A battery state of charge estimation method, device and equipment for a wide temperature range

Abstract

The present application relates to the technical field of battery state of charge monitoring, and provides a battery state of charge estimation method, device and equipment facing a wide temperature range, wherein the relaxation time distribution curve corresponding to any temperature of a battery to be estimated and measurement data are obtained in a target wide temperature range, and the relaxation time distribution curve is determined according to the electrochemical impedance spectrum curve corresponding to the battery to be estimated. According to the relaxation time distribution curve, a fractional order battery estimation model corresponding to the battery to be estimated is constructed, and the related parameters of the fractional order battery estimation model are identified. The fractional order model reflects the multi-time scale dynamics of the battery, avoids the problem that the multi-time scale dynamics of the battery cannot be captured, and thus more accurately realizes the estimation of the state of charge of the battery. For the measurement data, the fractional order adaptive extended Kalman filter is used to determine the state of charge of the battery to be estimated through the related parameters and the fractional order battery estimation model, and the accuracy of the estimation of the state of charge of the battery to be estimated is improved.

Description

Technical Field

[0001] This invention relates to the field of battery state of charge monitoring technology, and in particular to a method, apparatus and equipment for estimating the state of charge of batteries over a wide temperature range. Background Technology

[0002] Currently, lithium-ion batteries are the core power source for electric vehicles, and the accurate prediction and management of their performance directly impacts the user's driving experience, driving range, and vehicle safety. To achieve battery state prediction, control, and management, a Battery Management System (BMS) is typically used, employing various control algorithms to bridge the information gap between the user and the vehicle. Accurate estimation of the battery's State of Charge (SOC) is a core function of the BMS. It not only provides users with precise information about the remaining battery power but also helps the BMS flexibly adjust charging and discharging control strategies, effectively preventing overcharging and over-discharging, thereby simultaneously improving battery safety and cycle life. Therefore, accurate SOC estimation is crucial for the safety and efficiency of lithium-ion batteries and the overall operation of electric vehicles.

[0003] In existing technologies, SOC estimation of batteries can be achieved by using SOC estimation methods based on ampere-hour integration, SOC estimation methods based on open-circuit voltage, SOC estimation methods based on data-driven methods, and SOC estimation methods based on model methods.

[0004] However, SOC estimation methods based on the ampere-hour integration method suffer from strong dependence on the initial SOC and accumulated errors; SOC estimation methods based on the open-circuit voltage method require long periods of rest, resulting in low efficiency; SOC estimation methods based on the data-driven method suffer from over-reliance on training data, resulting in poor generalization ability of the model for obtaining battery SOC; and SOC estimation methods based on the model not only fail to capture the dynamics of the battery across multiple time scales and do not adequately characterize the nonlinear features of the internal electrochemical reactions, but also fail to consider the influence of ambient temperature and the time-varying characteristics of process noise and measurement noise under actual operating conditions, thus leading to reduced accuracy in battery SOC estimation. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, and device for estimating the state of charge of batteries over a wide temperature range to address the aforementioned technical problems.

[0006] In a first aspect, embodiments of the present invention provide a method for estimating the state of charge of a battery over a wide temperature range. The method includes: acquiring, within the target wide temperature range, a relaxation time distribution curve and measurement data of the battery to be estimated at any temperature, wherein the relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy curve of the battery to be estimated.

[0007] Based on the relaxation time distribution curve, construct a fractional-order battery estimation model corresponding to the battery to be estimated, and identify the relevant parameters of the fractional-order battery estimation model.

[0008] 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 the relevant parameters and the fractional-order battery estimation model.

[0009] In one embodiment, before acquiring the relaxation time distribution curve and measurement data of the battery to be estimated at any temperature within the target wide temperature range, the method further includes:

[0010] Obtain the electrochemical impedance spectroscopy curves of the battery to be estimated at any temperature;

[0011] The distribution function corresponding to the relaxation time distribution curve is determined based on the relative conversion function between the electrochemical impedance spectroscopy curve and the relaxation time distribution curve.

[0012] The relaxation time distribution curve is determined based on the preset objective function, the relative transformation calculation function, and the distribution function.

[0013] 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:

[0014] Based on the multiple characteristic peaks of the relaxation time distribution curve, determine the number of polarization elements used to construct the fractional-order equivalent circuit model;

[0015] Based on the number of polarization elements, a fractional-order equivalent circuit model is constructed;

[0016] Based on Kirchhoff's current-voltage law and the ampere-hour integration method, the initial fractional-order battery estimation model corresponding to the fractional-order equivalent circuit model in the time domain is obtained.

[0017] The initial fractional-order battery estimation model is discretized to obtain a fractional-order battery estimation model.

