Battery state of charge estimation method and device, electronic device, and storage medium

Abstract

The embodiment of the application provides a battery state of charge estimation method and device, electronic equipment and storage medium, and belongs to the technical field of battery state of charge. The method comprises the following steps: obtaining target model parameters of a pre-constructed battery model; constructing initial state expression data of the battery model according to the target model parameters; calculating the initial state expression data according to an external signal to obtain estimation state data; wherein the estimation state data comprises an estimated voltage; calculating compensation data according to the estimated voltage and the external signal; constructing disturbance expression data of the battery model according to the compensation data; merging the disturbance expression data and the initial state expression data to obtain target state expression data; and estimating the state of charge of the battery model based on the target state expression data. The embodiment of the application can improve the accuracy of SOC estimation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery state-of-charge, and particularly relates to a battery state-of-charge estimation method and device, an electronic device and a storage medium. BACKGROUND

[0002] Battery state-of-charge (SOC) refers to the percentage of the capacity that can be released by the battery under certain discharge conditions relative to the rated capacity of the battery. SOC estimation can help reduce excessive discharge or charging of the battery, thereby protecting the battery and prolonging the service life of the battery.

[0003] In related technologies, model-based estimation algorithms include an extended Kalman filter (EKF), an unscented Kalman filter (UKF), a proportional-integral observer (PIO), and the like. Among them, the effect of the PIO method is affected by the model nonlinearity and external noise. The EKF method and the UKF method involve a large number of matrix operations, resulting in a complex calculation process. Therefore, the methods in related technologies each have advantages, but have disadvantages in terms of computational complexity and the ability to handle nonlinear disturbance problems, which may affect the accuracy of SOC estimation. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a battery state-of-charge estimation method and device, an electronic device and a storage medium, which aims to improve the accuracy of battery state-of-charge estimation.

[0005] To achieve the above-mentioned purpose, a first aspect of the embodiments of the present application provides a battery state-of-charge estimation method, which comprises:

[0006] obtaining target model parameters of a pre-constructed battery model;

[0007] constructing initial state expression data of the battery model according to the target model parameters;

[0008] calculating the initial state expression data according to an external signal to obtain estimation state data; wherein the estimation state data comprises an estimated voltage;

[0009] calculating compensation data according to the estimated voltage and the external signal;

[0010] constructing disturbance expression data of the battery model according to the compensation data;

[0011] merge the perturbation expression data with the initial state expression data to obtain target state expression data;

[0012] perform battery state of charge estimation on the battery model based on the target state expression data.

[0013] In some embodiments, constructing the initial state expression data of the battery model according to the target model parameters comprises:

[0014] constructing voltage expression data of a response circuit in the battery model according to the target model parameters;

[0015] constructing state of charge expression data of the battery model according to the target model parameters;

[0016] constructing input expression data of the battery model according to the target model parameters;

[0017] merging the voltage expression data, the state of charge expression data and the input expression data to obtain original state expression data;

[0018] determining relationship data between state of charge and open circuit voltage of the battery model;

[0019] performing data transformation on the original state expression data according to the relationship data to obtain the initial state expression data.

[0020] In some embodiments, the target model parameters comprise battery internal resistance, response resistance and response capacitance, and performing data transformation on the original state expression data according to the relationship data to obtain the initial state expression data comprises:

[0021] calculating a first parameter according to the response capacitance and the response resistance;

[0022] calculating a second parameter according to the battery internal resistance and storage capacity of a target battery;

[0023] calculating a third parameter according to the storage capacity, the battery internal resistance and the first parameter;

[0024] calculating a fourth parameter according to the response capacitance;

[0025] performing data transformation on the original state expression data according to the relationship data, the first parameter, the second parameter, the third parameter and the fourth parameter to obtain the initial state expression data.

[0026] In some embodiments, the target model parameters comprise battery internal resistance and response capacitance, and constructing input expression data of the battery model according to the target model parameters comprises:

[0027] linearizing the state of charge and the open circuit voltage of the battery model to obtain linear parameters;

[0028] obtaining linear state expression data according to the linear parameters and the state of charge of the battery model;

[0029] constructing the input expression data according to the linear state expression data, the response capacitance and the internal resistance of the battery.

[0030] In some embodiments, the constructing the disturbance expression data of the battery model according to the compensation data comprises:

[0031] determining a target compensation range corresponding to the compensation data from a preset compensation range;

[0032] determining a target estimation function from a pre-constructed extended state estimation function according to the target compensation range;

[0033] constructing the disturbance expression data according to the target estimation function and a preset state gain.

