Battery state of charge value determination apparatus, method, storage medium, and electronic device

Abstract

The application discloses a kind of battery state of charge value determination device, method, storage medium and electronic equipment.It relates to battery management system technical field, the method includes: obtaining the actual impedance value of target frequency of battery to be measured;Based on actual impedance value, the initial equivalent circuit model of battery to be measured is optimized, and the target equivalent circuit model of battery to be measured is obtained;Obtain the state of charge reference value of battery to be measured;Based on target equivalent circuit model and state of charge reference value, determine the battery temperature of battery to be measured;Based on target equivalent circuit model, state of charge reference value and battery temperature, determine the target state of charge value of battery to be measured.The application solves the technical problem that high-precision battery state of charge and temperature estimation under low computing load is difficult to realize in the state estimation method of battery in the related art.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and more specifically, to a battery state-of-charge (SOC) determination device, method, storage medium, and electronic device. Background Technology

[0002] With the rapid development of electric vehicles and large-scale energy storage systems, lithium-ion batteries, as core energy devices, directly impact the performance of the entire system due to their operational safety, reliability, and lifespan. In battery management systems, accurately estimating the battery's state of charge (SOC) and internal temperature is a crucial technical step in ensuring safe battery operation, optimizing charge and discharge strategies, and extending battery life. However, related technologies have significant shortcomings in battery state estimation, mainly in the following aspects:

[0003] Electrochemical impedance spectroscopy (EIS) measurements rely on expensive offline experimental equipment, making them unsuitable for real-time application in battery management systems (BMS). Methods for estimating state of charge (SOC) and temperature have limitations; for example, coulomb counting methods suffer from cumulative errors, and methods based on complex models or data-driven approaches are computationally expensive and difficult to deploy on embedded platforms. Methods based on a single impedance parameter are highly susceptible to the cross-sensitivity between temperature and SOC, and frequency selection is challenging, making it difficult to achieve high-precision, real-time online SOC estimation in resource-constrained BMS systems. In summary, related technologies for battery SOC estimation suffer from limitations in achieving high-precision SOC and temperature estimation under low computational loads.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a battery state-of-charge (SOC) determination apparatus, method, storage medium, and electronic device to at least address the technical problem in related technologies where it is difficult to achieve high-precision battery SOC and temperature estimation under low computational load.

[0006] According to one aspect of the present invention, a method for determining the state of charge (SOC) value of a battery is provided, comprising: acquiring the actual impedance value of the battery under test at a target frequency; optimizing an initial equivalent circuit model of the battery under test based on the actual impedance value to obtain a target equivalent circuit model of the battery under test; acquiring a reference SOC value of the battery under test; determining the battery temperature of the battery under test based on the target equivalent circuit model and the reference SOC value; and determining the target SOC value of the battery under test based on the target equivalent circuit model, the reference SOC value, and the battery temperature.

[0007] Optionally, the actual impedance value of the battery under test at the target frequency is obtained, including: obtaining the voltage signal and current signal of the battery under test; performing frequency domain processing on the voltage signal to obtain the voltage frequency domain value of the voltage signal at the target frequency; performing frequency domain processing on the current signal to obtain the current frequency domain value of the current signal at the target frequency; and determining the actual impedance value based on the voltage frequency domain value and the current frequency domain value.

[0008] By using the above methods, voltage and current signals are converted in parallel and independently in the frequency domain. The actual impedance value at the target frequency is calculated recursively using intermediate variables. This makes impedance calculation require very little memory resources and computational instructions, thus enabling real-time impedance extraction under low computational load on resource-constrained embedded battery management system hardware.

[0009] Optionally, based on the actual impedance value, the initial equivalent circuit model of the battery under test is optimized to obtain the target equivalent circuit model of the battery under test. This includes: determining the reference impedance value of the battery under test at the target frequency based on the initial equivalent circuit model; determining the impedance error of the battery under test at the target frequency based on the reference impedance value and the actual impedance value; optimizing the initial equivalent circuit model with the minimum impedance error as the optimization objective until a preset termination condition is reached. The preset termination condition includes one of the following: reaching a preset number of iterations, the change in impedance error between two adjacent iterations being less than an error change threshold, or the difference in circuit model parameter changes between two adjacent iterations being less than a parameter change difference threshold; and determining the optimized equivalent circuit model obtained when the preset termination condition is reached as the target equivalent circuit model.

[0010] By using the above methods, the parameters of the initial equivalent circuit model are iteratively optimized. The difference between the reference impedance and the actual impedance is quantified using impedance error to guide the direction of parameter adjustment. Termination conditions are set, which can avoid the waste of computational resources caused by too many iterations and prevent getting trapped in local optima due to premature termination. Thus, high-precision identification of the target equivalent circuit model parameters can be achieved with limited computational overhead.

[0011] Optionally, obtaining the state of charge (SOC) reference value of the battery under test includes: obtaining the open-circuit voltage of the battery under test; and determining the SOC reference value based on a preset mapping relationship and the open-circuit voltage, wherein the preset mapping relationship is used to indicate the correspondence between multiple open-circuit voltages and multiple SOC reference values.

