Battery charge information determination method, determination apparatus, and electronic device

Abstract

The embodiment of the application discloses a battery charge information determination method, a determination device and electronic equipment, and the main technical scheme comprises the following steps: obtaining the charge parameter test data of the target battery in the charging and discharging process, wherein the charging and discharging process comprises a charging pulse state, a charging static state, a discharging pulse state and a discharging static state; based on the preset multi-stage equivalent circuit model, the time constant and the polarization resistance of each state of the target battery in the charging and discharging process are determined according to the charge parameter test data; based on the preset Kalman filter state equation, the corresponding time constant and polarization resistance are selected to determine the Kalman gain according to the state switching information between each state of the target battery in the charging and discharging process, and the posterior state value is estimated according to the Kalman gain, so as to obtain the charge information of the target battery, so that the application can capture the characteristic difference of the target battery in different states and state switching, thereby realizing real-time high-precision estimation of the charge information.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a method, apparatus and electronic device for determining battery charge information. Background Technology

[0002] Among the current mainstream methods for estimating battery charge information, electrochemical model-based methods can reflect the internal reactions of the battery, but the modeling is complex and computationally intensive. Equivalent circuit model-based methods are simple to model and have low computational cost, but they require parameter identification through mixed pulse power testing and are sensitive to parameter accuracy. At the same time, due to the significant differences in the characteristics of the battery in the pulse state of the current response segment and the static state of the current-free resting segment, existing methods cannot simultaneously take into account the estimation accuracy of the two states, resulting in large estimation errors for charge percentage, voltage, and other charge information, especially when switching between charging / discharging and static states, the accuracy drops significantly. Summary of the Invention

[0003] This application provides a method, apparatus, and electronic device for determining battery charge information, in order to improve the estimation accuracy of charge information.

[0004] This application provides the following solution:

[0005] According to the first aspect, a method for determining battery charge information is provided, including:

[0006] Acquire charge parameter test data of the target battery during the charging and discharging process, wherein the charging and discharging process includes charging pulse state, charging rest state, discharging pulse state and discharging rest state;

[0007] Based on a preset multi-order equivalent circuit model, the time constant and polarization resistance of the target battery in each state during the charging and discharging process are determined according to the charge parameter test data.

[0008] Based on a preset Kalman filter state equation, the state switching information between the states of the target battery during the charging and discharging process is obtained. The corresponding time constant and polarization resistance are selected according to the state switching information to determine the Kalman gain. The posterior state value is estimated according to the Kalman gain. The charge information of the target battery is determined based on the posterior state value. The charge information includes the estimated charge percentage and estimated voltage of the target battery.

[0009] As an optional approach, the charge parameter test data includes test data of the target battery under multiple reference charge percentages, wherein the multiple reference charge percentages are arranged in order of charge change.

[0010] The method based on a preset multi-order equivalent circuit model, determining the time constant and polarization resistance of the target battery in each state during the charging and discharging process according to the charge parameter test data, includes:

[0011] From the states of the charging and discharging process, select a combination of pulse state and rest state during the charging process, or a combination of pulse state and rest state during the discharging process, and select a target reference charge percentage from the multiple reference charge percentages.

[0012] Based on the charge parameter test data, obtain the test data of the target reference charge percentage under the state combination, and obtain the test data of the previous reference charge percentage of the target reference charge percentage.

[0013] Based on the test data of the target reference charge percentage and the previous reference charge percentage under the state combination, the parameters of the preset multi-order equivalent circuit model are fitted and estimated to obtain the time constant and polarization resistance of the target reference charge percentage under the state combination.

[0014] As an optional approach, the step of performing parameter fitting estimation on a preset multi-order equivalent circuit model based on test data of the target reference charge percentage under the state combination and the previous reference charge percentage, to obtain the time constant and polarization resistance of the target reference charge percentage under the state combination, includes:

[0015] In the pulse state of the state combination, the pulse start parameter is determined based on the test data of the target reference charge ratio and the previous reference charge ratio under the state combination, and the parameters of the preset multi-order equivalent circuit model are fitted and estimated according to the pulse start parameter to obtain the polarization internal resistance of the target reference charge ratio under the state combination, as well as the time constant and pulse end parameter of the target reference charge ratio in the pulse state of the state combination.

[0016] In the static state of the state combination, the static start parameter is determined according to the pulse end parameter, and the parameter fitting estimation of the multi-order equivalent circuit model is performed according to the static start parameter to obtain the time constant of the target reference charge ratio in the static state of the state combination.

[0017] As an optional approach, the pulse initiation parameters include the total pulse polarization internal resistance, and the determination of the pulse initiation parameters based on test data of the target reference charge percentage and the previous reference charge percentage under the state combination includes:

[0018] Based on the test data of the target reference charge percentage and the previous reference charge percentage under the state combination, the pulse end voltage, pulse start voltage, ohmic voltage drop, open circuit voltage drift compensation and pulse average current of the target battery are determined.

[0019] Subtract the pulse start voltage, ohmic voltage drop, and open-circuit voltage drift compensation from the pulse end voltage to obtain the pulse polarization voltage;

[0020] The total pulse polarization resistance is obtained based on the pulse polarization voltage and the pulse average current.

[0021] As an optional approach, the pulse initiation parameter includes the total pulse polarization resistance. The step of performing parameter fitting estimation on a preset multi-order equivalent circuit model based on the pulse initiation parameter to obtain the polarization resistance of the target reference charge ratio under the state combination includes:

[0022] The initial value of the polarization resistance in the multi-order equivalent circuit model is determined based on the pulse total polarization resistance of the target reference charge percentage, and the variation range of the polarization resistance is determined based on the pulse total polarization resistance of the target reference charge percentage and the pulse total polarization resistance of the previous reference charge percentage.

[0023] Based on the initial value and the range of variation, the polarization resistance is estimated by parameter fitting to obtain the polarization resistance of the target reference charge ratio under the state combination.

[0024] As an optional method, obtaining the state transition information of the target battery between different states during the charging and discharging process includes:

[0025] Obtain the current current flow direction and current polarization voltage of the target battery;

[0026] The state switching information of the target battery between each state during the charging and discharging process is determined based on the current current flow direction and the current polarization voltage.

[0027] As an optional approach, the step of selecting the corresponding time constant and polarization resistance based on the state switching information to determine the Kalman gain, and estimating the posterior state value based on the Kalman gain, includes:

[0028] The reference charge percentage of the target battery is determined based on the state switching information;

[0029] Based on the Kalman filter state equation, the prior state value and prior covariance are calculated according to the time constant and polarization internal resistance corresponding to the reference charge ratio of the target battery, so as to determine the Kalman gain.

