CN105093114A - Battery online modeling and state of charge combined estimating method and system - Google Patents

Abstract

The invention relates to a method and system for jointly estimating battery on-line modeling and state of charge, wherein the method includes: using a threshold model to perform piecewise linearization of the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model and can be mapped to the segmental linearization relationship between the battery terminal voltage and the state of charge SOC; only using the battery terminal voltage and current data obtained from online measurement, an autoregressive moving average model is established in each segmental interval; the obtained The autoregressive moving average model is transformed into the battery model described by the corresponding state space, and a state observer is constructed to estimate the state of charge as a state variable. Sliding time window, collect the next set of battery terminal voltage and current data to participate in the calculation. The method provided by the invention can perform online calculation of the model parameters and state of charge of the lithium-ion battery at any time with high accuracy and is easy to implement.

Description

A method and system for joint estimation of battery online modeling and state of charge

technical field

The invention relates to a method and system for jointly estimating battery online modeling and state of charge, and belongs to the technical field of lithium-ion battery management.

Background technique

In order to solve the problems of energy security and environmental pollution, in recent years, electric vehicles have achieved rapid development under the promotion of governments and automobile manufacturers. As the main energy carrier and power source of electric vehicles, batteries and their management systems are one of the core technologies of electric vehicles. Among them, lithium-ion batteries are widely used due to their high energy ratio, low self-discharge rate, no memory effect, high operating voltage platform, long service life and low manufacturing cost. And supporting it, lithium-ion power battery management system (BMS) has also been widely valued and researched and applied.

The core function of BMS is to effectively manage and control the working state of the battery by accurately tracking the dynamic behavior of the battery, which requires the establishment of a mathematical model that accurately describes the dynamic behavior of the battery. From the perspective of economical, safe and rational use of power batteries for electric vehicles, it is more critical to use battery model parameters to estimate the state of charge (SOC) of power batteries. In recent years, with the development of batteries, battery model identification methods emerge in an endless stream.

In the commercial application of electric vehicles, the high price of batteries is the main reason hindering its rapid promotion. People are looking for better battery packs to make full use of battery power and reduce battery costs. The grouping of batteries is mainly related to the consistency of batteries, which mainly depends on the identification efficiency of battery parameters and the improvement of SOC estimation accuracy, which will help the rational use of batteries and prolong the practical life of batteries. Therefore, it is necessary to find an accurate, fast and online method to obtain battery parameters and SOC. The present invention provides a lithium-ion power battery modeling and SOC joint estimation method, which just meets the above requirements.

Contents of the invention

The technical problem to be solved by the present invention is to provide a method and system for joint estimation of battery online modeling and state of charge, which is used to obtain battery parameters and SOC at the same time, and to achieve accurate and accurate battery parameters. Fast, online identification and accurate estimation of SOC.

The technical solution of the present invention to solve the above-mentioned technical problems is as follows: a method for joint estimation of battery online modeling and state of charge, specifically comprising the following steps:

Step 1: Collect battery terminal voltage value data and battery terminal current value data within the current time window;

Step 2: Divide the value range according to different voltage value data to obtain multiple segment intervals, establish an autoregressive moving average model for each segment interval, convert the autoregressive moving average model into a battery model, and identify battery model parameters ;

Step 3: Construct a state observer, estimate the state of charge SOC as a state variable, and obtain the estimated value of the state of charge SOC;

Step 4: Determine whether there is a time window that has not collected data, if yes, slide to obtain the next time window, use the obtained time window as the current time window, and perform step 1; otherwise, perform step 5;

Step 5: Complete the online modeling and state-of-charge estimation of the lithium-ion battery battery model.

The time window used in the present invention is a fixed time window. Taking data collected once per second as an example, 500 data collection points are used as the width of the time window, but it is not limited to this.

The beneficial effect of the present invention is: the present invention is used for obtaining battery parameter and SOC simultaneously, and realizes the accurate, quick, on-line identification of battery parameter and the accurate estimation of SOC; The model parameter and state of charge of lithium-ion battery can be checked online at any time Both have high precision and are easy to implement.

On the basis of the above technical solutions, the present invention can also be improved as follows.

Further, the step 2 specifically includes the following steps:

Step 2.1: Carry out value range division according to different voltage value data to obtain multiple segment intervals, and establish an autoregressive moving average model for each segment interval using the battery terminal voltage value data and battery terminal current value data obtained by online measurement;

Step 2.2: Transform the autoregressive moving average model into the battery model described by the corresponding state space, and identify the battery model parameters.

