Lithium battery SOC estimation method and related device

Abstract

The invention belongs to a battery parameter estimation method, and provides a lithium battery SOC estimation method and a related device for solving the technical problems that when a model-based method is adopted to estimate the SOC of a lithium battery, in an environment with high nonlinear intensity, the linearization error is easy to increase, and the estimation precision is obviously reduced or even diverged. And performing equivalent circuit modeling on the to-be-estimated lithium battery to obtain an equivalent circuit model. And establishing a voltage state equation and a transfer function based on the electrical characteristic parameters of the elements in the equivalent circuit model. And carrying out HPPC test on the lithium battery to be estimated, carrying out parameter identification by adopting a recursive least square method with a forgetting factor, and determining electrical characteristic parameter values of elements in the equivalent circuit model. And in combination with electrical characteristic parameter values of elements in the equivalent circuit model, estimating the SOC of the lithium battery by adopting an EKPF algorithm.

Description

Technical Field

[0001] The present application relates to a battery parameter estimation method, and specifically to a lithium battery SOC estimation method and related devices. Background Art

[0002] As the global climate change problem becomes increasingly serious, the risks of environmental pollution and resource depletion brought by traditional energy have aroused people's vigilance. Against this background, the global energy structure is undergoing a profound transformation, and renewable energy such as solar energy and wind energy are gradually becoming an important part of energy supply. However, these renewable energy sources are intermittent and unstable, requiring energy storage systems to regulate and balance. As an efficient energy storage device, energy storage lithium batteries play a key role in the access and consumption of renewable energy. In the application of lithium batteries, accurate estimation of lithium battery SOC (State of Charge) is of great significance for correct decision-making, safety control and performance maintenance.

[0003] Existing SOC estimation methods mainly include model-based methods, data-driven methods, and artificial intelligence-based methods. Among them, model-based methods are more widely used. Among model-based methods, the extended Kalman filter method has the advantages of fast calculation speed and low storage. In an environment with Gaussian white noise and low nonlinear intensity, the algorithm is stable, converges quickly, and has high estimation accuracy. However, in an environment with high nonlinear intensity, the linearization error is easy to increase, the estimation accuracy is significantly reduced, and even diverges. Summary of the invention

[0004] The present application provides a lithium battery SOC estimation method and related devices to address the technical problem that when a model-based method is used to estimate the SOC of a lithium battery, in an environment with large nonlinear intensity, the linearization error is easily increased, the estimation accuracy is significantly reduced, or even diverges.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application proposes a lithium battery SOC estimation method, comprising: Perform equivalent circuit modeling on the estimated lithium battery to obtain an equivalent circuit model; Based on the electrical characteristic parameters of the components in the equivalent circuit model, a voltage state equation and a transfer function are established; HPPC test is performed on the estimated lithium battery, and the recursive least square method with forgetting factor is used for parameter identification to determine the electrical characteristic parameter values ​​of the components in the equivalent circuit model; Combining the electrical characteristic parameter values ​​of the components in the equivalent circuit model, as well as the voltage state equation and transfer function, the EKPF algorithm is used to estimate the SOC of the lithium battery to be estimated.

[0006] Furthermore, the equivalent circuit modeling of the estimated lithium battery includes: A second-order fractional model is used to model the equivalent circuit of the estimated lithium battery.

[0007] Further, the equivalent circuit model includes a resistor R0, a resistor R1, a resistor R2, a constant phase angle element C1 and a constant phase angle element C2; The resistor R1 and the constant phase angle element C1 are connected in parallel to form a first branch; The resistor R2 and the constant phase angle element C2 are connected in parallel to form a second branch; The resistor R0, the first branch and the second branch are connected in series between the positive electrode of the lithium battery and the positive terminal voltage of the lithium battery facing the outside; the negative electrode of the lithium battery and the negative terminal voltage of the lithium battery facing the outside are connected.

[0008] Furthermore, the voltage state equation is expressed as:

[0009] in, represents the fractional order of the first branch, represents the fractional order of the second branch, represents the voltage across the constant phase angle element C1, represents the voltage across the constant phase angle element C2, represents the capacitance value of the constant phase angle element C1, Represents the resistance value of resistor R1, Indicates the estimated charging and discharging time of the lithium battery. represents the capacitance of the constant phase angle element C2, Represents the resistance value of resistor R2, Indicates the current of the lithium battery to be estimated, Indicates the terminal voltage of the lithium battery to be estimated. Indicates the open circuit voltage of the lithium battery to be estimated, Indicates the resistance value of resistor R0; The expression of the transfer function is:

[0010] in, represents the transfer function, After Laplace transformation, the function The independent variable of .

