the data, and preliminarily calculating the SOC of the power battery; adopting a segmented linear processing mode to establish a linear relationship between an open circuit voltage V oc and the SOC; establishing a power battery linear model considering internal and external information of the battery and a discrete state space equation thereof; adopting an adaptive forgetting factor least square algorithm to identify battery parameters on line; and estimating the SOC according to the on-line identified battery parameters through a noise adaptive Kalman algorithm. The application can overcome the problems that the existing lithium battery electrochemical model cannot consider the influence of internal and external factors of the battery on the battery model, the nonlinear battery model has high calculation burden, the estimation algorithm has high information redundancy, and the anti-noise capability is weak.

Description

Technical Field

[0001] This invention belongs to the field of automotive power lithium battery modeling and SOC estimation, and relates to an adaptive linear modeling and state of charge estimation method for power batteries that considers internal and external battery information. Background Technology

[0002] Lithium-ion batteries, with their significant advantages of low self-discharge rate, long cycle life, high energy density, and continuously decreasing cost, have become the core power source for the electric vehicle industry. However, with the booming development of the electric vehicle market and technological iterations, the widespread application of lithium-ion batteries has also brought a series of safety challenges, especially the frequent vehicle accidents caused by internal faults, which have attracted significant attention from the industry. Against this backdrop, the importance of the Battery Management System (BMS) is increasingly prominent, with accurate estimation of the battery's state of charge (SOC) becoming a core task of BMS management. A reliable and accurate SOC assessment system can not only provide crucial information on the remaining usable energy of the battery in real time, ensuring battery safety during driving, but also effectively extend the overall battery life and improve the operating efficiency of the battery system by optimizing charging and discharging strategies, which is of great significance for promoting the sustainable development of the electric vehicle industry.

[0003] Existing methods for estimating lithium-ion battery SoC can be broadly categorized into model-based methods and data-driven methods. Data-driven (deep learning) methods are computationally time-consuming, require massive amounts of data to train the model, and consume significant human and material resources. Model-based methods suffer from the following shortcomings: First, traditional lithium-ion battery electrochemical models struggle to account for the influence of internal and external factors. Second, traditional lithium-ion battery electrochemical models have low generalization capabilities. Third, traditional lithium-ion battery electrochemical models are nonlinear, resulting in high computational burden. Fourth, traditional estimation algorithms exhibit high information redundancy and weak noise resistance. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an adaptive linear modeling and state-of-charge estimation method for power batteries that considers internal and external battery information, so as to overcome the problems of existing lithium battery electrochemical models that cannot consider the influence of internal and external factors on the battery model, high computational burden of nonlinear battery models, high information redundancy of estimation algorithms, and weak noise resistance.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An adaptive linear modeling and state-of-charge estimation method for power batteries that considers internal and external battery information includes the following steps:

[0007] S1. Collect dynamic data of the power battery, preprocess the data, and preliminarily calculate the SOC of the power battery;

[0008] S2. Using a piecewise linear processing method, establish the open-circuit voltage V. oc Linear relationship with SOC;

[0009] S3. Establish a linear model of the power battery that considers internal and external information of the battery and its discrete state-space equations.

[0010] S4. Use the adaptive forgetting factor least squares algorithm to identify battery parameters online;

[0011] S5. The SOC is estimated based on the online identified battery parameters using the noise-adaptive Kalman algorithm.

[0012] Furthermore, in S1, the real-time data acquired from the power battery includes at least: current, open-circuit voltage, and temperature; among which, the state of charge (SOC) is initially calculated using the ampere-hour integration method.

[0013]

[0014] Where soc(t) is the SOC at time t; soc(0) is the initial SOC; C is the rated capacity of the battery in ampere-hours; and I(t) is the current at time t.

[0015] Furthermore, in S2, a piecewise linear regression method is used to handle higher-order nonlinear V. oc -SOC relationship, to obtain linear V oc The SOC relationship is expressed by the formula:

[0016] V oc (k)=f(soc)=b1(k)*soc(k)+b0(k)

[0017] Where soc(k) represents the SOC value at time step k, and b1(k) and b0(k) represent the V values ​​corresponding to soc(k). oc - The slope and intercept of the SOC linear piecewise curve.