[0012] Using the above method, without the need for complex iterative calculations or long periods of waiting for voltage stabilization, a reliable initial solution can be provided for subsequent state estimation based on impedance parameters.

[0013] Optionally, the battery temperature of the battery under test is determined based on the target equivalent circuit model and the state of charge reference value, including: obtaining the offset resistance parameters of the target element in the target equivalent circuit model; and determining the battery temperature based on the offset resistance parameters and the state of charge reference value.

[0014] By using the above methods, the cross-sensitivity interference of state of charge fluctuations on temperature estimation can be eliminated, thereby achieving high-precision real-time estimation of the battery's internal temperature without the need for complex electrothermal models.

[0015] Optionally, based on the target equivalent circuit model, the state of charge reference value, and the battery temperature, the target state of charge value of the battery under test is determined, including: obtaining the coefficient parameters of the target element in the target equivalent circuit model; obtaining the initial state of charge value of the battery under test based on the coefficient parameters, the state of charge reference value, and the battery temperature; and determining the target state of charge value based on the initial state of charge value and the state of charge reference value.

[0016] By using the above methods, the interference of temperature on impedance parameters can be eliminated, and the reference value of the state of charge can be introduced as a constraint condition to screen for a unique solution. This can effectively solve the problem of multiple solutions and ambiguity caused by nonlinear relationships in the impedance lookup table method, thereby achieving high-precision determination of the target state of charge value.

[0017] Optionally, when there are multiple initial state of charge (SOC) values, a target SOC value is determined based on the initial SOC value and a SOC reference value. This includes: obtaining SOC differences corresponding to each of the multiple initial SOC values ​​and the SOC reference value, wherein the SOC difference is used to indicate the degree of difference between the corresponding initial SOC value and the SOC reference value; and determining the initial SOC value corresponding to the SOC difference that is less than a preset SOC difference threshold among the multiple initial SOC values ​​as the target SOC value.

[0018] By using the above methods, the accurate state of charge can be uniquely determined when there are multiple feasible solutions, thereby improving the reliability of the estimation results.

[0019] According to another aspect of the present invention, a device for determining the state of charge (SOC) value of a battery is also provided, comprising: an impedance value acquisition module for acquiring the actual impedance value of the battery under test at a target frequency; a target equivalent circuit model determination module for optimizing an initial equivalent circuit model of the battery under test based on the actual impedance value to obtain a target equivalent circuit model of the battery under test; a SOC reference value acquisition module for acquiring a SOC reference value of the battery under test; a battery temperature determination module for determining the battery temperature of the battery under test based on the target equivalent circuit model and the SOC reference value; and a target SOC value determination module for determining the target SOC value of the battery under test based on the target equivalent circuit model, the SOC reference value, and the battery temperature.

[0020] According to another aspect of the present invention, a non-volatile storage medium is also provided, which stores a plurality of instructions adapted for a method for determining the state of charge of a battery, any one of which is loaded and executed by a processor.

[0021] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the following methods for determining the state of charge of a battery.

[0022] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of a method for determining the state of charge of a battery, any one of which is described above.

[0023] In this embodiment of the invention, the actual impedance value of the battery under test at the target frequency is obtained; based on the actual impedance value, the initial equivalent circuit model of the battery under test is optimized to obtain the target equivalent circuit model of the battery under test; the state of charge (SOC) reference value of the battery under test is obtained; based on the target equivalent circuit model and the SOC reference value, the battery temperature of the battery under test is determined; based on the target equivalent circuit model, the SOC reference value, and the battery temperature, the target SOC value of the battery under test is determined. This achieves the goal of rapidly extracting the target frequency impedance online to optimize the equivalent circuit model, and accurately determining the battery temperature and target SOC value of the battery under test using the SOC reference value. This achieves the technical effect of high-precision battery SOC and temperature estimation under low computational load, thereby solving the technical problem in related technologies where it is difficult to achieve high-precision battery SOC and temperature estimation under low computational load. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0025] Figure 1 This is a flowchart of a method for determining the state of charge (SOC) of a battery according to an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of an optional frequency selection analysis method according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of an optional Gertz algorithm according to an embodiment of the present invention;

[0028] Figure 4 This is a flowchart of an optional variable parameter method according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of an optional model-based state estimation method according to an embodiment of the present invention;

[0030] Figure 6 This is a flowchart of an optional method for determining the state of charge (SOC) of a battery according to an embodiment of the present invention.

[0031] Figure 7 This is a schematic diagram of a battery state-of-charge determination device according to an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] According to an embodiment of the present invention, a method for determining the state of charge (SOC) value of a battery is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] Figure 1 This is a flowchart of a method for determining the state of charge (SOC) of a battery according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0036] Step S102: Obtain the actual impedance value of the battery under test at the target frequency.