[0018] In one embodiment, the initial fractional-order battery estimation model may be defined by the following expression:

[0019]

[0020] in, This represents the fractional-order calculus operator of the fractional-order equivalent circuit model. Indicates in temperature, The fractional-order equivalent circuit model described at time t is in the charged state at the t. The order in each polarization stage Indicates in temperature, The fractional-order equivalent circuit model described at time t is in the charged state at the t. The polarization constant phase angle element capacitor in each polarization stage This refers to any temperature within the target wide temperature range. Indicates in temperature, The fractional-order equivalent circuit model described at time t is in the charged state at the t. Polarization resistance in each polarization stage The fractional-order equivalent circuit model represents the first... The voltage corresponding to each polarization stage Indicates in The estimated value of the state of charge at time t. Indicates the initial time. The state of charge value, Indicates Coulomb efficiency. Indicates in The input current of the fractional-order equivalent circuit model at time t. This represents the rated capacitance of the battery to be estimated. This represents the terminal voltage in the fractional-order equivalent circuit model. Indicates in temperature, The open-circuit voltage corresponding to the fractional-order equivalent circuit model under the charged state at any given time. This represents the input current of the fractional-order equivalent circuit model. Indicates in temperature, The ohmic resistance in the fractional-order equivalent circuit model at a given time under charged state, where n represents the number of polarization elements in the fractional-order equivalent circuit model;

[0021] The fractional-order battery estimation model can be defined by the following expression:

[0022]

[0023] in, The fractional-order equivalent circuit model represents the first... The voltage corresponding to the k-th discrete point under each polarization element. This represents the estimated state of charge at the (k+1)th discrete point. This represents the estimated state of charge at the k-th discrete point. Indicates in The fractional-order equivalent circuit model at temperature and the state of charge at the k-th discrete point is in the... Polarization resistance in each polarization stage Indicates in The fractional-order equivalent circuit model at temperature and the state of charge at the k-th discrete point is in the... The polarization constant phase angle element capacitor in each polarization stage j Represents integers from 1 to L+1, where L represents the fractional discrete memory length. Indicates in The fractional-order equivalent circuit model at temperature and the state of charge at the k-th discrete point is in the... The order in each polarization stage The fractional-order equivalent circuit model represents the first... The first polarization stage Voltages corresponding to discrete points This represents the input current at the k-th discrete point of the fractional-order equivalent circuit model. This represents the rated capacitance of the battery to be estimated. Indicates the discrete point interval. Indicates in The open-circuit voltage corresponding to the fractional-order equivalent circuit model at the temperature and the charged state at the k-th discrete point. Indicates in The ohmic resistance of the fractional-order equivalent circuit model at temperature and the state of charge at the k-th discrete point. The fractional-order equivalent circuit model represents the first... The voltage corresponding to the (k+1)th discrete point under each polarization element, where n represents the number of polarization elements in the fractional-order equivalent circuit model. This represents the terminal voltage at the (k+1)th discrete point of the fractional-order equivalent circuit model.

[0024] In one embodiment, identifying relevant parameters of the fractional-order battery estimation model includes:

[0025] Based on the relaxation time distribution curve, the distribution range of the relevant parameters is determined, wherein the distribution range includes multiple candidate relevant parameters;

[0026] Among the plurality of candidate related parameters, the candidate related parameter that makes the preset fitness function optimal is determined as the related parameter.

[0027] In one embodiment, determining the state of charge of the battery to be estimated using a fractional-order adaptive extended Kalman filter based on the measurement data, through the relevant parameters and the fractional-order battery estimation model, includes:

[0028] Based on the relevant parameters and the fractional-order battery estimation model, the state transition equation and observation equation are obtained;

[0029] The state of charge of the battery to be estimated is determined based on the state transition equation and the observation equation.

[0030] In one embodiment, determining the state of charge of the battery to be estimated based on the state transition equation and the observation equation includes:

[0031] Based on the posterior state estimate at time k and the posterior state estimates at the previous k times introduced by the fractional order, the prior state estimate at time k+1 is obtained through the state transition equation.

[0032] Based on the prior state estimate at time k+1, the prior observation at time k+1 is obtained through the observation equation.

[0033] Based on the posterior state covariance matrix at time k, the system noise covariance matrix, and the fractional-order posterior state covariance matrix at the previous k times, the prior state covariance matrix at time k+1 is obtained through the prior state covariance calculation formula.

[0034] Based on the prior state covariance matrix at time k+1 and the process noise covariance matrix at time k, the Kalman filter coefficients at time k+1 are obtained using the Kalman filter coefficient calculation formula.

[0035] The state of charge of the battery to be estimated is obtained based on the prior observations at time k+1, the Kalman filter coefficients, and the prior state estimate.

[0036] In one embodiment, the system noise covariance matrix at time k is determined based on the Kalman filter coefficients and prior residual covariance at time k and the filter coefficients at time k-1, wherein the prior residual covariance is determined based on the prior observations and actual observations at the previous k times.

[0037] The process noise covariance matrix at time k is determined based on the prior state covariance matrix and the posterior residual covariance at time k, as well as the filtering coefficients at time k-1. The posterior residual covariance is determined based on the posterior observations and actual observations from the previous k time steps.

[0038] Secondly, embodiments of the present invention provide a battery state-of-charge estimation device for a wide temperature range, comprising:

[0039] The acquisition module is used to acquire the relaxation time distribution curve and measurement data of the battery to be estimated at any temperature within a target wide temperature range, wherein the relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy curve corresponding to the battery to be estimated.

[0040] The fractional-order battery estimation model determination module is used to construct the fractional-order battery estimation model corresponding to the battery to be estimated based on the relaxation time distribution curve, and to identify the relevant parameters of the fractional-order battery estimation model.

[0041] The estimation module is used to determine the state of charge of the battery to be estimated by using fractional-order adaptive extended Kalman filtering on the measurement data, through the relevant parameters and the fractional-order battery estimation model.

[0042] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the battery state-of-charge estimation method for a wide temperature range described in the first aspect.

