CN105717460A - Power battery SOC estimation method and system based on nonlinear observer - Google Patents

Abstract

The invention discloses a power battery SOC estimation method based on a nonlinear observer. The method comprises the steps of: performing an intermittent discharging-standing experiment on a power battery, and obtaining a SOC and OCV expression; establishing a battery equivalent circuit model; determining each parameter value of the model; establishing a discrete state space model of the battery; designing a self-adaptive nonlinear observer; determining an optimizing target function; solving an optimal parameter value; an calculating an estimated value of the battery SOC. A power battery SOC estimation system based on the optimal self-adaptive nonlinear observer comprises a power battery SOC-OCV expression determining module, an equivalent circuit model establishing module, a parameter determining module, a power battery SOC estimation discrete state space model determining module, a self-adaptive nonlinear observer designing module, a parameter optimizing target function determining module, an optimal parameter solving module and a power battery SOC estimation module. According to the invention, the SOC estimation precision is improved, the algorithm calculated quantity is reduced, hardware realization is facilitated, and the power battery SOC estimation method and system based on the nonlinearity observer are applied to the field of electric vehicle power battery managing systems.

Description

Power battery SOC estimation method and system based on nonlinear observer

Technical Field

The invention relates to the field of power battery management systems of electric vehicles, in particular to a power battery SOC estimation method and system based on an optimal adaptive nonlinear observer.

Background

The power battery is one of the core components of the electric automobile, and directly influences performance indexes such as the driving mileage, the acceleration and the climbing capability of the whole automobile. The Battery Management System (BMS) is responsible for managing and controlling the Battery in aspects of state monitoring, electric quantity balancing, heat management, energy distribution and the like, and has important significance for prolonging the service life of the Battery, improving the safety of the Battery, reducing the use cost of the Battery in the whole life cycle and the like. The State of charge (SOC) is an important index for reflecting the remaining capacity and the work-doing capability of the battery, and is an important basis for the functions of battery charge-discharge control, health State monitoring, energy distribution, capacity equalization and the like. However, the SOC of the battery is affected by many factors such as temperature, current, cycle number, etc., and has significant uncertainty and strong nonlinear characteristics, so online accurate estimation of the SOC is considered as one of the core and difficult technologies in the research and design process of the battery management system.

At present, the power battery SOC estimation method disclosed at home and abroad mainly comprises the following steps: internal resistance method, ampere-hour integration method (also called coulometry method), open circuit voltage method, neural network method, kalman filter method, observer method, and the like. The internal resistance method calculates the SOC of the battery by detecting the internal resistance of the battery according to the functional relation between the internal resistance of the battery and the SOC, however, the online and accurate measurement of the internal resistance of the battery is difficult, and the application of the method in practical engineering is limited. The ampere-hour integration method is simple in principle and easy to implement, but cannot eliminate initial errors of the SOC and accumulated errors caused by inaccurate current measurement. The Open-Circuit Voltage method calculates the SOC of the battery according to a correspondence between Open-Circuit Voltage (OCV) and the SOC, and the OCV is measured only after the battery is sufficiently left, which is not suitable for online estimation of the SOC. The neural network method needs a large number of training samples, and because sample data capable of covering all practical working conditions cannot be obtained in practical application, the precision of the method is influenced to a certain extent, and the method has large calculation amount and is difficult to realize in hardware. The Kalman filtering method and the observer method can well correct the initial error of the SOC of the battery, have good anti-noise capability, but have relatively large calculated amount and difficult hardware realization. Therefore, the existing power battery SOC estimation methods have certain inconveniences and defects in practical applications to different degrees, and therefore further improvement is needed.

Disclosure of Invention

In order to solve the technical problems, the invention aims to provide a method and a system for estimating the SOC of a power battery based on a nonlinear observer, which have high accuracy, strong adaptability and small calculation amount.

The technical scheme adopted by the invention is as follows: a power battery SOC estimation method based on a non-linear observer comprises the following steps:

performing intermittent discharge-standing experiment on the power battery, and fitting to obtain SOC and OCV

A functional expression therebetween;

establishing an equivalent circuit model of the power battery;

determining each parameter value of the power battery equivalent circuit model;

establishing a discrete state space model of the power battery for SOC estimation;

designing an adaptive non-linear observer for estimating the SOC of the battery;

determining an objective function for parameter optimization of the adaptive nonlinear observer;

solving the optimized parameter value of the self-adaptive nonlinear observer;

and calculating the estimated value of the SOC of the battery by adopting an optimal self-adaptive nonlinear observer.