[0037] Optionally, the target frequency refers to a specific frequency point in the electrochemical impedance spectroscopy (EIS) that is significantly sensitive to the internal states of the battery (such as state of charge and temperature). The target frequency can be located in the low-frequency diffusion region of the electrochemical impedance spectroscopy. Figure 2This diagram illustrates an optional frequency selection analysis method according to an embodiment of the present invention. The diagram demonstrates a target frequency selection strategy based on a low-frequency diffusion range. The core basis for this strategy is the observation that the low-frequency impedance change of the battery under test is relatively stable under short resting times (e.g., approximately 5 minutes under low-current conditions). Furthermore, increases in temperature and state of charge both lead to an increase in the impedance inflection point frequency. Therefore, to balance real-time testing and prevent significant drift in the state of charge during testing, while ensuring the impedance's sensitivity to state parameters, a low-frequency diffusion range of 0.1Hz to 0.4Hz is ultimately selected as the target frequency range. This step, by selecting only the target frequency points sensitive to the battery's internal state (rather than full-band scanning), shortens data acquisition and calculation time, meeting the stringent real-time requirements of the battery management system. This allows the obtained actual impedance values ​​to provide a high signal-to-noise ratio and high-sensitivity data foundation for subsequent identification of the battery's internal state through an equivalent circuit model.

[0038] In one optional embodiment, obtaining the actual impedance value of the battery under test at the target frequency includes: obtaining the voltage signal and current signal of the battery under test; performing frequency domain processing on the voltage signal to obtain the voltage frequency domain value of the voltage signal at the target frequency; performing frequency domain processing on the current signal to obtain the current frequency domain value of the current signal at the target frequency; and determining the actual impedance value based on the voltage frequency domain value and the current frequency domain value.

[0039] Optionally, the voltage and current signals of the battery under test are acquired through a battery management system, and the voltage and current signals are processed in the frequency domain using the Goetzeel algorithm to obtain the voltage and current frequency domain values ​​at a specific target frequency, thereby determining the actual impedance value. In this embodiment, the Goetzeel algorithm, as an efficient frequency domain analysis tool, is derived from the Discrete Fourier Transform, but has a lower computational cost for calculations at a few specific target frequencies. Figure 3 This is a schematic diagram of an optional Gertz algorithm according to an embodiment of the present invention, as shown below. Figure 3 As shown, the input battery voltage signal u(t) and current signal i(t) are processed independently in parallel, and intermediate variables are introduced using a recursive structure to collect signal information. For the input signal at the current sampling time... (Voltage signal or current signal), the algorithm updates the intermediate variables at the current sampling time through iterative calculation. The recursive formula is ,in, Indicates the target frequency. This represents the sampling frequency of the input signal, and k represents the index of the current sampling time. This represents the intermediate variable from the previous sampling time. This represents the two sampling times preceding the current sampling time. Through this recursive process, intermediate variables obtained from processing the voltage signal are acquired. , , and intermediate variables obtained from processing current signals. , , Based on these intermediate variables, the voltage frequency domain value is then obtained. and current frequency domain value The complex frequency components at the target frequency can be calculated as follows: (Voltage frequency domain value or current frequency domain value), this complex frequency component contains the amplitude and phase information of the target frequency in the input signal, that is... Where j represents the imaginary unit, Figure 3 coefficients in The rotation factor in the figure Furthermore, to overcome the limitation that low-cost microcontrollers cannot directly perform complex number operations, the complex impedance equation is further decomposed into real and imaginary parts. The derivation process is as follows: Actual impedance value ; Target frequency Define constants Using Euler's formula, the complex expressions for voltage and current are transformed into Cartesian coordinates: By multiplying both the numerator and denominator by the complex conjugate of the denominator, the denominator is rationalized, thus separating the expression into real and imaginary parts. Subsequently, trigonometric identities are applied ( The simplified result yields: the real part of the impedance. Imaginary part of impedance That is, the actual impedance value is a complex impedance composed of the real part and the imaginary part of the impedance.

[0040] This embodiment employs the Gertz algorithm and performs calculations only for selected target frequencies, avoiding the enormous computational overhead of full-band scanning or Fast Fourier Transform, thus significantly reducing the algorithm's time and space complexity. Secondly, by introducing intermediate variables and utilizing their recursive properties, and by transforming complex number operations into addition, subtraction, multiplication, and division operations in the real number domain, the computational burden and memory usage of the microcontroller can be further reduced, enabling real-time impedance extraction on a low-cost hardware platform. Finally, this method does not rely on expensive external experimental equipment; it can obtain high-precision battery impedance information online using only the voltage and current signals routinely collected by the battery management system, thereby providing a reliable data foundation for subsequent state estimation based on the equivalent circuit model.

[0041] Step S104: Based on the actual impedance value, optimize the initial equivalent circuit model of the battery under test to obtain the target equivalent circuit model of the battery under test.