[0043] The technical solution provided by the embodiments of the present invention has the following advantages compared with the prior art:

[0044] This invention provides a method for estimating the state of charge (SOC) of a battery over a wide temperature range. The method acquires the relaxation time distribution curve and measurement data of the battery under test at any temperature within the target wide temperature range. The relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy (EIS) curve of the battery under test. Based on the relaxation time distribution curve, a fractional-order battery estimation model is constructed, and relevant parameters of the model are identified. This allows the fractional-order model to reflect the multi-timescale dynamics of the battery, avoiding the problem of existing technologies failing to capture these dynamics, thus achieving a more accurate estimation of the SOC. For the measurement data, a fractional-order adaptive extended Kalman filter is used to determine the SOC of the battery under test through relevant parameters and the fractional-order battery estimation model. By integrating adaptive mechanisms, fractional calculus, and extended Kalman filtering, the fractional-order battery estimation model is processed by fractional-order adaptive extended Kalman filtering. Iterative optimization is then performed to find the state or parameters that need to be estimated, thereby achieving an optimal estimation of the SOC in the sense of minimum variance, thus improving the accuracy of the SOC estimation. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0047] Figure 1 A flowchart illustrating a battery state-of-charge estimation method for a wide temperature range, provided in an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram illustrating how to obtain a relaxation time distribution curve and a fractional-order equivalent circuit model, as provided in an embodiment of the present invention.

[0049] Figure 3 A schematic diagram of an experimental result provided in an embodiment of the present invention;

[0050] Figure 4 A schematic diagram illustrating another experimental result provided in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of a battery state-of-charge estimation device for a wide temperature range, provided as an embodiment of the present invention.

[0052] Figure 5 In the middle: 10-Acquisition module; 11-Fractional order battery estimation model determination module; 12-Estimation module. Detailed Implementation

[0053] To better understand the above-mentioned objectives, features, and advantages of the present invention, the solutions of the present invention will be further described below. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention, but the invention may also be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of the invention, and not all embodiments.

[0055] In one embodiment, such as Figure 1 As shown, Figure 1 A flowchart illustrating a battery state-of-charge estimation method for a wide temperature range, provided by an embodiment of the present invention, specifically includes the following steps:

[0056] S10: Within the target wide temperature range, obtain the relaxation time distribution curve and measurement data of the battery to be estimated at any temperature.

[0057] The target wide temperature range refers to the fact that the operating temperature of automotive batteries, such as lithium-ion batteries, is typically controlled between 20°C and 40°C. However, in extremely cold regions, the battery's starting temperature may drop below 20°C, leading to starting failure. Therefore, when estimating the battery's state of charge, a target wide temperature range with a relatively wide temperature tolerance is set to avoid starting failure in extremely cold regions; for example, it could be 0°C to 40°C. "Any temperature" refers to any temperature within the target wide temperature range, such as 5°C, 10°C, 15°C, and 30°C. However, this invention is not limited to these values, and those skilled in the art can set the temperature according to actual conditions.

[0058] The relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy curve corresponding to the battery to be estimated. Electrochemical impedance spectroscopy is an analytical technique that studies the impedance of an electrochemical system as a function of frequency by applying a small AC voltage or current signal. It mainly analyzes the electrode process kinetics, interface characteristics, and material transport mechanisms by measuring the impedance response at different frequencies.

[0059] Measurement data refers to the data needed to estimate the state of charge of the battery to be estimated. Examples of measurement data include voltage, current, and ambient temperature. However, this invention is not limited to this, and those skilled in the art can configure the data according to actual conditions.

[0060] Specifically, for the battery to be estimated, within the target wide temperature range, the relaxation time distribution curves determined based on the electrochemical impedance spectroscopy curves of the battery to be estimated at any temperature are obtained, along with the measurement data required to estimate the state of charge of the battery to be estimated.

[0061] Optionally, based on the above embodiments, in some embodiments of the present invention, the method further includes the following before performing S10:

[0062] S20: Obtain the electrochemical impedance spectroscopy curve of the battery to be estimated at any temperature.

[0063] S21: Calculate the distribution function corresponding to the relaxation time distribution curve based on the relative conversion function between the electrochemical impedance spectroscopy curve and the relaxation time distribution curve.

[0064] The relative conversion calculation function is used to convert the electrochemical impedance spectroscopy curve to the relaxation time distribution curve.

[0065] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S21 may be:

[0066] S211: Construct a relative transformation calculation function for obtaining the relaxation time distribution curve. This relative transformation calculation function can be defined by the following expression:

[0067]

[0068] Where R0 represents the initial ohmic resistance, Let f represent the relaxation time distribution function, and let f represent the sampling frequency value. This represents a constant value for time.

[0069] S212: Based on the electrochemical impedance spectroscopy curve corresponding to the logarithmic scale, obtain the relative conversion calculation function corresponding to the logarithmic scale, which can be limited by the following expression:

[0070]

[0071] in, express The relaxation time distribution function described on a logarithmic scale, that is, the initial distribution function corresponding to the relaxation time distribution curve.

[0072] S213: Discretize the logarithmic scale-based relative transformation calculation function according to the Dirac distribution to obtain the discretized relative transformation calculation function and the initial discretized distribution function, which can be limited by the following expression:

[0073]

[0074]

[0075] in, This represents the unknowns obtained from subsequent fitting, where M is the number of time constant points. It is the time constant corresponding to the m-th time constant point. Indicates about The Dirac function.

[0076] S214: Initialize the discretized initial distribution function based on the Gaussian radial basis function. We obtain the distribution function corresponding to the relaxation time distribution curve, which can be defined by the following expression:

[0077]

[0078] S22: Determine the relaxation time distribution curve based on the preset objective function, relative transformation calculation function, and distribution function.

