Battery state-of-charge estimation method, device and equipment oriented to wide temperature interval

Abstract

The invention relates to the technical field of battery state-of-charge monitoring, and provides a wide-temperature-interval-oriented battery state-of-charge estimation method, device and equipment, and the method comprises the steps: obtaining a relaxation time distribution curve and measurement data corresponding to any temperature of a to-be-estimated battery in a target wide temperature interval; the relaxation time distribution curve is determined according to an electrochemical impedance spectrum curve corresponding to the battery to be estimated. According to the relaxation time distribution curve, constructing a fractional order battery estimation model corresponding to the to-be-estimated battery, identifying related parameters of the fractional order battery estimation model, and reflecting the multi-time-scale dynamic state of the battery by using the fractional order model, so that the problem that the multi-time-scale dynamic state of the battery cannot be captured is avoided; therefore, estimation of the state of charge of the battery is realized more accurately. For the measurement data, the fractional order adaptive extended Kalman filtering is adopted to determine the state of charge of the to-be-estimated battery through the related parameters and the fractional order battery estimation model, so that the accuracy of estimating the state of charge of the to-be-estimated battery is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery state of charge monitoring, and in particular to a battery state of charge estimation method, device and equipment for a wide temperature range. Background Art

[0002] Currently, lithium-ion batteries serve as the core power source of electric vehicles. Accurately predicting and managing their performance directly impacts the user's driving experience, mileage, and vehicle safety. To achieve battery status prediction, control, and management, a battery management system (BMS) is typically used, using various control algorithms to bridge the gap between user and vehicle information exchange. Accurately estimating the battery's state of charge (SOC) is a core function of a BMS. It not only provides users with precise information on the remaining charge in the electric vehicle, but also enables the BMS to flexibly adjust charge and discharge control strategies, effectively preventing overcharging and over-discharging, thereby improving battery safety and cycle life. Therefore, accurate SOC estimation is crucial to the safety and efficiency of lithium-ion batteries and the overall operation of electric vehicles.

[0003] In the prior art, battery SOC estimation can be achieved with the help of SOC estimation methods based on the ampere-hour integration method, SOC estimation methods based on the open circuit voltage method, SOC estimation methods based on the data-driven method, and SOC estimation methods based on the model method.

[0004] However, the SOC estimation method based on the ampere-hour integration method has the problems of strong dependence on the initial SOC and cumulative error; the SOC estimation method based on the open circuit voltage method requires a long period of static state, resulting in low efficiency; the SOC estimation method based on the data-driven method has the disadvantage of over-reliance on training data, which makes the model generalization ability of obtaining battery SOC poor; the SOC estimation method based on the model method, because the constructed battery model not only cannot capture the multi-time scale dynamics of the battery, but also fails to fully characterize the nonlinear characteristics of the electrochemical reaction inside the battery, and does not consider the influence of ambient temperature and the time-varying characteristics of process noise and measurement noise in actual working conditions, which leads to reduced accuracy of battery SOC estimation. Summary of the Invention

[0005] Based on this, it is necessary to provide a battery state of charge estimation method, device and equipment for a wide temperature range to address the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a method for estimating the state of charge of a battery over a wide temperature range. The method comprises: obtaining a relaxation time distribution curve and measurement data corresponding to a battery to be estimated at any temperature within a target wide temperature range, wherein the relaxation time distribution curve is determined based on an electrochemical impedance spectroscopy curve corresponding to the battery to be estimated; Constructing a fractional-order battery estimation model corresponding to the battery to be estimated according to the relaxation time distribution curve, and identifying relevant parameters of the fractional-order battery estimation model; For the measurement data, a fractional-order adaptive extended Kalman filter is used to determine the state of charge of the battery to be estimated through the relevant parameters and the fractional-order battery estimation model.

[0007] In one embodiment, before obtaining the relaxation time distribution curve and measurement data corresponding to the battery to be estimated at any temperature within the target wide temperature range, the method further includes: Obtaining an electrochemical impedance spectroscopy curve corresponding to the battery to be estimated at any temperature; Determining a distribution function corresponding to the relaxation time distribution curve according to a relative conversion calculation function between the electrochemical impedance spectroscopy curve and the relaxation time distribution curve; The relaxation time distribution curve is determined according to a preset objective function, the relative conversion calculation function, and the distribution function.

[0008] In one embodiment, constructing a fractional-order battery estimation model corresponding to the battery to be estimated based on the relaxation time distribution curve includes: Determining the number of polarization links used to construct a fractional-order equivalent circuit model based on a plurality of characteristic peaks of the relaxation time distribution curve; Constructing a fractional-order equivalent circuit model according to the number of polarization links; Obtaining an initial fractional-order battery estimation model corresponding to the fractional-order equivalent circuit model in the time domain according to Kirchhoff's current-voltage law combined with the ampere-hour integration method; Discretization processing is performed on the initial fractional-order battery estimation model to obtain a fractional-order battery estimation model.