[0011] Furthermore, the recursive least square method with forgetting factor is used for parameter identification to determine the electrical characteristic parameter values ​​of the components in the equivalent circuit model, including: At each moment in the discharge process of the lithium battery to be estimated, steps (1-1) to (1-4) are respectively performed to determine the value of the electrical characteristic parameter at the corresponding moment: (1-1) Calculated by the following formula System input at time :

[0012] in, Indicates that HPPC is testing Always estimate the input voltage of the lithium battery. Indicates that HPPC is testing Always estimate the input voltage of the lithium battery. Indicates that HPPC is testing The input current at the moment, Indicates that HPPC is testing The input current at the moment, Indicates that HPPC is testing Input current at the moment; (1-2) Calculated by the following formula The gain of time :

[0013] in, express The covariance of the time, represents the forgetting factor;

[0014] in, express The covariance of the time, express The gain of time, express System input at time; (1-3) Calculated by the following formula The parameter estimates at time :

[0015] in, express The parameter estimates at time , express Voltage estimation error at time t;

[0016] in, express Estimated voltage at time:

[0017] in, express The coefficient of express The coefficient of express The coefficient of express The coefficient of express The coefficient of (1-4) According to The parameter estimates at time , calculate the resistance of resistor R0 , the resistance value of resistor R1 , the resistance value of resistor R2 , the capacitance value of the constant phase angle element C1 and the capacitance of the constant phase angle element C2 .

[0018] Furthermore, the method of estimating the SOC of the lithium battery to be estimated by using the EKPF algorithm includes: (2-1) Prediction is performed using the following formula:

[0019] in, Indicates State variable estimation at time The prior state at the moment, express The state transfer matrix at time is expressed as:

[0020] in, is the sampling period of the signal in the time domain; express The input excitation matrix at time t is expressed as:

[0021] in, is the rated capacity of the lithium battery to be estimated; express The state variables at time, express Input incentives at all times, Indicates The output state of the moment The output status of each moment is updated; express The observation matrix of the lithium battery to be estimated at any time is expressed as:

[0022] in, Indicates the SOC value set during HPPC test; (2-2) The Kalman filter gain matrix is ​​calculated by the following formula:

[0023] in, express The Kalman filter gain matrix at time , express The observation matrix of the lithium battery to be estimated at any time, express The Kalman gain noise matrix at time t; Indicates State variable prediction at time The error covariance matrix at time , expressed as:

[0024] in, for The system process noise matrix at time , for The state error covariance update matrix at time ; (2-3) The SOC of the lithium battery to be estimated is estimated by the following formula:

[0025] in, express The SOC update value at the moment, express Always The estimated value of SOC at the moment, express The actual output value of the lithium battery is to be estimated at any time.

[0026] Furthermore, after estimating the SOC of the lithium battery to be estimated by using the EKPF algorithm, the method further includes: The weights are calculated by the following formula:

[0027] in, express Moment The weight of a particle, express Moment The weight of a particle, express and The importance density function of express Moment The state update value of times, ; The weight normalization result is calculated by the following formula:

[0028] in, represents the weight normalization result, Indicated in The number of status updates performed at any given moment; judge The relationship with the preset value is When the SOC of the lithium battery to be estimated is less than the preset value, the SOC of the lithium battery to be estimated is re-estimated; wherein the SOC of the lithium battery to be estimated is re-estimated to adjust The Kalman gain noise matrix at time After that, the SOC of the lithium battery to be estimated is estimated again through steps (2-2) and (2-3).

[0029] In a second aspect, the present application proposes a lithium battery SOC estimation system, comprising: A modeling module, used for performing equivalent circuit modeling on the estimated lithium battery to obtain an equivalent circuit model; An equation building module, used for building a voltage state equation and a transfer function based on electrical characteristic parameters of components in the equivalent circuit model; The parameter identification module is used to perform HPPC test on the estimated lithium battery, and uses the recursive least square method with forgetting factor to perform parameter identification to determine the electrical characteristic parameter values ​​of the components in the equivalent circuit model; The estimation module is used to estimate the SOC of the lithium battery to be estimated by using the EKPF algorithm based on the electrical characteristic parameter values ​​of the components in the equivalent circuit model, as well as the voltage state equation and the transfer function.