[0042] Optionally, the initial equivalent circuit model includes at least one resistive element and at least one capacitive element, particularly including a Warburg element for simulating the diffusion process of lithium ions in the electrode material. This initial equivalent circuit model can take the following form: ( ) ,in, ( () represents the complex impedance value of the Warburg element in the initial equivalent circuit model. Represents angular frequency. This represents the offset resistance used to simulate the resistance associated with a finite diffusion boundary or interface. Represents the coefficient parameter. This represents the diffusion parameters. It refers to the initial parameter set in the initial equivalent circuit model. The target parameter set was finally determined through iterative optimization. These are the parameters of the target equivalent circuit model, thus obtaining the target equivalent circuit model. This step adaptively adjusts the parameters of the equivalent circuit model so that its online calculated impedance response approximates the measured impedance, thereby accurately identifying key parameters reflecting the internal electrochemical characteristics of the battery (such as the Warburg coefficient and offset resistance). Finally, through high-precision parameter identification, the equivalent circuit model parameters closely related to the battery's state of charge and temperature are obtained, laying a solid data foundation for subsequent high-precision decoupled estimation of temperature and state of charge values ​​based on these parameters. This effectively overcomes the estimation bias problem caused by inaccurate model parameters in related technologies.

[0043] In one optional embodiment, based on the actual impedance value, the initial equivalent circuit model of the battery under test is optimized to obtain the target equivalent circuit model of the battery under test. This includes: determining the reference impedance value of the battery under test at the target frequency based on the initial equivalent circuit model; determining the impedance error of the battery under test at the target frequency based on the reference impedance value and the actual impedance value; optimizing the initial equivalent circuit model with the minimum impedance error as the optimization objective until a preset termination condition is reached, wherein the preset termination condition includes one of the following: reaching a preset number of iterations, the change in impedance error between two adjacent iterations being less than an error change threshold, or the difference in circuit model parameter changes between two adjacent iterations being less than a parameter change difference threshold; and determining the optimized equivalent circuit model obtained when the preset termination condition is reached as the target equivalent circuit model.

[0044] Optionally, the optimization process can be performed using the Variable Parameter Method (VPA). Figure 4This is a flowchart of an optional variable parameter method according to an embodiment of the present invention. Specifically, firstly, using an initial equivalent circuit model of the battery under test, a reference impedance value is calculated at a selected target frequency. Subsequently, when there are multiple target frequencies, the difference between the reference impedance value and the actual impedance value is calculated, and the root mean square error is used as a quantitative index to characterize the impedance error between the two. ,in, This represents the total number of multiple target frequencies. This represents the index of any one of multiple target frequencies. Indicates the reference impedance value. This represents the actual impedance value at any target frequency. Let represent any target frequency. Further, to find the optimal parameter combination, the circuit model parameters in the initial equivalent circuit model are fine-tuned. Specifically, each circuit model parameter in the initial parameter set is multiplied by (1+k) and (1-k), where k represents a preset perturbation factor, thereby generating multiple sets of fine-tuned candidate circuit model parameters. For each set of candidate circuit model parameters, its reference impedance at the target frequency is recalculated, and the corresponding impedance error is calculated. The impedance errors corresponding to all candidate circuit model parameters are compared, and the candidate circuit model parameter that minimizes the impedance error is selected as the result of the current iteration. This iterative process continues until a preset termination condition is met. When the preset termination condition is met, the equivalent circuit model corresponding to the currently obtained optimal circuit model parameters is determined as the target equivalent circuit model. This embodiment introduces a small perturbation factor to perform a local search of parameters and combines it with the root mean square error minimization criterion, enabling it to effectively approximate the global optimum without complex gradient calculations, thereby improving the accuracy of parameter identification. Secondly, by setting a preset iteration termination condition, the computation time and memory usage of the algorithm are strictly controlled while ensuring estimation accuracy, avoiding the waste of computational resources caused by excessive iteration. This allows the optimization algorithm to meet the stringent requirements of embedded battery management systems for real-time performance and computational efficiency, thus providing reliable data support for subsequent implementation.

[0045] Step S106: Obtain the state of charge reference value of the battery under test.

[0046] Optionally, by determining a state of charge reference value, the true state of charge level of the battery under test when it reaches electrochemical equilibrium after a long period of rest can be reflected. This allows the battery management system to significantly improve the overall robustness and accuracy of battery state estimation by integrating multiple information sources through software algorithms without increasing additional hardware costs.

[0047] In one optional embodiment, obtaining a state of charge (SOC) reference value for the battery under test includes: obtaining the open-circuit voltage of the battery under test; and determining the SOC reference value based on a preset mapping relationship and the open-circuit voltage, wherein the preset mapping relationship is used to indicate the correspondence between multiple open-circuit voltages and multiple SOC reference values.

[0048] Optionally, a preset mapping relationship characterizes the nonlinear correspondence between the battery's open-circuit voltage and state of charge (SCC), which can be obtained through offline experimental calibration. Specifically, the acquired open-circuit voltage is used as input, and matching or interpolation calculations are performed within the preset mapping relationship to determine the corresponding SCC reference value. Before acquiring the open-circuit voltage, the battery under test is kept in a static state for a preset duration (e.g., more than 3 hours) until the internal electrochemical processes of the battery tend to reach equilibrium and the terminal voltage stabilizes at the open-circuit voltage. Through this static voltage acquisition and lookup table mapping, a relatively accurate SCC estimate can be obtained. This method does not require complex online iterative calculations, has extremely low computational load, and can greatly reduce the resource consumption of the embedded battery management system, achieving a balance between high accuracy and low cost.