[0034] In some embodiments, the extended state estimation function is constructed according to the following method:

[0035] constructing state observation data of an n-order system based on a preset extended state observer and preset system disturbance data; wherein n is a positive integer greater than or equal to 1;

[0036] constructing state observation data of an n+1-order system based on the state observation data of the n-order system;

[0037] performing function transformation on the state observation data of the n+1-order system to obtain the extended state estimation function.

[0038] In some embodiments, the obtaining the target model parameters of the pre-constructed battery model comprises:

[0039] generating initialization model parameters of the battery model;

[0040] recursively performing the following steps until a preset condition is met: performing voltage error calculation on the battery model according to the initialization model parameters, and adjusting the initialization model parameters according to the calculated voltage error;

[0041] taking the adjusted initialization model parameters as the target model parameters.

[0042] To achieve the above object, a second aspect of the embodiments of the present application proposes a battery state of charge estimation device, which comprises:

[0043] a model parameter construction module, configured to obtain a target model parameter of a pre-constructed battery model;

[0044] an initial state representation data construction module, configured to construct initial state representation data of the battery model according to the target model parameter;

[0045] an estimated state data calculation module, configured to calculate the initial state representation data according to an external signal to obtain estimated state data, wherein the estimated state data comprises an estimated voltage;

[0046] a compensation data calculation module, configured to calculate compensation data according to the estimated voltage and the external signal;

[0047] a disturbance representation data construction module, configured to construct disturbance representation data of the battery model according to the compensation data;

[0048] a target state representation data determination module, configured to merge the disturbance representation data and the initial state representation data to obtain target state representation data;

[0049] a state of charge estimation module, configured to estimate a state of charge of the battery model based on the target state representation data.

[0050] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.

[0051] To achieve the above object, a fourth aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0052] The battery state of charge estimation method and device, electronic device and storage medium provided by the present application can obtain initial state representation data and disturbance representation data of a battery model by constructing the battery model. The disturbance representation data and the initial state representation data are merged to obtain target state representation data. The battery state of charge is estimated based on the target state representation data. Therefore, the disturbance representation data can be used as a new extended state in the target state representation data, so that the total disturbance of the battery model can be estimated and compensated in real time, and more accurate state of charge estimation and improved system response capability can be achieved. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a flowchart of the battery state of charge estimation method provided by the embodiments of the present application;

[0054] Figure 2 is a schematic diagram of a battery model provided by an embodiment of the present application;

[0055] Figure 3 is Figure 1 is a flowchart of step S101 in

[0056] Figure 4 is Figure 1 is a flowchart of step S102 in

[0057] Figure 5 is Figure 4 is a flowchart of step S403 in

[0058] Figure 6 is Figure 4 is a flowchart of step S406 in

[0059] Figure 7 is Figure 1 is a flowchart of step S105 in

[0060] Figure 8 is a flowchart of an extended state estimation function construction method provided by an embodiment of the present application;

[0061] Figures 9A-9B is a schematic diagram of fal function value provided by an embodiment of the present application;

[0062] Figure 10 is a schematic diagram of a battery state of charge estimation method rectification process provided by an embodiment of the present application;

[0063] Figure 11 is a structural schematic diagram of a battery state of charge estimation device provided by an embodiment of the present application;

[0064] Figure 12 is a hardware structure schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0066] It should be noted that although the functional modules are divided in the device schematic diagram, and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a manner different from the module division in the device or the sequence in the flowchart. The terms "first", "second", and the like in the specification and claims and the above-described drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification herein is for describing the embodiments of the application only and is not intended to limit the application.

[0068] Lithium-ion batteries (LIBs) have the characteristics of high energy density, high power output and long-term durability. As an indispensable part of battery applications, the battery management system (BMS) can be responsible for the overall management and protection of the battery, thereby improving the reliability, durability and safety of the battery. The state of charge (SOC) as a basic function of the battery management system is an important indicator of the remaining battery capacity. Accurate SOC estimation plays a crucial role in prolonging battery life and improving energy utilization.

[0069] In the related art, the SOC estimation method includes a model-based estimation method. The lithium-ion battery model includes an electrochemical model and an equivalent circuit model (ECM). The electrochemical model is based on the electrochemical reaction and mass transfer process inside the battery, which can describe the internal dynamics of the battery in detail, but such a model requires higher demand for computing resources, so the application of such a model is less. In contrast, ECM has been widely adopted in online applications due to its simplicity and ease of implementation. Based on an accurate battery model, a variety of SOC estimation methods have been developed, which usually combine battery models with Coulomb counting methods to optimize SOC estimation to reduce errors. The current popular model-based estimation algorithm includes the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the proportional-integral observer (PIO), and the sliding mode observer series (SMO).