As an improvement of the technical scheme, the battery model for identifying the battery parameters adopts an n-order RC equivalent circuit model, wherein n is more than or equal to 1.

As an improvement of the technical scheme, the battery model parameters are obtained by performing a pulse discharge-standing experiment on the battery and performing off-line identification by adopting an exponential fitting method according to a corresponding voltage response curve.

As an improvement of the technical scheme, the SOC estimation method adopts an optimal self-adaptive nonlinear observer.

As an improvement of the technical solution, the value of the gain matrix of the adaptive non-linear observer is expressed by a formula:

$K=\left[\begin{array}{cccccc}{a}_{11}{k}_1& 0& 0& 0& \mathrm{...}& 0\\ 0& a_{22}{k}_2& 0& 0& \mathrm{...}& 0\\ .& .& .& .& \mathrm{...}& .\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ 0& 0& \mathrm{...}& a_{ii}{k}_i& \mathrm{...}& 0\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ 0& 0& 0& 0& \mathrm{...}& (a_{11}+a_{22}+...+a_{nn})k_{n+1}\end{array}\right]$

wherein, a_ij(i, j ═ 1, 2.. times, n) is the ith row and j column elements of the coefficient matrix in the battery system state space model;

k_i＝α_i(0.1+|e_y|/(|e_y|+0.1)(i＝1,2,...,n+1)，α_iis a proportionality coefficient (real number greater than zero), e_yIs the systematic observation error.

As an improvement of the technical scheme, the proportional coefficient of the gain matrix of the optimal adaptive nonlinear observer is optimally designed by adopting a group intelligent search algorithm.

As an improvement of the technical scheme, the swarm intelligent search algorithm for the optimal design of the gain matrix proportionality coefficient of the optimal adaptive non-linear observer adopts a particle swarm optimization algorithm.

Further, the optimal design of the gain matrix scaling factor of the optimal adaptive non-linear observer is aimed at minimizing the absolute average error between the estimated battery SOC value and the reference value under the test condition, wherein:

objective function minZ (α)₁,α₂,α₃)＝mean(abs(SOC_r-SOC_e))

Where mean (-) is the averaging function, abs (-) is the absolute value function, SOC_rIs a reference value of SOC (usually obtained by ampere-hour integration), SOC_eIs an estimate of the SOC.

The invention also provides a power battery SOC estimation system based on the optimal adaptive nonlinear observer, which comprises the following steps:

the power battery SOC-OCV relational expression determining module is used for performing intermittent discharge-standing experiments on the power battery and fitting to obtain a functional expression between the SOC and the OCV; the power battery system equivalent circuit model building module is used for building an equivalent circuit model of the power battery;

the power battery system equivalent circuit model parameter determination module is used for determining each parameter value of the power battery equivalent circuit model;

the power battery SOC estimation discrete state space model determining module is used for establishing a discrete state space model of the power battery for SOC estimation;

the adaptive nonlinear observer design module for the SOC estimation of the power battery is used for designing an adaptive nonlinear observer for the SOC estimation of the battery;

the adaptive nonlinear observer parameter optimization objective function determination module is used for determining an objective function for parameter optimization of the adaptive nonlinear observer;

the particle swarm optimization algorithm solving module is used for solving the optimized parameter value of the adaptive nonlinear observer;

and the power battery SOC estimation module based on the optimal nonlinear observer is used for calculating the estimation value of the battery SOC by adopting the optimal adaptive nonlinear observer.

The invention has the beneficial effects that: compared with the traditional Kalman filtering, sliding-mode observer, neural network and other methods, the nonlinear observer has the advantages that the accuracy is equivalent, the calculation complexity is lower, the hardware implementation is easier, and compared with the traditional constant-gain nonlinear observer, the value of a gain matrix of the adaptive nonlinear observer can be adaptively adjusted according to the observation error of the system, so that the noise and adaptive capacity of the estimation algorithm and the estimation accuracy of the SOC are improved; the optimal adaptive nonlinear observer optimizes the proportional coefficient of the gain matrix on the basis of the adaptive nonlinear observer, and can further improve the SOC estimation precision. Therefore, the method has the outstanding advantages of improving the SOC estimation precision, reducing the calculation amount of the algorithm and being beneficial to hardware realization.