[0049] Step S108: Determine the battery temperature of the battery under test based on the target equivalent circuit model and the state of charge reference value.

[0050] Optionally, by introducing a state of charge reference value for bidirectional decoupling correction, the problem of cross-sensitivity between temperature and state of charge in impedance response can be effectively solved, significantly improving the accuracy and robustness of temperature estimation and avoiding temperature estimation deviations caused by changes in state of charge.

[0051] In one optional embodiment, determining the battery temperature of the battery under test based on the target equivalent circuit model and the state of charge reference value includes: obtaining the offset resistance parameter of the target element in the target equivalent circuit model; and determining the battery temperature based on the offset resistance parameter and the state of charge reference value.

[0052] Optionally, in the battery temperature determination process, firstly, the offset resistance parameter corresponding to the target element (i.e., the Warburg element) used to simulate the lithium-ion diffusion process is extracted from the target equivalent circuit model. This offset resistance parameter is highly sensitive to changes in battery temperature but relatively insensitive to changes in state of charge (SOC). However, SOC still has a certain coupling effect on the offset resistance parameter, so directly using the offset resistance parameter to look up a table to estimate the temperature will result in errors. Therefore, a SOC reference value is introduced as a correction input. Subsequently, the temperature is estimated using a pre-constructed three-dimensional lookup table model of "offset resistance parameter-SOC-temperature". This three-dimensional lookup table model is formed by fitting the mapping relationship between the offset resistance parameter and temperature and SOC using a variable parameter method under different temperatures and SOC conditions through offline experiments. Using the currently acquired offset resistance parameter and SOC reference value as two-dimensional inputs, linear interpolation calculations are performed in this three-dimensional lookup table model to output the battery temperature of the battery under test. This embodiment improves the sensitivity of temperature estimation by selecting the offset resistance parameter, which is sensitive to temperature but relatively insensitive to SOC, as the main feature quantity. By using a pre-built offline lookup table instead of complex online nonlinear fitting, the computational load on the embedded platform can be greatly reduced, achieving a balance between high-precision temperature estimation and low computational complexity, thus meeting the real-time requirements of the battery management system.

[0053] Step S110: Based on the target equivalent circuit model, the state of charge reference value, and the battery temperature, determine the target state of charge value of the battery under test.

[0054] Optionally, this process also relies on pre-built offline lookup tables and simple interpolation operations, which can avoid complex online iterative optimization or machine learning inference, greatly reducing computational complexity. This allows high-precision state-of-charge estimation to be deployed at low cost on resource-constrained embedded battery management system platforms, achieving a good balance between real-time performance and accuracy.

[0055] In one optional embodiment, determining the target state of charge (SOC) value of the battery under test based on the target equivalent circuit model, the SOC reference value, and the battery temperature includes: obtaining the coefficient parameters of the target element in the target equivalent circuit model; obtaining the initial SOC value of the battery under test based on the coefficient parameters, the SOC reference value, and the battery temperature; and determining the target SOC value based on the initial SOC value and the SOC reference value.

[0056] Optionally, firstly, key coefficient parameters are extracted from the determined target equivalent circuit model. Specifically, coefficient parameters sensitive to the state of charge (SOC) of the target components are extracted. Since these coefficient parameters are simultaneously affected by the nonlinear coupling of battery SOC and temperature, directly using them for table lookup or mapping may result in multiple solutions or errors. Therefore, the determined battery temperature and SOC reference value are used as input variables to query a pre-stored offline calibration data table. This offline calibration data table establishes a three-dimensional mapping relationship between coefficient parameters, battery temperature, and SOC. Based on the coefficient parameters, battery temperature, and SOC reference value, a linear interpolation algorithm is used to find the initial SOC value that best matches the current state in the three-dimensional mapping table. Subsequently, to further improve estimation accuracy, a joint determination is made based on the initial SOC value and the SOC reference value to accurately determine the target SOC value of the battery under test. This embodiment, by introducing battery temperature as a decoupling variable, can effectively eliminate the interference of temperature changes on coefficient parameters and solve the problem of SOC estimation error caused by temperature drift in single-parameter estimation in related technologies.

[0057] In one optional embodiment, when there are multiple initial state of charge (SOC) values, determining a target SOC value based on the initial SOC values ​​and a SOC reference value includes: obtaining SOC differences corresponding to each of the multiple initial SOC values ​​and the SOC reference value, wherein the SOC difference is used to indicate the degree of difference between the corresponding initial SOC value and the SOC reference value; and determining the initial SOC value corresponding to the SOC difference that is less than a preset SOC difference threshold among the multiple initial SOC values ​​as the target SOC value.