[0018] Furthermore, in S3, considering the influence of internal and external factors on the power battery, a linear equivalent model of the power battery is established, which includes the following steps:

[0019] S31, Design Temperature Weighting Factor T weigh Aging-weighted factor A weight and SOC weighting factor SOC weight Among them, the temperature weighting factor T weight Represented as:

[0020]

[0021] Where, α TIt is the temperature effect coefficient, T ref This is a reference temperature;

[0022] Aging weighting factor A weight Represented as:

[0023]

[0024] Where, α A This is the aging impact coefficient, where A represents the degree of aging.

[0025] SOC weighting factor SOC weig Represented as:

[0026]

[0027] Where, α c It is the SOC influence coefficient, and SOC(k) is the SOC value at time step K;

[0028] S32. Limit the range of values ​​for each weighting factor to 0-1 using the Sigmoid activation function:

[0029]

[0030] V t =V oc (k)-Vd k -R0(p k ).I k = b1(k)*soc(k)+b0(k)-Vd k -R0(p k ).I k

[0031] Where, p k Defined as the total impact factor, it represents the combined influence of internal and external factors on the model parameters; Soc k and Soc k+1 Vd represents the SOC at the current time and the next time, respectively; k and Vd k+1 Represents the RC dynamic voltage at the current and next time points; R0(p k To consider p k The influence of battery internal resistance, R d (p k To consider p k The influence of polarization internal resistance, C d (p k To consider p k Influence of polarization capacitance; I k V t These represent the input current and output voltage at time step k, respectively; Ts Represents the sampling time, η represents the battery charge / discharge rate, and C n The values ​​represent the nominal capacity of the battery, and b1(k) and b0(k) represent the V values ​​corresponding to soc(k). oc - The slope and intercept of the SOC linear piecewise curve.

[0032] Furthermore, in S4, the process of online identification of battery parameters using the adaptive forgetting factor least squares algorithm includes the following steps:

[0033] S41, Combining the adaptive influence gain matrix M k Discretize the system output equation into least squares form;

[0034] S42. The recursive forgetting factor least squares algorithm is used to identify the parameter matrix θ online. k ;

[0035] S43. Design an adaptive strategy to adaptively update the forgetting factor λ;

[0036] S44. Calculate the battery internal resistance and polarization network parameters affected by internal and external factors based on the identified parameters.

[0037] Furthermore, in S41, let y = V oc -V t The system output equation is then expressed as:

[0038] V oc (k)-V t (k)=b1(V oc (k-1)-V t (k-1))+a1I k +a2I k-1

[0039] The discretized least squares form is:

[0040]

[0041] In the formula, and θ k = [b1, a1, a2] are the data matrix and the parameter vector matrix to be identified, respectively;

[0042] Adaptive influence gain matrix M k Represented as:

[0043]

[0044] Where m1, m2, and m3 are combinations of SOC weighting factors, temperature weighting factors, and aging weighting factors;

[0045] In S42, we have:

[0046]

[0047] In the formula, P k-1 and P k Let represent the covariance matrices at time k-1 and time k, respectively; K represents the input matrix; LS，k This represents the gain value of the RLS algorithm at time k;

[0048] In S43, we have:

[0049]

[0050] Where λ(k) is the forgetting factor, λ min λ represents the minimum value of the set forgetting factor. max This represents the maximum value of the set forgetting factor, e. base The expected error value is represented by e(k), which represents the estimation error at time step K, and ε(k) represents the output voltage estimation error at time k; h is a constant value, representing the algorithm's sensitivity to error.

[0051] In S44, we have:

[0052]

[0053] Where b1, a1, a2 are parameters identified by the RLS algorithm, used to calculate the battery parameter R0(p k ), R d (p k C d (p k ), R0(p k R represents the battery's internal resistance, which is affected by internal and external factors. d (p k C represents the polarization resistance influenced by internal and external factors. d (p k ) is the polarization capacitance affected by internal and external factors.

[0054] Furthermore, in S5, the identified battery parameters are passed to the noise-adaptive Kalman filter algorithm for SOC estimation, which includes the following process:

[0055] S51. Considering noise, obtain the discretized state-space equation of the battery model used for SOC estimation.

[0056] S52. The optimal estimate of SOC at each time step is obtained by using the recursive process of the standard Kalman filter algorithm.

[0057] S53. The noise value is adjusted by adopting a residual-based noise adaptive strategy.

