CN111308371A - Lithium ion battery state of charge estimation method - Google Patents

Lithium ion battery state of charge estimation method Download PDF

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CN111308371A
CN111308371A CN201911199741.6A CN201911199741A CN111308371A CN 111308371 A CN111308371 A CN 111308371A CN 201911199741 A CN201911199741 A CN 201911199741A CN 111308371 A CN111308371 A CN 111308371A
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state
battery
charge
estimation
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袁涛
陈湘晖
危棋
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Hunan Haibo Ruide Electronic Intelligence Control Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator

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Abstract

The invention provides a lithium ion battery state of charge estimation method, which comprises the following steps: establishing an equivalent battery model, and identifying the first-order Thevenin battery equivalent model parameters for the first time through a genetic algorithm and a state of charge estimation algorithm; performing function linearization on the first-order Thevenin battery equivalent model by a weighted statistical linear regression method; estimating the state of charge estimation of the battery by a standard Kalman filtering algorithm and a weighted statistical linear regression method; determining an initial state of charge value of the battery by an open circuit voltage method; measuring the accumulated use electric quantity of the battery in the running state by an Ah metering method; compensating the initial state of charge value and the accumulated used electric quantity according to the state of charge value; and determining the current state of charge estimation of the battery according to the compensated initial state of charge value and the accumulated used electric quantity. The method has higher operation efficiency and estimation precision for estimating the state of charge of the battery.

