CN115327416A - Lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering - Google Patents

Abstract

The invention discloses a lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering, which comprises the steps of establishing a Thevenin equivalent circuit model and describing the dynamic characteristics of a battery; calculating each parameter value corresponding to each sampling point in a complete working condition by an online parameter identification method of recursive least squares; and (4) combining a battery state space model, and finishing the estimation of the SOC value of the lithium ion battery by using a firefly algorithm and particle filtering. By adopting the method disclosed by the invention, the estimation precision is improved under the condition of controllable calculated quantity.

Description

Lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering

Technical Field

The invention belongs to the technical field of power battery measurement and control, and particularly relates to a lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering.

Background

In the whole life cycle of the lithium ion Battery, the monitoring and the adjustment of a Battery Management System (BMS) on a core parameter SOC will affect the effect and the safety of the emergency power output; therefore, it is very necessary to monitor the change of the parameter in real time and ensure the working performance of the lithium ion battery based on the change; because the grouped SOC estimation technology in the BMS is not mature, potential safety hazards existing in the use process seriously restrict the development of the lithium ion battery; for lithium ion batteries, reliable BMS management relies on accurate SOC values; under the condition that the value is known, reliable energy management and safety control are carried out on the lithium ion battery, the lithium ion battery is prevented from being damaged in advance, and the service life of the lithium ion battery is prolonged; therefore, the SOC value is accurately estimated, and the method is vital to guarantee the working performance, energy and safety management of the lithium ion battery; the construction and accurate estimation values of an SOC estimation model of the lithium ion battery are obtained, and become the core problem of energy and safety management of the lithium ion battery; the lithium ion battery is formed by combining lithium cobaltate battery monomers with high energy density and closed circuit voltage, and the safety of the lithium ion battery is influenced by the working state of the lithium ion battery; the SOC represents the residual capacity of the lithium ion battery and is a key parameter which is the most basic and the most important of a battery management system; in addition, the charge and discharge process of the lithium ion battery comprises the links of complicated electric energy, chemical energy, heat energy conversion and the like, the overcharge and overdischarge phenomena are easy to cause safety accidents, and the accurate SOC estimation plays an important role in preventing the overcharge and overdischarge; in the application of lithium ion batteries, the safety of the lithium ion batteries is still the most concerned, and the SOC estimation is the basis and precondition for the safe use of the lithium ion batteries.

For the necessity and urgent needs of SOC estimation of lithium ion batteries, related research institutions and universities, such as the massachusetts institute of technology, state university, southern card university, british litz university, robert university, united states department of renewable energy, leidend energy company, germany english-flying-slush science and technology company, qinghua university, beijing aerospace university, beijing university of technology, beijing university of transportation, university of tokyo, university of science and technology, and harabin industry university, etc., have developed a lot of research and have conducted intensive research on SOC estimation; many periodicals at home and abroad, such as Journal of Power Sources, applied Energy, IEEE Transactions on Power Systems, power technology and the like, establish highly targeted columns for relevant research result display; aiming at the problem of SOC estimation of the lithium ion battery, relevant research workers at home and abroad make great research progress at present; as described in Hu et al, there are currently mainly an Ampere-hour integration method (Ah), an Open Circuit Voltage method (OCV), kalman filtering and its extended algorithm, a Particle Filter method (PF), a Neural Network method (NN), and the like; due to the influence of various factors such as charging and discharging current, temperature, internal resistance, self-discharge, aging and the like, the performance change of the lithium ion battery can obviously influence the SOC estimation precision, and a universal method for realizing the accurate estimation of the SOC value is not available; in addition, the consistency among the single batteries is influenced in the grouping working process, and the lithium ion battery still lacks an effective SOC estimation method; at present, SOC estimation in practical application is realized by a basic ampere-hour integration method, but the estimation error is large, and the accumulation effect is obvious under the influence of a plurality of factors; aiming at the SOC estimation research of the lithium ion battery, the related research provides thought reference; on the basis, an SOC estimation method under the driving condition of the new energy automobile is explored, and effective SOC estimation of the lithium ion battery is achieved; an SOC estimation model with parameter correction and adjustment capabilities is built, a parameter estimation theory based on an equivalent circuit model is applied, the development trend of SOC estimation is formed, an optimal balance point is sought between the improvement of precision and the reduction of calculated amount, and an estimation method is optimized and improved continuously.