[0030] In a third aspect, the present application proposes an electronic device, comprising: a memory, and one or more processors; the memory is coupled to the processor; wherein computer program code is stored in the memory, and the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the above-mentioned lithium battery SOC estimation method.

[0031] In a fourth aspect, the present application proposes a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned lithium battery SOC estimation method are implemented.

[0032] Compared with the prior art, this application has the following beneficial effects: This application proposes a lithium battery SOC estimation method, which uses the EKPF algorithm to estimate the SOC of the lithium battery to be estimated. The EKPF algorithm combines the linear prediction capability of the Kalman filter and the nonlinear processing capability of the particle filter to achieve a more accurate estimation of the nonlinear system state. Compared with a single Kalman filter or particle filter, when the system state undergoes nonlinear changes, the EKPF algorithm can more accurately capture the changes, thereby providing more reliable estimation results. In addition, the EKPF algorithm can handle the uncertainty of the system state, thereby enhancing the robustness of the system. Furthermore, by analyzing the estimation results, fault detection and diagnosis can be achieved, which helps to promptly discover and repair potential problems and improve the reliability and safety of the system.

[0033] The present application also proposes a lithium battery SOC estimation system, an electronic device and a computer storage medium, which have all the advantages of the above-mentioned lithium battery SOC estimation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A schematic diagram of a flow chart of a lithium battery SOC estimation method of the present application; Figure 2 is a schematic diagram of an equivalent circuit model in an embodiment of the present application; Figure 3 This is a schematic diagram of the FFRLS process in the embodiment of the present application; Figure 4 A schematic diagram of a lithium battery SOC estimation system of the present application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0037] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0038] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0039] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invented product is usually placed when used. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0040] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0041] In the description of the embodiments of the present application, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0042] Energy storage lithium batteries can store electricity when there is excess electricity and release electricity when there is a power shortage, thereby improving the stability and reliability of the power system. In addition, energy storage lithium batteries have also shown broad application prospects in the industrial and commercial fields and household fields. Industrial and commercial users can reduce electricity costs by using energy storage lithium batteries and provide power guarantee during power outages. Household energy storage lithium batteries can provide independent power supply for families and reduce dependence on the power grid. Therefore, as an efficient energy storage device, energy storage lithium batteries play a key role in the access and consumption of renewable energy. However, the high initial cost of energy storage lithium batteries (hereinafter referred to as lithium batteries) has also prompted researchers to seek more economical design solutions to offset their cost disadvantages.

[0043] In the application of lithium batteries, accurate estimation of lithium battery SOC (State of Charge) is of great significance for correct decision-making, safety control and maintenance. SOC reflects the remaining power of lithium batteries and is directly related to the service life, endurance and performance of energy storage systems of lithium batteries. Therefore, accurate estimation of lithium battery SOC not only helps to improve the performance of the system, but also ensures the safety of lithium batteries. However, the estimation of lithium battery SOC also faces many challenges: First, lithium batteries exhibit complex nonlinear relationships during the charging and discharging process, making it difficult to accurately estimate SOC. Secondly, the internal impedance of lithium batteries will cause the terminal voltage to gradually decrease during the discharge process, and the SOC of lithium batteries is not a linear relationship with the terminal voltage, which further increases the difficulty of SOC estimation. In addition, when lithium batteries are close to full charge and empty state, the capacity characteristics will change nonlinearly, which also increases the complexity of SOC estimation.

[0044] The background of lithium battery SOC estimation involves many aspects, including the application and development of lithium batteries, the importance of SOC estimation, the challenges faced, and research progress. With the continuous advancement of technology and the in-depth expansion of applications, the estimation method of lithium battery SOC will continue to be improved and optimized, providing strong support for the performance improvement and safety assurance of energy storage systems such as new energy vehicles.

[0045] In order to meet the above challenges, researchers have proposed a variety of SOC estimation methods. These methods can be roughly divided into model-based methods, data-driven methods, and artificial intelligence-based methods. Among them, model-based methods usually rely on the physical and chemical properties of lithium batteries and estimate the state and capacity of lithium batteries through various models. The equivalent circuit model can simplify the complexity of lithium batteries into an equivalent circuit model, thereby simulating the dynamic behavior of the battery. Kalman filtering is a recursive algorithm used to estimate state variables in linear systems, and can use information such as current and voltage of lithium batteries to estimate SOC.