Drawings

The following further describes embodiments of the present invention with reference to the accompanying drawings:

FIG. 1 is a flow chart of a battery SOC estimation method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a second-order RC equivalent circuit model of a battery according to an embodiment of the present invention;

FIG. 3 is a voltage response curve of a battery pulse discharge according to an embodiment of the present invention;

FIG. 4 is a flowchart of solving the proportional coefficient of the gain matrix of the adaptive nonlinear observer by the particle swarm optimization algorithm according to an embodiment of the present invention;

FIG. 5 is a convergence curve of a global optimum value in a process of solving a gain matrix scale coefficient of an adaptive nonlinear observer by a particle swarm optimization algorithm according to an embodiment of the present invention;

FIG. 6 is a test condition current response curve for verifying battery SOC estimation accuracy, in accordance with an embodiment of the present invention;

FIG. 7 is a test condition voltage response curve for verifying battery SOC estimation accuracy, in accordance with an embodiment of the present invention;

FIG. 8 is a schematic diagram illustrating SOC test results of a battery SOC estimation method according to an embodiment of the present invention;

FIG. 9 is a schematic diagram illustrating a SOC error test result of the battery SOC estimation method according to an embodiment of the present invention;

fig. 10 is a functional block diagram of a battery SOC estimation system according to an embodiment of the present invention.

Detailed Description

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

In order to improve the accuracy and the high efficiency of SOC estimation of a power battery for an electric vehicle, the embodiment of the invention provides a power battery SOC estimation method and system based on an optimal adaptive non-linear observer.

A power battery SOC estimation method based on a non-linear observer comprises the following steps:

performing an intermittent discharge-standing experiment on the power battery, and fitting to obtain a function expression between the SOC and the OCV;

establishing an equivalent circuit model of the power battery;

determining each parameter value of the power battery equivalent circuit model;

establishing a discrete state space model of the power battery for SOC estimation;

designing an adaptive non-linear observer for estimating the SOC of the battery;

determining an objective function for parameter optimization of the adaptive nonlinear observer;

solving the optimized parameter value of the self-adaptive nonlinear observer;

and calculating the estimated value of the SOC of the battery by adopting an optimal self-adaptive nonlinear observer.

As an improvement of the technical scheme, the battery model for identifying the battery parameters adopts an n-order RC equivalent circuit model, wherein n is more than or equal to 1.

As an improvement of the technical scheme, the battery model parameters are obtained by performing a pulse discharge-standing experiment on the battery and performing off-line identification by adopting an exponential fitting method according to a corresponding voltage response curve.

As an improvement of the technical scheme, the SOC estimation method adopts an optimal self-adaptive nonlinear observer.

As an improvement of the technical solution, the value of the gain matrix of the adaptive non-linear observer is expressed by a formula:

$K=\left[\begin{array}{cccccc}{a}_{11}{k}_1& 0& 0& 0& \mathrm{...}& 0\\ 0& a_{22}{k}_2& 0& 0& \mathrm{...}& 0\\ .& .& .& .& \mathrm{...}& .\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ 0& 0& \mathrm{...}& a_{ii}{k}_i& \mathrm{...}& 0\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ 0& 0& 0& 0& \mathrm{...}& (a_{11}+a_{22}+...+a_{nn})k_{n+1}\end{array}\right]$

wherein, a_ij(i, j ═ 1, 2.. times, n) is the ith row and j column elements of the coefficient matrix in the battery system state space model;

k_i＝α_i(0.1+|e_y|/(|e_y|+0.1)(i＝1,2,...,n+1)，α_iis a proportionality coefficient (real number greater than zero), e_yIs the systematic observation error.

As an improvement of the technical scheme, the proportional coefficient of the gain matrix of the optimal adaptive nonlinear observer is optimally designed by adopting a group intelligent search algorithm.

As an improvement of the technical scheme, the swarm intelligent search algorithm for the optimal design of the gain matrix proportionality coefficient of the optimal adaptive non-linear observer adopts a particle swarm optimization algorithm.