[0079] The preset objective function is used to solve for the distribution function, and the preset objective function can be limited by the following expression:

[0080]

[0081] in, This represents the real part of the function corresponding to the relative transformation calculation during the experimental process. This represents the real part of the function corresponding to the relative transformation during the calculation process. This represents the imaginary part of the relative transformation calculation function during the experimental process. This represents the imaginary part of the function corresponding to the relative transformation in the calculation process. The weights of the squared standard deviation of the real part error are represented by their respective values. The weights representing the squared standard deviation of the imaginary part error are... is the regularization parameter, and L is the regularization matrix.

[0082] Specifically, the relative transformation calculation function and the distribution function are substituted into the preset objective function, and the relaxation time distribution curve is determined by adjusting the regularization matrix and regularization parameters to make the preset objective function optimal.

[0083] For example, refer to Figure 2 As shown, the electrochemical impedance spectroscopy curve is converted into a relaxation time distribution curve. However, the present invention is not limited to this, and those skilled in the art can make adjustments according to the actual situation.

[0084] S11: Based on the relaxation time distribution curve, construct the fractional-order battery estimation model corresponding to the battery to be estimated, and identify the relevant parameters of the fractional-order battery estimation model.

[0085] The fractional-order battery estimation model and related parameters are used to subsequently estimate the state of charge of the battery to be estimated.

[0086] Specifically, after obtaining the relaxation time distribution curve, a fractional-order battery estimation model corresponding to the estimated battery is constructed based on the relaxation time distribution curve, and the relevant parameters of the fractional-order battery estimation model are identified.

[0087] Optionally, based on the above embodiments, in some embodiments of the present invention, one way to construct the fractional-order battery estimation model corresponding to the estimated battery according to the relaxation time distribution curve is as follows:

[0088] S111: Determine the number of polarization elements used to construct the fractional-order equivalent circuit model based on the multiple characteristic peaks of the relaxation time distribution curve.

[0089] 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 elements is used to determine the number of parallel resistor-capacitor structures in the equivalent circuit model.

[0090] S112: Construct a fractional-order equivalent circuit model based on the number of polarization elements.

[0091] Specifically, after obtaining the relaxation time distribution curve, the number of polarization elements used to construct the fractional-order equivalent circuit model is determined based on the multiple characteristic peaks of the relaxation time distribution curve. The fractional-order equivalent circuit model is then constructed based on the number of polarization elements.

[0092] Example, 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 elements in constructing the fractional-order equivalent circuit model is determined to be 5. Therefore, in the fractional-order equivalent circuit model, the number of parallel resistor-capacitor structures is 5. However, this invention is not limited to this, and those skilled in the art can set it according to the actual situation.

[0093] S113: Based on Kirchhoff's current-voltage law and the ampere-hour integration method, obtain the initial fractional-order battery estimation model corresponding to the fractional-order equivalent circuit model in the time domain.

[0094] 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 using Kirchhoff's current-voltage law combined with the ampere-hour integration method.

[0095] Optionally, based on the above embodiments, in some embodiments of the present invention, the initial fractional-order battery estimation model may be defined by the following expression:

[0096]

[0097] in, The fractional-order calculus operator represents the fractional-order equivalent circuit model. Indicates in temperature, The fractional-order equivalent circuit model under charged state at time 1 The order in each polarization stage Indicates in temperature, The fractional-order equivalent circuit model under charged state at time 1 The polarization constant phase angle element capacitor in each polarization stage This represents any temperature within the target wide temperature range. Indicates in temperature, The fractional-order equivalent circuit model under charged state at time 1 Polarization resistance in each polarization stage The fractional-order equivalent circuit model represents the first... The voltage corresponding to each polarization stage Indicates in The estimated value of the state of charge at time t. Indicates the initial time. The state of charge value, Indicates Coulomb efficiency. Indicates in The input current of the fractional-order equivalent circuit model at time step. This represents the rated capacitance of the battery to be estimated. This represents the terminal voltage in a fractional-order equivalent circuit model. Indicates in temperature, The open-circuit voltage corresponding to the fractional-order equivalent circuit model under charged state at any given time. This represents the input current of the fractional-order equivalent circuit model. Indicates in temperature, The ohmic resistance in the fractional-order equivalent circuit model at time n under charged state, where n represents the number of polarization elements in the fractional-order equivalent circuit model.

[0098] S114: Discretize the initial fractional-order battery estimation model to obtain the fractional-order battery estimation model.

[0099] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S114 may be:

[0100] Because Grünwald-Letnikov fractional order theory has good numerical stability and computability, and can flexibly adjust the fractional order, this paper uses Grünwald-Letnikov fractional order theory to discretize the initial fractional-order battery estimation model, and determines the discretized model. It can be qualified by the following expressions:

[0101]

[0102] in, Describes a fractional calculus operator. Indicates about t Integral-differential operators, To indicate the order, it should be noted that when When >0, Denotes the fractional derivative; when When =0, =1. This represents the sampling time, k is the memory length, and h is the step size. Denotes the coefficients of the Newton binomial. For positive integers between 0 and k, when When it is a non-integer, It can be qualified by the following expression:

[0103]

[0104] in, A function can be qualified by the following expressions:

[0105]

[0106] Furthermore, since performing infinite-dimensional GL derivatives in continuous time results in discretization and truncation to a short, fixed memory length, the discretized data... Processing is performed to obtain It can be qualified by the following expressions:

[0107]

[0108] Where k represents the kth discrete point.