[0070] The P1O is used to minimize the voltage prediction error and adjust the SOC estimation. However, the effect of this method can be affected by the model nonlinearity and external noise. The traditional Kalman filter (KF) is a linear estimation method, in order to extend the application of this method in nonlinear battery system, the EKF and UKF methods are developed. Based on the ability of EKF and UKF to eliminate measurement noise and process noise, accurate estimation of SOC is realized. However, due to the EKF and UKF method involves a large number of matrix operations, resulting in a complex calculation process. In addition, the SMO method is also a method that can robustly estimate SOC in the presence of parameter uncertainty and disturbance. However, determining the uncertainty boundary and reducing the jitter phenomenon are the main problems that this method needs to overcome. Although the above methods have their own advantages, but in the calculation complexity, the sensitivity to initial value, the ability to deal with nonlinear disturbance problem, etc. These deficiencies may affect the accuracy of SOC estimation.

[0071] Based on this, the embodiments of the present application provide a battery state of charge estimation method and device, electronic equipment and storage medium, aiming to improve the accuracy of SOC estimation.

[0072] Figure 1 is an optional flowchart of the battery state of charge estimation method provided by the embodiments of the present application, Figure 1 The method in the above embodiment can include but is not limited to including steps S101 to S107.

[0073] Step S101, obtaining the target model parameter of the pre-constructed battery model;

[0074] Step S102, constructing the initial state expression data of the battery model according to the target model parameter;

[0075] Step S103, calculating the initial state expression data according to the external signal to obtain the estimated state data; wherein the estimated state data includes the estimated voltage;

[0076] Step S104, calculating the compensation data according to the estimated voltage and the external signal;

[0077] Step S105, constructing the disturbance expression data of the battery model according to the compensation data;

[0078] Step S106, merging the disturbance expression data and the initial state expression data to obtain the target state expression data;

[0079] Step S107, estimating the battery state of charge of the battery model based on the target state expression data.

[0080] The steps S101 to S107 shown in the embodiments of the present application can obtain initial state expression data and disturbance expression data of the battery model by constructing the battery model. The target state expression data is obtained by merging the disturbance expression data and the initial state expression data. The battery state of charge is estimated by the battery model through the target state expression data. Therefore, the embodiments of the present application can regard the disturbance expression data as a new extended state in the target state expression data. Thus, by estimating and compensating the total disturbance of the battery model in real time, more accurate state of charge estimation can be achieved, and the response capability of the system can be improved.

[0081] In step S101 of some embodiments, the battery model can refer to a model constructed in advance based on the ECM. The battery model can be a first-order ECM. The battery model can be used to describe the dynamic behavior of the battery. The battery model can abstract the battery as a power supply with internal resistance, and describe the behavior of the battery by using a first-order linear differential equation. As shown in Figure 2 The battery model can include a battery internal resistance R0, a resistance R1 and a capacitance C1. The resistance R1 and the capacitance C1 can constitute an RC circuit, and the RC circuit can be used to describe the dynamic behavior of the battery. b represents an input signal, C n represents all the energy stored in the battery, U SOC(t) represents the relationship between the state of charge (SOC) of the battery and the open circuit voltage (OCV). The target model parameters can refer to specific parameter values corresponding to the battery internal resistance R0, the resistance R1 and the capacitance C1. The offline parameter identification of the battery model can be performed by using a particle swarm optimization (PSO) algorithm to obtain the target model parameters. It can be understood that the PSO algorithm is an optimization technique based on swarm intelligence. The PSO algorithm searches for the optimal solution by simulating the foraging behavior of a bird swarm. The PSO algorithm searches the parameter space by updating the speed and position of the particles, wherein the position of the particle represents a set of possible model parameters, and the speed can be used to guide the moving direction and distance of the particle in the search space.

[0082] Referring to Figure 3 In some embodiments, step S101 can include but is not limited to steps S301 to S303.

[0083] Step S301, generating initialization model parameters of the battery model;

[0084] Step S302, performing the following steps in a loop until a preset condition is reached: performing voltage error calculation on the battery model according to the initialization model parameters, and adjusting the initialization model parameters according to the calculated voltage error;

[0085] Step S303, taking the adjusted initialization model parameters as the target model parameters.