[0030] The prior state value is corrected by the Kalman gain to obtain the posterior state value and the posterior covariance;

[0031] Based on the posterior state value and the posterior covariance, the reference charge ratio and the prior covariance are iteratively updated. The above calculation and correction process is repeated until the prior covariance is less than a preset convergence threshold. Based on the posterior state value, the charge information of the target battery is determined.

[0032] As an optional approach, the equation parameters of the Kalman filter state equation include a state transition matrix, a control input matrix, a state vector, and a control input quantity. The state transition matrix and the control input matrix are determined by the time constant and polarization resistance corresponding to the reference charge percentage of the target battery. The state vector is determined by the charge information of the target battery, and the control input quantity is determined by the real-time current of the target battery.

[0033] According to a second aspect, a battery charge information determination device is provided, comprising:

[0034] The data acquisition module is configured to acquire charge parameter test data of the target battery during the charging and discharging process, wherein the charging and discharging process includes charging pulse state, charging rest state, discharging pulse state and discharging rest state.

[0035] The parameter determination module is configured to determine the time constant and polarization resistance of the target battery in each state during the charging and discharging process based on a preset multi-order equivalent circuit model and the charge parameter test data.

[0036] The charge estimation module is configured to acquire state switching information between states of the target battery during the charging and discharging process based on a preset Kalman filter state equation, select the corresponding time constant and polarization resistance according to the state switching information to determine the Kalman gain, estimate the posterior state value according to the Kalman gain, and determine the charge information of the target battery based on the posterior state value. The charge information includes the estimated charge percentage and estimated voltage of the target battery.

[0037] According to a third aspect, an electronic device is provided, comprising:

[0038] One or more processors; and

[0039] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any one of the first aspects.

[0040] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0041] The solution provided in this application obtains the charge parameter test data of the target battery under all states of charging pulse, charging rest, discharging pulse, and discharging rest. It combines a multi-order equivalent circuit model to fit the time constant and polarization internal resistance corresponding to each state, and dynamically selects the appropriate parameters for posterior estimation based on the Kalman filter state equation and state switching information. This can accurately capture the characteristic differences of the target battery under different states and state switching, effectively reduce the estimation error of charge information, improve the estimation accuracy under charging / discharging and resting state switching scenarios, and thus achieve real-time high-precision estimation of charge information.

[0042] Of course, any product implementing this application does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating the battery charge information determination method provided in this application embodiment.

[0045] Figure 2 This is a schematic block diagram of a battery charge information determination device provided in an embodiment of this application.

[0046] Figure 3 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0048] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0049] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0050] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0051] Among the current mainstream methods for estimating battery charge information, electrochemical model-based methods can reflect the internal reactions of the battery, but the modeling is complex and computationally intensive. Equivalent circuit model-based methods are simple to model and have low computational cost, but they require parameter identification through mixed pulse power testing and are sensitive to parameter accuracy. At the same time, due to the significant differences in the characteristics of the battery in the pulse state of the current response segment and the static state of the current-free resting segment, existing methods cannot simultaneously take into account the estimation accuracy of the two states, resulting in large estimation errors for charge percentage, voltage, and other charge information, especially when switching between charging / discharging and static states, the accuracy drops significantly.

[0052] To address the aforementioned issues, the inventors of this application noted a fundamental difference in the polarization response of a battery between pulsed and stationary states. In the pulsed state, sudden current changes trigger a rapid polarization effect, while in the stationary state, the polarization voltage exhibits slow relaxation characteristics. Using a uniform parameter model for state estimation inevitably leads to lag in tracking the dynamic process. Further research revealed that parameters at each order in the multi-order equivalent circuit model correspond to polarization processes at different time scales. By extracting the time constant and polarization internal resistance from different states, the transient and steady-state behavior of the battery can be characterized more accurately. Simultaneously, the Kalman filter algorithm possesses the ability to dynamically adjust gain; combining state switching information with parameter selection can effectively improve the estimation accuracy during state switching.

[0053] Therefore, this application provides a method, apparatus, and electronic device for determining battery charge information, which captures the characteristic differences of a target battery in different states and during state transitions, thereby achieving real-time high-precision estimation of charge information.

[0054] The present application will be further described below with reference to the accompanying drawings.

[0055] Figure 1 A flowchart of the battery charge information determination method provided in the embodiments of this application; as follows: Figure 1 As shown, this application provides a method for determining battery charge information, which may include at least the following steps:

[0056] Step 101: Obtain the charge parameter test data of the target battery during the charging and discharging process, wherein the charging and discharging process includes charging pulse state, charging rest state, discharging pulse state and discharging rest state.

[0057] Step 102: Based on the preset multi-order equivalent circuit model, determine the time constant and polarization resistance of the target battery in each state during the charging and discharging process according to the charge parameter test data.

[0058] Step 103: Based on the preset Kalman filter state equation, obtain the state switching information between each state of the target battery during the charging and discharging process, select the corresponding time constant and polarization internal resistance according to the state switching information to determine the Kalman gain, estimate the posterior state value according to the Kalman gain, and determine the charge information of the target battery based on the posterior state value.

[0059] Based on steps 101 to 103, it can be seen that the solution provided in this application embodiment obtains the charge parameter test data of the target battery under all states of charging pulse, charging rest, discharging pulse, and discharging rest. It combines a multi-order equivalent circuit model to fit the time constant and polarization internal resistance corresponding to each state, and based on the Kalman filter state equation, dynamically selects the adaptation parameters for posterior estimation according to the state switching information. This can accurately capture the characteristic differences of the target battery under different states and state switching, effectively reduce the estimation error of charge information, improve the estimation accuracy under charging, discharging and resting state switching scenarios, and thus achieve real-time high-precision estimation of charge information.