[0009] In one embodiment, the initial fractional-order battery estimation model can be defined by the following expression:

[0010] in, represents the fractional-order calculus operator of the fractional-order equivalent circuit model, Indicates temperature, The fractional order equivalent circuit model under the charge state at the moment The order of the polarization link, Indicates temperature, The fractional order equivalent circuit model under the charge state at the moment The polarization constant phase angle element capacitance in each polarization link, represents any temperature within the target wide temperature range, Indicates temperature, The fractional order equivalent circuit model under the charge state at the moment The polarization resistance in each polarization link, The fractional-order equivalent circuit model is represented in The voltage corresponding to each polarization link is Indicates The estimated state of charge at the moment, Indicates the initial time The state of charge value, represents the Coulomb efficiency, Indicates The input current of the fractional-order equivalent circuit model at time , represents the rated capacitance of the battery to be estimated, represents the terminal voltage in the fractional-order equivalent circuit model, Indicates temperature, The open circuit voltage corresponding to the fractional-order equivalent circuit model at the charged state at the moment is, represents the input current of the fractional-order equivalent circuit model, Indicates temperature, The ohmic resistance in the fractional-order equivalent circuit model at the charged state at the moment, n represents the number of polarization links in the fractional-order equivalent circuit model; The fractional-order battery estimation model can be defined by the following expression:

[0011] in, The fractional-order equivalent circuit model is represented in The voltage corresponding to the kth discrete point under the polarization link is, represents the estimated state of charge at the k+1th discrete point, represents the estimated state of charge at the kth discrete point, Indicates The fractional order equivalent circuit model is the same as the charge state under the temperature and the kth discrete point. The polarization resistance in each polarization link, Indicates The fractional order equivalent circuit model is the same as the charge state under the temperature and the kth discrete point. The polarization constant phase angle element capacitance in each polarization link, j represents an integer from 1 to L+1, L represents the length of the fractional discrete memory, Indicates The fractional order equivalent circuit model is the same as the charge state under the temperature and the kth discrete point. The order of the polarization link, The fractional-order equivalent circuit model is represented in The first polarization link The voltage corresponding to each discrete point is represents the input current corresponding to the kth discrete point of the fractional-order equivalent circuit model, represents the rated capacitance of the battery to be estimated, represents the discrete point interval, Indicates The open circuit voltage corresponding to the fractional-order equivalent circuit model under the charge state at the temperature and the kth discrete point, Indicates Temperature, ohmic resistance of the fractional-order equivalent circuit model under the state of charge at the kth discrete point, The fractional-order equivalent circuit model is represented in The voltage corresponding to the k+1th discrete point under the polarization link, n represents the number of polarization links in the fractional-order equivalent circuit model, represents the terminal voltage corresponding to the k+1th discrete point of the fractional-order equivalent circuit model.

[0012] In one embodiment, identifying relevant parameters of the fractional-order battery estimation model includes: Determining a distribution range to which the relevant parameter belongs according to the relaxation time distribution curve, wherein the distribution range includes a plurality of relevant parameters to be selected; Among the multiple candidate related parameters, the candidate related parameter that makes the preset fitness function optimal is determined to be the related parameter.

[0013] In one embodiment, the method of determining the state of charge of the battery to be estimated by using the relevant parameters and the fractional-order battery estimation model using a fractional-order adaptive extended Kalman filter for the measurement data includes: Obtaining a state transfer equation and an observation equation according to the relevant parameters and the fractional-order battery estimation model; The state of charge of the battery to be estimated is determined according to the state transfer equation and the observation equation.

[0014] In one embodiment, determining the state of charge of the battery to be estimated according to the state transition equation and the observation equation includes: According to the posterior state estimate at the kth moment and the posterior state estimate at the first k moments introduced by the fractional order, the prior state estimate at the k+1th moment is obtained through the state transfer equation; According to the prior state estimation value at the k+1th moment, the prior observation value at the k+1th moment is obtained through the observation equation; According to the posterior state covariance matrix at the kth moment, the system noise covariance matrix and the posterior state covariance matrix of the first k moments introduced by the fractional order, the prior state covariance matrix at the k+1th moment is obtained by the prior state covariance calculation formula; According to the prior state covariance matrix at the k+1th moment and the process noise covariance matrix at the kth moment, the Kalman filter coefficient at the k+1th moment is obtained by the Kalman filter coefficient calculation formula; The state of charge of the battery to be estimated is obtained according to the prior observation value, the Kalman filter coefficient and the prior state estimation value at the k+1th moment.

[0015] In one embodiment, the system noise covariance matrix at the kth moment is determined based on the Kalman filter coefficient and the prior residual covariance at the kth moment and the filter coefficient at the k-1th moment, wherein the prior residual covariance is determined based on the prior observation values ​​and the actual observation values ​​at the previous k moments; The process noise covariance matrix at the kth moment is determined based on the prior state covariance matrix and the posterior residual covariance at the kth moment and the filter coefficient at the k-1th moment, and the posterior residual covariance is determined based on the posterior observation values ​​and actual observation values ​​at the previous k moments.

[0016] In a second aspect, an embodiment of the present invention provides a battery state of charge estimation device for a wide temperature range, comprising: An acquisition module, configured to acquire a relaxation time distribution curve and measurement data corresponding to a battery to be estimated at any temperature within a target wide temperature range, wherein the relaxation time distribution curve is determined based on an electrochemical impedance spectroscopy curve corresponding to the battery to be estimated; A fractional-order battery estimation model determination module is used to construct a fractional-order battery estimation model corresponding to the battery to be estimated according to the relaxation time distribution curve, and identify relevant parameters of the fractional-order battery estimation model; The estimation module is configured to determine the state of charge of the battery to be estimated by using the relevant parameters and the fractional-order battery estimation model based on the measurement data using a fractional-order adaptive extended Kalman filter.

[0017] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the battery state of charge estimation method for a wide temperature range described in the first aspect when executing the computer program.