[0086] In step S301 of some embodiments, a group of particles is randomly generated according to the PSO algorithm, each particle representing a set of possible parameters of the battery model, i.e., initializing the model parameters. It can be understood that the initializing model parameters include specific parameter values corresponding to the battery internal resistance R0, the resistance R1 and the capacitance C1.

[0087] In step S302 of some embodiments, for each particle, the fitness of the corresponding particle is calculated according to the cost function. The fitness can be used to measure the pros and cons of the particle solution. For example, in the embodiments of the present application, the cost function can be as shown in the following formula (1). That is, the embodiments of the present application take the voltage error between the estimated voltage T and the actual voltage V as the cost function. Wherein, N represents the number of particles.

[0088]

[0089] As can be seen from formula (1), the cost function of the embodiments of the present application is to minimize the voltage error. The initializing model parameters are updated according to the voltage error.

[0090] The two steps of calculating the cost function and updating the initializing model parameters are executed in a loop until a preset condition is reached. The preset condition can refer to a loop stop condition, such as the preset condition can include that the number of loops reaches a preset number, the voltage error reaches a preset threshold, etc., which is not specifically limited by the embodiments of the present application.

[0091] In step S303 of some embodiments, the initializing model parameters corresponding to the time when the preset condition is reached are taken as the target model parameters.

[0092] The advantages of steps S301 to S303 are that taking the voltage error as the cost function can improve the accuracy of determining the target model parameters.

[0093] In step S102 of some embodiments, the initial state expression data can refer to data capable of expressing the dynamic state of the battery, and the initial state expression data can be constructed according to the target model parameters. For example, referring to Figure 2 , the initial state expression data can include the expression of V T , the expression of SOC and the expression of U1

[0094] Referring to Figure 4 , in some embodiments, step S102 can include but is not limited to steps S401 to S406.

[0095] In step S401, the voltage expression data of the response circuit in the battery model is constructed according to the target model parameters;

[0096] Step S402, constructing state of charge expression data of the battery model according to the target model parameter;

[0097] Step S403, constructing input expression data of the battery model according to the target model parameter;

[0098] Step S404, merging the voltage expression data, the state of charge expression data and the input expression data to obtain original state expression data;

[0099] Step S405, determining relationship data between the state of charge and the open circuit voltage of the battery model;

[0100] Step S406, performing data transformation on the original state expression data according to the relationship data to obtain initial state expression data.

[0101] In steps S401 to S404 of some embodiments, the response circuit in the battery model can refer to an RC circuit including a resistor R1 and a capacitor C1. Therefore, as shown in FIG. 4, the voltage expression data can refer to expression data of the voltage U1. The state of charge expression data can refer to expression data of the state of charge SOC of the battery model. The input expression data can refer to expression data of an external signal (such as a voltage signal V T ) of the battery model. It can be understood that the voltage expression data and the state of charge expression data can be constructed based on the Kirchhoff's law. Figure 2

[0102] Referring to Figure 5 , in some embodiments, the construction step of the input expression data (i.e., step S403) can include but is not limited to steps S501 to S503.

[0103] Step S501, performing linearization processing on the state of charge and the open circuit voltage of the battery model to obtain linear parameters;

[0104] Step S502, obtaining linear state expression data according to the linear parameters and the state of charge of the battery model;

[0105] Step S503, constructing the input expression data according to the linear state expression data, the response capacitance and the internal resistance of the battery.

[0106] In steps S501 to S503 of some embodiments, the nonlinear relationship between the state of charge SOC and the open circuit voltage OCV can be approximated as a set of linear segments. Therefore, the open circuit voltage OCV can be expressed as a linear function of the state of charge SOC using a piecewise linearization method. The linear parameters can include m i and b i . m i may refer to the slope after linearization processing of each segment, and b i ​may refer to the intercept after each linearization process. In this way, linear state expression data can be obtained as shown in the following equation (2).

[0107] U oc (SOC)=m i SOC+b i ......equation (2)

[0108] In step S503 of some embodiments, input expression data can be constructed according to the linear state expression data, the voltage U1 of the response capacitor C1, and the voltage U0 of the battery internal resistance R0, as shown in the following equation (3).

[0109] V T =U OC (SOC)+U1+U0......equation (3)

[0110] Data merging of the voltage expression data, the state of charge expression data, and the input expression data can obtain original state expression data as shown in the following equation (4).

[0111]

[0112] In step S405 of some embodiments, the relationship data between the state of charge SOC and the open circuit voltage OCV in the battery model is determined, which can be the following equation (5).