[0060] The charge parameter test data refers to the voltage and current variation curves over time collected by charge-discharge test equipment. This can be achieved using a test protocol that alternates between constant current charge-discharge and rest, reflecting the dynamic response characteristics of the battery under different reference charge percentages. The multi-order equivalent circuit model refers to a multi-level RC network structure composed of ohmic resistors, polarization resistors, and capacitors. This can be implemented using a third- or fourth-order topology, and its parameter identification accuracy is positively correlated with the order. The time constant and polarization resistance refer to the time constant and polarization resistance values ​​of each RC element in the equivalent circuit model. This can be obtained by piecewise fitting of the voltage response curves during the pulse and rest phases, used to quantify the polarization decay rate and amplitude under different states. The Kalman filter state equation is a mathematical model describing the relationship between system state transitions and observations. This can be implemented using discrete-time linear system equations, with its state transition matrix and control input matrix dynamically constructed from the time constant and polarization resistance corresponding to the current state. State switching information refers to the state switching information triggered by changes in current direction and amplitude during charge-discharge. This can be determined by real-time monitoring of current sensor signals, used to trigger dynamic switching of the equivalent circuit model parameters. Posterior state value estimation refers to the process of correcting prior estimates using Kalman gain. Specifically, it can be achieved through iterative updates of the covariance matrix and weighted fusion of state vectors, which is used to eliminate the influence of measurement noise and model errors on charge information.

[0061] Specifically, during the charge / discharge testing phase, this application can acquire voltage and current data across four states by alternately applying constant current pulses and allowing the battery to rest. For each state, a multi-order equivalent circuit model is used for parameter fitting. For example, during the charging pulse phase, the small time constant and large polarization resistance corresponding to rapid polarization are fitted based on the current step response curve; during the charging rest phase, the large time constant and small polarization resistance corresponding to slow polarization are fitted based on the voltage relaxation curve. Furthermore, upon detecting a state transition, such as from charging pulse to charging rest, the time constant and polarization resistance parameters of the corresponding state are automatically retrieved, and the Kalman gain is adjusted in real time. Based on the Kalman gain, a posteriori state value is estimated to obtain the target battery's charge information. This charge information can include the estimated charge percentage and estimated voltage of the target battery, thereby achieving real-time, high-precision estimation of charge information.

[0062] In some embodiments, this application can cover the charging and discharging process of the target battery under different temperature conditions (e.g., multiple temperature points within the range of -20℃ to 55℃), different current intensities (e.g., multiple charge / discharge rates within the range of 0.1C to 2C), and different reference charge percentages (e.g., discrete nodes such as 10%, 30%, 50%, 70%, and 90%) based on typical operating conditions in energy storage scenarios. HPPC (Hybrid Pulse Power Characteristic Test) and OCV (Open Circuit Voltage Test) are performed to collect charge parameter test data, ensuring that it comprehensively reflects the dynamic characteristics of the battery under complex operating conditions. Temperature conditions can be precisely controlled by a constant temperature chamber, current intensity can be adjusted in steps by the charge / discharge test equipment, and the reference charge percentage is defined by preset charge / discharge cutoff conditions. The specific data collected for charge parameter test can include voltage and current change curves over time under each state, thus providing multi-dimensional input for subsequent parameter fitting.

[0063] In some embodiments, each multi-order equivalent circuit model corresponds to multiple time constants and polarization internal resistances, the number of which can be determined by the order of the multi-order equivalent circuit model. For example, when using a third-order equivalent circuit model, three time constants and three polarization internal resistances need to be determined, each parameter corresponding to the polarization response of the battery at different time scales (e.g., fast polarization, medium-speed polarization, slow polarization). If a fourth-order model is used, it corresponds to four time constants and four polarization internal resistances, to more finely characterize the hierarchical differences in the polarization process. These parameters are extracted from the charge parameter test data using a state-separated fitting algorithm. Each reference charge percentage, each temperature, and each current condition forms an independent parameter group, constructing a parameter library covering all operating conditions, providing a basis for parameter calls during state switching.

[0064] In some embodiments, the multi-order equivalent circuit model in this application can be a third-order equivalent circuit model, and the target battery can be a lithium-ion battery, wherein the voltage U of the target battery is... t This can be expressed as the following formula:

[0065] ;

[0066] Among them, U OCV U is the open-circuit voltage of the target battery, and U1, U2, and U3 are the polarization voltage components of the target battery at each polarization internal resistance R1, R2, and R3. r The ohmic voltage of the target battery can be determined based on Kirchhoff's current-voltage law and Ohm's law, where U1, U2, U3, and U... r The calculation method can be expressed as the following formula:

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] Where dt is the time length, I is the current, R0 is the ohmic internal resistance of the target battery, and C1, C2, and C3 are the polarization capacitances of the target battery. For the above U1, U2, U3, and U... r The calculation formula can be solved by first-order partial differential equations to obtain:

[0072] ;

[0073] ;

[0074] ;

[0075] in, , and The time constant of the target battery. Multiplication is represented by the following method:

[0076] ;

[0077] ;

[0078] ;

[0079] Based on the above formula, the time constant and polarization resistance can be calculated. The time constant... The value i = 1, 2, or 3 can characterize the decay rate of the polarization process in the fast, medium, and slow stages, and can be used to construct the state transition matrix A of the Kalman filter state equation; the polarization internal resistance R i The equivalent resistance representing each polarization process can be used to construct the control input matrix B of the Kalman filter state equation.

[0080] Understandably, traditional equivalent circuit model parameter identification methods do not consider parameter differences during state transitions, leading to the Kalman filter using a fixed gain during the transition between charging / discharging and resting states, failing to respond promptly to rapid changes in polarization characteristics. This scheme extracts time constants and polarization resistances by state, establishing a state-dependent parameter library. This allows the Kalman filter state equation to dynamically adjust the system matrix based on real-time state transitions, achieving synchronous switching between parameters and states. This dynamic parameter matching mechanism effectively solves the model mismatch problem encountered by traditional methods during state transitions.

[0081] Through the above technical solution, this application can accurately track the dynamic changes of charge information under complex operating conditions where the battery frequently switches between charging / discharging and resting states. By identifying sub-state parameters and adjusting the dynamic Kalman gain, the estimation error at the moment of state switching is reduced, the real-time performance and robustness of charge state estimation are improved, and reliable charge information input is provided for the battery management system.

[0082] This application further proposes to select a combination of pulse state and static state during charging or a combination of pulse state and static state during discharging, and to select a target reference charge percentage from multiple reference charge percentages; to obtain test data of the target reference charge percentage under the state combination based on charge parameter test data, and to obtain test data of the previous reference charge percentage of the target reference charge percentage; and to perform parameter fitting estimation on a preset multi-order equivalent circuit model based on the test data of the target reference charge percentage under the state combination, so as to obtain the time constant and polarization resistance of the target reference charge percentage under the state combination.