[0018] The technical solution provided by the embodiment of the present invention has the following advantages compared with the existing technology: An embodiment of the present invention provides a battery state-of-charge (SOC) estimation method for a wide temperature range. This method obtains a relaxation time distribution curve and measurement data corresponding to the battery to be estimated at any temperature within a target wide temperature range. The relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy (EIS) curve of the battery to be estimated. Based on the relaxation time distribution curve, a fractional-order battery estimation model corresponding to the battery to be estimated is constructed, and relevant parameters of the fractional-order battery estimation model are identified. This allows the fractional-order model to reflect the multi-time-scale dynamics of the battery, avoiding the problem of existing technologies that fail to capture multi-time-scale dynamics, thereby achieving more accurate SOC estimation. Based on the measurement data, a fractional-order adaptive extended Kalman filter (FADKF) is used to determine the SOC of the battery to be estimated using relevant parameters and the FADKF model. By integrating an adaptive mechanism, fractional-order calculus, and the EADKF, the FADKF is applied to the fractional-order battery estimation model. The state or parameters to be estimated are determined through iterative optimization, thereby achieving an optimal SOC estimate of the battery to be estimated with minimum variance, thereby improving the accuracy of the SOC estimation of the battery to be estimated. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 A schematic flow chart of a method for estimating the state of charge of a battery in a wide temperature range provided by an embodiment of the present invention; Figure 2 A schematic diagram of obtaining a relaxation time distribution curve and a fractional-order equivalent circuit model provided by an embodiment of the present invention; Figure 3 A schematic diagram of an experimental result provided by an embodiment of the present invention; Figure 4A schematic diagram of another experimental result provided by an embodiment of the present invention; Figure 5 A schematic structural diagram of a battery state of charge estimation device for a wide temperature range provided by an embodiment of the present invention.

[0022] Figure 5 Middle: 10-acquisition module; 11-fractional order battery estimation model determination module; 12-estimation module. DETAILED DESCRIPTION

[0023] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features therein can be combined with each other.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all the embodiments.

[0025] In one embodiment, Figure 1 As shown, Figure 1 A flowchart of a method for estimating the state of charge of a battery over a wide temperature range provided by an embodiment of the present invention specifically includes the following steps: S10: Obtaining a relaxation time distribution curve and measurement data corresponding to the battery to be estimated at any temperature within a target wide temperature range.

[0026] Among them, the target wide temperature range refers to the fact that the operating temperature of automotive batteries such as lithium-ion batteries is usually controlled between 20℃-40℃. However, in extremely cold areas, the starting temperature of the battery may be lower than 20℃, which may lead to startup failure. Based on this, when estimating the battery state of charge, in order to avoid startup failure in extremely cold areas, a target wide temperature range with a wider temperature adaptability range will be set, for example, it can be: 0℃-40℃. Any temperature refers to any temperature in the target wide temperature range, for example, it can be: 5℃, 10℃, 15℃ and 30℃, but the present invention is not limited to this, and those skilled in the art can set it according to actual conditions.

[0027] The relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy (EIS) curve for the battery being evaluated. EIS is an analytical technique that studies how the impedance of an electrochemical system changes with frequency by applying a small AC voltage or current signal. By measuring the impedance response at different frequencies, it can be used to analyze electrode process dynamics, interfacial properties, and material transport mechanisms.

[0028] The measurement data refers to the data required for estimating the state of charge of the battery to be estimated, and the measurement data may be, for example, voltage, current, and ambient temperature. However, the present invention is not limited thereto, and those skilled in the art may configure the measurement data according to actual conditions.

[0029] Specifically, for the battery to be estimated, within the target wide temperature range, a relaxation time distribution curve corresponding to the battery to be estimated at any temperature is obtained based on the electrochemical impedance spectroscopy curve corresponding to the battery to be estimated, as well as the measurement data required for estimating the state of charge of the battery to be estimated.

[0030] Optionally, based on the above embodiment, in some embodiments of the present invention, before executing S10, the following steps are further included: S20: Obtain an electrochemical impedance spectroscopy curve corresponding to the battery to be estimated at any temperature.

[0031] S21: determining a distribution function corresponding to the relaxation time distribution curve according to a relative conversion calculation function between the electrochemical impedance spectroscopy curve and the relaxation time distribution curve.

[0032] Among them, the relative conversion calculation function is used to realize the conversion between the electrochemical impedance spectroscopy curve and the relaxation time distribution curve.

[0033] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation of S21 may be: S211: Construct a relative conversion calculation function for obtaining a relaxation time distribution curve. The relative conversion calculation function can be defined by the following expression:

[0034] Where R0 represents the initial ohmic resistance, represents the relaxation time distribution function, f represents the sampling frequency value, Indicates the time constant value.

[0035] S212: According to the electrochemical impedance spectrum curve corresponding to the electrochemical impedance spectrum curve, a relative conversion calculation function corresponding to the relative conversion calculation function is obtained, which can be defined by the following expression:

[0036] in, express The relaxation time distribution function depicted on a logarithmic scale is the initial distribution function corresponding to the relaxation time distribution curve.

[0037] S213: Discretize the relative conversion calculation function described on a logarithmic scale according to the Dirac distribution to obtain the discretized relative conversion calculation function and the discretized initial distribution function, which can be defined by the following expression:

[0038]

[0039] in, represents the unknown term obtained by subsequent fitting, M is the number of time constant points, is the time constant corresponding to the mth time constant point. Express about The Dirac function.

[0040] S214: Initialize the discretized initial distribution function according to the Gaussian radial basis function , we get the distribution function corresponding to the relaxation time distribution curve, which can be defined by the following expression:

[0041] S22: Determine a relaxation time distribution curve according to a preset target function, a relative conversion calculation function, and a distribution function.

[0042] The preset objective function is used to solve the distribution function, and the preset objective function can be defined by the following expression:

[0043] in, Represents the real part of the relative conversion calculation function during the experimental process, Indicates the real part of the calculation process relative to the conversion calculation function. Represents the imaginary part of the relative conversion calculation function during the experimental process, Indicates the imaginary part of the calculation process relative to the conversion calculation function. represents the weight of the square of the real part error standard deviation, represents the weight of the square of the imaginary error standard deviation, is the regularization parameter and L is the regularization matrix.