[0109] Finally, based on the above Discretizing the initial fractional-order battery estimation model yields a fractional-order battery estimation model in the frequency domain, which can be defined by the following expression:

[0110]

[0111] in, The fractional-order equivalent circuit model represents the first... The voltage corresponding to the k-th discrete point under each polarization element. This represents the estimated state of charge at the (k+1)th discrete point. This represents the estimated state of charge at the k-th discrete point. Indicates in The fractional-order equivalent circuit model at temperature and the k-th discrete point under the state of charge is in the... Polarization resistance in each polarization stage Indicates in The fractional-order equivalent circuit model at temperature and the k-th discrete point under the state of charge is in the... The polarization constant phase angle element capacitor in each polarization stage j Represents integers from 1 to L+1, where L represents the fractional discrete memory length. Indicates in The fractional-order equivalent circuit model at temperature and the k-th discrete point under the state of charge is in the... The order in each polarization stage The fractional-order equivalent circuit model represents the first... The first polarization stage Voltages corresponding to discrete points This represents the input current at the k-th discrete point in the fractional-order equivalent circuit model. This represents the rated capacitance of the battery to be estimated. Indicates the discrete point interval. Indicates in The open-circuit voltage corresponding to the fractional-order equivalent circuit model at the temperature and the charged state at the k-th discrete point. Indicates in The ohmic resistance of the fractional-order equivalent circuit model under temperature and the charged state at the k-th discrete point. The fractional-order equivalent circuit model represents the first... The voltage corresponding to the (k+1)th discrete point under a polarization element, where n represents the number of polarization elements in the fractional-order equivalent circuit model. This represents the terminal voltage at the (k+1)th discrete point of the fractional-order equivalent circuit model.

[0112] Optionally, based on the above embodiments, in some embodiments of the present invention, one way to identify the relevant parameters of the fractional-order battery estimation model may be:

[0113] S115: Determine the distribution range of the relevant parameters based on the relaxation time distribution curve.

[0114] Among them, relevant parameters include: polarization resistance, polarization constant phase angle element capacitance, polarization stage order, and the distribution range includes multiple selectable relevant parameters.

[0115] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation 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 of the relevant parameters containing multiple candidate relevant parameters.

[0116] S116: Among multiple candidate related parameters, determine the candidate related parameter that makes the preset fitness function optimal as the related parameter.

[0117] The preset fitness function is used to identify relevant parameters of the fractional-order battery estimation model. The preset fitness function can be defined by the following expression:

[0118]

[0119] Here, Len represents the length of multiple candidate related parameters within the distribution range. This represents the terminal voltage data at time k determined through a pulse relaxation experiment. This indicates the output voltage.

[0120] Specifically, each of the multiple candidate related parameters is substituted into the 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 related parameter.

[0121] 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.

[0122] Fractional-order adaptive extended Kalman filtering integrates adaptive mechanisms and fractional-order calculus into Kalman filtering, applying fractional-order adaptive extended Kalman filtering to the fractional-order battery estimation model. Through iterative optimization, it finds the states or parameters that need to be estimated, and then makes the optimal estimate of the battery's state of charge (SOC) in the sense of minimizing variance, thereby improving the accuracy of the SOC estimation.

[0123] Specifically, after obtaining the measurement data, a fractional-order adaptive extended Kalman filter is used to estimate the state of charge of the battery to be estimated using relevant parameters and a fractional-order battery estimation model.

[0124] Thus, this embodiment provides a battery state-of-charge (SOC) estimation method for a wide temperature range. It acquires the relaxation time distribution curve and measurement data of the battery under test at any temperature within the target wide temperature range. The relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy (EIS) curve of the battery under test. Based on the relaxation time distribution curve, a fractional-order battery estimation model is constructed, and relevant parameters of the fractional-order model are identified. This allows the fractional-order model to reflect the multi-timescale dynamics of the battery, avoiding the problem of existing technologies failing to capture multi-timescale dynamics, thereby achieving more accurate estimation of the battery SOC. For the measurement data, a fractional-order adaptive extended Kalman filter is used to determine the SOC of the battery under test through relevant parameters and the fractional-order battery estimation model. By integrating the adaptive mechanism, fractional-order calculus, and extended Kalman filtering, the fractional-order battery estimation model is processed by fractional-order adaptive extended Kalman filtering. Iterative optimization is performed to find the state or parameters that need to be estimated, thereby making an optimal estimate of the SOC of the battery under test in the sense of minimum variance, thus improving the accuracy of the SOC estimation.

[0125] Optionally, based on the above embodiments, in some embodiments of the present invention, S12 may be implemented as follows:

[0126] S121: Based on the relevant parameters and the fractional-order battery estimation model, obtain the state transition equation and observation equation.

[0127] Specifically, based on the relevant parameters of the fractional-order battery estimation model and the fractional-order battery estimation model itself, the corresponding state transition equation and observation equation are determined.

[0128] Optionally, based on the above embodiments, in some embodiments of the present invention, the state transition equation may be defined by the following expression:

[0129]

[0130] The observation equation can be defined by the following expression:

[0131]

[0132] in, This represents the state transition matrix of the battery to be estimated at time k, under the state of charge at temperature T and time k. This represents the control matrix of the battery to be estimated at time k, under the state of charge at temperature T and time k. This represents the state vector at time k+1. , Let represent the observation matrix at time k+1 corresponding to the state of charge at temperature T and time k. , Let represent the ohmic resistance of the battery to be estimated at time k+1, under the state of charge at temperature T and time k. This represents the open-circuit voltage of the battery to be estimated at temperature T and time k, under the state of charge.