[0113] U OC =g(U soc )......equation (5)

[0114] In step S406 of some embodiments, for the battery model, the original state expression data (such as equation (4)) is data transformed according to the relationship data (such as equation (5)) to obtain initial state expression data.

[0115] Referring Figure 6 , in some embodiments, step S406 can include but is not limited to steps S601 to S605.

[0116] Step S601, calculating a first parameter according to the response capacitor and the response resistance;

[0117] Step S602, calculating a second parameter according to the battery internal resistance and the storage capacity of the target battery;

[0118] Step S603, calculating a third parameter according to the storage capacity, the battery internal resistance, and the first parameter;

[0119] Step S604, calculating a fourth parameter according to the response capacitor;

[0120] In step S605, the original state expression data is transformed according to the relationship data, the first parameter, the second parameter, the third parameter and the fourth parameter, to obtain initial state expression data.

[0121] In step S601 to step S605 of some embodiments, the original state expression data is transformed according to the relationship data, to obtain initial state expression data as shown in the following formula (6).

[0122]

[0123] Wherein, x1=V T , x2=SOC, x3=U1. The first parameter can be represented by a1, a1=1 / R1C1, R1 represents a response resistance, and C1 represents a response capacitance. The second parameter can be represented by a2, a2=1 / R0C n , R0 represents a battery internal resistance, and C n represents all the energy stored in the battery. The third parameter can be represented by b1, b1=((1 / C n )+(1 / R0)+(R0 / R1C1)). The fourth parameter can be represented by b2, b2=1 / C1. Therefore, the original state expression data can be transformed in a data expression form according to the relationship data, the first parameter, the second parameter, the third parameter and the fourth parameter, to obtain initial state expression data.

[0124] In step S103 of some embodiments, the external signal can refer to a signal input to the battery model from the outside, such as an external signal referring to a current. The initial state expression data is solved according to the external current of each second, to obtain estimated state data. The estimated state data can refer to data obtained by estimating the state of the battery model based on the external signal, such as the estimated state data including specific parameter values of V T , SOC and U1.

[0125] In step S104 of some embodiments, the external signal can also refer to an actual voltage provided from the outside. The estimated voltage and the external signal can be calculated by difference, to obtain compensation data. That is, the compensation data can refer to error data between the estimated terminal voltage and the actual voltage.

[0126] In step S105 of some embodiments, the disturbance expression data can refer to expression data capable of representing the disturbance suffered by the battery model. It can be understood that when the battery model is disturbed, an error will be generated between the terminal voltage of the battery model and the actual voltage. Therefore, the disturbance expression data of the battery model can be constructed based on the compensation data.

[0127] Referring to Figure 7 In some embodiments, step S105 can include but is not limited to steps S701 to S703.

[0128] Step S701: Determine the target compensation range corresponding to the compensation data from the preset compensation range;

[0129] Step S702: Determine the target estimation function from the pre-constructed extended state estimation function based on the target compensation range:

[0130] Step S703: Based on the target estimation function and the preset state gain, the perturbation expression data is constructed.

[0131] In steps S701 to S702 of some embodiments, multiple extended state estimation functions can be pre-constructed, each corresponding to a different range of voltage error (the error between the terminal voltage and the actual voltage). The range into which the current voltage error falls is taken as the target compensation range, and the extended state estimation function corresponding to the target compensation range is taken as the target estimation function.

[0132] Reference Figure 8 In some embodiments, the method for constructing the extended state estimation function may include, but is not limited to, steps S801 to S803.

[0133] Step S801: Construct state observation data for an n-order system based on a preset extended state observer and preset system disturbance data; where n is a positive integer greater than or equal to 1.

[0134] Step S802: Construct state observation data for the (n+1)th order system based on the state observation data of the nth order system;

[0135] Step S803: Perform a function transformation on the state observation data of the (n+1)th order system to obtain the extended state estimation function.

[0136] In step S801 of some embodiments, the preset Extended State Observer (ESO) is an observer that can include the total system disturbance in the extended state. By incorporating the total system disturbance into the state vector, the ESO can estimate the total disturbance together with the system state, thereby achieving real-time disturbance elimination. An nth-order nonlinear system can be constructed as shown in Equation (7).