[0044] Specifically, the relative conversion calculation function and the distribution function are substituted into the preset objective function, and the relaxation time distribution curve is determined when the preset objective function is optimized by adjusting the regularization matrix and the regularization parameter.

[0045] Exemplary, reference Figure 2 As shown, the electrochemical impedance spectroscopy curve is converted into a relaxation time distribution curve. However, the present invention is not limited thereto, and those skilled in the art can make settings according to actual conditions.

[0046] S11: Construct a fractional-order battery estimation model corresponding to the battery to be estimated according to the relaxation time distribution curve, and identify relevant parameters of the fractional-order battery estimation model.

[0047] The fractional-order battery estimation model and related parameters are used to subsequently estimate the state of charge of the battery to be estimated.

[0048] Specifically, after obtaining the relaxation time distribution curve, a fractional-order battery estimation model corresponding to the estimated battery is constructed according to the relaxation time distribution curve, and relevant parameters of the fractional-order battery estimation model are identified.

[0049] Optionally, based on the above embodiment, in some embodiments of the present invention, one implementation method of constructing a fractional-order battery estimation model corresponding to the estimated battery according to the relaxation time distribution curve may be: S111: Determine the number of polarization links used to construct a fractional-order equivalent circuit model based on multiple characteristic peaks of the relaxation time distribution curve.

[0050] The fractional-order model reflects the multi-timescale dynamics of the battery, enabling a more accurate estimation of the battery's state of charge. Based on this, a fractional-order equivalent circuit model is constructed. The number of polarization links determines the number of parallel resistor and capacitor structures in the equivalent circuit model.

[0051] S112: Construct a fractional-order equivalent circuit model based on the number of polarization links.

[0052] Specifically, after obtaining the relaxation time distribution curve, the number of polarization links used to construct the fractional-order equivalent circuit model is determined based on multiple characteristic peaks of the relaxation time distribution curve. The fractional-order equivalent circuit model is constructed based on the number of polarization links.

[0053] Exemplary, reference Figure 2 As shown, for the multiple characteristic peaks a, b, c, d, and e of the relaxation time distribution curve, the number of polarization links for constructing the fractional-order equivalent circuit model is determined to be 5. Then, in the fractional-order equivalent circuit model, the number of resistor-capacitor parallel structures is 5. However, the present invention is not limited to this, and those skilled in the art can make settings according to actual conditions.

[0054] S113: According to Kirchhoff's current-voltage law combined with the ampere-hour integration method, an initial fractional-order battery estimation model corresponding to the fractional-order equivalent circuit model in the time domain is obtained.

[0055] Specifically, after constructing the fractional-order equivalent circuit model, an initial fractional-order battery estimation model corresponding to the fractional-order equivalent circuit model in the time domain is constructed by adopting Kirchhoff's current-voltage law combined with the ampere-hour integration method.

[0056] Optionally, based on the above embodiment, in some embodiments of the present invention, the initial fractional-order battery estimation model may be defined by the following expression:

[0057] in, The fractional-order calculus operator representing the fractional-order equivalent circuit model, Indicates temperature, The fractional-order equivalent circuit model of the charge state at the moment The order of the polarization link, Indicates temperature, The fractional-order equivalent circuit model of the charge state at the moment The polarization constant phase angle element capacitance in each polarization link, Indicates any temperature within the target wide temperature range, Indicates temperature, The fractional-order equivalent circuit model of the charge state at the moment The polarization resistance in each polarization link, Indicates that the fractional-order equivalent circuit model is The voltage corresponding to each polarization link is Indicates The estimated state of charge at the moment, Indicates the initial time The state of charge value, represents the Coulomb efficiency, Indicates The input current of the fractional-order equivalent circuit model at time , represents the rated capacity of the battery to be estimated, represents the terminal voltage in the fractional-order equivalent circuit model, Indicates temperature, The open circuit voltage corresponding to the fractional-order equivalent circuit model at the moment of charge state is: represents the input current of the fractional-order equivalent circuit model, Indicates temperature, The ohmic resistance in the fractional-order equivalent circuit model at the moment of charge, n represents the number of polarization links in the fractional-order equivalent circuit model.

[0058] S114: Discretize the initial fractional-order battery estimation model to obtain a fractional-order battery estimation model.

[0059] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation of S114 may be: Since the Grünwald-Letnikov fractional-order theory has good numerical stability and computability, and can flexibly adjust the fractional order, based on this, the initial fractional-order battery estimation model is discretized by the Grünwald-Letnikov fractional-order theory to determine the discretized , which can be defined by the following expression:

[0060] in, represents the fractional calculus operator, Express about t The integral differential operator of Indicates the order. It should be noted that when >0, represents the fractional derivative; when =0, =1. represents the sampling time, k is the memory length, h is the step length, represents the Newton binomial coefficient, is a positive integer between 0 and k. When is a non-integer, It can be qualified by the following expression:

[0061] in, Functions can be qualified by the following expressions:

[0062] Furthermore, since infinite-dimensional GL derivatives are discretized and truncated to a short and fixed memory length in continuous time, Process and obtain , which can be defined by the following expression:

[0063] Where k represents the kth discrete point.