[0133] Optionally, based on the above embodiments, in some embodiments of the present invention, the state transition matrix may be defined by the following expression:

[0134]

[0135] Optionally, based on the above embodiments, in some embodiments of the present invention, the control matrix may be defined by the following expression:

[0136]

[0137] It should be noted that the elements in the state transition matrix, control matrix, and state vector are composed of relevant parameters from the fractional-order battery estimation model. After obtaining the state transition equation and observation equation, the corresponding relevant data are initialized, including: the state estimation matrix, prior covariance matrix, system noise covariance matrix, process noise covariance matrix, and filter coefficients.

[0138] S122: Determine the state of charge of the battery to be estimated based on the state transition equation and the observation equation.

[0139] Optionally, based on the above embodiments, in some embodiments of the present invention, one implementation of S122 may be:

[0140] S1221: Based on the posterior state estimate at time k and the posterior state estimate at the first k times introduced by the fractional order, the prior state estimate at time k+1 is obtained through the state transition equation.

[0141] S1222: Based on the prior state estimate at time k+1, obtain the prior observation at time k+1 through the observation equation.

[0142] S1223: Based on the posterior state covariance matrix at time k, the system noise covariance matrix, and the fractional-order posterior state covariance matrix at the previous k times, the prior state covariance matrix at time k+1 is obtained through the prior state covariance calculation formula.

[0143] Optionally, based on the above embodiments, in some embodiments of the present invention, the formula for calculating the prior state covariance may be limited by the following expression:

[0144]

[0145] in, This represents the fractional matrix at time k=1. This represents the transpose of the fractional matrix at time k=j. This represents the fractional matrix at time k=j. ,in . Let the system noise covariance matrix at time k be denoted as . Let represent the posterior state covariance matrix at time k.

[0146] Optionally, based on the above embodiments, in some embodiments of the present invention, the formula for calculating the posterior state covariance matrix may be limited by the following expression:

[0147]

[0148] in, Represents the identity matrix. Let the prior state covariance matrix at time k be denoted as . This represents the Kalman filter coefficients at time k. This represents the observation matrix at temperature T and time k-1, showing the state of charge.

[0149] S1224: Based on the prior state covariance matrix at time k+1 and the process noise covariance matrix at time k, obtain the Kalman filter coefficients at time k+1 using the Kalman filter coefficient calculation formula.

[0150] Optionally, based on the above embodiments, in some embodiments of the present invention, the formula for calculating the Kalman filter coefficients may be limited by the following expression:

[0151]

[0152] in, Let the prior state covariance matrix at time k+1 be represented. Let represent the process noise covariance matrix at time k. This represents the observation matrix corresponding to the charged state at temperature T and time k.

[0153] S1225: Obtain the state of charge of the battery to be estimated based on the prior observations at time k+1, the Kalman filter coefficients, and the prior state estimates.

[0154] Specifically, the posterior state estimate at time k and the posterior state estimates from the previous k times (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 then substituted into the observation equation to calculate the prior observation at time k+1. Based on the posterior state covariance matrix at time k, the system noise covariance matrix, and the posterior state covariance matrix from the previous k times (introduced by the fractional order), the prior state covariance matrix at time k+1 is calculated using the prior state covariance calculation formula. Based on the prior state covariance matrix at time k+1 and the process noise covariance matrix at time k, the Kalman filter coefficients at time k+1 are calculated using the Kalman filter coefficient calculation formula. Finally, based on the prior observation at time k+1, the Kalman filter coefficients, and the prior state estimate, the state of charge of the battery to be estimated is further obtained.

[0155] Optionally, based on the above embodiments, in some embodiments of the present invention, the system noise covariance matrix at time k is determined based on the Kalman filter coefficients and prior residual covariance at time k, and the filter coefficients at time k-1, wherein the prior residual covariance is determined based on the prior observations and actual observations from the previous k times. The process noise covariance matrix at time k is determined based on the prior state covariance matrix and posterior residual covariance at time k, and the filter coefficients at time k-1, wherein the posterior residual covariance is determined based on the posterior observations and actual observations from the previous k times.

[0156] Specifically, the Kalman filter coefficients and prior residual covariance at time k, and the filter coefficients at time k-1 are substituted into the system noise covariance calculation formula to obtain the system noise covariance matrix at time k. Similarly, the posterior state covariance matrix and posterior residual covariance at time k, and the filter coefficients at time k-1 are substituted into the process noise covariance calculation formula to obtain the process noise covariance matrix at time k.

[0157] Optionally, based on the above embodiments, in some embodiments of the present invention, the formula for calculating the system noise covariance may be limited by the following expression:

[0158]

[0159] in, This represents the Kalman filter coefficients at time k. This represents the filter coefficients at time k-1, i.e., the Sage-Husa filter coefficients. , b The value represents the forgetting factor, which ranges from 0 to 1. It should be noted that the filter coefficient gradually increases with time. The filter coefficient can be used to learn the characteristics of system noise, thereby eliminating system noise and improving the accuracy of obtaining the state of charge of the battery to be estimated. Let represent the prior residual covariance at time k.