In the application of the conventional lithium ion battery BMS, an SOC estimation method based on ampere-hour integration and open-circuit voltage cannot accurately represent accumulated errors existing in SOC estimation and cannot be combined with the current state for parameter correction; through analysis of the existing SOC estimation method, based on group intelligent optimization and particle filter algorithm research, battery terminal voltage and current are used as real-time input parameters, working condition information of the lithium ion battery is considered in the SOC estimation process, and the defects of large error, gradual accumulation and the like caused by insufficient real-time correction of the traditional SOC estimation method are overcome; meanwhile, aiming at SOC estimation errors caused by the fact that traditional particle filtering is prone to falling into particle degradation and particle diversity reduction, SOC particles are regarded as fireflies in the nature based on a method of group intelligent optimization and particle filtering, and the behaviors that the fireflies attract each other through fluorescence brightness are simulated, so that the SOC particles are close to the optimal value, and concentration of group particle clusters to the optimal value is achieved; aiming at the SOC estimation problem of the lithium ion battery, by combining with an equivalent circuit modeling method and the advantage analysis of an iterative computation process, an algorithm based on group intelligent optimization and particle filtering is provided, the iterative computation method is researched, and the construction and experimental verification of a high-precision SOC estimation model are realized.

Disclosure of Invention

Aiming at the problems in the prior art, the invention discloses a lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering, which improves the estimation precision.

In order to achieve the purpose, the invention adopts the following technical scheme: the lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering comprises the following steps:

s1, establishing a Thevenin equivalent circuit model to describe the dynamic characteristics of a battery;

s2, calculating each parameter value corresponding to each sampling point in a complete working condition by adopting a recursive least square online parameter identification method;

and S3, combining a battery state space model, and finishing the estimation of the SOC value of the lithium ion battery by using a firefly algorithm and particle filtering.

Further the battery state space model is as follows:

in the formula, SOC _k Is the SOC value at time k; SOC _k+1 And U _p，k+1 The SOC value and the polarization voltage value at the moment of k +1 are respectively; u shape _L，k+1 Outputting an observation variable for the working voltage; n is the number of SOC particles; u shape _OC，k+1 For the estimated open circuit voltage at the next moment, U _p Is the polarization voltage, R, on the RC circuit in the Thevenin model _p 、R ₀ The resistance values are respectively the polarization resistance and the ohmic resistance of the lithium ion battery, Δ t is sampling time, and Δ t =0.1s is set in the present study; τ is R _p And a polarization capacitor C _p The product of (a); q _N The calibrated capacity of the battery is obtained; i is _k The current magnitude at the current moment; w is a _1，k And w _2，k Is a system noise parameter; v. of _k To observe the noise parameters.

And S2, taking open-circuit voltage values corresponding to ten points of 0.1-1 SOC under the HPPC working condition, and performing parameter fitting by using MATLAB to obtain an OCV-SOC curve for subsequent SOC estimation.

The S3 comprises the following steps:

a. initializing N =200 SOC particles, calculating one-step prediction of state space variables and a variance matrix thereof, and directly obtaining the following prediction equation according to a battery state space equation:

SOC(k|k-1)＝A(k)SOC(k-1|k-1)+BI(k)

wherein, SOC (k-1) is the SOC value at the current moment, the SOC value estimated at the next moment of SOC (k | k-1), A (k) is a state transition matrix, the system variable is predicted, and B is a system control input matrix;

b. calculating corresponding terminal voltage values through the obtained SOC values of 200 particles, comparing the terminal voltage values with observation variables, obtaining the weight of each particle by judging whether the estimated value is good or bad, and realizing weight normalization:

in the formula, ω ⁱ _k Representing the weight of the ith particle at time k;

c. simulating the SOC particles with the highest weight to be the brightest firefly individuals, calculating the fluorescence brightness of the brightest individuals and the attraction degree of each SOC particle, completing the updating of the particle positions through the attraction degrees, and realizing the particle optimization process through the approach of the rest particles to the SOC particles:

x _i ＝x _i +β×(x _j -x _i )+α×(rand-0.5)

in the formula I ₀ Is the maximum fluorescence intensity of firefly i, and γ is the light intensity absorption coefficient, set to 0.98 in this study. r is _ij Is the spatial distance between fireflies i and j, referred to herein as the estimated difference, β, between SOC particles i and j ₀ At the light sourceAttraction (r = 0), which is the maximum attraction of the light source firefly, x _i And x _j Representing the spatial positions of fireflies i and j, representing the SOC values of particles i and j in the PF algorithm, and a step factor alpha is [0,1 ]]A constant of above, having a value of 0.05, rand of [0,1 ]]The last random factor is subjected to uniform distribution;

d. if the resampling condition is satisfied, namely the number of the particles with the weight not meeting the condition is larger than the set threshold value, resampling the particles:

in the formula, ω ⁱ _k Represents the weight of the ith particle at time k, N _eff Represents a set threshold;

e. after resampling is finished, determining an SOC estimated value at the next moment by the updated particle weight:

compared with the prior art, the SOC estimation method of the lithium ion battery based on the group intelligent optimization and the particle filtering is provided based on the experimental analysis of the power application requirements and the working characteristics of the lithium ion battery and the research thought of the modern control theory, and has strong applicability; aiming at the accurate estimation target of the SOC value of the lithium ion battery, the invention utilizes the equivalent modeling idea to establish a Thevenin model to describe the working characteristics of the battery, solves the defects of particle degradation and particle diversity reduction by introducing an intelligent firefly algorithm into particle filtering, realizes the mathematical description of SOC estimation and improves the calculation reliability; the method can provide method reference for the establishment of the lithium ion battery SOC estimation model and the calculation of the SOC value under different application scenes, and has the advantages of strong robustness, good adaptability and high precision.

Drawings

FIG. 1 is a flow chart of the estimation method of the present invention.

Detailed Description

For ease of understanding, the present disclosure is described in further detail below with reference to fig. 1.

Firstly, the equivalent modeling of the battery is the basis of SOC estimation, the dynamic characteristics of the battery can be described through the establishment of circuit elements such as a capacitor and a resistor, and a functional relation among various parameters such as the capacitor, the resistor, the voltage and the current is established. Considering that the calculation amount needs to be reduced and the calculation accuracy is improved, the Thevenin model establishes the equivalent circuit model, so that the battery characteristics can be well represented, and the calculation amount is relatively small, therefore, the Thevenin equivalent model is selectively established to describe the dynamic characteristics of the battery.

Secondly, after modeling, the parameter values in the model are acquired for application in the subsequent SOC estimation. However, in the process of charging and discharging the battery, the internal chemical reaction is severe, and the values of the parameters are dynamically changed and are not invariable. Therefore, in order to obtain more accurate battery model parameter values, a recursive least square online parameter identification method is adopted to calculate each parameter value corresponding to each sampling point in a complete working condition. Wherein, it is necessary to know the open circuit voltage value corresponding to each SOC point, so that the open circuit voltage values corresponding to ten points of SOC equal to 0.1-1 under the condition of HPPC are taken, and MATLAB is used for parameter fitting to obtain an OCV-SOC curve for subsequent SOC estimation. The point taking is to take one point every 0.1, and the point taking is just 10 points from 0.1 to 1. And the SOC value is obtained based on the iterative calculation of group intelligent optimization and particle filtering by combining a state space model of the lithium ion battery, and when the SOC value is used for tracking the output voltage of the lithium ion battery, the average estimation error is 0.015V, and the maximum estimation error is 0.04V.