[0064] Finally, according to the above The initial fractional-order battery estimation model is discretized to obtain a fractional-order battery estimation model in the frequency domain, which can be defined by the following expression:

[0065] in, Indicates that the fractional-order equivalent circuit model is The voltage corresponding to the kth discrete point under the polarization link is, represents the estimated state of charge at the k+1th discrete point, represents the estimated state of charge at the kth discrete point, Indicates The fractional order equivalent circuit model of the charge state under the temperature and the kth discrete point is The polarization resistance in each polarization link, Indicates The fractional order equivalent circuit model of the charge state under the temperature and the kth discrete point is The polarization constant phase angle element capacitance in each polarization link, j represents an integer from 1 to L+1, L represents the length of the fractional discrete memory, Indicates The fractional order equivalent circuit model of the charge state under the temperature and the kth discrete point is The order of the polarization link, Indicates that the fractional-order equivalent circuit model is The first polarization link The voltage corresponding to each discrete point is represents the input current corresponding to the kth discrete point of the fractional-order equivalent circuit model, represents the rated capacity of the battery to be estimated, represents the discrete point interval, Indicates The open circuit voltage corresponding to the fractional-order equivalent circuit model under the charge state at the temperature and the kth discrete point, Indicates Temperature, ohmic resistance of the fractional-order equivalent circuit model under the state of charge at the kth discrete point, Indicates that the fractional-order equivalent circuit model is The voltage corresponding to the k+1th discrete point under the polarization link, n represents the number of polarization links in the fractional-order equivalent circuit model, Represents the terminal voltage corresponding to the k+1th discrete point of the fractional-order equivalent circuit model.

[0066] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation method for identifying relevant parameters of the fractional-order battery estimation model may be: S115: Determine the distribution range of the relevant parameters according to the relaxation time distribution curve.

[0067] Examples of relevant parameters include polarization resistance, polarization constant phase angle element capacitance, polarization link order, and a distribution range including multiple relevant parameters to be selected.

[0068] Optionally, based on the above embodiments, in some embodiments of the present invention, an implementation method of S115 may be: calculating the peak area and time constant corresponding to each characteristic peak in the relaxation time distribution curve, and determining the distribution range corresponding to the relevant parameters including multiple selected relevant parameters.

[0069] S116: Determine, among multiple candidate related parameters, a candidate related parameter that optimizes the preset fitness function as the relevant parameter.

[0070] The preset fitness function is used to identify the relevant parameters of the fractional-order battery estimation model. The preset fitness function can be defined by the following expression:

[0071] Wherein, Len represents the length of multiple related parameters to be selected within the distribution range. represents the terminal voltage data at the kth moment determined by the pulse relaxation experiment, Indicates the output voltage.

[0072] Specifically, each candidate related parameter among the multiple candidate related parameters is substituted into a preset fitness function. When the preset fitness function is optimal, the candidate related parameter that makes the preset fitness function optimal is determined as the relevant parameter.

[0073] S12: Based on the measurement data, a fractional-order adaptive extended Kalman filter is used to determine the state of charge of the battery to be estimated through relevant parameters and a fractional-order battery estimation model.

[0074] The fractional-order adaptive extended Kalman filter (FAKF) integrates adaptive mechanisms and fractional-order calculus into the Kalman filter, performing FAF on the fractional-order battery estimation model. By performing iterative optimization to determine the state or parameters to be estimated, the optimal estimate of the battery's state of charge (SOC) is made with minimum variance, thereby improving the accuracy of the estimated battery's SOC.

[0075] Specifically, after obtaining the measurement data, a fractional-order adaptive extended Kalman filter is used for the measurement data to estimate the state of charge of the battery to be estimated through relevant parameters and a fractional-order battery estimation model.

[0076] Thus, this embodiment provides a battery state-of-charge estimation method for a wide temperature range. This method obtains a relaxation time distribution curve and measurement data corresponding to the battery to be estimated at any temperature within a target wide temperature range. The relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy curve corresponding to the battery to be estimated. Based on the relaxation time distribution curve, a fractional-order battery estimation model corresponding to the battery to be estimated is constructed, and relevant parameters of the fractional-order battery estimation model are identified. This allows the fractional-order model to reflect the multi-time-scale dynamics of the battery, avoiding the problem of existing technologies that cannot capture multi-time-scale dynamics of batteries, thereby achieving more accurate battery state-of-charge estimation. Based on the measurement data, a fractional-order adaptive extended Kalman filter is used to determine the state-of-charge of the battery to be estimated using relevant parameters and the fractional-order battery estimation model. By integrating an adaptive mechanism, fractional-order calculus, and the extended Kalman filter, the fractional-order battery estimation model is processed using a fractional-order adaptive extended Kalman filter. Through iterative optimization, the state or parameters to be estimated are obtained, and then the optimal estimate of the state of charge of the battery to be estimated is made in the sense of minimum variance, thereby improving the accuracy of the estimated state-of-charge of the battery to be estimated.

[0077] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation of S12 may be: S121: Obtain a state transfer equation and an observation equation based on relevant parameters and a fractional-order battery estimation model.

[0078] Specifically, the corresponding state transfer equation and observation equation are determined according to the relevant parameters of the fractional-order battery estimation model and the fractional-order battery estimation model.

[0079] Optionally, based on the above embodiment, in some embodiments of the present invention, the state transition equation may be defined by the following expression:

[0080] The observation equation can be defined by the following expression:

[0081] in, It represents the state transition matrix of the battery to be estimated at the kth moment under the temperature T and the state of charge at the kth moment. It represents the control matrix of the battery to be estimated at the kth moment under the temperature T and the state of charge at the kth moment, represents the state vector at the k+1th moment, , represents the observation matrix at the k+1th moment corresponding to the state of charge at the kth moment at the temperature T, , represents the ohmic resistance of the battery to be estimated at the k+1th moment under the state of charge at the kth moment at the temperature T. It represents the open circuit voltage of the battery to be estimated at the temperature T and the state of charge at the kth moment.