[0083] The reference charge percentage refers to multiple discrete state points of the battery arranged in the order of charge change during charging and discharging. For example, these could be nodes with reference charge percentages of 20%, 40%, and 60%. This division can be achieved through a preset charge change gradient, used to fit battery parameters under different charge states in segments. State combinations refer to the pairing of pulse states and resting states during the same charging and discharging process, such as a combination of charging pulses and charging resting states, or a combination of discharging pulses and discharging resting states. These combinations can be identified through current direction or voltage change characteristics, used to distinguish battery characteristics at different dynamic response stages. Parameter fitting estimation refers to the numerical optimization and solution of unknown parameters in the equivalent circuit model using test data. For example, the least squares method or gradient descent algorithm can be used to minimize the error between the model output and the measured data through iterative calculations, thereby obtaining accurate time constants and polarization resistance.

[0084] It is worth noting that the reference charge percentage is a pre-defined discrete state point used to divide the data acquisition interval in HPPC and OCV tests. By collecting voltage and current data at these nodes, parameters such as the time constant and polarization resistance of the corresponding state are fitted and used as a benchmark reference for subsequent estimation. The estimated charge percentage is a dynamic estimate of the battery's current state of charge based on real-time test data and Kalman filtering algorithm, which can more accurately reflect the continuously changing remaining capacity percentage of the battery during charging and discharging.

[0085] Understandably, during the charging and discharging process, the pulse state and its corresponding resting state are first selected as the state combination to be analyzed based on the current direction and voltage change trend. For example, during charging, test data from the charging pulse phase and its subsequent resting phase are extracted. Next, the current target reference charge percentage is determined according to the preset change sequence of the reference charge percentage. For example, when the reference charge percentage increases from 30% to 35%, 35% is selected as the target reference charge percentage. By comparing the test data of the target reference point and its previous reference point, such as comparing the voltage response curves corresponding to 35% and 30%, the parameter constraints of the multi-order equivalent circuit model are established. This allows for the joint estimation of the time constant and polarization resistance in the multi-order equivalent circuit model.

[0086] Furthermore, after each charging pulse ends, the battery enters a charging resting state for a preset time. For example, after a charging pulse at 95% reference charge percentage ends, the voltage gradually decreases from the end of the pulse until it reaches a stable OCV value. The OCV value is the open-circuit voltage value obtained from the open-circuit voltage test mentioned above. At this time, a charging resting state curve can be fitted. The slow voltage decrease in the curve reflects the polarization decay process. The large time constant of the charging resting state can be obtained through this curve fitting.

[0087] Furthermore, after each discharge pulse ends, the battery enters a discharge resting state for a preset time. For example, after a discharge pulse at 95% reference charge percentage ends, the voltage gradually rises from the end of the pulse until it reaches a stable OCV value. At this point, the charge-discharge resting state curve can be fitted, and the large time constant under the discharge resting state can be obtained through this curve fitting.

[0088] Understandably, existing methods typically use a single test data segment for global parameter identification, failing to consider the impact of different reference charge percentages on battery dynamic characteristics, leading to deviations in model parameters during state transitions. This application effectively addresses the estimation error problem caused by parameter sensitivity in existing multi-order equivalent circuit models during hybrid pulse power testing. Through staged parameter fitting, it significantly improves the consistency of charge information estimation between pulse and resting states. For example, when switching from a charging pulse to a resting state, the transient change in polarization resistance is accurately captured, avoiding accumulated errors in voltage estimation, thereby improving the joint estimation accuracy of charge percentage and voltage.

[0089] It is worth noting that the above curve fitting process can be understood as splitting the curve according to the state, calculating the total polarization internal resistance of the pulse based on the pulse start parameters as the initial value of the polarization internal resistance, and fitting the voltage rapid change segment through a preset algorithm to optimize the polarization internal resistance and the time constant of the pulse state; furthermore, in the static state, using the pulse end parameters as the initial conditions, fitting the voltage relaxation segment to optimize the static state time constant, ensuring that the model matches the slow decay characteristics of the polarization voltage.

[0090] Corresponding to the above curve fitting process, this application further proposes that in the pulse state of the state combination, the pulse start parameter is determined based on the test data of the target reference charge ratio and the previous reference charge ratio under the state combination, and the parameters of the preset multi-order equivalent circuit model are estimated by fitting the parameters according to the pulse start parameter to obtain the polarization internal resistance of the target reference charge ratio under the state combination, as well as the time constant and pulse end parameter of the target reference charge ratio under the pulse state of the state combination; in the stationary state of the state combination, the stationary start parameter is determined based on the pulse end parameter, and the parameters of the multi-order equivalent circuit model are estimated by fitting the parameters according to the stationary start parameter to obtain the time constant of the target reference charge ratio under the stationary state of the state combination.

[0091] Among them, the pulse initiation parameters refer to the initial conditions used for parameter fitting under the pulse state. Specifically, they can be implemented using the total pulse polarization resistance, which is calculated through the pulse end voltage, pulse initiation voltage, ohmic voltage drop, and open-circuit voltage drift compensation. These parameters are used to establish the initial parameters for a multi-order equivalent circuit model. Parameter fitting estimation refers to the process of adjusting the model parameters to match the test data using mathematical optimization algorithms. Specifically, it can be implemented using the least squares method or gradient descent method to improve the calculation accuracy of polarization resistance and time constant. Pulse end parameters refer to the intermediate results obtained at the end of the pulse state, which can include polarization voltage or current decay values, and are used as initial conditions for parameter fitting under the stationary state.

[0092] In some embodiments, this application can extract the pulse end voltage, pulse start voltage, ohmic voltage drop, open-circuit voltage drift compensation, and pulse average current based on the test data corresponding to the target reference charge percentage and the previous reference charge percentage. Then, the pulse end voltage can be subtracted from the pulse start voltage, ohmic voltage drop, and open-circuit voltage drift compensation to obtain the pulse polarization voltage caused only by the polarization effect.