[0160] The formula for calculating process noise covariance can be defined by the following expression:

[0161]

[0162] in, Let the prior state covariance matrix at time k be denoted as . Let represent the posterior residual covariance at time k.

[0163] It should be noted that since the fractional adaptive Kalman filter obtains the estimated parameter values ​​through iterative optimization, S1221-S1225 are repeatedly executed to obtain the state of charge of the battery to be estimated at any temperature.

[0164] Optionally, based on the above embodiments, in some embodiments of the present invention, to verify that the present invention can improve the accuracy of the estimation of the state of charge of the battery to be estimated, experiments were conducted using measurement data from two different vehicle operating conditions, such as Dynamic Stress Test (DST) and Urban Dynamometer Driving Schedule (UDDS), at arbitrary temperatures within the target wide temperature range, such as 5°C and 35°C. The effectiveness of the present invention was evaluated using the mean absolute error performance index. The experimental results are shown in […]. Figure 3 as well as Figure 4 .

[0165] Specifically, for DST vehicle operating conditions, such as Figure 3As shown, the state of charge (SOC) of the battery estimated by this invention can simulate the actual SOC quite well. The mean absolute error calculated by this invention is controlled within 1%, and the mean absolute error gradually decreases as the ambient temperature increases, i.e., from 5°C to 35°C. For UDDS vehicle operating conditions, such as... Figure 4 As shown, the state of charge (SOC) of the battery estimated by this invention can simulate the actual SOC quite well. Although the mean absolute error increases at 35°C, it is still controlled within 2.5%, which meets the industry estimation standard. This indicates that this invention can adapt to various complex vehicle operating conditions and can more accurately estimate the SOC of the battery under different vehicle operating conditions.

[0166] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0167] In one embodiment, such as 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.

[0168] The acquisition module 10 is used to acquire the relaxation time distribution curve and measurement data of the battery to be estimated at any temperature within the target wide temperature range. The relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy curve of the battery to be estimated.

[0169] The fractional-order battery estimation model determination module 11 is used to construct the fractional-order battery estimation model corresponding to the battery to be estimated based on the relaxation time distribution curve, and to identify the relevant parameters of the fractional-order battery estimation model.

[0170] The estimation module 12 is used to determine the state of charge of the battery to be estimated by using fractional-order adaptive extended Kalman filtering based on the measurement data, relevant parameters, and a fractional-order battery estimation model.

[0171] In the above embodiments, the acquisition module obtains the relaxation time distribution curve and measurement data of the battery to be estimated at any temperature within the 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. 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 the relevant parameters of the fractional-order battery estimation model. In this way, the fractional-order model can reflect the multi-timescale dynamics of the battery, avoiding the problem of not being able to capture the multi-timescale dynamics of the battery in the prior art, thereby achieving a more accurate estimation of the battery's state of charge. The estimation module uses fractional-order adaptive extended Kalman filtering to determine the state of charge of the battery to be estimated through relevant parameters and the fractional-order battery estimation model based on the measurement data. By integrating the adaptive mechanism, fractional-order calculus, and extended Kalman filtering, the fractional-order battery estimation model is processed by fractional-order adaptive extended Kalman filtering. Through iterative optimization, the state or parameters to be estimated are obtained, and then the optimal estimation 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 estimation of the state of charge of the battery to be estimated.

[0172] Specific limitations regarding the battery state-of-charge estimation device for a wide temperature range can be found in the limitations of a battery state-of-charge estimation method for a wide temperature range described above, and will not be repeated here. The various modules in the aforementioned server can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the corresponding operations of each module.

[0173] This invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can implement a battery state-of-charge estimation method for a wide temperature range provided by this invention. For example, when the processor executes the computer program, it can implement... Figures 1 to 4 The technical solutions of any of the method embodiments shown are similar in implementation principle and technical effect, and will not be described again here.

[0174] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can 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), etc.

[0175] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0176] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for estimating the state of charge of a battery over a wide temperature range, characterized in that, include: Within the target wide temperature range, the relaxation time distribution curve and measurement data of the battery to be estimated at any temperature are obtained, wherein the relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy curve corresponding to the battery to be estimated. Based on the multiple characteristic peaks of the relaxation time distribution curve, determine the number of polarization elements used to construct the fractional-order equivalent circuit model; Based on the number of polarization elements, a fractional-order equivalent circuit model is constructed; Based on Kirchhoff's current-voltage law and the ampere-hour integration method, the initial fractional-order battery estimation model corresponding to the fractional-order equivalent circuit model in the time domain is obtained. The initial fractional-order battery estimation model is discretized to obtain a fractional-order battery estimation model, and the relevant parameters of the fractional-order battery estimation model are identified. 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 the relevant parameters and the fractional-order battery estimation model.