[0082] Optionally, based on the above embodiment, in some embodiments of the present invention, the state transition matrix may be defined by the following expression:

[0083] Optionally, based on the above embodiment, in some embodiments of the present invention, the control matrix may be defined by the following expression:

[0084] It should be noted that the elements of the state transfer matrix, control matrix, and state vector are composed of relevant parameters of the fractional-order battery estimation model. After obtaining the state transfer equation and observation equation, the corresponding relevant data are initialized. The relevant data includes: state estimation matrix, prior covariance matrix, system noise covariance matrix, process noise covariance matrix, and filter coefficients.

[0085] S122: Determine the state of charge of the battery to be estimated based on the state transfer equation and the observation equation.

[0086] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation of S122 may be: S1221: Based on the posterior state estimate at the kth moment and the posterior state estimate at the first k moments introduced by the fractional order, the prior state estimate at the k+1th moment is obtained through the state transfer equation.

[0087] S1222: Based on the prior state estimate at the k+1th moment, obtain the prior observation value at the k+1th moment through the observation equation.

[0088] S1223: According to the posterior state covariance matrix at the kth moment, the system noise covariance matrix and the posterior state covariance matrix of the first k moments introduced by the fractional order, the prior state covariance matrix at the k+1th moment is obtained by the prior state covariance calculation formula.

[0089] Optionally, based on the above embodiment, in some embodiments of the present invention, the prior state covariance calculation formula may be defined by the following expression:

[0090] in, represents the fractional-order matrix at the k=1th moment, represents the transpose of the fractional matrix at the k=j moment, represents the fractional order matrix at the k=j moment, ,in . represents the system noise covariance matrix at the kth moment, represents the posterior state covariance matrix at the kth moment.

[0091] Optionally, based on the above embodiment, in some embodiments of the present invention, the calculation formula of the posterior state covariance matrix can be defined by the following expression:

[0092] in, represents the identity matrix, represents the prior state covariance matrix at the kth moment, represents the Kalman filter coefficient at the kth moment, represents the observation matrix at temperature T and the charge state at the k-1th moment.

[0093] S1224: Obtain the Kalman filter coefficient at the k+1th moment according to the prior state covariance matrix at the k+1th moment and the process noise covariance matrix at the kth moment using the Kalman filter coefficient calculation formula.

[0094] Optionally, based on the above embodiment, in some embodiments of the present invention, the Kalman filter coefficient calculation formula may be defined by the following expression:

[0095] in, represents the prior state covariance matrix at the k+1th moment, represents the process noise covariance matrix at the kth moment. Represents the observation matrix corresponding to the state of charge at the kth moment at temperature T.

[0096] S1225: Obtain the state of charge of the battery to be estimated based on the prior observation value at the k+1th moment, the Kalman filter coefficient, and the prior state estimation value.

[0097] Specifically, the posterior state estimate at time k and the posterior state estimate at the previous k moments introduced by the fractional order are substituted into the state transition equation to calculate the prior state estimate at time k+1. The prior state estimate at time k+1 is substituted into the observation equation to calculate the prior observation at time k+1. The prior state covariance calculation formula is used to calculate the prior state covariance matrix at time k+1, the system noise covariance matrix, and the posterior state covariance matrix at the previous k moments introduced by the fractional order. The Kalman filter coefficient calculation formula is used to calculate the Kalman filter coefficient at time k+1 based on the prior state covariance matrix at time k+1 and the process noise covariance matrix at time k. Finally, the state of charge of the battery to be estimated is further obtained based on the prior observation value at time k+1, the Kalman filter coefficient, and the prior state estimate.

[0098] Optionally, based on the above embodiment, in some embodiments of the present invention, the system noise covariance matrix at the kth moment is determined based on the Kalman filter coefficient and the prior residual covariance at the kth moment and the filter coefficient at the k-1th moment, wherein the prior residual covariance is determined based on the prior observations and actual observations at the previous k moments. The process noise covariance matrix at the kth moment is determined based on the prior state covariance matrix and the posterior residual covariance at the kth moment and the filter coefficient at the k-1th moment, wherein the posterior residual covariance is determined based on the posterior observations and actual observations at the previous k moments.

[0099] Specifically, the Kalman filter coefficients and the prior residual covariance at the kth moment, as well as the filter coefficients at the k-1th moment, are substituted into the system noise covariance calculation formula to obtain the system noise covariance matrix at the kth moment. The posterior state covariance matrix and the posterior residual covariance at the kth moment, as well as the filter coefficients at the k-1th moment, are substituted into the process noise covariance calculation formula to obtain the process noise covariance matrix at the kth moment.

[0100] Optionally, based on the above embodiment, in some embodiments of the present invention, the system noise covariance calculation formula may be defined by the following expression:

[0101] in, represents the Kalman filter coefficient at the kth moment, represents the filter coefficient at the k-1th moment, that is, the Sage-Husa filter coefficient, , bIt represents the forgetting factor, and its value range is 0-1. It should be noted that as time increases, the filter coefficient gradually increases. The filter coefficient can learn the system noise characteristics, thereby eliminating the system noise, thereby improving the accuracy of obtaining the charge state of the battery to be estimated. represents the prior residual covariance at the kth moment.

[0102] The process noise covariance calculation formula can be defined by the following expression:

[0103] in, represents the prior state covariance matrix at the kth moment, represents the posterior residual covariance at the kth moment.

[0104] It should be noted that, since the fractional-order adaptive Kalman filter obtains estimated parameter values ​​through iterative optimization, based on this, S1221-S1225 are repeatedly executed to obtain the state of charge of the battery to be estimated at any temperature.

[0105] Optionally, based on the above embodiments, in some embodiments of the present invention, in order to verify that the present invention can improve the accuracy of the estimated battery state of charge, two different vehicle operating conditions, such as dynamic stress test (DST) and urban Dynamometer Driving Schedule (UDDS), are selected for experiments at any temperature within the target wide temperature range, such as 5°C and 35°C, and the mean absolute error performance index is used to evaluate the effect of the present invention. The experimental results are shown in Figure 3 as well as Figure 4 .