[0093] The pulse-end voltage refers to the battery terminal voltage measured at the end of the pulse state, which can be obtained in real time by a voltage sensor and reflects the voltage change after the accumulation of polarization effects. The pulse-start voltage refers to the battery terminal voltage measured at the beginning of the pulse state, which can be obtained by a voltage sensor at the instant current is applied. The ohmic voltage drop refers to the instantaneous voltage change caused by the battery's ohmic internal resistance, which can be calculated by multiplying the current value by the ohmic internal resistance and is used to compensate for voltage fluctuations caused by sudden current changes. Open-circuit voltage drift compensation refers to the open-circuit voltage deviation caused by self-discharge or temperature changes during battery resting, which can be estimated by fitting historical resting data or using empirical formulas and is used to eliminate the influence of non-polarization factors on voltage. The pulse average current refers to the average current flowing through the battery during the pulse state, which can be calculated by taking the arithmetic mean after sampling by a current sensor and is used as the calculation benchmark for quantifying polarization internal resistance. The pulse total polarization internal resistance refers to the sum of equivalent internal resistances caused by polarization effects, which can be calculated by the ratio of pulse polarization voltage to pulse average current and is used to characterize the dynamic response characteristics of the battery under pulse conditions.

[0094] This application further proposes that the pulse initiation parameter includes the total pulse polarization resistance. Based on the pulse initiation parameter, a parameter fitting estimation is performed on a preset multi-order equivalent circuit model to obtain the polarization resistance of the target reference charge percentage under the state combination. This includes: determining the initial value of the polarization resistance in the multi-order equivalent circuit model based on the pulse total polarization resistance of the target reference charge percentage, and determining the variation range of the polarization resistance based on the pulse total polarization resistance of the target reference charge percentage and the pulse total polarization resistance of the previous reference charge percentage; based on the initial value and the variation range, parameter fitting estimation is performed on the polarization resistance to obtain the polarization resistance of the target reference charge percentage under the state combination.

[0095] This application calculates the total polarization resistance of a pulse by dividing the pulse polarization voltage by the average pulse current. This resistance serves as an input parameter for a multi-order equivalent circuit model. A nonlinear fitting algorithm optimizes the initial value of the polarization resistance in the model, while simultaneously constraining the parameter variation range using the resistance data from the previous reference charge percentage, thereby determining the precise value of the polarization resistance under the current reference charge percentage. Furthermore, based on the correlation between the pulse end parameters and the time constant, the time constant under the pulse state is further derived. In the stationary state, using the pulse end parameters as initial conditions, the voltage relaxation process is backfitted through the equivalent circuit model, thereby separating the time constant under the stationary state.

[0096] Specifically, the multi-order equivalent circuit model in this application can be a third-order equivalent circuit model. Based on the formula definition of the third-order equivalent circuit model in the above embodiments, this application can further perform parameter fitting, as follows:

[0097] Based on the charge parameter test data, the temperature is set for each fitting operation, and the reference charge percentage and current are used. The preprocessed data is then input into the script for fitting the data to perform charge percentage matching, and the ohmic internal resistance is calculated according to Ohm's law. :

[0098] ;

[0099] in, This is the starting voltage in the static state. This is the voltage at the end of the pulse state. Let || be the terminal current of the current pulse state, and || be the absolute value operation.

[0100] Understandably, to prevent jumps, it is necessary to define the initial values ​​and upper and lower limits of each parameter before performing parameter fitting. First, the total pulse polarization resistance during the pulse period is calculated as follows:

[0101] ;

[0102] ;

[0103] Where / represents division. The voltage at the end of the pulse. This is the pulse start voltage. This indicates the ohmic voltage drop. The OCV voltage difference, representing the percentage of charge between adjacent reference charges, is used to characterize open-circuit voltage drift compensation. The pulse average current in the pulse state. It is the pulse polarization voltage. This represents the total pulse polarization internal resistance.

[0104] Furthermore, when the proportion of the adjacent reference charge is the initial value, , and The initial values ​​are respectively set to and When performing fitting later, it can be , and The upper and lower limits are limited according to the following calculation formula:

[0105] ;

[0106] Where / represents division. The total polarization resistance is the percentage of the previous reference charge. The total polarization resistance is used as the target reference charge percentage. The coefficient of change of total polarization internal resistance, if If it is greater than 1, then it can be , and The upper limit is set to , and The lower limit value is set to , and ,in , and These are the polarization internal resistance values ​​obtained by fitting the previous reference charge percentage; if If less than or equal to 1, then... , and The lower limit is set to , and The upper limit is set to , and ,in , and These are the polarization resistance values ​​obtained by fitting the previous reference charge percentage, which can control the stability of the fitted polarization resistance values ​​and avoid sudden and large jumps in the polarization resistance values.

[0107] Understandably, existing methods typically ignore open-circuit voltage drift compensation when calculating polarization internal resistance, and there are cases where the fitted value of polarization internal resistance jumps significantly, resulting in systematic errors and poor stability in polarization voltage calculation. This application, by introducing open-circuit voltage drift compensation, effectively eliminates the interference of non-polarization factors on voltage changes. Furthermore, by establishing parameter correlation constraints between adjacent reference charge percentages, it can suppress parameter estimation deviations caused by test data noise or operating condition fluctuations, thereby improving the accuracy of charge information estimation when the battery switches between charge / discharge pulse state and rest state.

[0108] In some embodiments, given the initial values ​​of the time constant and polarization resistance, parameter fitting can be performed using built-in MATLAB functions; for example, setting the initial values ​​during the first fitting. , and The upper limit is , , The lower limit is , , The initial value is , , The pulse state can be determined using MATLAB's built-in fmincon function. , To perform fitting; and in , After fitting is complete, the fitted time constant and the set time constant will be used. , and The initial value is input as a parameter into the MATLAB built-in lsqcurvefit function for processing. , and The fitting process is performed, and the polarization voltage value at the end of the current during the fitting iteration process is obtained. , and Preservation; and thus, the polarization internal resistance can be controlled. , and and time constant , After fitting, use the obtained , and The initial polarization voltage of the stationary state is used to set the time constant of the stationary state. , initial value, , , Using MATLAB's built-in fmincon function , The fitting of the data allows for the determination of the time constants and polarization resistance of the target battery in each state during the charging and discharging process.

[0109] This application further proposes to obtain state switching information between states of the target battery during charging and discharging, including: obtaining the current current flow direction and current polarization voltage of the target battery; and determining the state switching information between states of the target battery during charging and discharging based on the current current flow direction and current polarization voltage.

[0110] Among these, the current flow direction refers to the direction of the current during battery charging and discharging, which can be achieved by using a current sensor to measure the current direction in real time, and is used to determine whether the battery is in a charging or discharging state. The current polarization voltage refers to the open-circuit voltage offset generated by the battery under polarization effects, which can be obtained by measuring the battery terminal voltage with a voltage sensor and calculating it using an open-circuit voltage model, and is used to characterize the battery polarization state. State switching information refers to the transition characteristics between different operating states during charging and discharging, which can be identified by changes in current direction and polarization voltage trends, and is used to determine the timing of parameter switching in the equivalent circuit model.