[0046] The Kalman filter algorithm has been widely used in model-based methods. By establishing an equivalent circuit model of a lithium battery, determining the state variables, measurement variables and noise matrix, and then applying the Kalman filter algorithm for state estimation. Extended Kalman Filter (EKF) is an extension of Kalman Filter (KF), which is mainly used to deal with state estimation problems in nonlinear systems. The core idea of ​​the extended Kalman filter is to approximate the nonlinear system model and observation model into a linear system, and then apply the Kalman filter algorithm for state estimation. The advantages of the EKF algorithm include fast calculation speed, low storage volume, and obvious advantages in engineering applications. Therefore, in an environment with Gaussian white noise and low nonlinear intensity, the algorithm is stable, converges quickly, and has high estimation accuracy. However, in an environment with high nonlinear intensity, the linearization error is easy to increase, and the estimation accuracy will be significantly reduced or even diverge.

[0047] The problem of large errors under nonlinear conditions can be effectively solved by introducing the particle filter (PF) algorithm. The particle filter algorithm is a non-parametric method that breaks away from the constraint that random quantities must satisfy the Gaussian distribution when solving nonlinear filtering problems. It can express a wider distribution than the Gaussian model and has a stronger modeling capability for the nonlinear characteristics of variable parameters.

[0048] Based on the above situation, the present application proposes a lithium battery SOC estimation method and related devices, and the present application is described in detail below in conjunction with embodiments and drawings.

[0049] like Figure 1 As shown, it is a flow chart of a lithium battery SOC estimation method of the present application, which may include: S101, performing equivalent circuit modeling on the estimated lithium battery to obtain an equivalent circuit model.

[0050] Equivalent circuit modeling is an effective way to simulate the dynamic behavior of lithium batteries. It simplifies complex electrochemical processes into a combination of circuit elements (such as resistance, capacitance, inductance, etc.) to approximately describe the working state of lithium batteries. In practical applications, appropriate equivalent circuit models can be selected according to application requirements, such as first-order RC (Resistance-Capacitance) model and second-order RC model. The first-order RC model is relatively simple and suitable for quick estimation. Second-order and higher RC models can more accurately reflect the dynamic characteristics of lithium batteries.

[0051] S102, establishing a voltage state equation and a transfer function based on electrical characteristic parameters of components in the equivalent circuit model.

[0052] The relationship between lithium battery voltage, current, SOC and other variables is described by mathematical equations, providing a theoretical basis for subsequent parameter identification and SOC estimation. In practical applications, differential equations or difference equations describing the change of lithium battery voltage over time can be derived based on Kirchhoff's laws and circuit theory. For linear systems, the state equation can be converted into a transfer function form through Laplace transform, which is convenient for frequency domain analysis and control design. For nonlinear systems, the nonlinear system can be approximated as a linear system by performing Taylor series expansion on the nonlinear terms and truncating them near the operating point.

[0053] S103, performing an HPPC test on the lithium battery to be estimated, using a recursive least square method with a forgetting factor to perform parameter identification, and determining electrical characteristic parameter values ​​of components in the equivalent circuit model.

[0054] The HPPC test is specifically Hybrid Pulse Power Characterization, which is a commonly used lithium battery performance test method. It obtains the dynamic characteristics of the lithium battery by applying a series of pulse currents and measuring the voltage response. The optimization algorithm is then used to identify the unknown parameters in the equivalent circuit model. When performing parameter identification, the recursive least squares method with a forgetting factor can be used to fit the data, and the equivalent circuit model parameters are iteratively updated until the preset convergence conditions are reached. The introduction of the forgetting factor enables the algorithm to adapt to changes in the state of the lithium battery more quickly.

[0055] S104, combining the electrical characteristic parameter values ​​of the components in the equivalent circuit model, as well as the voltage state equation and the transfer function, an EKPF algorithm is used to estimate the SOC of the lithium battery.

[0056] In practical applications, the initial state can be set, including the initial estimated value of SOC and the covariance matrix, etc., and the SOC and covariance matrix at the next moment can be predicted based on the voltage state equation.

[0057] The following is an embodiment of the lithium battery SOC estimation method of the present application, which provides a more specific description of the present application: (1) Lithium battery equivalent modeling.

[0058] To estimate the SOC of a lithium battery, it is first necessary to model the equivalent circuit of the lithium battery. In this embodiment, the equivalent circuit model includes electrical components such as resistors, capacitors, and inductors. The equivalent circuit model is relatively simple and easy to build, and can fully reflect the electrical characteristics of the lithium battery.