[0137] y (n) (t)=f(y (n-1) (t),...y(t),d(t))+b·u(t)...Equation (7)

[0138] Where d(t) represents external interference, y(t) represents the output signal, and u(t) represents the input signal. f(y (n-1)(t), ..., y(t), d(t)) represent the preset system disturbance data, i.e., the total system disturbance. The total system disturbance can be a nonlinear and time-varying value. It can be understood that the total system disturbance can include internal disturbances and external disturbances. For example, in the battery SOC scenario, internal disturbances can include battery model parameters, while external disturbances can include changes in operating conditions, sensor acquisition problems, and other disturbances that affect the estimation accuracy.

[0139] By taking the total disturbance of the system as a new extended state, the state space of the system (i.e., the state observation data of the nth-order system) can be expressed as follows (8).

[0140]

[0141] In step S802 of some embodiments, equation (8) serves as a new extended system of equation (7), and the total system disturbance f(y) is... (n -1) (t), ..., y(t), d(t)) are extended states Z n+1 Thus, an n+1 order ESO system can be constructed based on the original n-order system, that is, an n+1 order state relation system (as shown in equation (9) below).

[0142]

[0143] In step S803 of some embodiments, in equation (9), e represents the difference between the estimated value and the actual value. NESO uses a nonlinear function fal(·) to enhance the estimation performance. In the nonlinear fal function, α and δ are important parameters affecting the convergence characteristics. δ is inversely proportional to the slope of the linear segment of the fal function. Specifically, when |e|≤δ, the observer operates in the linear region, thus avoiding high-frequency oscillations. When |e|>δ, the observer operates nonlinearly. α affects the magnitude of the fal function. Figure 9A and 9B As shown, different values ​​of α and δ affect the value of the nonlinear fal function, thus affecting the overall convergence characteristics of the system. For the system, the characteristics of the nonlinear function in ESO affect the observation performance. Although the fal function is continuous in its domain, it is prone to high-frequency chattering in the neighborhood of the origin and has many tuning parameters, which can easily lead to poor observation results due to excessive gain. When the fal function is used in ESO, although it can improve the error feedback gain and thus improve performance, it may also bring negative effects. Based on this, the embodiment of this application improves the fal function and constructs the gal function. Based on the gal function, the state observation data of the n+1 order system are transformed by function to obtain the extended state estimation function shown in the following equation (10).

[0144]

[0145] Wherein, σ is an adjustable parameter of the gal function, σ > 0. Compared with the fal function, the gal function can reflect small errors, large gains, large errors, and small gains, and can reduce the problem of high-frequency chattering of the system caused by excessive gain near zero due to the defects of the fal function itself. The gal function has better smoothness, continuity and convergence near the origin. In addition, the gal function has only one adjustable parameter, and is easy to set. By using fixed constant output at both ends of the gal function, the difficulty of setting the parameter σ is further reduced. Thus, the observer of the embodiments of the present application uses a nonlinear smooth gal function, which can not only simplify the entire observer system, but also improve the observation ability of the observer, thereby improving the anti-interference ability and parameter estimation ability of the system.

[0146] In some embodiments, it is determined which compensation range the compensation data e1 falls into in formula (10) |e|≤1, |e|>1, |e|<-1, and the compensation range is taken as the target compensation range. The extended state estimation function corresponding to the target compensation range is taken as the target estimation function. For example, if the compensation data e1 falls into |e|≤1, the target estimation function is

[0147] In step S703 of some embodiments, the disturbance expression data shown in the following formula (11) can be constructed.

[0148]

[0149] Wherein, gal(e1, α) is equivalent to gal(e, σ), that is, the compensation data e1 is equivalent to e, and the parameter α is equivalent to σ. k4 is an adjustable gain, that is, it represents a preset state gain. represents the total disturbance of the battery model.

[0150] In step S106 of some embodiments, the disturbance expression data and the initial state expression data are combined to obtain the target state expression data of the battery model. That is, the disturbance expression data is taken as a new extended state, so that when the dynamic state of the battery is estimated, the influence of the disturbance can be considered at the same time, thereby improving the accuracy of SOC estimation.

[0151] Specifically, due to the complex chemical reaction inside the battery, the battery is a strong nonlinear and time-varying system, and therefore, the initial state expression data shown in formula (6) cannot well describe the battery characteristics. Therefore, the external and internal disturbances of the battery model need to be added, so that the target state expression data shown in the following formula (12) can be obtained.

[0152]

[0153] Among them, k1, k2, and k3 are adjustable gains.

[0154] To better adapt to computer programming, equation (12) can be discretized to obtain the final expression of the target state data (as shown in equation (13) below).

[0155]

[0156] Among them, T s This indicates the adoption time in practical applications.