Description

Lithium ion battery state of charge estimation method
Technical Field
The invention relates to the technical field of new energy batteries, in particular to a lithium ion battery state of charge estimation method.
Background
With the shortage of primary energy sources and the problem of environmental pollution becoming more and more serious all over the world, the replacement of fossil fuel automobiles by electric automobiles has become a major trend. A high-efficiency, stable and safe power battery management system is a key technology for electric vehicle development. However, the battery management technology is still far from being mature, and particularly, the estimation aspect of the State of Charge (SOC) of the battery cannot meet the accuracy requirement.
The SOC estimation of the battery is one of the main functions of the battery management system, and the accurate SOC estimation not only can reflect the endurance mileage and give full play to the performance of the battery, but also can ensure the safety of the battery and prolong the service life. However, because the working characteristics of the lithium ion battery are nonlinear and are easily affected by factors such as current, temperature, self-aging and the like in actual work, accurate estimation of the SOC becomes a difficult point.
Disclosure of Invention
Based on the above, the invention provides a lithium ion battery state of charge estimation method, which has higher operation efficiency and estimation accuracy for estimating the battery state of charge.
In order to achieve the purpose, the invention adopts the following technical scheme:
a lithium ion battery state of charge estimation method comprises the following steps:
establishing an equivalent battery model, and identifying the first-order Thevenin battery equivalent model parameters for the first time through a genetic algorithm and a state of charge estimation algorithm;
performing secondary identification on the first-order Thevenin battery equivalent model parameters through a least square algorithm and a state of charge estimation algorithm;
comparing the parameter values obtained by the first identification and the second identification, and ensuring that the difference value of the parameter values obtained by the first identification and the second identification is within a preset value range;
performing function linearization on the first-order Thevenin battery equivalent model by a weighted statistical linear regression method; estimating the state of charge estimation of the battery by a standard Kalman filtering algorithm and a weighted statistical linear regression method;
determining an initial state of charge value of the battery by an open circuit voltage method; measuring the accumulated use electric quantity of the battery in the running state by an Ah metering method; compensating the initial state of charge value and the accumulated used electric quantity according to the state of charge value;
and determining the current state of charge estimation of the battery according to the compensated initial state of charge value and the accumulated used electric quantity.
The further improvement of the scheme is as follows:
the preset numerical range is 2%.
In the foregoing solution, preferably, the step of estimating the state of charge value of the battery by using a standard kalman filter algorithm and a weighted statistical linear regression method includes:
state variable prediction and mean square estimation error prediction are realized through a standard Kalman filtering method; constructing and obtaining a set number of sampling points according to the state variables and the predicted value of the mean square estimation error;
carrying out nonlinear propagation on the sampling points according to an observation equation to obtain new sampling points;
and according to the new sampling points and the corresponding weights, realizing the prediction of the state of charge of the observation variables to obtain the estimated value of the state of charge.
In the foregoing solution, preferably, the first-order thevenin battery equivalent model parameters include an open-circuit voltage, a terminal current, and a resistance.
According to the scheme, the lithium ion battery state of charge estimation method disclosed by the invention has the advantages that the battery equivalent model parameters are identified by adopting a genetic algorithm and a least square algorithm, and the two algorithms are matched with the state of charge estimation algorithm in the actual identification process. The genetic algorithm realizes parameter initial value identification, the least square algorithm realizes parameter secondary identification, and comparison is carried out simultaneously, so that the maximum error value is ensured to be within 2%. This improves the accuracy of identification.
The method has the advantages that the model function linearization is realized by adopting a weighted statistical linear regression method, the algorithm has higher operation efficiency and estimation precision than a Kalman filtering algorithm based on the linear characteristic of a battery model state equation and the combination mode of the standard Kalman filtering algorithm and the weighted statistical linear regression method, the problems of inaccurate initial values and accumulative errors of an open-circuit voltage method and an Ah metering algorithm can be compensated, and the estimated charge state is more accurate.
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FIG. 1 is a schematic flow chart of a method for estimating a state of charge of a lithium ion battery according to an embodiment of the present invention;
fig. 2 is a system block diagram illustrating a method for estimating a state of charge of a lithium ion battery according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1-2, the embodiment of the present invention first illustrates the structure and principle of the module of the present invention in a particularly preferred embodiment.
The State Of Charge (SOC) Of the power battery is an important secondary quantity that cannot be directly measured by the battery, reflects the remaining available energy Of the battery, and is an important parameter Of an energy optimization algorithm Of the electric vehicle, so the SOC estimation algorithm is also one Of the key technologies Of the battery management system Of the electric vehicle. The charge state of the battery is accurately estimated, the overcharge or the overdischarge of the battery can be effectively prevented, and the service life of the battery is prolonged.
The invention researches the estimation algorithm of the state of charge by taking the most commonly used lithium ion power battery in the electric automobile as an object and taking the application of the electric automobile as a background.
Comprises the following steps:
the first step is as follows: the invention adopts a first-order Thevenin equivalent battery model with clear structure, reflects the instantaneous change of the battery voltage under the condition of current mutation by series resistance, and describes the relaxation effect of the slow change of the terminal voltage after the battery is kept stand by an RC (resistance-capacitance circuit) parallel link.
Wherein the lithium ion battery pack is equivalent to an ideal voltage source, an internal resistance and a first-order RC circuit, wherein UocIs an ideal voltage source, representing the battery open circuit voltage, as a function of the battery SOC; y iskIs terminal voltage; i.e. ikRepresenting the instantaneous terminal current of the battery at a certain moment; r1 represents the internal resistance of the battery, and represents the abrupt change characteristic of the voltage in the charging process of the battery pack; the first-order RC represents the polarization capacitance and resistance of a polar plate in the charging and discharging process of the battery and represents the gradual change characteristic of voltage.
According to kirchhoff's law, by solving differential equations, equation discretization and other operations, the following battery model expression can be obtained:
Figure BDA0002295569650000041
yk＝Uoc-R1*ik-Up,k
in the formula of Up,kRepresenting the cell polarization resistance R2Voltage of two stages, t time, c capacitance, UocThe characteristic battery open-circuit voltage R1 represents the battery internal resistance, X represents a random variable, and Q represents a triode.
The second step is that: and identifying the parameters of the battery equivalent model by adopting a genetic algorithm and a least square algorithm, wherein the two algorithms are matched with a state of charge estimation algorithm in the actual identification process. The genetic algorithm realizes parameter initial value identification, the least square algorithm realizes parameter secondary identification, and comparison is carried out simultaneously, so that the maximum error value is ensured to be within 2%. Thus, the identification accuracy is improved as much as possible.
The parameter identification refers to a process of determining unknown data in a model through experiments or actually measured signals or data on the basis of wearing the Winan model, and parameters are related parameters in the Winan model. The above processes are accuracy rates for realizing model parameter identification, so that the model fitting effect is identical with the actual working condition.
The third step: the method adopts a better Kalman filtering algorithm to realize the estimation of the state of charge of the battery, and adopts a weighted statistical linear regression method to realize the linearization of a model function, wherein the linearization lays a foundation for building the solution of a state equation of a battery model. Based on the linear characteristic of a state equation of the battery model, the algorithm has higher operation efficiency and estimation precision than the Kalman filtering algorithm by combining the standard Kalman filtering algorithm with a linear regression method based on weighted statistics. The basic principle is as follows: when the automobile is started, the battery enters a discharging state from a standing state, and the battery terminal voltage is close to the battery electromotive force at the moment, so that the initial state of charge value is determined by adopting an open-circuit voltage method in the method of the embodiment; when the electric automobile is in a running state, the power battery is in a discharging state, the accumulated calculation of the used battery power is realized by adopting an Ah metering method, and meanwhile, the influences of the environmental temperature and the charge-discharge rate on the charge state are also considered and corrected if necessary; in order to solve the problems of inaccurate initial values and accumulative errors of an open-circuit voltage method and an Ah metering algorithm, the combined filtering algorithm is adopted for repairing.
The combined filtering algorithm combines the Kalman filtering algorithm based on the weighted statistical linear regression method and the standard Kalman filtering algorithm, and greatly reduces the operation amount on the premise of not influencing the estimation precision. When the weighted statistical linear regression method is applied to SOC estimation of the lithium battery of the electric automobile, the statistical linearization process of the battery state composite model is as follows:
1. according to statistical properties (X) of the estimated statei,Px) Generating the discrete sampling points ξ with the same statistical properties as the estimated stateiAnd corresponding first order statistical characteristic weight coefficient Wi mAnd a second order statistical characteristic weight coefficient Wi c
2. Calculating the result r of the sampling point after the propagation of the nonlinear functioniWherein L is a final value of the number of times of calculation:
Figure BDA0002295569650000051
3. calculating the posterior mean of a random variable x
Figure BDA0002295569650000052
Figure BDA0002295569650000053
4. Computing the autocovariance P of a random variable xz：
Figure BDA0002295569650000054
5. Computing the cross-covariance P of the random variable xxzWherein
Figure BDA0002295569650000055
Is the mean of x:
Figure BDA0002295569650000056
the basic derivation process after the combination of the two is as follows:
1. state variable prediction and mean square estimation error prediction are realized according to standard Kalman filtering;
2. constructing and obtaining 2n +1 sampling points by using the state variables and the predicted values of the mean square estimation errors;
3. carrying out nonlinear propagation on the sampling points according to an observation equation so as to obtain new sampling points;
4. and according to the new sampling point and the corresponding weight, realizing the prediction of the SOC of the observation variable.
By the method, the conversion efficiency of the battery is determined by introducing the ambient temperature proportional factor and the charging rate proportional factor at the same time, so that the aim of correction is fulfilled.
As shown in FIG. 2, an embodiment method of the present invention may be implemented in a systematic manner. The system mainly comprises a current integration module, an open-circuit voltage look-up table correction module and an auxiliary battery model estimation module. The current integration module is realized through a hardware circuit, provides accurate data for calculating the state of charge, and can compensate the estimated SOC to a certain extent according to the discharge rate and the battery temperature; under the condition of enough standing time, an open-circuit voltage table look-up correction module is used, and the module is based on a battery equivalent model and then calculates parameters such as total battery pressure, SOC and the like through open-circuit voltage and battery internal resistance; the auxiliary battery model estimation module mainly adopts an algorithm superior to Kalman filtering to realize the estimation of the state of charge of the battery, thereby improving the operation efficiency and the estimation precision.
According to the scheme, the battery state of charge estimation method for the lithium ion battery adopts the genetic algorithm and the least square algorithm to identify the battery equivalent model parameters, and the two algorithms need to be matched with the state of charge estimation algorithm in the actual identification process. The genetic algorithm realizes parameter initial value identification, the least square algorithm realizes parameter secondary identification, and comparison is carried out simultaneously, so that the maximum error value is ensured to be within 2%. This improves the accuracy of identification.
The method is characterized in that a weighted statistical linear regression method is adopted to realize model function linearization, and the algorithm has higher operation efficiency and estimation precision than a Kalman filtering algorithm by combining a standard Kalman filtering algorithm and the method based on the weighted statistical linear regression method based on the linear characteristic of a state equation of a battery model.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (4)