[0059] At present, the equivalent circuit modeling of lithium batteries is still mainly based on integer-order models. This type of model is simple to calculate, but the actual estimation often has large errors. Considering the electric double layer effect, the second-order model is characterized by a pair of parallel resistors and capacitors, and its impedance diagram presents a standard semicircle. However, due to the existence of the capacitance dispersion effect in lithium batteries, the characteristics of the actual electric double layer are greatly offset from the ideal RC parallel network. Therefore, in this embodiment, CPE (Constant Phase Angle Element) is used to replace the ideal capacitor in the parallel network. Figure 2 Shown is a schematic diagram of the equivalent circuit model in this embodiment, which can more accurately describe the characteristics of the electric double layer, that is, adopt a second-order fractional model.

[0060] in accordance with Figure 2 The equivalent circuit model shown in the figure shows that according to Kirchhoff's law, the voltage state equation of the lithium battery can be expressed as:

[0061] in, represents the fractional order of the first branch, represents the fractional order of the second branch, represents the voltage across the constant phase angle element C1, represents the voltage across the constant phase angle element C2, represents the capacitance value of the constant phase angle element C1, Represents the resistance value of resistor R1, Indicates the estimated charging and discharging time of the lithium battery. represents the capacitance of the constant phase angle element C2, Represents the resistance value of resistor R2, Indicates the current of the lithium battery to be estimated, Indicates the terminal voltage of the lithium battery to be estimated. Indicates the open circuit voltage of the lithium battery to be estimated, Indicates the resistance value of resistor R0.

[0062] The transfer function expression of the equivalent circuit model of lithium battery is:

[0063] in, represents the transfer function, After Laplace transformation, the function The independent variable of .

[0064] (2) Parameter identification.

[0065] The online identification of lithium battery parameters based on the equivalent circuit model can use the recursive least squares method to implement recursive operations on the basis of one estimation to obtain updated results. However, the accumulation of old data in the recursive process will reduce the correction effect of new data. In order to solve the problem of weakening the effect of the gain matrix correction estimate in the recursive process, the recursive least squares method with forgetting factor (FFRLS) can be introduced to ensure real-time correction and rapid convergence during the calculation process.

[0066] like Figure 3 As shown, it is a schematic diagram of the process of FFRLS in this embodiment. FFRLS includes an initial data input layer, a gain and covariance update layer, an estimated parameter update layer and a model parameter calculation layer. The specific calculation methods of each layer include: (1-1) At the initial data input layer, the following formula is used to calculate System input at time :

[0067] in, Indicates that HPPC is testing Always estimate the input voltage of the lithium battery. Indicates that HPPC is testing Always estimate the input voltage of the lithium battery. Indicates that HPPC is testing The input current at the moment, Indicates that HPPC is testing The input current at the moment, Indicates that HPPC is testing The input current at that moment.

[0068] (1-2) In the gain and covariance update layer, the following formula is used to calculate The gain of time :

[0069] in, express The covariance of the time, Represents the forgetting factor, which is generally 0.95-1;

[0070] in, express The covariance of the time, express The gain of time, express The system input at the time.

[0071] It should be noted that the gain is usually used to adjust the parameter estimate to minimize the prediction error, while the covariance reflects the uncertainty of the parameter estimate.

[0072] (1-3) In the estimation parameter update layer, the following formula is used to calculate The parameter estimates at time :

[0073] in, express The parameter estimates at time , express Voltage estimation error at time t;

[0074] in, express Estimated voltage at time:

[0075] in, express The coefficient of express The coefficient of express The coefficient of express The coefficient of express The coefficient of .

[0076] (1-4) In the model parameter calculation layer, according to The parameter estimates at time , calculate the resistance of resistor R0 , the resistance value of resistor R1 , the resistance value of resistor R2 , the capacitance value of the constant phase angle element C1 and the capacitance of the constant phase angle element C2 .

[0077] It can be calculated by the following formula:

[0078] (3) Estimate SOC using the EKPF algorithm.

[0079] The EKPF algorithm achieves a more accurate estimation of the state of a nonlinear system by integrating the linear prediction capability of the Kalman filter and the nonlinear processing capability of the particle filter. Compared with a single Kalman filter or particle filter, the EKPF algorithm has higher estimation accuracy in complex nonlinear systems. When the system state undergoes nonlinear changes, the EKPF algorithm can capture these changes more accurately, thereby providing more reliable estimation results. The EKPF algorithm can also handle the uncertainty of the system state, including noise, model errors, etc., thereby enhancing the robustness of the system. In practical applications, the EKPF algorithm can cope with various complex environmental conditions and system changes and provide stable estimation results. By analyzing the estimation results and residual information of the EKPF algorithm, the detection and diagnosis of system faults can be achieved, which helps to timely discover and repair potential problems in the system and improve the reliability and safety of the system.