Further, the step 3 specifically includes the following steps:

Step 3.1: Use the threshold model to perform piecewise linearization of the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model, and map it to the piecewise linearization relationship between the battery terminal voltage and the state of charge SOC;

Step 3.2: Construct a state observer according to the linear relationship in the battery model, estimate the state of charge SOC as a state variable, and obtain the estimated value of the state of charge SOC.

Further, the key to segmentally linearize the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model by using the threshold model is to linearize the open circuit voltage OCV and the state of charge SOC of the lithium-ion battery Model parameters, to determine the battery model parameters.

Further, the determination of the next time window may scale the length of the time window according to the degree of nonlinearity.

The technical solution of the present invention to solve the above-mentioned technical problems is as follows: a battery online modeling and charge state joint estimation system, including an acquisition module, a battery model module, a state variable estimation module and a judgment module;

The collection module is used to collect battery terminal voltage value data and battery terminal current value data within the current time window;

The battery model module is used to divide the value range according to different voltage value data to obtain multiple segment intervals, establish an autoregressive moving average model for each segment interval, convert the autoregressive moving average model into a battery model, and Identify battery model parameters;

The estimation module is used for constructing a state observer, estimating the state of charge SOC as a state variable, and obtaining an estimated value of the state of charge SOC;

The judging module is used to judge whether there is a time window for which data has not been collected, and if so, slide to obtain the next time window, use the obtained time window as the current time window, and collect the next set of battery terminal voltage and current data to participate in the calculation; otherwise , to complete the online modeling and state of charge estimation of the lithium-ion battery battery model.

The beneficial effect of the present invention is: the present invention is used for obtaining battery parameter and SOC simultaneously, and realizes the accurate, quick, on-line identification of battery parameter and the accurate estimation of SOC; The model parameter and state of charge of lithium-ion battery can be checked online at any time Both have high precision and are easy to implement.

On the basis of the above technical solutions, the present invention can also be improved as follows.

Further, the battery model module includes a modeling module and a model conversion module;

The modeling module is used to divide the value range according to different voltage value data to obtain a plurality of segmented intervals, and to establish an auto-regression for each segmented interval using the battery terminal voltage value data and battery terminal current value data obtained by online measurement moving average model;

The model conversion module converts the autoregressive moving average model into a battery model described by the corresponding state space, and identifies battery model parameters.

Further, the state variable estimation module includes a linearization module and an estimated value module;

The linearization module is used to linearize the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model by using the threshold model, and can be mapped to a segmented relationship between the battery terminal voltage and the state of charge SOC Linearization relationship;

The estimated value module is used to construct a state observer according to the linear relationship in the battery model, estimate the state of charge SOC as a state variable, and obtain an estimated value of the state of charge SOC.

Further, the key to segmentally linearize the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model by using the threshold model is to linearize the open circuit voltage OCV and the state of charge SOC of the lithium-ion battery Model parameters, to determine the battery model parameters.

Further, the determination of the next time window may scale the length of the time window according to the degree of nonlinearity.

Description of drawings

Fig. 1 is a flow chart of a joint estimation method for battery online modeling and state of charge according to the present invention;

Fig. 2 is a schematic diagram of the lithium ion power battery modeling and SOC joint estimation method of the present invention;

Fig. 3 is the battery model equivalent circuit diagram of the present invention;

Fig. 4 is the SOC estimation result under the FUDS working condition of the present invention;

Fig. 5 is the SOC estimation error under the FUDS working condition of the present invention;

Fig. 6 is a structural block diagram of a battery online modeling and state of charge joint estimation system according to the present invention.

In the accompanying drawings, the list of parts represented by each label is as follows:

1. Acquisition module, 2. Battery model module, 3. State variable estimation module, 4. Judgment module, 21. Modeling module, 22. Model conversion module, 31. Linearization module, 32. Estimated value module.

Detailed ways

The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

As shown in Figure 1, it is a method for joint estimation of battery online modeling and state of charge according to the present invention, which specifically includes the following steps:

Step 1: Collect battery terminal voltage value data and battery terminal current value data within the current time window;

Step 2: Divide the value range according to different voltage value data to obtain multiple segment intervals, establish an autoregressive moving average model for each segment interval, convert the autoregressive moving average model into a battery model, and identify battery model parameters ;

Step 3: Construct a state observer, estimate the state of charge SOC as a state variable, and obtain the estimated value of the state of charge SOC;

Step 4: Determine whether there is a time window that has not collected data, if yes, slide to obtain the next time window, use the obtained time window as the current time window, and perform step 1; otherwise, perform step 5;

Step 5: Complete the online modeling and state-of-charge estimation of the lithium-ion battery battery model.