1. A lithium ion battery state of charge estimation method is characterized by comprising the following steps:
establishing an equivalent battery model, and identifying the first-order Thevenin battery equivalent model parameters for the first time through a genetic algorithm and a state of charge estimation algorithm;
performing secondary identification on the first-order Thevenin battery equivalent model parameters through a least square algorithm and a state of charge estimation algorithm;
comparing the parameter values obtained by the first identification and the second identification, and ensuring that the difference value of the parameter values obtained by the first identification and the second identification is within a preset value range;
performing function linearization on the first-order Thevenin battery equivalent model by a weighted statistical linear regression method; estimating the state of charge estimation of the battery by a standard Kalman filtering algorithm and a weighted statistical linear regression method;
determining an initial state of charge value of the battery by an open circuit voltage method; measuring the accumulated use electric quantity of the battery in the running state by an Ah metering method; compensating the initial state of charge value and the accumulated used electric quantity according to the state of charge value;
and determining the current state of charge estimation of the battery according to the compensated initial state of charge value and the accumulated used electric quantity.
2. The method of claim 1, wherein the predetermined value range is 2%.
3. The method of claim 1, wherein the step of estimating the state of charge of the battery by a standard kalman filter algorithm and a weighted statistical linear regression method comprises:
state variable prediction and mean square estimation error prediction are realized through a standard Kalman filtering method; constructing and obtaining a set number of sampling points according to the state variables and the predicted value of the mean square estimation error;
carrying out nonlinear propagation on the sampling points according to an observation equation to obtain new sampling points;
and according to the new sampling points and the corresponding weights, realizing the prediction of the state of charge of the observation variables to obtain the estimated value of the state of charge.
4. The method of claim 1, wherein the first order Thevenin battery equivalent model parameters comprise open circuit voltage, terminal current, and resistance.
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Application publication date: 20200619