[0058] Furthermore, in S51, the discretized state-space equation is expressed as:

[0059]

[0060] In the formula, w k V k These are system state noise and system measurement noise, respectively. k B k C k They represent A respectively k B k C k Let represent the state transition matrix, input matrix, and observation matrix, respectively, and we have:

[0061]

[0062] C k = [-1b1(k)]

[0063] R0(p) identified online k ), R d (p k C d (p k )Parameters are updated in real time A k B k matrix.

[0064] Furthermore, in S52, the process of obtaining the optimal estimate of SOC at each time step using the standard Kalman filter algorithm includes:

[0065] State prediction: x k - =A k-1 x k-1 +B k-1 μ k-1 ;

[0066] State prediction covariance: P k - =A k-1 P k-1 A k-1 T +Q k-1 ;

[0067] Calculate the Kalman gain: K k =P k - C k T (C k P k - C kT +R k ) -1 ;

[0068] Status update: x k + =x k - +K k (y k -C k x k - );

[0069] Covariance update: P k + =(IK k C k )P k - .

[0070] Furthermore, in S53, the residual-based noise adaptive strategy is expressed as:

[0071] ero k =V k -V e

[0072]

[0073] Among them, V k V represents the true value of the terminal voltage at time step k. e ero represents the estimated terminal voltage of the Kalman filter output at time step k. k H represents the error between the Kalman estimated voltage and the actual terminal voltage at time k; e The sliding window technique is used to calculate the sum of squared residuals, where M1 represents the size of the sliding window, and H... e K represents the mean of the sum of squared residuals. k R represents the Kalman gain. k For the adaptively updated input noise value, Q k The output noise value is updated adaptively.

[0074] The beneficial effects of this invention are as follows:

[0075] This invention establishes a power lithium-ion battery model that considers internal and external battery information. It invents weighting factors for internal and external factors such as SOC and temperature, and an adaptive gain weighting matrix to consider the dynamic influence of SOC, temperature, and other internal and external factors on the battery model, improving model accuracy and estimation precision. Furthermore, this invention establishes a linear power battery model, reducing the computational load of automotive BMS and improving real-time applicability. It also employs an adaptive forgetting factor least squares algorithm for parameter identification, dynamically adjusting the forgetting factor in real time to solve information redundancy and improve algorithm stability. Finally, it uses a noise-adaptive Kalman filter algorithm for SOC estimation and invents a noise-adaptive strategy to adjust noise parameters, solving the problems of low accuracy and weak noise resistance of lithium battery models caused by environmental noise changes.

[0076] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0077] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0078] Figure 1 This is a simplified flowchart of the adaptive linear modeling and state-of-charge estimation method for power batteries that considers internal and external battery information according to the present invention.

[0079] Figure 2 The linear V obtained by the piecewise linear method oc -SOC relationship diagram;

[0080] Figure 3 A power battery model diagram considering internal and external factors such as SOC and temperature;

[0081] Figure 4 This is a detailed flowchart illustrating the adaptive linear modeling and state-of-charge estimation method for power batteries that considers internal and external battery information according to the present invention. Detailed Implementation

[0082] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0083] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0084] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0085] Please see Figures 1-4 This is an adaptive linear modeling and state-of-charge estimation method for power batteries that takes into account internal and external information of the battery.

[0086] Example

[0087] This embodiment proposes an adaptive linear modeling and state-of-charge estimation method for power batteries that considers both internal and external battery information, such as... Figure 1 As shown, it includes the following steps:

[0088] S1: Collect dynamic data of the power lithium-ion battery, preprocess the input current and voltage data to obtain preprocessed battery data, and preliminarily calculate the SOC of the power battery.

[0089] S2: Piecewise linear processing of open-circuit voltage V oc -SOC data, to obtain linear V oc -SOC relationship;

[0090] S3: Establish a linear model of the power battery that considers internal and external information of the battery, and obtain the discrete state-space equation of the model;

[0091] S4: Online identification of battery parameters using an adaptive forgetting factor least squares algorithm.

[0092] S5: The identified battery parameters are passed to the noise-adaptive Kalman algorithm for online SOC estimation.

[0093] In S1 of this embodiment, battery data and information are acquired in real time, and the data processing includes: acquiring data from the actual lithium-ion battery to obtain information such as current, open-circuit voltage, and temperature, and preliminarily calculating the SOC using the ampere-hour integration method.

[0094]

[0095] Where soc(t) is the SOC at time t, soc(0) is the initial SOC (typically ranging from 0 to 1 or 0% to 100%), C is the rated capacity of the battery (in ampere-hours, Ah), and I(t) is the current at time t (positive during charging and negative during discharging).