Further, the optimal design of the gain matrix scaling factor of the optimal adaptive non-linear observer is aimed at minimizing the absolute average error between the estimated battery SOC value and the reference value under the test condition, wherein:

objective function minZ (α)₁,α₂,α₃)＝mean(abs(SOC_r-SOC_e))

Where mean (-) is the averaging function, abs (-) is the absolute value function, SOC_rIs a reference value of SOC (usually obtained by ampere-hour integration), SOC_eIs an estimate of the SOC.

An optimal adaptive non-linear observer-based power battery SOC estimation method is shown in FIG. 1, and includes the following steps:

s101, performing intermittent discharge-standing experiment on the power battery at room temperature, and fitting a relational expression (noted as OCV ═ f) of SOC-OCV according to obtained experimental data_ocv(SOC)) and as a reference for finding OCV values corresponding to different SOC values in the SOC estimation process.

As an embodiment of the present invention, the SOC-OCV relational expression obtained by fitting a 5 th order polynomial is:

OCV＝f_ocv(SOC)

＝12.5801×SOC⁵-35.3081×SOC⁴+36.3924×SOC³-16.7012×SOC²+4.0110×SOC+3.2030

and S102, establishing an equivalent circuit model of the power battery.

Common battery models include: the system comprises an electrochemical model, a neural network model, an equivalent circuit model and the like, wherein the equivalent circuit model is more suitable for battery parameter identification and SOC estimation. As an embodiment of the present invention, fig. 2 shows a second-order equivalent circuit model of the battery, which is easily known from fig. 2:

$V_t=V_{oc}-R_o{I}_t-R_{p1}{I}_t{e}^{-t/{\mathrm{\τ}}_1}-R_{p2}{I}_t{e}^{-t/{\mathrm{\τ}}_2}---\left(1\right)$

wherein, I_tRepresenting the battery terminal current, R_oIndicating the ohmic internal resistance, R_p1And C_p1Respectively showing the internal resistance and capacitance of active polarization, R_p2And C_p2Respectively representing concentration polarization internal resistance and concentration polarization capacitance, tau₁＝R_p1C_p1，τ₂＝R_p2C_p2。

S103, determining each parameter value of the equivalent circuit model of the power battery.

As an embodiment of the present invention, a pulse discharge-standing experiment was performed on the battery using a 2C constant current value at an SOC of 70%, the voltage response during the period was recorded, and each parameter value of the battery equivalent circuit model was identified by an exponential fitting method based on the obtained voltage response curve (fig. 3). The principle and method of use of exponential function fitting is briefly as follows:

(1) it is apparent that the AB segment in the voltage response curve shown in fig. 3 can be expressed as an exponential function as follows:

$V_t=k_0-k_1{e}^{-{\mathrm{\λ}}_{1}t}-k_2{e}^{-{\mathrm{\λ}}_{2}t}---\left(2\right)$

(2) comparing equations (1) and (2) yields:

$R_{p1}=\frac{k_1}{I_t},R_{p2}=\frac{k_2}{I_t},C_{p1}=\frac{1}{{\mathrm{\λ}}_1{R}_{p1}},C_{p2}=\frac{1}{{\mathrm{\λ}}_2{R}_{p2}}$

(3) in addition, the battery model parameter R_oCan be obtained from the following equation:

$R_o=\frac{\mathrm{\Δ}V}{I_t}$

and S104, establishing a discrete state space model of the power battery for SOC estimation.

As an embodiment of the present invention, a discrete state space model for battery SOC estimation is established according to the battery second-order RC equivalent circuit model shown in fig. 2 as follows:

the state equation is as follows: x (k +1) ═ ax (k) + Bu (k +1)

An output equation: y (k) ═ h (x (k)) + Du (k)