The state equation of the battery state space is constructed as follows:

in the formula, SOC _k Is the SOC value at time k; SOC _k+1 And U _p，k+1 The SOC value and the polarization voltage value at the moment of k +1 are respectively; u shape _L，k+1 Outputting an observation variable for the working voltage; n is SOC particleThe number of the cells; u shape _OC，k+1 For the estimated open circuit voltage at the next instant, U _p Is the polarization voltage, R, on the RC circuit in the Thevenin model _p 、R ₀ The resistance values are respectively the polarization resistance and the ohmic resistance of the lithium ion battery, Δ t is sampling time, and Δ t =0.1s is set in the present study; τ is R _p And a polarization capacitor C _p The product of (a); q _N The calibrated capacity of the battery is obtained; i is _k The current is the current at the current moment; w is a _1，k And w _2，k Is a system noise parameter; v. of _k To observe the noise parameters.

Finally, under the influence of random noise, aiming at the accurate estimation of the SOC of the lithium ion battery, the estimation at different moments is realized by the following steps:

a. initializing N =200 SOC particles, calculating one-step prediction of state space variables and a variance matrix thereof, and directly obtaining the following prediction equation according to a state equation:

SOC(k|k-1)＝A(k)SOC(k-1|k-1)+BI(k)；

the SOC (k-1) is the SOC value at the current moment, the SOC value estimated at the next moment of the SOC (k | k-1), A (k) is a state transition matrix, system variables are predicted, and B is a system control input matrix.

b. Calculating corresponding terminal voltage values through the obtained SOC values of 200 particles, comparing the terminal voltage values with observation variables, obtaining the weight of each particle by judging whether the estimated value is good or bad, and realizing weight normalization:

wherein, ω is ⁱ _k Representing the weight of the ith particle at time k.

c. Simulating the SOC particles with the highest weight to be the brightest firefly individuals, calculating the fluorescence brightness of the brightest individuals and the attraction degree of each SOC particle, completing the updating of the particle positions through the attraction degrees, and realizing the particle optimization process through the approach of the rest particles to the SOC particles:

I ₀ is the maximum fluorescence intensity of firefly i. The better the value of the objective function, the higher the brightness of the firefly itself. For the SOC estimation of the lithium ion battery in the PF algorithm, the higher the weight of the particles is, the higher the brightness of the particles is. γ is the light intensity absorption coefficient and is set to 0.98 in this study. r is a radical of hydrogen _ij Is the spatial distance between fireflies i and j, referred to herein as the estimated difference between SOC particles i and j.

β ₀ Is the attraction at the light source (r = 0), is the maximum attraction of the light source firefly.

x _i ＝x _i +β×(x _j -x _i )+α×(rand-0.5)

Since the formulation of the firefly algorithm is combined with PF and applied in SOC estimation, x _i And x _j Originally, the spatial positions of fireflies i and j are indicated, but here they represent the SOC values of particles i and j in the PF algorithm. The step factor alpha is [0,1 ]]One constant of (2) above, the value is 0.05.rand is [0,1 ]]The last random factor, follows a uniform distribution.

d. If the resampling condition is satisfied, namely the number of the particles with the weight not meeting the condition is larger than the set threshold value, resampling the particles:

where ω represents the weight of each SOC particle, N _eff The representative set threshold is used to determine whether a resampling condition is reached.

e. After resampling is finished, determining an SOC estimated value at the next moment by the updated particle weight:

where ω represents the weight of each SOC particle after the weight update, x _x (i) Representing the SOC value, x, of each particle _k The SOC estimate for the next time is determined based on the updated particle weights.

In the SOC estimation process of the lithium ion battery, iteration is carried out through the series of formulas, and output x _k Namely the SOC value is obtained. The method is based on a particle filter algorithm framework to realize an iterative computation process; in the one-step prediction calculation process of SOC estimation, the particle optimization precision and speed are improved by introducing an intelligent firefly algorithm, and a more accurate SOC estimation method of the lithium ion battery is provided.