Figure 2 is a schematic diagram of the joint estimation method of lithium-ion power battery modeling and SOC. As shown in Figure 2, a method for jointly estimating battery online modeling and state of charge according to the present invention specifically includes the following steps:

Step 1: Use the threshold model to perform piecewise linearization of the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model, and map it to the piecewise linearization relationship between the battery terminal voltage and the state of charge SOC. The relationship between the open circuit voltage (OCV) of the lithium-ion power battery and the SOC of the lithium-ion battery state of charge is: V _oc = f(SOC), f(·) is the nonlinear relationship between the open circuit voltage OCV and the SOC of the battery state of charge . It can be approximated piecewise linearly by a threshold model, namely,

λ ₁ ,...,λ _k are constant coefficients, r ₁ ,...,r _k-1 are constants representing thresholds, and k is the number of segmented regions. The piecewise linear relationship between the mapped battery terminal voltage and the state of charge SOC can be expressed as,

V and I represent the measured battery terminal voltage and current data respectively, V _p1 is the first polarization voltage in the lithium-ion battery, V _p2 is the second polarization voltage in the lithium-ion battery, and R ₀ is the polarization voltage in the lithium-ion battery A load resistance that responds to changes in current.

Step 2: In the selected time window, divide the battery terminal voltage obtained by online measurement into the value range, and only use the battery terminal voltage and current data obtained by online measurement to establish an ARMA model in each subsection. can be expressed as,

_{V t} and _{I t} represent the time series of measured battery terminal voltage and current data respectively; Expressed as the order of the model, and i=1,...k; φ _0,1 ,...,φ _0,k are the constant items of the built ARMA model; e _t,k represent the prediction error.

Step 3: Transform the ARMA model into a battery model described by the corresponding state space, and identify the battery model parameters. This embodiment utilizes the battery model equivalent circuit shown in FIG. 3 . The establishment of state space equations that can be equivalent to the ARMA models of each segmented area, and the establishment of coefficient solution equations, for the ith segmented area, the state space expression of the electrical circuit model of the battery can be established as

$\begin{array}{c}\stackrel{\&Center\; Dot;}{x}=\mathrm{AX}+\mathrm{BI}\\ V=\mathrm{CX}+\mathrm{DI}\end{array},$ in,

$\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; sd</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}& \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right]<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; =</span>{\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}& \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}& {\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right]}^{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; \&lambda;SOC</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; ,</span>$

The state X of the lithium-ion battery = [SOCV _p1 V _p2 ] ^T . It and the ARMA model have the following solution equations under the continuous system ${\mathrm{no}}_{0,i}=\frac{1}{R_{p1}{R}_{p2}{R}_{\mathrm{sd}}{C}_{p1}{C}_{p2}{C}_c},{\mathrm{no}}_{1,i}=\frac{1}{R_{p1}{R}_{\mathrm{sd}}{C}_{p1}{C}_c}+\frac{1}{R_{p2}{R}_{\mathrm{sd}}{C}_{p2}{C}_c}+\frac{1}{R_{p1}{R}_{p2}{C}_{p1}{C}_{p2}},$ ${\mathrm{no}}_{2,i}=\frac{1}{R_{\mathrm{sd}}{C}_c}+\frac{1}{R_{p1}{C}_{p1}}+\frac{1}{R_{p2}{C}_{p2}},$

$\begin{array}{c}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; sd</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; sd</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; sd</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; ,</span>\\ {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; sd</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; sd</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; ,</span>\\ <span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; sd</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; sd</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}\end{array}$