Wherein u ═ I_t，x＝[x₁x₂x₃]^T＝[V_cp1V_cp2SOC]^T，y＝V_t，

$A=\left[\begin{array}{ccc}1-\frac{T_s}{R_{p1}{C}_{p1}}& 0& 0\\ 0& 1-\frac{T_s}{R_{p2}{C}_{p2}}& 0\\ 0& 0& 1\end{array}\right],B=\left[\begin{array}{c}\frac{T_s}{C_{p1}}\\ \frac{T_s}{C_{p2}}\\ -\frac{T_s}{Q_n}\end{array}\right],$

h(x)＝V_oc-V_cp1-V_cp2＝f_ocv(SOC)-V_cp1-V_cp2，D＝R_o。

Wherein, I_tRepresenting the battery terminal current, R_oIndicating the ohmic internal resistance, R_p1And C_p1Respectively showing the internal resistance and capacitance of active polarization, R_p2And C_p2Respectively representing concentration polarization internal resistance and concentration polarization capacitance, V_cp1Represents the polarization capacitance C_p1Terminal voltage of, V_cp2Represents the polarization capacitance C_p2Terminal voltage of, V_ocRepresents an open circuit voltage, f_OCV(. Q) is a non-linear functional relationship between OCV and SOC of the battery_nFor rated capacity of battery, T_sIs the sampling period of the current and voltage.

And S105, designing an adaptive non-linear observer for estimating the SOC of the battery.

According to the discrete state space model of the battery system established in step S104, the estimated values of the state variables and the output variables are calculated as follows:

state estimation value:

$\hat{x}(k+1)=A\hat{x}\left(k\right)+Bu(k+1)+K(k+1){\stackrel{\·}{h}}^T\left(\hat{x}\right(k\left)\right)\left(y\right(k)-\hat{y}(k\left)\right)$

outputting an estimated value: $\hat{y}\left(k\right)=h\left(\hat{x}\right(k\left)\right)+Du\left(k\right)$

wherein,andrespectively representing the estimated values of the state variable and the output variable,representing the first derivative of the non-linear function matrix h.

The value of the gain matrix K of the nonlinear observer must satisfy the following conditions:

A^TK^-1+K^-1A＝-Q

wherein, the matrix Q must satisfy the condition: (1) the rank is the same as A; (2) all eigenvalues are greater than zero, from which the matrices K and K are known^-1Are all positive definite matrices. As an embodiment of the present invention, the order of the equation of the system state space is 3 (i.e. there are 3 state variables), so the value of K has the following form:

$K=\left[\begin{array}{ccc}{a}_{11}{k}_1& 0& 0\\ 0& a_{22}{k}_2& 0\\ 0& 0& (a_{11}+a_{22})k_3\end{array}\right]$

wherein, a_ijI row and j column element of A, k_i＝α_i(0.1+|e_y|/(|e_y|+0.1)(i＝1,2,3)，α_iFor real numbers greater than zero, the value is chosen according to the actual application, e_yIndicating an output error.

And S106, determining an objective function for parameter optimization of the adaptive nonlinear observer.

The objective of the adaptive nonlinear observer parameter optimization is defined as finding α where the absolute mean error of the SOC estimate from the reference is minimized₁，α₂And α₃The objective function can then be expressed as:

minZ(α₁,α₂,α₃)＝mean(abs(SOC_r-SOC_e))

where mean (-) is the averaging function, abs (-) is the absolute value function, SOC_rIs a reference value of SOC (usually obtained by ampere-hour integration), SOC_eIs an estimate of the SOC.

And S107, solving the optimized parameter value of the self-adaptive nonlinear observer.

As one of the present inventionFIG. 4 shows an embodiment of solving α using Particle Swarm Optimization (PSO) algorithm₁，α₂And α₃Fig. 5 shows a convergence process of the global optimum value (i.e. the absolute average error between the SOC estimation value and the reference value).

And S108, calculating the estimated value of the SOC of the battery by adopting an optimal self-adaptive nonlinear observer.

FIG. 8 and FIG. 9 show the estimation of battery SOC under the conditions of FIG. 6 and FIG. 7, where the parameters of the optimal adaptive non-linear observer (α) are shown as an example of the present invention₁＝20.0，α₂5.9 and α₃1.3) is obtained by the step S106, and the parameter (α) of the non-linear observer is adapted₁＝10.0，α₂5.0 and α₃1.0) is set artificially.