[0157] In step S107 of some embodiments, the SOC of the battery model can be estimated based on the target state representation data. Specifically, as shown... Figure 10 As shown, ΔV T This indicates voltage error. (From...) Figure 10 As can be seen, the observer in this embodiment includes a state sub-observer and a disturbance sub-observer. By feeding the observation results of the state sub-observer and the observation results of the disturbance sub-observer back to the battery model, the SOC estimation result can be obtained.

[0158] The battery state-of-charge estimation method provided in this application has the following advantages:

[0159] 1. Compared to state observers in related technologies, this application embodiment treats the total disturbance of the battery model as a new extended state and performs real-time estimation and compensation. Thus, this application embodiment provides a novel method for handling the uncertainty and dynamic changes of battery state.

[0160] 2. The embodiments of this application demonstrate high robustness to time-varying internal and external disturbances, and can stably provide accurate state estimates under different ambient temperatures, driving cycles, and noise levels. Furthermore, the method provided in these embodiments is less dependent on accurate battery model parameters, thus making it applicable to various types and brands of lithium-ion batteries. This high versatility allows these embodiments to be deployed in different application scenarios.

[0161] In a specific embodiment, the battery state-of-charge estimation method provided in this application may include the following steps:

[0162] 1. Use the PSO algorithm to obtain the model parameters of the first-order battery model (including the specific parameter values ​​of battery internal resistance R0, resistance R1 and capacitance C1).

[0163] 2. Construct the state-space equations of the battery model (i.e., the initial state representation data, as shown in equation (6)).

[0164] 3. Calculate estimated state data (including V) based on the external current every second. T, the specific parameter values of SOC and U1.

[0165] 4. Observer design compensation. The error between the estimated voltage (i.e., V T ) of the battery model and the actual voltage is taken as compensation, and the fourth extended state of the observer is constructed according to the compensation, i.e., the fourth extended state is the total disturbance of the system.

[0166] Please refer to Figure 11 , the embodiment of the application further provides a battery state of charge estimation device, which can implement the above battery state of charge estimation method, and the device comprises:

[0167] A model parameter construction module 1101 is configured to acquire target model parameters of a pre-constructed battery model.

[0168] An initial state expression data construction module 1102 is configured to construct initial state expression data of the battery model according to the target model parameters.

[0169] An estimated state data calculation module 1103 is configured to calculate the initial state expression data according to an external signal to obtain estimated state data, wherein the estimated state data comprises an estimated voltage.

[0170] A compensation data calculation module 1104 is configured to calculate compensation data according to the estimated voltage and the external signal.

[0171] A disturbance expression data construction module 1105 is configured to construct disturbance expression data of the battery model according to the compensation data.

[0172] A target state expression data determination module 1106 is configured to merge the disturbance expression data and the initial state expression data to obtain target state expression data.

[0173] A state of charge estimation module 1107 is configured to estimate the state of charge of the battery model based on the target state expression data.

[0174] The specific implementation of the battery state of charge estimation device is basically the same as the specific embodiment of the above battery state of charge estimation method, and will not be repeated here.

[0175] The embodiment of the application further provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the above battery state of charge estimation method when executing the computer program. The electronic device can be any intelligent terminal, such as a tablet computer or a vehicle-mounted computer.

[0176] Please refer to Figure 12 , Figure 12 the hardware structure of the electronic device of another embodiment is shown, which comprises:​

[0177] The processor 1201 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0178] The memory 1202 can be implemented by a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 1202 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 1202 and are called and executed by the processor 1201 to implement the battery state of charge estimation method of the embodiments of the present application.

[0179] The input / output interface 1203 is configured to implement information input and output.

[0180] The communication interface 1204 is configured to implement the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0181] The bus 1205 is configured to transmit information between various components (for example, the processor 1201, the memory 1202, the input / output interface 1203, and the communication interface 1204) of the device.

[0182] The processor 1201, the memory 1202, the input / output interface 1203, and the communication interface 1204 are connected to each other through the bus 1205 to realize the communication connection between the device.

[0183] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the above battery state of charge estimation method.

[0184] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely from the processor, which can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0185] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0186] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than shown in the figures, or combine certain steps or different steps.

[0187] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0188] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and appropriate combinations thereof.

[0189] The terms "first", "second", "third", "fourth" and the like used in the specification of the present application and the above-described drawings, if any, are used to distinguish similar objects, and do not necessarily have to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0190] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B, and A and B existing at the same time, wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, and c can be single or multiple.