[0096] In S2 of this embodiment, as Figure 2 As shown, a piecewise linear regression method is used to handle higher-order nonlinear V. oc -SOC relationship, to obtain linear V oc The SOC relationship is expressed by the formula:

[0097] V oc (k)=f(soc)=b1(k)*soc(k)+b0(k)

[0098] Where soc(k) represents the SOC value at time step k, and b1(k) and b0(k) represent the v corresponding to soc(k) respectively. oc - The slope and intercept of the SOC linear piecewise curve.

[0099] In S3 of this embodiment, considering the influence of internal and external factors such as SOC and temperature on the power battery, and combining LPV theory, a linear equivalent model of the power battery considering the influence of factors such as temperature change and SOC on battery parameters is established, such as... Figure 3 As shown, the specific steps include:

[0100] S31, Using p k The sigmoid activation function is used to quantify the impact of internal and external battery information on the power battery, and a temperature weighting factor is designed:

[0101]

[0102] Where, α TIt is the temperature effect coefficient, T ref This is a reference temperature.

[0103] Design aging weighting factor:

[0104]

[0105] Where, α A It is the aging effect coefficient. A is the degree of aging, with a value between (0,1). New battery: very few cycles: A≈0; moderate aging: A≈0.5-0.8; severe aging: A>0.8.

[0106] Design SOC weighting factor:

[0107]

[0108] Where, α c It is the SOC influence coefficient, and SOC(k) is the SOC value at time step K.

[0109] S32. The Sigmoid activation function is used to limit the values ​​of each weighting factor to the range of 0-1:

[0110]

[0111] V t =V oc (k)-Vd k -R0(p k ).I k = b1(k)*soc(k)+b0(k)-Vd k -R0(p k ).I k

[0112] Where, p k Defined as the total impact factor, it represents the combined influence of internal factors such as SOC and aging degree, and external factors such as operating environment temperature and noise on model parameters. k+1 Vd represents the SOC at the next time step. k+1 Soc represents the RC dynamic voltage at the next moment. k Vd represents the current state of charge (SOC). k R0(p) represents the current RC dynamic voltage. k To consider p k The influence of battery internal resistance, R d (p k To consider p k Influence of polarization internal resistance, C d (p k To consider p k The influence of polarization capacitance. Ik V t These represent the input current and output voltage at time step k, respectively. s Represents the sampling time, η represents the battery charge / discharge rate, and C n The values ​​represent the nominal capacity of the battery, and b1(k) and b0(k) represent the V values ​​corresponding to soc(k). oc - The slope and intercept of the SOC linear piecewise curve.

[0113] In S4 of this embodiment, the battery parameters are identified online using the Adaptive Forgetting Factor Least Squares (AFFRLS) algorithm, specifically including the following steps:

[0114] S41. Adaptive influence gain matrix M is used. k The impact of internal and external factors such as SOC and temperature on the power battery model is quantified. Among them, SOC, temperature, aging and other factors have different effects on the model parameters. Practical applications should be considered according to the requirements.

[0115] Combine M k Let y = V oc -V t Discretize the system output equation into least squares form, where the system output equation is expressed as:

[0116] V oc (k)-V t (k)=b1(V oc (k-1)-V t (k-1))+a1I k +a2I k-1

[0117] The discretized least squares form is:

[0118]

[0119] In the formula, and θ k = [b1, a1, a2] are the data matrix and the parameter vector matrix to be identified, respectively.

[0120] Adaptive influence gain matrix M k Represented as:

[0121]

[0122] Where m1, m2, and m3 are combinations of weighted factors for SOC, temperature, and aging. When considering the impact of SOC on the battery, SOC primarily affects the dynamic response rate of current to voltage, where m1 = 1.

[0123] S42. The recursive forgetting factor least squares (FFRLS) algorithm is used to identify the parameter matrix θ online. k ;

[0124]

[0125] In the formula, P k-1 and P k Let represent the covariance matrices at time k-1 and time k, respectively; K represents the input matrix; LS,k This represents the gain value of the RLS algorithm at time k;

[0126] S43. Design an adaptive strategy to adaptively update the forgetting factor λ;

[0127]

[0128] Where, λ min λ represents the minimum value of the set forgetting factor. max This represents the maximum value of the set forgetting factor, typically λ. max Take 0.999. e base Let λ represent the expected error value, and e(k) represent the estimated error at time step K. h is a constant value, representing the algorithm's sensitivity to error. When h approaches 1, the change of λ tends to slow down, resulting in a slower response; conversely, when h approaches 0, λ oscillates violently, resulting in a fast response time but lower accuracy.