[0111] Specifically, during charging and discharging, when a change in current direction is detected, such as switching from a charging pulse state to a discharging pulse state, the polarization voltage will exhibit a reverse trend. By monitoring the changes in current direction and the amplitude of polarization voltage changes in real time, the critical point of state switching can be accurately identified. During state switching, the corresponding time constant and polarization resistance parameters are selected based on the current charging / discharging stage. For example, the time constant parameters used during the charging resting stage are used, while the polarization resistance parameters used during the discharging pulse stage are used. Substituting the selected parameters into the Kalman filter state equation, the Kalman gain coefficient is calculated. This coefficient determines the correction weight of the measured data on the state estimation. By iteratively updating the posterior state value and covariance matrix, accurate estimates of the charge percentage and voltage are gradually obtained through convergence.

[0112] Understandably, when the current direction is discharging, it is defined as positive, and the parameters during discharging are used; when the current direction is negative, the parameters during charging are used; when the polarization voltage is positive, the parameters under pulse state are used; and when the polarization voltage is negative, the parameters under rest state are used. Furthermore, considering the current sampling deviation issue during actual use, -1A and 1A can be used as the switching thresholds for transitioning from charging / discharging to rest.

[0113] In some embodiments, the equation parameters of the Kalman filter state equation include a state transition matrix, a control input matrix, a state vector, and a control input quantity. The state transition matrix and the control input matrix are determined by the time constant and polarization resistance corresponding to the reference charge percentage of the target battery. The state vector is determined by the charge information of the target battery, and the control input quantity is determined by the real-time current of the target battery.

[0114] The state transition matrix can be understood as follows: based on the multi-order equivalent circuit model, the differential equation in the continuous time domain is transformed into the difference equation in the discrete time domain through discretization, and its matrix elements are determined by the time constant exponential function corresponding to each state.

[0115] The control input matrix can be understood as follows: based on the structure of the multi-order equivalent circuit model, it is determined by analyzing the influence relationship of current on each state variable, and its matrix elements are determined by the ratio of polarization internal resistance to time constant.

[0116] The state vector is determined by the charge information of the target battery and may include: charge percentage and estimated voltage. The estimated voltage consists of multiple polarization voltage components, which correspond to the voltages of each RC parallel link in the multi-order equivalent circuit model and are used to describe the polarization effect inside the battery.

[0117] The control input is determined by the real-time current value and direction of the target battery.

[0118] Specifically, the multi-order equivalent circuit model in this application can be a third-order equivalent circuit model. Based on the formula definition of the third-order equivalent circuit model in the above embodiments, this application can construct the Kalman filter state equation, as follows:

[0119] The state equation for a Kalman filter can be expressed as follows:

[0120] + ;

[0121] In the formula Refers to the state vector. The control input in a state system refers to the real-time current, and A and B are the state transition matrix and control input matrix, respectively. The state vector represents the percentage of the previous reference charge. The control input is the percentage of the previous reference charge.

[0122] Based on this, the prior state value estimate of the Kalman filter state equation can be expressed as follows:

[0123] + ;

[0124] in, These are prior state values. This is the prior state value of the previous reference charge percentage. The real-time current value is the percentage of the previous reference charge.

[0125] The prior state covariance can be expressed as follows:

[0126] A +Q;

[0127] in, For the prior state covariance, The prior state covariance of the previous reference charge percentage. is the transpose of the state transition matrix, and Q is the noise covariance, which can be set according to the current sampling error of the target battery;

[0128] The Kalman gain can be expressed as follows:

[0129] ;

[0130] in, Let H be the Kalman gain, and H be the preset state-to-observation mapping matrix. Let R be the transpose of the mapping matrix, and let R be the preset sensor measurement noise.

[0131] The posterior state values ​​are estimated as follows:

[0132] + );

[0133] in, The posterior state value represents the updated prior state value; The measured value of the target battery terminal voltage;

[0134] The posterior covariance is estimated as follows:

[0135] (I- H) ;

[0136] in, Here, denoted as posterior covariance, represents the updated prior covariance.

[0137] Understandably, based on the third-order equivalent circuit model described above, and substituting it into the Kalman filter state equation, the state equation of the target battery can be expressed as:

[0138] + I;

[0139] =OCV+ - ;

[0140] in, The total battery capacity of the target battery. This represents the estimated percentage of charge at the previous time step, where t represents time. This indicates the estimated percentage of charge at the current moment. Indicates estimated voltage.

[0141] Combining the Kalman equation of state, we can obtain:

[0142] = A= B= ;

[0143] Based on the above formula, this application further proposes to determine the reference charge percentage of the target battery according to the state switching information; based on the Kalman filter state equation, the prior state value and prior covariance are calculated according to the time constant and polarization internal resistance corresponding to the reference charge percentage of the target battery to determine the Kalman gain; the prior state value is corrected by the Kalman gain to obtain the posterior state value and posterior covariance; the reference charge percentage and prior covariance are iteratively updated based on the posterior state value and posterior covariance, and the above calculation and correction process is repeated until the prior covariance is less than the preset convergence threshold, so that the charge information of the target battery can be determined based on the posterior state value.

[0144] Among them, the Kalman gain determines the model's confidence in real-time measurement data, which can be used to optimize the accuracy of state estimation. The posterior state value refers to the state estimation result after Kalman gain correction. Specifically, it can be calculated by a linear combination of the prior state value and the measurement residual. This value reflects the optimal estimate after fusing prediction information and measured data, and is used to improve the calculation accuracy of charge information.

[0145] Specifically, when the battery's operating state switches, such as from a charging pulse state to a resting state, the exact moment of the state switch needs to be determined based on the current current flow and changes in polarization voltage. A corresponding reference charge percentage can be selected based on the switched state; for example, in the charging resting state, a time constant and polarization resistance parameter matching this charge percentage can be chosen. Further, based on the Kalman filter state equation, the prior state value and its covariance matrix are calculated using the selected parameters. The Kalman gain is updated using real-time acquired current data, and the prior state value is corrected using the Kalman gain to obtain the posterior state value and its covariance. During the iteration process, the value of the covariance matrix gradually decreases with calculation. The iteration stops when a preset convergence threshold is reached. The final output posterior state value is the estimated charge percentage and estimated voltage.