[0058] Optionally, when multiple initial state of charge (SOC) values ​​are obtained through table lookup or model identification, this embodiment introduces a SOC reference value as a correction benchmark to select the target SOC value that best approximates the actual state of the battery. First, the absolute difference between each initial SOC value and the SOC reference value is calculated, i.e., the SOC difference. This SOC difference quantifies the degree of deviation between the corresponding initial SOC value and the SOC reference value. Then, the calculated SOC differences are compared with a preset SOC difference threshold. This preset SOC difference threshold is a safety range pre-set based on battery impedance characteristics, sensor accuracy, and the fault tolerance range of the estimation algorithm. Initial SOC values ​​with SOC differences less than the preset SOC difference threshold are selected and determined as the target SOC values. If multiple initial state-of-charge (POC) values ​​satisfy the threshold conditions exist during the screening process, optionally, the one with the smallest deviation from the POC reference value can be selected, or a weighted average of these POC values ​​can be taken to determine the final target POC value. This embodiment introduces an independent POC reference value as a constraint, leveraging its high correlation with the actual POC to rapidly narrow down the range of feasible solutions, thereby quickly identifying the most probable target POC. Furthermore, this screening mechanism has simple computational logic, requiring only numerical comparison operations without complex optimization algorithms, which greatly reduces the computational load on the embedded processor and ensures the real-time operation capability of the state estimation algorithm on resource-constrained battery management system hardware. Figure 5 This is a schematic diagram of an optional model-based state estimation method according to an embodiment of the present invention, specifically including: firstly, modeling the low-frequency diffusion part using experimental data, employing an equivalent circuit model containing Warburg elements (such as...). Figure 5 (As shown on the left) to fit the electrochemical impedance spectrum; then, extract key parameters using the target equivalent circuit model; subsequently, obtain the offset resistance parameters through offline variable parameter fitting. Coefficient parameters A three-dimensional lookup table showing the relationship between battery temperature and state of charge (SOC); in actual online estimation, parameters that are sensitive to temperature and less affected by SOC are first utilized. By combining the state of charge reference value obtained from the open-circuit voltage qOCV by looking up the table, the accurate temperature value is estimated by looking up the table; then, using the estimated temperature value and the parameter state of charge (which is sensitive to the state of charge), the final high-precision target state of charge value is determined by looking up the table, thus realizing the bidirectional decoupling and accurate estimation of temperature and state of charge.

[0059] Through the above steps S102 to S110, the goal of optimizing the equivalent circuit model by rapidly extracting the target frequency impedance online and accurately determining the battery temperature and target state of charge value of the battery under test can be achieved. This achieves the technical effect of high-precision battery state of charge and temperature estimation under low computational load, thereby solving the technical problem in related technologies where it is difficult to achieve high-precision battery state of charge and temperature estimation under low computational load.

[0060] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 6 This is a flowchart of an optional method for determining the state of charge (SOC) of a battery according to an embodiment of the present invention, as shown below. Figure 6 As shown, the method includes:

[0061] S1: Acquire the voltage and current signals of the battery under test, and calculate the actual impedance value at the target frequency based on the Goertzel Algorithm (GZ). Specifically, this includes: acquiring the real-time voltage and current signals of the battery under test at the target frequency through the battery management system, extracting intermediate variables and calculating complex impedance using the Goertzel Algorithm, and then obtaining the real and imaginary parts of the battery impedance at each target frequency point through the real and imaginary parts, thereby obtaining the actual impedance value Z. The specific implementation process is the same as in the aforementioned embodiment, and will not be repeated here.

[0062] S2: The equivalent circuit model parameters are optimized based on the Variable Parameter Algorithm (VPA). Specifically, this includes: selecting an equivalent circuit model containing Warburg elements; generating multiple sets of parameter combinations based on the initial circuit model parameters through small perturbations; calculating the reference impedance of each circuit model parameter at the target frequency point; evaluating the difference between the reference impedance and the measured impedance (i.e., impedance error) using the root mean square error; iteratively updating the parameters until the preset termination condition is met, thereby obtaining the target equivalent circuit model parameters that fit the measured impedance. The specific implementation process is the same as in the aforementioned embodiments and will not be repeated here.

[0063] S3: Determine the battery temperature of the battery under test, specifically including: extracting the offset resistance parameters that are sensitive to temperature but relatively insensitive to the state of charge from the fitted target equivalent circuit model. The reference value of the state of charge (i.e., reference value) is obtained by looking up the table using the open-circuit voltage qOCV. Using the three-dimensional lookup table model of offline calibrated offset resistance parameters, state of charge and temperature, the current battery temperature is calculated by linear interpolation. The specific implementation process is the same as the previous embodiment, and will not be repeated here.

[0064] S4: Determine the target state of charge (SOC) value of the battery under test, specifically including: extracting SOC-sensitive coefficient parameters from the fitted target equivalent circuit model. By combining the obtained battery temperature, and using the offline calibrated coefficient parameters, temperature, and state of charge lookup table model, multiple initial state of charge values ​​are obtained. Furthermore, by combining the state of charge reference value, the target state of charge value (SOC) is accurately determined. The specific implementation process is the same as that in the aforementioned embodiments, and will not be repeated here.