[0106] Specifically, for DST vehicle operating conditions, such as Figure 3 As shown in the figure, the estimated state of charge of the battery to be estimated by the present invention can better simulate the actual state of charge. The average absolute error calculated by the present invention is controlled within 1%, and as the ambient temperature increases, that is, from 5°C to 35°C, the average absolute error of the present invention gradually decreases. For UDDS vehicle operating conditions, such as Figure 4 As shown, the state of charge of the battery to be estimated estimated by the present invention can better simulate the actual state of charge. At 35°C, although the mean absolute error increases, it is still controlled within 2.5%, which meets the industry estimation standard. This shows that the present invention can adapt to various complex vehicle operating conditions and more accurately estimate the state of charge of the battery to be estimated under different vehicle operating conditions.

[0107] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The order of execution of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0108] In one embodiment, Figure 5 As shown, a battery state of charge estimation device for a wide temperature range is provided, including: an acquisition module 10, a fractional-order battery estimation model determination module 11 and an estimation module 12.

[0109] The acquisition module 10 is used to obtain the relaxation time distribution curve and measurement data corresponding to the battery to be estimated at any temperature in the target wide temperature range, wherein the relaxation time distribution curve is determined according to the electrochemical impedance spectroscopy curve corresponding to the battery to be estimated.

[0110] The fractional-order battery estimation model determination module 11 is used to construct a fractional-order battery estimation model corresponding to the battery to be estimated according to the relaxation time distribution curve, and identify relevant parameters of the fractional-order battery estimation model.

[0111] The estimation module 12 is used to determine the state of charge of the battery to be estimated based on the measurement data by using a fractional-order adaptive extended Kalman filter through relevant parameters and a fractional-order battery estimation model.

[0112] In the above embodiment, the acquisition module acquires the relaxation time distribution curve and measurement data corresponding to the battery to be estimated at any temperature over a wide target temperature range. The relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy curve corresponding to the battery to be estimated. The fractional-order battery estimation model determination module constructs a fractional-order battery estimation model corresponding to the battery to be estimated based on the relaxation time distribution curve and identifies relevant parameters of the fractional-order battery estimation model. This allows the fractional-order model to reflect the multi-time-scale dynamics of the battery, avoiding the problem of the existing technology that cannot capture multi-time-scale dynamics of the battery, thereby more accurately estimating the battery's state of charge. The estimation module uses a fractional-order adaptive extended Kalman filter based on the measurement data, using relevant parameters and the fractional-order battery estimation model to determine the state of charge of the battery to be estimated. By integrating an adaptive mechanism, fractional-order calculus, and the extended Kalman filter, the fractional-order battery estimation model is processed by the fractional-order adaptive extended Kalman filter. Through iterative optimization, the state or parameters to be estimated are obtained, and the optimal estimate of the state of charge of the battery to be estimated is made in the sense of minimum variance, thereby improving the accuracy of the estimated state of charge of the battery to be estimated.

[0113] For the specific definition of the battery state of charge estimation device for a wide temperature range, please refer to the definition of a battery state of charge estimation method for a wide temperature range above, which will not be repeated here. Each module in the above-mentioned server can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0114] An embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for estimating the state of charge of a battery in a wide temperature range provided by an embodiment of the present invention can be implemented. For example, when the processor executes the computer program, Figures 1 to 4 The technical solutions of any of the illustrated method embodiments have similar implementation principles and technical effects, which will not be described in detail here.

[0115] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes in the above-described method embodiments. Any reference to memory, database, or other media used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM).

[0116] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A battery state of charge estimation method for a wide temperature range, characterized in that: include: Obtaining a relaxation time distribution curve and measurement data corresponding to the battery to be estimated at any temperature within a target wide temperature range, wherein the relaxation time distribution curve is determined based on an electrochemical impedance spectroscopy curve corresponding to the battery to be estimated; Constructing a fractional-order battery estimation model corresponding to the battery to be estimated according to the relaxation time distribution curve, and identifying relevant parameters of the fractional-order battery estimation model; For the measurement data, a fractional-order adaptive extended Kalman filter is used to determine the state of charge of the battery to be estimated through the relevant parameters and the fractional-order battery estimation model.

2. The method according to claim 1, characterized in that Before obtaining the relaxation time distribution curve and measurement data corresponding to the battery to be estimated at any temperature within the target wide temperature range, the method further includes: Obtaining an electrochemical impedance spectroscopy curve corresponding to the battery to be estimated at any temperature; Determining a distribution function corresponding to the relaxation time distribution curve according to a relative conversion calculation function between the electrochemical impedance spectroscopy curve and the relaxation time distribution curve; The relaxation time distribution curve is determined according to a preset objective function, the relative conversion calculation function, and the distribution function.

3. The method according to claim 2, characterized in that The step of constructing a fractional-order battery estimation model corresponding to the battery to be estimated according to the relaxation time distribution curve includes: Determining the number of polarization links used to construct a fractional-order equivalent circuit model based on a plurality of characteristic peaks of the relaxation time distribution curve; Constructing a fractional-order equivalent circuit model according to the number of polarization links; Obtaining an initial fractional-order battery estimation model corresponding to the fractional-order equivalent circuit model in the time domain according to Kirchhoff's current-voltage law combined with the ampere-hour integration method; Discretization processing is performed on the initial fractional-order battery estimation model to obtain a fractional-order battery estimation model.