$m_{2,i}=\frac{R_0}{R_{\mathrm{sd}}{C}_c}+\frac{R_0}{R_{p1}{C}_{p1}}+\frac{R_0}{R_{p2}{C}_{p2}}+\frac{\mathrm{\&lambda;}}{C_c}+\frac{1}{C_{p1}}+\frac{1}{C_{p2}},$ m _3,i =R ₀ . Among them, R _sd is the self-discharge energy loss resistance of the lithium-ion battery, C _c is the full-capacity capacitance of the lithium-ion battery, R _p1 is the first polarization resistance in the lithium-ion battery, and R _p2 is the second polarization in the lithium-ion battery Resistance, C _p1 is the first set of polarized capacitors in the lithium-ion battery, and C _p2 is the second set of polarized capacitors in the lithium-ion battery. The form of the ARMA model for the i-th segmented region is: Using the corresponding transformation method, such as bilinear transformation etc., the continuous system can be converted into a discrete system, and then φ _j,i and θ _l,i can be established to solve equations with various internal parameters of the lithium-ion battery. The battery parameters R ₀ , R _sd , R _p1 , R _p2 , C _p1 , and C _p2 to be identified can be obtained by using corresponding numerical analysis methods, such as the Newton iterative method.

Step 4: Construct a state observer to estimate the state of charge as a state variable. The equation of state for lithium ions uses

$\begin{array}{c}\stackrel{\stackrel{<span\; class="notranslate">\; \&Center\; Dot;</span>}{<span\; class="notranslate">\; ^</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; A</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; BI</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; )</span>\\ \stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; C</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; DI</span>}\end{array}<span\; class="notranslate">\; ,</span>$

Among them, L=[ _L1L2L3 ] ^T , L ₁ is the gain coefficient of the error feedback of the first derivative of the state of charge of the lithium-ion battery; L ₂ is the error feedback of the first derivative of the polarization voltage of the lithium-ion battery The gain factor of; L ₃ is the gain factor of the error feedback amount to the first derivative of the second group of polarization voltage of the lithium-ion battery; V is the actual value of the terminal voltage of the lithium-ion battery; Estimated value for the terminal voltage of a Li-ion battery. The gain coefficient can be solved using the pole placement method or the linear quadratic method.

Step 5: Sliding the time window, collecting the next set of battery terminal voltage and current data to participate in the calculation.

Choose one LiMn ₂ O ₄ battery, the nominal voltage of the single cell is 3.6V, and the nominal capacity is 15Ah. Under the FUDS working condition, the battery model parameters are obtained by using the method, and then the SOC estimation result is shown in Figure 4, and the estimation error is shown in Figure 5.

As shown in Figure 6, it is a battery online modeling and charge state joint estimation system according to the present invention, including an acquisition module 1, a battery model module 2, a state variable estimation module 3 and a judgment module 4;

The collection module 1 is used to collect battery terminal voltage value data and battery terminal current value data within the current time window;

The battery model module 2 is used to divide the value range according to different voltage value data to obtain multiple segment intervals, establish an autoregressive moving average model for each segment interval, and convert the autoregressive moving average model into a battery model, And identify the battery model parameters;

The estimation module 3 is used to construct a state observer, estimate the state of charge SOC as a state variable, and obtain an estimated value of the state of charge SOC;

The judging module 4 is used to judge whether there is a time window for which data has not been collected, and if so, slide to obtain the next time window, use the obtained time window as the current time window, and collect the next group of battery terminal voltage and current data to participate in the calculation; Otherwise, complete the online modeling and state of charge estimation of the battery model of the lithium-ion battery.

The battery model module 2 includes a modeling module 21 and a model conversion module 22;

The modeling module 21 is used to divide the value range according to different voltage value data to obtain a plurality of segmented intervals, and for each segmented interval, the battery terminal voltage value data and battery terminal current value data obtained by online measurement are used to establish regression moving average model;

The model conversion module 22 converts the autoregressive moving average model into a battery model described by the corresponding state space, and identifies battery model parameters.

The state variable estimation module 3 includes a linearization module 31 and an estimated value module 32;

The linearization module 31 is used to linearize the non-linear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model by using the threshold model, and can be mapped to a breakdown of the battery terminal voltage and the state of charge SOC. Segment linearization relationship;

The estimated value module 32 is used to construct a state observer according to the linear relationship in the battery model, estimate the state of charge SOC as a state variable, and obtain an estimated value of the state of charge SOC.

The key to performing piecewise linearization of the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model by using the threshold model is to use the linearization model parameters of the open circuit voltage OCV and the state of charge SOC of the lithium-ion battery , to determine the battery model parameters.

The determination of the next time window may scale the length of the time window according to the strength of the nonlinearity.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection of the present invention. within range.