[0065] This embodiment can achieve at least one of the following effects: (1) It can realize the online and embedded application of electrochemical impedance spectroscopy technology, significantly reducing costs and improving practicality. By adopting an optimized impedance calculation algorithm and selecting a limited number of target frequency points, this embodiment can get rid of the dependence on large and expensive laboratory instruments, so that the high-value electrochemical impedance spectroscopy analysis technology can be integrated into the embedded hardware of the battery management system, and achieve deep perception of the internal state of the battery at extremely low additional costs. (2) It can achieve the unity of high precision and high real-time performance, effectively solving the problem of state estimation under dynamic working conditions. This embodiment significantly shortens the calculation and estimation time while ensuring accuracy through a partial spectrum matching strategy. (3) Through an innovative bidirectional decoupling correction mechanism, it can significantly improve the accuracy and robustness of battery state of charge and temperature estimation. This embodiment uses temperature-sensitive parameters and state of charge-sensitive parameters, and designs a bidirectional compensation process: the initial value of the state of charge is introduced into the temperature estimation for correction, and the estimated temperature is introduced into the state of charge estimation for compensation. This mechanism effectively eliminates the cross-sensitivity between temperature and state of charge in the electrochemical impedance spectroscopy response, fundamentally solving the problem of decreased accuracy caused by coupling interference when estimating a single parameter. (4) The overall scheme has low computational load and low resource requirements, making it easy to deploy and promote on the battery management system platform. This embodiment optimizes the design of the limited computing resources of the embedded platform from impedance calculation and parameter iteration to state lookup, avoiding the huge computational overhead caused by complex models or large neural networks. This enables this embodiment to easily achieve functional integration and performance improvement without significantly upgrading the existing battery management system hardware, and has high commercial conversion value.

[0066] This embodiment also provides a battery state-of-charge (SOC) determination device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0067] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described method for determining the state of charge (SOC) value of a battery is also provided. Figure 7 This is a schematic diagram of a battery state-of-charge determination device according to an embodiment of the present invention, as shown below. Figure 7 As shown, the above-mentioned battery state-of-charge (POC) determination device includes: an impedance value acquisition module 700, a target equivalent circuit model determination module 702, a POC reference value acquisition module 704, a battery temperature determination module 706, and a target POC value determination module 708, wherein:

[0068] Impedance acquisition module 700 is used to acquire the actual impedance value of the battery under test at the target frequency;

[0069] The target equivalent circuit model determination module 702 is connected to the impedance value acquisition module 700. It is used to optimize the initial equivalent circuit model of the battery under test based on the actual impedance value to obtain the target equivalent circuit model of the battery under test.

[0070] The state of charge reference value acquisition module 704 is connected to the target equivalent circuit model determination module 702 and is used to acquire the state of charge reference value of the battery under test.

[0071] The battery temperature determination module 706 is connected to the state of charge reference value acquisition module 704 and is used to determine the battery temperature of the battery under test based on the target equivalent circuit model and the state of charge reference value.

[0072] The target state of charge (SOC) value determination module 708 is connected to the battery temperature determination module 706 and is used to determine the target SOC value of the battery under test based on the target equivalent circuit model, the SOC reference value, and the battery temperature.

[0073] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0074] It should be noted that the impedance value acquisition module 700, the target equivalent circuit model determination module 702, the state of charge reference value acquisition module 704, the battery temperature determination module 706, and the target state of charge value determination module 708 correspond to steps S102 to S110 in the embodiments. The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0075] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0076] The aforementioned battery state of charge determination device may further include a processor and a memory. The impedance value acquisition module 700, the target equivalent circuit model determination module 702, the state of charge reference value acquisition module 704, the battery temperature determination module 706, and the target state of charge value determination module 708 are all stored in the memory as program modules. The processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0077] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0078] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device containing the non-volatile storage medium to execute the method for determining the state of charge value of any of the aforementioned batteries.

[0079] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0080] Optionally, during program execution, the device containing the non-volatile storage medium may be controlled to perform the following functions: obtain the actual impedance value of the battery under test at the target frequency; optimize the initial equivalent circuit model of the battery under test based on the actual impedance value to obtain the target equivalent circuit model of the battery under test; obtain the state of charge reference value of the battery under test; determine the battery temperature of the battery under test based on the target equivalent circuit model and the state of charge reference value; and determine the target state of charge value of the battery under test based on the target equivalent circuit model, the state of charge reference value, and the battery temperature.

[0081] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described methods for determining the state of charge (SOC) value of a battery.

[0082] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the steps of the battery state-of-charge determination method described above.

[0083] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: obtaining the actual impedance value of the battery under test at a target frequency; optimizing the initial equivalent circuit model of the battery under test based on the actual impedance value to obtain the target equivalent circuit model of the battery under test; obtaining the state of charge reference value of the battery under test; determining the battery temperature of the battery under test based on the target equivalent circuit model and the state of charge reference value; and determining the target state of charge value of the battery under test based on the target equivalent circuit model, the state of charge reference value, and the battery temperature.