4. The method according to claim 3, characterized in that The initial fractional-order battery estimation model can be defined by the following expression: ; in, represents the fractional-order calculus operator of the fractional-order equivalent circuit model, Indicates temperature, The fractional order equivalent circuit model under the charge state at the moment The order of the polarization link, Indicates temperature, The fractional order equivalent circuit model under the charge state at the moment The polarization constant phase angle element capacitance in each polarization link, represents any temperature within the target wide temperature range, Indicates temperature, The fractional order equivalent circuit model under the charge state at the moment The polarization resistance in each polarization link, The fractional-order equivalent circuit model is represented in The voltage corresponding to each polarization link is Indicates The estimated state of charge at the moment, Indicates the initial time The state of charge value, represents the Coulomb efficiency, Indicates The input current of the fractional-order equivalent circuit model at time , represents the rated capacitance of the battery to be estimated, represents the terminal voltage in the fractional-order equivalent circuit model, Indicates temperature, The open circuit voltage corresponding to the fractional-order equivalent circuit model at the charged state at the moment is, represents the input current of the fractional-order equivalent circuit model, Indicates temperature, The ohmic resistance in the fractional-order equivalent circuit model at the charged state at the moment, n represents the number of polarization links in the fractional-order equivalent circuit model; The fractional-order battery estimation model can be defined by the following expression: ; in, The fractional-order equivalent circuit model is represented in The voltage corresponding to the kth discrete point under the polarization link is, represents the estimated state of charge at the k+1th discrete point, represents the estimated state of charge at the kth discrete point, Indicates The fractional order equivalent circuit model is the same as the charge state under the temperature and the kth discrete point. The polarization resistance in each polarization link, Indicates The fractional order equivalent circuit model is the same as the charge state under the temperature and the kth discrete point. The polarization constant phase angle element capacitance in each polarization link, j represents an integer from 1 to L+1, L represents the length of the fractional discrete memory, Indicates The fractional order equivalent circuit model is the same as the charge state under the temperature and the kth discrete point. The order of the polarization link, The fractional-order equivalent circuit model is represented in The first polarization link The voltage corresponding to each discrete point is represents the input current corresponding to the kth discrete point of the fractional-order equivalent circuit model, represents the rated capacitance of the battery to be estimated, represents the discrete point interval, represents the open circuit voltage corresponding to the fractional-order equivalent circuit model under the state of charge at the kth discrete point at temperature T, Indicates Temperature, ohmic resistance of the fractional-order equivalent circuit model under the state of charge at the kth discrete point, The fractional-order equivalent circuit model is represented in The voltage corresponding to the k+1th discrete point under the polarization link, n represents the number of polarization links in the fractional-order equivalent circuit model, represents the terminal voltage corresponding to the k+1th discrete point of the fractional-order equivalent circuit model.

5. The method according to claim 4, characterized in that Identify the relevant parameters of the fractional-order battery estimation model, including: Determining a distribution range to which the relevant parameter belongs according to the relaxation time distribution curve, wherein the distribution range includes a plurality of relevant parameters to be selected; Among the multiple candidate related parameters, the candidate related parameter that makes the preset fitness function optimal is determined to be the related parameter.

6. The method according to claim 5, characterized in that The method of determining the state of charge of the battery to be estimated by using the relevant parameters and the fractional-order battery estimation model based on the measurement data includes: Obtaining a state transfer equation and an observation equation according to the relevant parameters and the fractional-order battery estimation model; The state of charge of the battery to be estimated is determined according to the state transfer equation and the observation equation.

7. The method according to claim 6, characterized in that Determining the state of charge of the battery to be estimated according to the state transfer equation and the observation equation includes: According to the posterior state estimate at the kth moment and the posterior state estimate at the first k moments introduced by the fractional order, the prior state estimate at the k+1th moment is obtained through the state transfer equation; According to the prior state estimation value at the k+1th moment, the prior observation value at the k+1th moment is obtained through the observation equation; According to the posterior state covariance matrix at the kth moment, the system noise covariance matrix and the posterior state covariance matrix of the first k moments introduced by the fractional order, the prior state covariance matrix at the k+1th moment is obtained by the prior state covariance calculation formula; According to the prior state covariance matrix at the k+1th moment and the process noise covariance matrix at the kth moment, the Kalman filter coefficient at the k+1th moment is obtained by the Kalman filter coefficient calculation formula; The state of charge of the battery to be estimated is obtained according to the prior observation value, the Kalman filter coefficient and the prior state estimation value at the k+1th moment.

8. The method according to claim 7, characterized in that The system noise covariance matrix at the kth moment is determined based on the Kalman filter coefficient and the prior residual covariance at the kth moment and the filter coefficient at the k-1th moment, wherein the prior residual covariance is determined based on the prior observation values ​​and the actual observation values ​​at the previous k moments; The process noise covariance matrix at the kth moment is determined based on the prior state covariance matrix and the posterior residual covariance at the kth moment and the filter coefficient at the k-1th moment, and the posterior residual covariance is determined based on the posterior observation values ​​and actual observation values ​​at the previous k moments.

9. A battery state of charge estimation device for a wide temperature range, characterized in that: include: An acquisition module, configured to acquire a relaxation time distribution curve and measurement data corresponding to a battery to be estimated at any temperature within a target wide temperature range, wherein the relaxation time distribution curve is determined based on an electrochemical impedance spectroscopy curve corresponding to the battery to be estimated; A fractional-order battery estimation model determination module is used to construct a fractional-order battery estimation model corresponding to the battery to be estimated according to the relaxation time distribution curve, and identify relevant parameters of the fractional-order battery estimation model; The estimation module is configured to determine the state of charge of the battery to be estimated by using the relevant parameters and the fractional-order battery estimation model based on the measurement data using a fractional-order adaptive extended Kalman filter.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for estimating the state of charge of a battery in a wide temperature range according to any one of claims 1 to 8 are implemented.

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