Claims (4)

1. The lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering is characterized by comprising the following steps of:

s1, establishing a Thevenin equivalent circuit model to describe the dynamic characteristics of a battery;

s2, calculating each parameter value corresponding to each sampling point in a complete working condition by adopting a recursive least square online parameter identification method;

and S3, combining a battery state space model, and finishing the estimation of the SOC value of the lithium ion battery by using a firefly algorithm and particle filtering.

2. The lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering according to claim 1, wherein the battery state space model is as follows:

in the formula, SOC _k Is the SOC value at time k; SOC (system on chip) _k+1 And U _p，k+1 The SOC value and the polarization voltage value at the moment of k +1 are respectively; u shape _L，k+1 Outputting an observation variable for the working voltage; n is the number of SOC particles; u shape _OC，k+1 For the estimated open circuit voltage at the next instant, U _p Is the polarization voltage, R, on the RC circuit in the Thevenin model _p 、R ₀ The resistance values are respectively the polarization resistance and the ohmic resistance of the lithium ion battery, Δ t is sampling time, and is set to be Δ t =0.1s in the research; tau is R _p And a polarization capacitor C _p The product of (a); q _N Calibrating the capacity of the battery; I.C. A _k The current is the current at the current moment; w is a _1，k And w _2，k Is a system noise parameter; v. of _k To observe the noise parameters.

3. The population intelligence optimization and particle filter-based lithium ion battery SOC estimation method according to claim 1, wherein in S2, the open-circuit voltage values corresponding to ten points of 0.1-1 SOC under HPPC working conditions are taken, and MATLAB is used for performing parameter fitting to obtain an OCV-SOC curve for subsequent SOC estimation.

4. The lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering according to claim 2, wherein the step S3 includes the following steps:

a. initializing N =200 SOC particles, calculating one-step prediction of state space variables and a variance matrix thereof, and directly obtaining the following prediction equation according to a battery state space equation:

SOC(k|k-1)＝A(k)SOC(k-1|k-1)+BI(k)

in the formula, SOC (k-1) is an SOC value at the current moment, an SOC value estimated at the next moment of the SOC (k | k-1), A (k) is a state transition matrix and used for predicting system variables, and B is a system control input matrix;

b. calculating corresponding terminal voltage values through the obtained SOC values of 200 particles, comparing the terminal voltage values with observation variables, obtaining the weight of each particle by judging whether the estimated value is good or bad, and realizing weight normalization:

in the formula, ω ⁱ _k Represents the ith particle at time kThe weight of (c);

c. simulating the SOC particles with the highest weight as the brightest firefly individuals, calculating the fluorescence brightness of the brightest individuals and the attraction degree of each SOC particle, completing the updating of the particle positions through the attraction degrees, and realizing the particle optimization process through the approach of the rest particles to the SOC particles:

x _i ＝x _i +β×(x _j -x _i )+α×(rand-0.5)

in the formula I ₀ Is the maximum fluorescence intensity of firefly i, and γ is the light intensity absorption coefficient, set to 0.98 in this study. r is a radical of hydrogen _ij Is the spatial distance between fireflies i and j, referred to herein as the estimated difference, β, between SOC particles i and j ₀ Is the attraction at the light source (r = 0), is the maximum attraction of the light source firefly, x _i And x _j Representing the spatial positions of fireflies i and j, representing the SOC values of particles i and j in the PF algorithm, and a step factor alpha is [0,1 ]]A constant of above, having a value of 0.05, rand of [0,1 ]]The last random factor is subjected to uniform distribution;

d. if the resampling condition is satisfied, namely the number of the particles with the weight not meeting the condition is larger than the set threshold value, resampling the particles:

in the formula, ω ⁱ _k Represents the weight of the ith particle at time k, N _eff Representing a set threshold;

e. after resampling is finished, determining the SOC estimated value at the next moment through the updated particle weight:

Images