[0191] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0192] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0193] In addition, the functional units in each embodiment of the application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0194] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0195] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A battery state-of-charge estimation method, characterized by, The method comprises: acquiring target model parameters of a pre-constructed battery model; constructing initial state expression data of the battery model according to the target model parameters; calculating the initial state expression data according to an external signal to obtain estimated state data; wherein the estimated state data comprises an estimated voltage; calculating compensation data according to the estimated voltage and the external signal; constructing perturbation expression data of the battery model according to the compensation data; merging the perturbation expression data and the initial state expression data to obtain target state expression data; estimating the state of charge of the battery model based on the target state expression data; wherein the compensation data comprises: determining a target compensation range corresponding to the compensation data from a preset compensation range; determining a target estimation function from a pre-constructed extended state estimation function according to the target compensation range; constructing the perturbation expression data according to the target estimation function and a preset state gain; The extended state estimation function is constructed according to the following method: constructing state observation data of an n-order system based on a preset extended state observer and preset system disturbance data; wherein n is a positive integer greater than or equal to 1; constructing state observation data of an n+1-order system based on the state observation data of the n-order system; performing function transformation on the state observation data of the n+1-order system to obtain the extended state estimation function.

2. The method of claim 1, wherein, The initial state expression data of the battery model is constructed according to the target model parameters, comprising: constructing voltage expression data of a response circuit in the battery model according to the target model parameters; constructing state of charge expression data of the battery model according to the target model parameters; constructing input expression data of the battery model according to the target model parameters; merging the voltage expression data, the state of charge expression data and the input expression data to obtain original state expression data; determining relationship data between the state of charge and the open circuit voltage of the battery model; performing data transformation on the original state expression data according to the relationship data to obtain the initial state expression data.

3. The method of claim 2, wherein, The target model parameters comprise battery internal resistance, response resistance and response capacitance, and the data transformation on the original state expression data according to the relationship data to obtain the initial state expression data comprises: calculating a first parameter according to the response capacitance and the response resistance; calculating a second parameter according to the battery internal resistance and the storage capacity of a target battery; calculating a third parameter according to the storage capacity, the battery internal resistance and the first parameter; calculating a fourth parameter according to the response capacitance; performing data transformation on the original state expression data according to the relationship data, the first parameter, the second parameter, the third parameter and the fourth parameter to obtain the initial state expression data.

4. The method of claim 2, wherein, The target model parameters comprise battery internal resistance and response capacitance, and the construction of the input expression data of the battery model according to the target model parameters comprises: linearize the state of charge and the open circuit voltage of the battery model to obtain linear parameters; obtain linear state expression data according to the linear parameters and the state of charge of the battery model; construct the input expression data according to the linear state expression data, the response capacitance and the battery internal resistance.

5. The method according to any one of claims 1 to 4, characterized in that, The target model parameters of the pre-constructed battery model are obtained, including: generating initialization model parameters of the battery model; performing the following steps in a loop until a preset condition is reached: performing voltage error calculation on the battery model according to the initialization model parameters, and adjusting the initialization model parameters according to the calculated voltage error; the adjusted initialization model parameters are used as the target model parameters.

6. A battery state of charge estimation device characterized by comprising: The device includes: a model parameter construction module for obtaining target model parameters of a pre-constructed battery model; an initial state expression data construction module for constructing initial state expression data of the battery model according to the target model parameters; an estimated state data calculation module for calculating the initial state expression data according to an external signal to obtain estimated state data; wherein the estimated state data includes an estimated voltage; a compensation data calculation module for calculating compensation data according to the estimated voltage and the external signal; a disturbance expression data construction module for constructing disturbance expression data of the battery model according to the compensation data; a target state expression data determination module for merging the disturbance expression data with the initial state expression data to obtain target state expression data; a state of charge estimation module for estimating the state of charge of the battery model based on the target state expression data; wherein the disturbance expression data of the battery model is constructed according to the compensation data, including: determining a target compensation range corresponding to the compensation data from a preset compensation range; determining a target estimation function from a pre-constructed extended state estimation function according to the target compensation range; constructing the disturbance expression data according to the target estimation function and a preset state gain; The extended state estimation function is constructed according to the following method: constructing state observation data of an n-order system based on a preset extended state observer and preset system disturbance data; wherein n is a positive integer greater than or equal to 1; constructing state observation data of an n+1-order system based on the state observation data of the n-order system; performing function transformation on the state observation data of the n+1-order system to obtain the extended state estimation function.

7. An electronic device, comprising: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the method of any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program is executed by the processor to implement the method of any one of claims 1-5.

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