[0146] refer to Figure 2 , Figure 2 This is a schematic block diagram of a battery charge information determination device provided in an embodiment of this application; as shown below. Figure 2 This application also provides a battery charge information determination device 200, comprising:

[0147] The data acquisition module 201 is configured to acquire charge parameter test data of the target battery during the charging and discharging process, wherein the charging and discharging process includes charging pulse state, charging rest state, discharging pulse state and discharging rest state.

[0148] The parameter determination module 202 is configured to determine the time constant and polarization resistance of the target battery in each state during charging and discharging based on a preset multi-order equivalent circuit model and the charge parameter test data.

[0149] The charge estimation module 203 is configured to acquire state switching information between states of the target battery during charging and discharging based on a preset Kalman filter state equation, select the corresponding time constant and polarization internal resistance according to the state switching information to determine the Kalman gain, estimate the posterior state value according to the Kalman gain, and determine the charge information of the target battery based on the posterior state value. The charge information includes the estimated charge percentage and estimated voltage of the target battery.

[0150] As an optional approach, the charge parameter test data includes test data of the target battery under multiple reference charge percentages, with the multiple reference charge percentages arranged in the order of charge change. The parameter determination module 202 can also be configured to: select a combination of pulse state and rest state during charging, or a combination of pulse state and rest state during discharging, from the various states of the charging and discharging process, and select a target reference charge percentage from multiple reference charge percentages; based on the charge parameter test data, obtain the test data of the target reference charge percentage under the state combination, and obtain the test data of the previous reference charge percentage of the target reference charge percentage; based on the test data of the target reference charge percentage under the state combination and the previous reference charge percentage, perform parameter fitting estimation on a preset multi-order equivalent circuit model to obtain the time constant and polarization resistance of the target reference charge percentage under the state combination.

[0151] As an optional approach, the parameter determination module 202 can also be configured to: in the pulse state of the state combination, determine the pulse start parameter based on the test data of the target reference charge percentage and the previous reference charge percentage under the state combination, and perform parameter fitting estimation on the preset multi-order equivalent circuit model according to the pulse start parameter to obtain the polarization internal resistance of the target reference charge percentage under the state combination, as well as the time constant and pulse end parameter of the target reference charge percentage under the pulse state of the state combination; in the stationary state of the state combination, determine the stationary start parameter according to the pulse end parameter, and perform parameter fitting estimation on the multi-order equivalent circuit model according to the stationary start parameter to obtain the time constant of the target reference charge percentage under the stationary state of the state combination.

[0152] As an optional approach, the parameter determination module 202 can also be configured to: determine the pulse end voltage, pulse start voltage, ohmic voltage drop, open-circuit voltage drift compensation, and pulse average current of the target battery based on the test data of the target reference charge percentage and the previous reference charge percentage under the state combination; subtract the pulse start voltage, ohmic voltage drop, and open-circuit voltage drift compensation from the pulse end voltage to obtain the pulse polarization voltage; and obtain the total pulse polarization internal resistance based on the pulse polarization voltage and the pulse average current.

[0153] As an optional approach, the parameter determination module 202 can also be configured to: determine the initial value of the polarization resistance in the multi-order equivalent circuit model based on the pulse total polarization resistance of the target reference charge percentage, and determine the range of polarization resistance variation based on the pulse total polarization resistance of the target reference charge percentage and the pulse total polarization resistance of the previous reference charge percentage; and perform parameter fitting estimation on the polarization resistance based on the initial value and the range of variation to obtain the polarization resistance of the target reference charge percentage under the state combination.

[0154] As an optional approach, the charge estimation module 203 can also be configured to: acquire the current current flow direction and current polarization voltage of the target battery; and determine the state switching information between different states of the target battery during the charging and discharging process based on the current current flow direction and current polarization voltage.

[0155] As an optional approach, the charge estimation module 203 can also be configured to: determine the reference charge percentage of the target battery based on the state switching information; calculate the prior state value and prior covariance based on the Kalman filter state equation, according to the time constant and polarization internal resistance corresponding to the reference charge percentage of the target battery, to determine the Kalman gain; correct the prior state value through the Kalman gain to obtain the posterior state value and posterior covariance; iteratively update the reference charge percentage and prior covariance based on the posterior state value and posterior covariance, repeating the above calculation and correction process until the prior covariance is less than a preset convergence threshold.

[0156] As an optional approach, the equation parameters of the Kalman filter state equation used in the charge estimation module 203 may include a state transition matrix, a control input matrix, a state vector, and a control input quantity. The state transition matrix and the control input matrix are determined by the time constant and polarization resistance corresponding to the reference charge percentage of the target battery, the state vector is determined by the charge information of the target battery, and the control input quantity is determined by the real-time current of the target battery.

[0157] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of any of the methods in the foregoing method embodiments.

[0158] And an electronic device, comprising:

[0159] One or more processors; and

[0160] A memory associated with one or more processors, the memory being used to store program instructions that, when read and executed by one or more processors, perform the steps of any of the methods in the foregoing method embodiments.

[0161] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the methods in the foregoing method embodiments.

[0162] in, Figure 3 The architecture of an electronic device is illustrated, which may include a processor 310, a video display adapter 311, a disk drive 312, an input / output interface 313, a network interface 314, and a memory 320. The processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, and memory 320 can communicate with each other via a communication bus 330.

[0163] The processor 310 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs in order to implement the technical solution provided in this application.

[0164] The memory 320 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 320 can store the operating system 321 for controlling the operation of the electronic device 300, and the basic input / output system (BIOS) 322 for controlling the low-level operations of the electronic device 300. Additionally, it can store a web browser 323, a data storage management system 324, and a battery charge information determination device 200, etc. The aforementioned battery charge information determination device 200 can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when the technical solution provided in this application is implemented through software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310.

[0165] Input / output interface 313 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0166] Network interface 314 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0167] Bus 330 includes a pathway for transmitting information between various components of the device, such as processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, and memory 320.

[0168] It should be noted that although the above-described device only shows the processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, memory 320, bus 330, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0169] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer program product. This computer program product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of the embodiments of this application.