[0084] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: obtaining the actual impedance value of the battery under test at a target frequency; optimizing the initial equivalent circuit model of the battery under test based on the actual impedance value to obtain a target equivalent circuit model of the battery under test; obtaining a state of charge (SOC) reference value of the battery under test; determining the battery temperature of the battery under test based on the target equivalent circuit model and the SOC reference value; and determining the target SOC value of the battery under test based on the target equivalent circuit model, the SOC reference value, and the battery temperature.

[0085] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0086] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0088] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0089] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0090] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0091] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A device for determining the state of charge (SOC) value of a battery, characterized in that, include: Impedance acquisition module is used to acquire the actual impedance value of the battery under test at the target frequency; The target equivalent circuit model determination module is used to optimize the initial equivalent circuit model of the battery under test based on the actual impedance value, so as to obtain the target equivalent circuit model of the battery under test. A state of charge reference value acquisition module is used to acquire the state of charge reference value of the battery under test; A battery temperature determination module is used to determine the battery temperature of the battery under test based on the target equivalent circuit model and the state of charge reference value. The target state of charge (SOC) determination module is used to determine the target SOC of the battery under test based on the target equivalent circuit model, the SOC reference value, and the battery temperature.

2. A method for determining the state of charge (SOC) value of a battery, characterized in that, include: Obtain the actual impedance value of the battery under test at the target frequency; Based on the actual impedance value, the initial equivalent circuit model of the battery under test is optimized to obtain the target equivalent circuit model of the battery under test. Obtain the state of charge reference value of the battery under test; Based on the target equivalent circuit model and the state of charge reference value, the battery temperature of the battery under test is determined; Based on the target equivalent circuit model, the state of charge reference value, and the battery temperature, the target state of charge value of the battery under test is determined.

3. The method according to claim 2, characterized in that, The process of obtaining the actual impedance value of the battery under test at the target frequency includes: Acquire the voltage and current signals of the battery under test; The voltage signal is processed in the frequency domain to obtain the voltage frequency domain value of the voltage signal at the target frequency; The current signal is subjected to the frequency domain processing to obtain the current frequency domain value of the current signal at the target frequency; The actual impedance value is determined based on the voltage frequency domain value and the current frequency domain value.

4. The method according to claim 2, characterized in that, The optimization of the initial equivalent circuit model of the battery under test based on the actual impedance value to obtain the target equivalent circuit model of the battery under test includes: Based on the initial equivalent circuit model, the reference impedance value of the battery under test at the target frequency is determined; Based on the reference impedance value and the actual impedance value, the impedance error of the battery under test at the target frequency is determined; The initial equivalent circuit model is optimized with the goal of minimizing the impedance error until a preset termination condition is reached. The preset termination condition includes one of the following: reaching a preset number of iterations, the change in impedance error between two adjacent iterations being less than an error change threshold, or the difference in circuit model parameter changes between two adjacent iterations being less than a parameter change difference threshold. The optimized equivalent circuit model obtained when the preset termination condition is met is determined as the target equivalent circuit model.

5. The method according to claim 2, characterized in that, The process of obtaining the state of charge reference value of the battery under test includes: Obtain the open-circuit voltage of the battery under test; Based on a preset mapping relationship and the open-circuit voltage, the state of charge reference value is determined, wherein the preset mapping relationship is used to indicate the correspondence between multiple open-circuit voltages and multiple state of charge reference values.

6. The method according to claim 2, characterized in that, The step of determining the battery temperature of the battery under test based on the target equivalent circuit model and the state of charge reference value includes: Obtain the offset resistance parameters of the target element in the target equivalent circuit model; The battery temperature is determined based on the offset resistor parameters and the state of charge reference value.

7. The method according to any one of claims 2 to 6, characterized in that, The step of determining the target state of charge (SOC) value of the battery under test based on the target equivalent circuit model, the SOC reference value, and the battery temperature includes: Obtain the coefficient parameters of the target element in the target equivalent circuit model; Based on the coefficient parameters, the state of charge reference value, and the battery temperature, the initial state of charge value of the battery under test is obtained. The target state of charge value is determined based on the initial state of charge value and the state of charge reference value.

8. The method according to claim 7, characterized in that, When there are multiple initial state of charge values, determining the target state of charge value based on the initial state of charge value and the state of charge reference value includes: Based on multiple initial state of charge values ​​and the state of charge reference value, a state of charge difference value corresponding to each of the multiple initial state of charge values ​​is obtained, wherein the state of charge difference value is used to indicate the degree of difference between the corresponding initial state of charge value and the state of charge reference value. The initial state of charge value corresponding to the initial state of charge difference that is less than the preset state of charge difference threshold among the multiple initial state of charge values ​​is determined as the target state of charge value.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions adapted for loading and execution by a processor of the battery state-of-charge determination method according to any one of claims 2 to 8.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for determining the state of charge of a battery as described in any one of claims 2 to 8.

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