Claims (10)

1. A method for jointly estimating battery online modeling and state of charge, characterized in that, specifically comprising the following steps: Step 1: Collect battery terminal voltage value data and battery terminal current value data within the current time window; Step 2: Divide the value range according to different voltage value data to obtain multiple segment intervals, establish an autoregressive moving average model for each segment interval, convert the autoregressive moving average model into a battery model, and identify battery model parameters ; Step 3: Construct a state observer, estimate the state of charge SOC as a state variable, and obtain the estimated value of the state of charge SOC; Step 4: Determine whether there is a time window that has not collected data, if yes, slide to obtain the next time window, use the obtained time window as the current time window, and perform step 1; otherwise, perform step 5; Step 5: Complete the online modeling and state-of-charge estimation of the lithium-ion battery battery model. 2. A method for jointly estimating battery online modeling and state of charge according to claim 1, wherein said step 2 specifically comprises the following steps: Step 2.1: Carry out value range division according to different voltage value data to obtain multiple subsection intervals, and establish an autoregressive moving average model for each subsection interval using the battery terminal voltage value data and battery terminal current value data obtained by online measurement; Step 2.2: Transform the autoregressive moving average model into the battery model described by the corresponding state space, and identify the battery model parameters. 3. A method for jointly estimating battery online modeling and state of charge according to claim 1 or 2, wherein the step 3 specifically includes the following steps: Step 3.1: Use the threshold model to perform piecewise linearization of the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model, and map it to the piecewise linearization relationship between the battery terminal voltage and the state of charge SOC; Step 3.2: Construct a state observer according to the linear relationship in the battery model, estimate the state of charge SOC as a state variable, and obtain the estimated value of the state of charge SOC. 4. A method for jointly estimating battery online modeling and state of charge according to claim 3, characterized in that the threshold model is used to compare the difference between the open circuit voltage OCV contained in the battery model and the state of charge SOC The key to the piecewise linearization of the linear relationship is to determine the battery model parameters according to the linearization model parameters of the open circuit voltage OCV and the state of charge SOC of the lithium-ion battery. 5. A method for jointly estimating battery online modeling and state of charge according to claim 1, characterized in that the determination of the next time window can be carried out according to the degree of nonlinear strength, the length of the time window zoom. 6. A joint estimation system for battery online modeling and state of charge, comprising an acquisition module, a battery model module, a state variable estimation module and a judgment module; The collection module is used to collect battery terminal voltage value data and battery terminal current value data within the current time window; The battery model module is used to divide the value range according to different voltage value data to obtain multiple segment intervals, establish an autoregressive moving average model for each segment interval, convert the autoregressive moving average model into a battery model, and Identify battery model parameters; The estimation module is used for constructing a state observer, estimating the state of charge SOC as a state variable, and obtaining an estimated value of the state of charge SOC; The judging module is used to judge whether there is a time window for which data has not been collected, and if so, slide to obtain the next time window, use the obtained time window as the current time window, and collect the next set of battery terminal voltage and current data to participate in the calculation; otherwise , to complete the online modeling and state of charge estimation of the lithium-ion battery battery model. 7. A system for jointly estimating battery online modeling and state of charge according to claim 6, wherein the battery model module includes a modeling module and a model conversion module; The modeling module is used to divide the value range according to different voltage value data to obtain a plurality of segmented intervals, and to establish an auto-regression for each segmented interval using the battery terminal voltage value data and battery terminal current value data obtained by online measurement moving average model; The model conversion module converts the autoregressive moving average model into a battery model described by the corresponding state space, and identifies battery model parameters. 8. A system for jointly estimating battery online modeling and state of charge according to claim 6 or 7, wherein the state variable estimation module includes a linearization module and an estimated value module; The linearization module is used to linearize the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC contained in the battery model by using the threshold model, and can be mapped to a segmented relationship between the battery terminal voltage and the state of charge SOC Linearization relationship; The estimated value module is used to construct a state observer according to the linear relationship in the battery model, estimate the state of charge SOC as a state variable, and obtain an estimated value of the state of charge SOC. 9. A system for jointly estimating battery online modeling and state of charge according to claim 8, wherein the threshold model is used to compare the difference between the open circuit voltage OCV contained in the battery model and the state of charge SOC The key to the piecewise linearization of the linear relationship is to determine the battery model parameters according to the linearization model parameters of the open circuit voltage OCV and the state of charge SOC of the lithium-ion battery. 10. A joint battery online modeling and state of charge estimation system according to claim 6, characterized in that the determination of the next time window can be performed on the length of the time window according to the degree of nonlinear strength zoom.