[0129] S44. Calculate the battery internal resistance and polarization network parameters affected by internal and external factors such as SOC and temperature based on the identified parameters.

[0130]

[0131] In S5 of this embodiment, the battery parameters identified by the Adaptive Forgetting Factor Least Squares (AFFRLS) algorithm are passed to the Noise Adaptive Kalman Filtering algorithm for SOC estimation, specifically including the following steps:

[0132] S51. Considering noise, the discretized state-space equations of the battery model used for SOC estimation are obtained:

[0133]

[0134] Among them, w k V k These are system state noise and system measurement noise, respectively. k B k C k These represent the state transition matrix, input matrix, and observation matrix, respectively.

[0135]

[0136] C k = [-1b1(k)]

[0137] Using the identified R0(p k ), R d (p k C d (p k (Update A in real time) k B k matrix.

[0138] S52. Using the recursive process of the standard Kalman filter algorithm, the optimal estimate of SOC at each time step can be obtained, including:

[0139] State prediction: x k - =A k-1 x k-1 +B k-1 μ k-1

[0140] State prediction covariance: P k - =A k-1 P k-1 A k-1 T +Q k-1

[0141] Calculate the Kalman gain: K k =P k - C k T (C k P k - C k T +R k ) -1

[0142] Status update: x k + =x k - +K k (y k -C k x k - ).

[0143] S53. Under the actual operating conditions of the vehicle, the noise at the input and output terminals of the power lithium-ion battery is not Gaussian white noise under ideal conditions. Therefore, in order to improve the estimation accuracy of the algorithm under actual noise variation environment and enhance the robustness of the algorithm, a noise adaptive strategy based on residuals is invented.

[0144] ero k =V k -V e

[0145]

[0146] Among them, V k V represents the true value of the terminal voltage at time step k. e ero represents the estimated terminal voltage of the Kalman filter output at time step k. k This represents the error between the Kalman-estimated voltage and the actual terminal voltage at time k. H e The sliding window technique is used to calculate the sum of squared residuals, where M1 represents the size of the sliding window, and H... e K represents the mean of the sum of squared residuals. k R represents the Kalman gain. k For the adaptively updated input noise value, Q k For adaptively updated output noise values. For example... Figure 4 As shown, by combining a low computational burden, highly accurate model with a robust and accurate algorithm, real-time SOC estimates can be output quickly, accurately, and stably.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for adaptive linear modeling and state-of-charge estimation of a power battery that considers internal and external battery information, characterized in that: The method includes the following steps: S1. Collect dynamic data of the power battery, preprocess the data, and preliminarily calculate the SOC of the power battery; S2. Using a piecewise linear processing method, the open-circuit voltage is established. Linear relationship with SOC; S3. Establish a linear model of the power battery that considers internal and external information of the battery and its discrete state-space equations. In S3, considering the influence of internal and external factors on the power battery, a linear equivalent model of the power battery is established, which includes the following steps: S31, Design Temperature Weighting Factor Aging-weighted factor and SOC weighting factor Among them, temperature weighting factor Represented as: in, It is the temperature influence coefficient. This is a reference temperature; Aging weighting factor Represented as: in, It is the aging effect coefficient. A It refers to the degree of aging; SOC weighting factor Represented as: in, It is the SOC influence coefficient. It is the SOC value at time step K; S32. Limit the range of values ​​for each weighting factor to 0-1 using the Sigmoid activation function: = + in, Defined as the total impact factor, it represents the combined influence of internal and external factors on the model parameters; and These represent the SOC at the current time and the next time, respectively. and This represents the RC dynamic voltage at the current and next time points; For consideration The impact on battery internal resistance, For consideration The influence of polarization internal resistance, For consideration The influence of polarization capacitance; , These represent the input current and output voltage at time step k, respectively. Represents the sampling time. Represents the battery charge / discharge rate. This represents the battery's nominal capacity. and Represent correspond -Slope and intercept of the SOC linear piecewise curve; S4. The battery parameters are identified online using an adaptive forgetting factor least squares algorithm. In S4, the process of identifying battery parameters online using the adaptive forgetting factor least squares algorithm includes the following steps: S41, Combining the adaptive influence gain matrix Discretize the system output equation into least squares form; in S41, let The system output equation is then expressed as: The discretized least squares form is: In the formula, and These are the data matrix and the parameter vector matrix to be identified, respectively. Adaptive influence gain matrix Represented as: = in, , , It is a combination of SOC weighting factor, temperature weighting factor, and aging weighting factor; S42. The recursive forgetting factor least squares algorithm is used to identify the parameter matrix online. ; S43. Design an adaptive strategy to adaptively update the forgetting factor. ; S44. Calculate the battery internal resistance and polarization network parameters affected by internal and external factors based on the identified parameters. S5. Estimate the State of Charge (SOC) based on the online identified battery parameters using a noise-adaptive Kalman filter algorithm. In S5, the identified battery parameters are passed to the noise-adaptive Kalman filter algorithm for SOC estimation, which includes the following steps: S51. Considering noise, obtain the discretized state-space equation of the battery model used for SOC estimation. S52. The optimal estimate of SOC at each time step is obtained by using the recursive process of the standard Kalman filter algorithm. S53. The noise value is adjusted by adopting a residual-based noise adaptive strategy.