[0080] 2. Enhanced robustness The standard process of the EKPF algorithm is as follows: ① Prediction stage:

[0081] in, Indicates State variable estimation at time The prior state at the moment, express The state transfer matrix at time is expressed as:

[0082] in, is the sampling period of the signal in the time domain; express The input excitation matrix at time t is expressed as:

[0083] in, is the rated capacity of the lithium battery to be estimated; express The state variables at time, express Input incentives at all times, Indicates The output state of the moment The output status of each moment is updated; express The observation matrix of the lithium battery to be estimated at any time is expressed as:

[0084] in, Indicates the open circuit voltage of the lithium battery to be estimated, Indicates the SOC value set during the HPPC test.

[0085] ②Update phase: First calculate the Kalman filter gain matrix:

[0086] in, express The Kalman filter gain matrix at time , express The observation matrix of the lithium battery to be estimated at any time, express The Kalman gain noise matrix at time t; Indicates State variable prediction at time The error covariance matrix at time , expressed as:

[0087] in, for The system process noise matrix at time , for The state error covariance update matrix at time .

[0088] Next, update the status:

[0089] in, express The SOC update value at the moment, express Always The estimated value of SOC at the moment, express The actual output value of the lithium battery is to be estimated at any time.

[0090] Finally, the error covariance matrix can be updated so that:

[0091] in, express The observation matrix of the lithium battery to be estimated at any time, express Updated value of the error covariance matrix at time instant.

[0092] In some embodiments of the present application, a PF (Particle Filter) algorithm may also be used to verify the estimation result. The PF algorithm uses a large number of sampling points and sample weights to describe the prediction accuracy of the state. Less than threshold Resampling is performed when , which enhances the effectiveness of the estimation results. The calculation process is as follows: ① Update weights:

[0093] in, express Moment The weight of a particle, express Moment The weight of a particle, express and The importance density function of express Moment The state update value of times, .

[0094] ②Weight normalization:

[0095] in, represents the weight normalization result, Indicated in The number of status updates performed at a given moment. As the effective number of particles The EKPF algorithm reduces the consumption of computing resources and improves the algorithm's computing efficiency by optimizing the distribution and number of particles. This enables the EKPF algorithm to have better performance in systems with high real-time requirements. The particle filter part of the EKPF algorithm has natural parallel computing capabilities and can make full use of multi-core processors or distributed computing resources to further improve computing efficiency.

[0096] The EKPF algorithm estimates the SOC by establishing a nonlinear state space model. It can reflect the dynamic changes of lithium batteries during the charging and discharging process, which is the basis of the EKPF algorithm estimation. The EKPF algorithm combines the recursive algorithm, uses the estimated value of the previous moment and the observed value of the current moment, and realizes the minimum variance estimation of SOC through prediction and update steps. In the EKPF algorithm estimation, considering the relationship between the open circuit voltage and SOC of the lithium battery is crucial for accurately estimating the SOC because it reflects the voltage response of the lithium battery in different states. In addition, in some cases, in order to further improve the accuracy of the estimation, the parameters of the EKPF algorithm can be adaptively adjusted. For example, according to the slope of OCV (Open Circuit Voltage Curve, open circuit voltage curve), the entire OCV range is divided into different regions, and the parameters of the EKPF algorithm are adjusted to adapt to the characteristics of different regions. Therefore, compared with the traditional time-based integration method and coulomb counting method, the present application can effectively overcome the influence of factors such as changes in the internal resistance and temperature of the lithium battery, and improve the accuracy of SOC estimation. The EKPF algorithm can estimate SOC in real time and meet the real-time control requirements of the lithium battery management system, which is particularly important for application scenarios that require rapid response, such as electric vehicles. This application can also adapt to different lithium battery equivalent circuit models and working conditions, and has good robustness, which means that the EKPF algorithm can provide stable SOC estimation results in different usage environments. The EKPF algorithm can not only give an estimated value of SOC, but also give the range of estimation error, which helps to evaluate the reliability of the estimation results and provide a basis for subsequent control and decision-making.