To facilitate a better understanding and appreciation of the relevant methods of the present invention by those skilled in the art, the basic principles of Particle Swarm Optimization (PSO) algorithms are now set forth as follows:

the PSO algorithm has the advantages of small calculated amount, high convergence speed, strong global optimization capability and the like. In the PSO algorithm, the potential solution of each optimization problem is considered as a point on the D-dimensional search space called a "particle", and all particles have an adaptive value determined by the objective function and a velocity that determines their flight direction and distance. Suppose the position of a particle is denoted x_i＝(x_i1,x_i2,…,x_iD)^TVelocity is denoted by v_i＝(v_i1,v_i2,…,v_iD)^TThe update rule of speed and position is as follows:

and (3) updating the speed: $v_{id}^{k+1}={\mathrm{wv}}_{id}^k+c_1{r}_1(p_{id}^k-x_{id}^k)+c_2{r}_2(g_d^k-x_{id}^k)$

and (3) updating the position: $x_{id}^{k+1}=x_{id}^k+v_{id}^{k+1}$

wherein, i ∈ [1, m]M is the number of particles, D ∈ [1, D]D is the dimension of the solution vector; k is the number of iterations;andrespectively representing the position, the speed and the d-dimension component of the individual optimal value of the particle i in the k-th iteration;d-dimension component of the global optimal value g at the k-th iteration; c. C₁And c₂For learning factors, used to adjust the maximum step size for flying to the individual and global optima, respectively, usually take c₁＝c₂2.0; w is the inertial weight and generally ranges from 0.4 to 0.9]；r₁And r₂Is [0,1 ]]A random number in between. To improve the search performance of the algorithm, one can adoptWith linearly decreasing inertial weight:

$w=w_{max}-\frac{k}{k_{max}}(w_{max}-w_{\min })$

wherein, w_maxAnd w_minThe upper limit and the lower limit of the inertia weight are respectively set as 0.9 and 0.4, k is the current iteration number, k is_maxIs the maximum number of iterations.

Corresponding to the above method embodiment, an embodiment of the present invention further provides a power battery SOC estimation system based on an optimal adaptive non-linear observer, and as shown in fig. 10, the power battery SOC estimation system may include:

the power battery SOC-OCV relational expression determining module 201 is configured to determine a functional relational expression between SOC and OCV required for calculating a current Open Circuit Voltage (OCV) value according to a current SOC value in a battery SOC estimation process.

The power battery system equivalent circuit model establishing module 202 is used for establishing a battery system equivalent circuit model for offline battery parameter identification, and comprises the selection of the type of the equivalent circuit model and the derivation of a voltage response function.

The power battery system equivalent circuit model parameter determining module 203 is used for identifying parameter values of the battery equivalent circuit model in an off-line mode, and comprises the steps of collecting a battery pulse discharge-standing voltage response curve, selecting a voltage response curve fitting function, deducing a relational expression between battery parameters and voltage response curve fitting function coefficients, selecting a battery parameter identification method, identifying the battery parameters and the like.

The discrete state space model determining module 204 for estimating the SOC of the power battery is used for establishing a discrete state space equation for estimating the SOC of the battery, and the discrete state space equation comprises selection of system state variables and output variables, derivation of a state equation and an output equation, discretization of the state equation and the output equation and the like.

The adaptive nonlinear observer design module 205 for power battery SOC estimation is configured to establish a mathematical model of an adaptive nonlinear observer for power battery SOC estimation and derive an expression of a gain matrix of the adaptive nonlinear observer.

And the adaptive nonlinear observer parameter optimization objective function determination module 206 is configured to establish an adaptive nonlinear observer parameter optimization objective function.

The particle swarm optimization algorithm solving module 207 for the optimization parameters of the adaptive nonlinear observer is used for solving the optimization parameters of the adaptive nonlinear observer by adopting the particle swarm optimization algorithm, and comprises the initialization of the parameters of the particle swarm optimization algorithm, the updating of the flight speed and the position of the particles, the calculation of the current fitness value, the updating of the optimal position of the individual particles, the updating of the optimal position of the particle swarm, and the like.

And the optimal nonlinear observer-based power battery SOC estimation module 208 is used for calculating an estimated value of the battery SOC through the designed optimal adaptive nonlinear observer, wherein the estimated value comprises the real-time acquisition of the battery voltage and current, the calculation of an observation error, the updating of a state variable and an output variable and the like.