2. The adaptive linear modeling and state-of-charge estimation method for a power battery considering internal and external battery information as described in claim 1, characterized in that: In S1, the real-time data acquired from the power battery includes at least: current, open-circuit voltage, and temperature; among which, the state of charge (SOC) is initially calculated using the ampere-hour integration method. in, soc(t) It is time t SOC at that time; soc(0) It is the initial SOC; C This is the battery's rated capacity, measured in ampere-hours (AH). I (t) It is time t Current at any given moment.

3. The adaptive linear modeling and state-of-charge estimation method for a power battery considering internal and external battery information as described in claim 2, characterized in that: In S2, a piecewise linear regression method is used to handle higher-order nonlinearities. -SOC relationship, to obtain linear The SOC relationship is expressed by the formula: in, represent k SOC value at time step and Represent correspond - The slope and intercept of the SOC linear piecewise curve.

4. The adaptive linear modeling and state-of-charge estimation method for a power battery considering internal and external battery information as described in claim 1, characterized in that: In S42, we have: In the formula, and Let represent the covariance matrices at time k-1 and time k, respectively; Represents the input matrix; This represents the gain value of the RLS algorithm at time k; In S43, we have: in, Forgetting factor, This represents the minimum value of the set forgetting factor. This represents the maximum value of the set forgetting factor. This represents the expected error value. This represents the estimation error at time step K. This represents the output voltage estimation error at time k; h This is a constant value, representing the algorithm's sensitivity to errors; In S44, we have: in, , , The parameters identified by the RLS algorithm are used to calculate battery parameters. , , , The internal resistance of the battery is affected by internal and external factors. The polarization resistance is affected by internal and external factors. Polarized capacitors are affected by internal and external factors.

5. The adaptive linear modeling and state-of-charge estimation method for a power battery considering internal and external battery information as described in claim 1, characterized in that: In S51, the discretized state-space equations are expressed as: In the formula, , These are system state noise and system measurement noise, respectively. Let represent the state transition matrix, input matrix, and observation matrix, respectively, and we have: Identified online 、 、 Parameters are updated in real time , matrix.

6. The adaptive linear modeling and state-of-charge estimation method for a power battery considering internal and external battery information as described in claim 5, characterized in that: In S52, the process of obtaining the optimal estimate of SOC at each time step using the standard Kalman filter algorithm includes: State prediction: ; State prediction covariance: ; Calculate the Kalman gain: ; Status Update: - ); Covariance update: ) ; in, This represents the adaptively updated output noise value at time step k-1. The input noise value is adaptively updated at time step k. The Kalman gain at time step k.

7. The adaptive linear modeling and state-of-charge estimation method for a power battery considering internal and external battery information as described in claim 6, characterized in that: In S53, the residual-based noise adaptive strategy is expressed as: in, This represents the true value of the terminal voltage at time step k. This represents the estimated terminal voltage output of the Kalman filter at time step k. This represents the error between the Kalman-estimated voltage and the actual terminal voltage at time k; Calculate the sum of squared residuals using the sliding window technique. This represents the size of the sliding window value. This represents the mean of the sum of squared residuals.

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