[0097] like Figure 4 As shown, it is a schematic diagram of a lithium battery SOC estimation system of the present application, which may include: A modeling module, used for performing equivalent circuit modeling on the estimated lithium battery to obtain an equivalent circuit model; An equation building module, used for building a voltage state equation and a transfer function based on electrical characteristic parameters of components in the equivalent circuit model; The parameter identification module is used to perform HPPC test on the estimated lithium battery, and uses the recursive least square method with forgetting factor to perform parameter identification to determine the electrical characteristic parameter values ​​of the components in the equivalent circuit model; The estimation module is used to estimate the SOC of the lithium battery to be estimated by using the EKPF algorithm based on the electrical characteristic parameter values ​​of the components in the equivalent circuit model, as well as the voltage state equation and the transfer function.

[0098] It should be noted that in the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of each module is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The module described as a separate component may or may not be physically separated. The component displayed as a module may be a physical unit or multiple physical units, that is, it may be located in one place, or it may be distributed in multiple different places. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0099] In addition, each module in each embodiment of the present invention may be integrated into a processing unit, each module may exist physically separately, or two or more modules may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0100] An embodiment of the present application also provides an electronic device, which may include one or more processors, a memory, and a communication interface.

[0101] The memory, the communication interface and the processor are coupled, for example, the memory, the communication interface and the processor may be coupled together via a bus.

[0102] The communication interface is used for data transmission with other devices. The memory stores computer program code. The computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the above-mentioned lithium battery SOC estimation method.

[0103] Wherein, the processor can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the present disclosure. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessors, and the like. The processor can be used to support electronic devices to execute the method steps provided in the above embodiments.

[0104] The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The above bus may be divided into an address bus, a data bus, a control bus, etc.

[0105] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned lithium battery SOC estimation method are implemented.

[0106] The computer-readable storage medium involved in the present application includes random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field.

[0107] For the description of the relevant parts of the lithium battery SOC estimation system, electronic device and computer-readable storage medium provided in the embodiments of the present application, please refer to the detailed description of the corresponding parts in the lithium battery SOC estimation method provided in the embodiments of the present application, which will not be repeated here. In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive elaboration.

[0108] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A lithium battery SOC estimation method, characterized in that: include: Perform equivalent circuit modeling on the estimated lithium battery to obtain an equivalent circuit model; Based on the electrical characteristic parameters of the components in the equivalent circuit model, a voltage state equation and a transfer function are established; HPPC test is performed on the estimated lithium battery, and the recursive least square method with forgetting factor is used for parameter identification to determine the electrical characteristic parameter values ​​of the components in the equivalent circuit model; Combining the electrical characteristic parameter values ​​of the components in the equivalent circuit model, as well as the voltage state equation and transfer function, the EKPF algorithm is used to estimate the SOC of the lithium battery to be estimated.

2. The lithium battery SOC estimation method according to claim 1, characterized in that: The equivalent circuit modeling of the estimated lithium battery includes: A second-order fractional model is used to model the equivalent circuit of the estimated lithium battery.

3. The lithium battery SOC estimation method according to claim 2, characterized in that: The equivalent circuit model includes a resistor R0, a resistor R1, a resistor R2, a constant phase angle element C1 and a constant phase angle element C2; The resistor R1 and the constant phase angle element C1 are connected in parallel to form a first branch; The resistor R2 and the constant phase angle element C2 are connected in parallel to form a second branch; The resistor R0, the first branch and the second branch are connected in series between the positive electrode of the lithium battery and the positive terminal voltage of the lithium battery facing the outside; the negative electrode of the lithium battery and the negative terminal voltage of the lithium battery facing the outside are connected.

4. The lithium battery SOC estimation method according to claim 3, characterized in that: The expression of the voltage state equation is: in, represents the fractional order of the first branch, represents the fractional order of the second branch, represents the voltage across the constant phase angle element C1, represents the voltage across the constant phase angle element C2, represents the capacitance value of the constant phase angle element C1, represents the resistance value of resistor R1, Indicates the estimated charging and discharging time of the lithium battery. represents the capacitance value of the constant phase angle element C2, Represents the resistance value of resistor R2, Indicates the current of the lithium battery to be estimated, Indicates the terminal voltage of the lithium battery to be estimated. Indicates the open circuit voltage of the lithium battery to be estimated, Indicates the resistance value of resistor R0; The expression of the transfer function is: in, represents the transfer function, After Laplace transformation, the function The independent variable of .