[0170] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for determining battery charge information, characterized in that, include: Acquire charge parameter test data of the target battery during the charging and discharging process, wherein the charging and discharging process includes charging pulse state, charging rest state, discharging pulse state and discharging rest state; Based on a preset multi-order equivalent circuit model, the time constant and polarization resistance of the target battery in each state during the charging and discharging process are determined according to the charge parameter test data. Based on the preset Kalman filter state equation, the state switching information between the states of the target battery during the charging and discharging process is obtained. The corresponding time constant and polarization internal resistance are selected according to the state switching information to determine the Kalman gain. The posterior state value is estimated according to the Kalman gain. The charge information of the target battery is determined based on the posterior state value. The charge parameter test data includes test data of the target battery under multiple reference charge percentages, and the charge information includes the estimated charge percentage and estimated voltage of the target battery. The charge percentage is used to represent the state of charge of the target battery. The step of determining the time constant and polarization resistance of the target battery in each state during the charging and discharging process based on the charge parameter test data includes: selecting the corresponding combination of pulse state and static state during the charging and discharging process for parameter fitting estimation, and determining the time constant and polarization resistance of the target battery in each state.

2. The method according to claim 1, characterized in that, The step of selecting corresponding combinations of pulse states and rest states during the charging and discharging process for parameter fitting estimation to determine the time constant and polarization resistance of the target battery in each state includes: From the states of the charging and discharging process, select a combination of pulse state and rest state during the charging process, or a combination of pulse state and rest state during the discharging process, and select a target reference charge percentage from the multiple reference charge percentages. Based on the charge parameter test data, obtain the test data of the target reference charge percentage under the state combination, and obtain the test data of the previous reference charge percentage of the target reference charge percentage. Based on the test data of the target reference charge percentage and the previous reference charge percentage under the state combination, the parameters of the preset multi-order equivalent circuit model are fitted and estimated to obtain the time constant and polarization resistance of the target reference charge percentage under the state combination.

3. The method according to claim 2, characterized in that, Based on the test data of the target reference charge percentage and the previous reference charge percentage under the state combination, the parameters of the preset multi-order equivalent circuit model are fitted and estimated to obtain the time constant and polarization resistance of the target reference charge percentage under the state combination, including: In the pulse state of the state combination, the pulse start parameter is determined based on the test data of the target reference charge ratio and the previous reference charge ratio under the state combination, and the parameters of the preset multi-order equivalent circuit model are fitted and estimated according to the pulse start parameter to obtain the polarization internal resistance of the target reference charge ratio under the state combination, as well as the time constant and pulse end parameter of the target reference charge ratio in the pulse state of the state combination. In the static state of the state combination, the static start parameter is determined according to the pulse end parameter, and the parameter fitting estimation of the multi-order equivalent circuit model is performed according to the static start parameter to obtain the time constant of the target reference charge ratio in the static state of the state combination.

4. The method according to claim 3, characterized in that, The pulse initiation parameters include the total pulse polarization internal resistance. The determination of the pulse initiation parameters based on test data of the target reference charge percentage and the previous reference charge percentage under the state combination includes: Based on the test data of the target reference charge percentage and the previous reference charge percentage under the state combination, the pulse end voltage, pulse start voltage, ohmic voltage drop, open circuit voltage drift compensation and pulse average current of the target battery are determined. Subtract the pulse start voltage, ohmic voltage drop, and open-circuit voltage drift compensation from the pulse end voltage to obtain the pulse polarization voltage; The total pulse polarization resistance is obtained based on the pulse polarization voltage and the pulse average current.

5. The method according to claim 3, characterized in that, The pulse initiation parameters include the total pulse polarization resistance. The step of performing parameter fitting estimation on a preset multi-order equivalent circuit model based on the pulse initiation parameters to obtain the polarization resistance of the target reference charge percentage under the state combination includes: The initial value of the polarization resistance in the multi-order equivalent circuit model is determined based on the pulse total polarization resistance of the target reference charge percentage, and the variation range of the polarization resistance is determined based on the pulse total polarization resistance of the target reference charge percentage and the pulse total polarization resistance of the previous reference charge percentage. Based on the initial value and the range of variation, the polarization resistance is estimated by parameter fitting to obtain the polarization resistance of the target reference charge ratio under the state combination.

6. The method according to claim 1, characterized in that, The step of obtaining the state switching information of the target battery between various states during the charging and discharging process includes: Obtain the current current flow direction and current polarization voltage of the target battery; The state switching information of the target battery between each state during the charging and discharging process is determined based on the current current flow direction and the current polarization voltage.

7. The method according to claim 2, characterized in that, The step of selecting the corresponding time constant and polarization resistance based on the state switching information to determine the Kalman gain, and estimating the posterior state value based on the Kalman gain, includes: The reference charge percentage of the target battery is determined based on the state switching information; Based on the Kalman filter state equation, the prior state value and prior covariance are calculated according to the time constant and polarization internal resistance corresponding to the reference charge ratio of the target battery, so as to determine the Kalman gain. The prior state value is corrected by the Kalman gain to obtain the posterior state value and the posterior covariance; Based on the posterior state value and the posterior covariance, iteratively update the reference charge ratio and the prior covariance, repeating the above calculation and correction process until the prior covariance is less than a preset convergence threshold.

8. The method according to any one of claims 2 to 7, characterized in that, The Kalman filter state equation parameters include a state transition matrix, a control input matrix, a state vector, and a control input quantity. The state transition matrix and the control input matrix are determined by the time constant and polarization resistance corresponding to the reference charge percentage of the target battery. The state vector is determined by the charge information of the target battery, and the control input quantity is determined by the real-time current of the target battery.

9. A battery charge information determination device, characterized in that, include: The data acquisition module is configured to acquire charge parameter test data of the target battery during the charging and discharging process, wherein the charging and discharging process includes charging pulse state, charging rest state, discharging pulse state and discharging rest state. The parameter determination module is configured to determine the time constant and polarization resistance of the target battery in each state during the charging and discharging process based on a preset multi-order equivalent circuit model and the charge parameter test data. The charge estimation module is configured to acquire state switching information between states of the target battery during the charging and discharging process based on a preset Kalman filter state equation, select the corresponding time constant and polarization internal resistance according to the state switching information to determine the Kalman gain, estimate the posterior state value according to the Kalman gain, and determine the charge information of the target battery based on the posterior state value. The charge parameter test data includes test data of the target battery under multiple reference charge percentages, and the charge information includes the estimated charge percentage and estimated voltage of the target battery. The charge percentage is used to represent the state of charge of the target battery. The step of determining the time constant and polarization resistance of the target battery in each state during the charging and discharging process based on the charge parameter test data includes: selecting the corresponding combination of pulse state and static state during the charging and discharging process for parameter fitting estimation, and determining the time constant and polarization resistance of the target battery in each state.

10. An electronic device, characterized in that, include: One or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1 to 8.

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