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

3. The method according to claim 1, characterized in that, The initial fractional-order battery estimation model can be defined by the following expression: ； in, This represents the fractional-order calculus operator of the fractional-order equivalent circuit model. Indicates in temperature, The fractional-order equivalent circuit model described at time t is in the charged state at the t. The order in each polarization stage Indicates in temperature, The fractional-order equivalent circuit model described at time t is in the charged state at the t. The polarization constant phase angle element capacitor in each polarization stage This refers to any temperature within the target wide temperature range. Indicates in temperature, The fractional-order equivalent circuit model described at time t is in the charged state at the t. Polarization resistance in each polarization stage The fractional-order equivalent circuit model represents the first... The voltage corresponding to each polarization stage Indicates in The estimated value of the state of charge at time t. Indicates the initial time. The state of charge value, Indicates Coulomb efficiency. Indicates in The input current of the fractional-order equivalent circuit model at time t. This represents the rated capacitance of the battery to be estimated. This represents the terminal voltage in the fractional-order equivalent circuit model. Indicates in temperature, The open-circuit voltage corresponding to the fractional-order equivalent circuit model under the charged state at any given time. This represents the input current of the fractional-order equivalent circuit model. Indicates in temperature, The ohmic resistance in the fractional-order equivalent circuit model at a given time under charged state, where n represents the number of polarization elements 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 represents the first... The voltage corresponding to the k-th discrete point under each polarization element. This represents the estimated state of charge at the (k+1)th discrete point. This represents the estimated state of charge at the k-th discrete point. Indicates in The fractional-order equivalent circuit model at temperature and the state of charge at the k-th discrete point is in the... Polarization resistance in each polarization stage Indicates in The fractional-order equivalent circuit model at temperature and the state of charge at the k-th discrete point is in the... The polarization constant phase angle element capacitor in each polarization stage j Represents integers from 1 to L+1, where L represents the fractional discrete memory length. Indicates in The fractional-order equivalent circuit model at temperature and the state of charge at the k-th discrete point is in the... The order in each polarization stage The fractional-order equivalent circuit model represents the first... The first polarization stage Voltages corresponding to discrete points This represents the input current at the k-th discrete point of the fractional-order equivalent circuit model. This represents the rated capacitance of the battery to be estimated. Indicates the discrete point interval. This represents the open-circuit voltage corresponding to the fractional-order equivalent circuit model under the charged state at temperature T and the k-th discrete point. Indicates in The ohmic resistance of the fractional-order equivalent circuit model at temperature and the state of charge at the k-th discrete point. The fractional-order equivalent circuit model represents the first... The voltage corresponding to the (k+1)th discrete point under each polarization element, where n represents the number of polarization elements in the fractional-order equivalent circuit model. This represents the terminal voltage at the (k+1)th discrete point of the fractional-order equivalent circuit model.

4. The method according to claim 3, characterized in that, Identify the relevant parameters of the fractional-order battery estimation model, including: Based on the relaxation time distribution curve, the distribution range of the relevant parameters is determined, wherein the distribution range includes multiple candidate relevant parameters; Among the plurality of candidate related parameters, the candidate related parameter that makes the preset fitness function optimal is determined as the related parameter.

5. The method according to claim 4, characterized in that, The step of determining the state of charge of the battery to be estimated using fractional-order adaptive extended Kalman filtering on the measurement data, through the relevant parameters and the fractional-order battery estimation model, includes: Based on the relevant parameters and the fractional-order battery estimation model, the state transition equation and observation equation are obtained; The state of charge of the battery to be estimated is determined based on the state transition equation and the observation equation.

6. The method according to claim 5, characterized in that, The step of determining the state of charge of the battery to be estimated based on the state transition equation and the observation equation includes: Based on the posterior state estimate at time k and the posterior state estimates at the previous k times introduced by the fractional order, the prior state estimate at time k+1 is obtained through the state transition equation. Based on the prior state estimate at time k+1, the prior observation at time k+1 is obtained through the observation equation. Based on the posterior state covariance matrix at time k, the system noise covariance matrix, and the fractional-order posterior state covariance matrix at the previous k times, the prior state covariance matrix at time k+1 is obtained through the prior state covariance calculation formula. Based on the prior state covariance matrix at time k+1 and the process noise covariance matrix at time k, the Kalman filter coefficients at time k+1 are obtained using the Kalman filter coefficient calculation formula. The state of charge of the battery to be estimated is obtained based on the prior observations at time k+1, the Kalman filter coefficients, and the prior state estimates.

7. The method according to claim 6, characterized in that, The system noise covariance matrix at time k is determined based on the Kalman filter coefficients and prior residual covariance at time k and the filter coefficients at time k-1. The prior residual covariance is determined based on the prior observations and actual observations at the previous k times. The process noise covariance matrix at time k is determined based on the prior state covariance matrix and the posterior residual covariance at time k, as well as the filtering coefficients at time k-1. The posterior residual covariance is determined based on the posterior observations and actual observations from the previous k time steps.

8. A battery state-of-charge estimation device for a wide temperature range, characterized in that, include: The acquisition module is used to acquire the relaxation time distribution curve and measurement data of the battery to be estimated at any temperature within a target wide temperature range, wherein the relaxation time distribution curve is determined based on the electrochemical impedance spectroscopy curve of the battery to be estimated. The fractional-order battery estimation model determination module is used to determine the number of polarization elements used to construct the fractional-order equivalent circuit model based on multiple characteristic peaks of the relaxation time distribution curve. Based on the number of polarization elements, a fractional-order equivalent circuit model is constructed; Based on Kirchhoff's current-voltage law and the ampere-hour integration method, the initial fractional-order battery estimation model corresponding to the fractional-order equivalent circuit model in the time domain is obtained. The initial fractional-order battery estimation model is discretized to obtain a fractional-order battery estimation model, and the relevant parameters of the fractional-order battery estimation model are identified. The estimation module is used to determine the state of charge of the battery to be estimated by using fractional-order adaptive extended Kalman filtering on the measurement data, through the relevant parameters and the fractional-order battery estimation model.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the battery state-of-charge estimation method for a wide temperature range as described in any one of claims 1 to 7.

Images