5. The lithium battery SOC estimation method according to claim 4, characterized in that: The recursive least square method with forgetting factor is used for parameter identification to determine the electrical characteristic parameter values ​​of the components in the equivalent circuit model, including: At each moment in the discharge process of the lithium battery to be estimated, steps (1-1) to (1-4) are respectively performed to determine the value of the electrical characteristic parameter at the corresponding moment: (1-1) Calculated by the following formula System input at time : in, Indicates that HPPC is testing Always estimate the input voltage of the lithium battery. Indicates that HPPC is testing Always estimate the input voltage of the lithium battery. Indicates that HPPC is testing The input current at the moment, Indicates that HPPC is testing The input current at the moment, Indicates that HPPC is testing Input current at the moment; (1-2) Calculated by the following formula The gain of time : in, express The covariance of time, represents the forgetting factor; in, express The covariance of time, express The gain of time, express System input at time (1-3) Calculated by the following formula The parameter estimates at time : in, express The parameter estimates at time , express Voltage estimation error at time t; in, express Estimated voltage at time: in, express The coefficient of express The coefficient of express The coefficient of express The coefficient of express The coefficient of (1-4) According to The parameter estimates at time , calculate the resistance of resistor R0 , the resistance value of resistor R1 , the resistance value of resistor R2 , the capacitance value of the constant phase angle element C1 and the capacitance of the constant phase angle element C2 .

6. The lithium battery SOC estimation method according to claim 5, characterized in that: The method of estimating the SOC of the lithium battery to be estimated by using the EKPF algorithm includes: (2-1) Prediction is performed using the following formula: in, Indicates State variable estimation at time The prior state at the moment, express The state transfer matrix at time is expressed as: in, is the sampling period of the signal in the time domain; express The input excitation matrix at time t is expressed as: in, is the rated capacity of the lithium battery to be estimated; express The state variables at time, express Input incentives at all times, Indicates The output state of the moment The output status of each moment is updated; express The observation matrix of the lithium battery to be estimated at any time is expressed as: in, Indicates the SOC value set during HPPC test; (2-2) The Kalman filter gain matrix is ​​calculated by the following formula: in, express The Kalman filter gain matrix at time , express The observation matrix of the lithium battery to be estimated at any time, express The Kalman gain noise matrix at time t; Indicates State variable prediction at time The error covariance matrix at time , expressed as: in, for The system process noise matrix at time , for The state error covariance update matrix at time ; (2-3) The SOC of the lithium battery to be estimated is estimated by the following formula: in, express The SOC update value at the moment, express Always The estimated value of SOC at the moment, express The actual output value of the lithium battery is to be estimated at any time.

7. The lithium battery SOC estimation method according to claim 1, characterized in that: After the SOC of the lithium battery to be estimated is estimated by using the EKPF algorithm, the method further includes: The weights are calculated by the following formula: in, express Moment The weight of a particle, express Moment The weight of a particle, express and The importance density function of express Moment The state update value of times, ; The weight normalization result is calculated by the following formula: in, represents the weight normalization result, Indicated in The number of status updates performed at any given moment; judge The relationship with the preset value is When the SOC of the lithium battery to be estimated is less than the preset value, the SOC of the lithium battery to be estimated is re-estimated; wherein the SOC of the lithium battery to be estimated is re-estimated to adjust The Kalman gain noise matrix at time After that, the SOC of the lithium battery to be estimated is estimated again through steps (2-2) and (2-3).

8. A lithium battery SOC estimation system, characterized in that: include: A modeling module, used for performing equivalent circuit modeling on the estimated lithium battery to obtain an equivalent circuit model; An equation building module, used for building a voltage state equation and a transfer function based on electrical characteristic parameters of components in the equivalent circuit model; The parameter identification module is used to perform HPPC test on the estimated lithium battery, and uses the recursive least square method with forgetting factor to perform parameter identification to determine the electrical characteristic parameter values ​​of the components in the equivalent circuit model; The estimation module is used to estimate the SOC of the lithium battery to be estimated by using the EKPF algorithm based on the electrical characteristic parameter values ​​of the components in the equivalent circuit model, as well as the voltage state equation and the transfer function.

9. An electronic device, characterized in that: include: A memory and one or more processors; the memory is coupled to the processor; wherein the memory stores computer program code, the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the lithium battery SOC estimation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the lithium battery SOC estimation method according to any one of claims 1 to 7 are implemented.

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