While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (9)

1. A power battery SOC estimation method based on a nonlinear observer is characterized by comprising the following steps:

performing an intermittent discharge-standing experiment on the power battery, and fitting to obtain a function expression between the SOC and the OCV;

establishing an equivalent circuit model of the power battery;

determining each parameter value of the power battery equivalent circuit model;

establishing a discrete state space model of the power battery for SOC estimation;

designing an adaptive non-linear observer for estimating the SOC of the battery;

determining an objective function for parameter optimization of the adaptive nonlinear observer;

solving the optimized parameter value of the self-adaptive nonlinear observer;

and calculating the estimated value of the SOC of the battery by adopting an optimal self-adaptive nonlinear observer.

2. The nonlinear observer-based power battery SOC estimation method according to claim 1, wherein: the battery model for battery parameter identification adopts an n-order RC equivalent circuit model, wherein n is more than or equal to 1.

3. The nonlinear observer-based power battery SOC estimation method according to claim 2, characterized in that: the battery model parameters are obtained by performing a pulse discharge-standing experiment on the battery and performing off-line identification by adopting an exponential fitting method according to a corresponding voltage response curve.

4. The nonlinear observer-based power battery SOC estimation method according to claim 3, wherein: the SOC estimation method adopts an optimal self-adaptive nonlinear observer.

5. The nonlinear observer-based power battery SOC estimation method according to claim 4, wherein: the value of the gain matrix of the adaptive nonlinear observer is expressed by a formula as follows:

$K=\left[\begin{array}{cccccc}{a}_{11}{k}_1& 0& 0& 0& ...& 0\\ 0& a_{22}{k}_2& 0& 0& ...& 0\\ \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}\\ 0& 0& ...& a_{ii}{k}_i& ...& 0\\ \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}\\ 0& 0& 0& 0& ...& (a_{11}+a_{22}+...+a_{nn})k_{n+1}\end{array}\right]$

wherein, a_ij(i, j ═ 1, 2.. times, n) is the ith row and j column elements of the coefficient matrix in the battery system state space model;

k_i＝α_i(0.1+|e_y|/(|e_y|+0.1)(i＝1,2,...,n+1)，α_iis a proportionality coefficient (real number greater than zero), e_yIs the systematic observation error.

6. The nonlinear observer-based power battery SOC estimation method according to claim 5, wherein: and the proportional coefficient of the gain matrix of the optimal adaptive nonlinear observer is optimally designed by adopting a group intelligent search algorithm.

7. The nonlinear observer-based power battery SOC estimation method according to claim 6, wherein: the swarm intelligent search algorithm for the optimal design of the gain matrix proportionality coefficient of the optimal adaptive non-linear observer adopts a particle swarm optimization algorithm.

8. The nonlinear observer-based power battery SOC estimation method according to claim 7, wherein: the optimal design of the gain matrix proportionality coefficient of the optimal adaptive nonlinear observer aims at minimizing the absolute average error between the battery SOC estimated value and the reference value under the test working condition, wherein:

objective function minZ (α)₁,α₂,α₃)＝mean(abs(SOC_r-SOC_e))

Where mean (-) is the averaging function, abs (-) is the absolute value function, SOC_rIs a reference value of SOC (usually obtained by ampere-hour integration), SOC_eIs an estimate of the SOC.

9. An optimal adaptive nonlinear observer-based power battery SOC estimation system is characterized by comprising:

the power battery SOC-OCV relational expression determining module is used for performing intermittent discharge-standing experiments on the power battery and fitting to obtain a functional expression between the SOC and the OCV;

the power battery system equivalent circuit model building module is used for building an equivalent circuit model of the power battery;

the power battery system equivalent circuit model parameter determination module is used for determining each parameter value of the power battery equivalent circuit model;

the power battery SOC estimation discrete state space model determining module is used for establishing a discrete state space model of the power battery for SOC estimation;

the adaptive nonlinear observer design module for the SOC estimation of the power battery is used for designing an adaptive nonlinear observer for the SOC estimation of the battery;

the adaptive nonlinear observer parameter optimization objective function determination module is used for determining an objective function for parameter optimization of the adaptive nonlinear observer;

the particle swarm optimization algorithm solving module is used for solving the optimized parameter value of the adaptive nonlinear observer;

and the power battery SOC estimation module based on the optimal nonlinear observer is used for calculating the estimation value of the battery SOC by adopting the optimal adaptive nonlinear observer.