[regression prediction] lithium ion battery life prediction based on particle filter with matlab code

1 Content introduction

With modern production and life, the reliability of system equipment 、 Improvement of safety requirements , From cost 、 reliable

From the perspective of sex , The electronic system is gradually changing from regular maintenance to condition based maintenance （CBM,

Condition Based Maintenance）.CBM  As an advanced equipment maintenance concept , In complexity

High degree and important equipment 、 The field of system maintenance and support has received more and more attention , And support  CBM

Of The key technology is failure Prediction and Health management （ PHM , Prognostics and Health

Management） So it becomes very necessary to study . stay  PHM  Prediction in technology （Prognostics） yes

Its core content and technical challenges , Prediction is to estimate and predict the remaining service life of a device or system

（RUL,Remaining Useful Life）. The remaining life prediction is not necessary for the maintenance of system equipment

Less important information , according to  RUL  The analysis of prediction results can manage the system equipment well , can

To improve the availability and reliability of the system or equipment , At the same time, reduce or avoid major losses caused by faults .

Due to the complex structure of electronic equipment or system in practical application , And most of them are nonlinear systems , Worked

Most of the process is seriously disturbed by noise , Therefore, the measurement data must be considered in the prediction process 、 State estimation

meter 、 Model error 、 Load change 、 Working conditions and other uncertain factors . For nonlinear non Gaussian systems

Fault prediction and health management of , Particle filter has been proved to be a very effective method , The method also

Have the ability to dynamically adjust the parameters of the nonstationary state estimation model . At the same time, the prediction method with excellent performance

The key is not only able to accurately estimate the remaining life , At the same time, it can also give an uncertain prediction result

Confidence assessment of sex . This kind of confidence interval calculation is often evaluated through the probability density function （PDF） Come on

Express . This uncertainty estimation constitutes a special part of the current life prediction of electronic equipment or systems

Challenge . Particle filter uses a set of particles with weights to describe the posterior estimation of the state , Multiple weighted particles

Representation of the state space of the child , Be able to give the probability expression of the prediction results , This Monte Carlo description

Valued at the true posterior probability density function , The uncertainty of the results is effectively expressed . Make the prediction result

Estimation characteristics with uncertainty . This makes particle filter an ideal method for state tracking and prediction .

Battery is the energy supply of many vital devices or systems , It is the core of many systems

parts . Battery based power supply has penetrated into all levels of current life , From the tiny Bluetooth Ear

machine 、 mobile phone 、 The camera 、 Laptop to hybrid electric vehicle 、 Aerospace complex systems , The battery

Are relatively important and key components . meanwhile , Battery failure may cause system performance degradation , what

To the catastrophic failure and loss of the system , Especially in aerospace systems , For battery degradation

modeling 、 Life prediction and health management , It will greatly improve the reliability of the battery system , therefore , Battery life

Prediction has very important practical value and significance . Compared with other batteries , In sex

It has more outstanding advantages , Its application scope is also more and more extensive , Therefore, the residual life prediction in this paper

The physical model is lithium ion battery .

This topic will study the remaining life prediction algorithm of lithium-ion battery based on particle filter , Based on lithium ion

Based on the degradation data of battery, the degradation model and prediction algorithm are established to realize  RUL  forecast , And give with inaccuracy

Qualitative expression of prediction results and quantitative representation of uncertainty , Evaluate the performance of the remaining life prediction method

Estimate . at present , In the field of electronic systems  PHM  Research and application is still in its infancy , Establish a good

RUL  The prediction framework can provide support for the maintenance and guarantee of the system , So as to further improve the scalability of the system

Usability and reliability , At the same time, reduce maintenance costs . therefore , Carry out residual life prediction based on particle filter

Methods to study , It can not only provide the necessary technical guarantee for the application and management of lithium-ion batteries , along with it

It can be used in electronic system  PHM  Technology research and application provide important reference ​

2 Simulation code

​%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%   The functionality ： Particle filter is used to predict the life of power supply % function main clc close all clear allload Battery_CapacityN=length(A12Cycle); % error(' The following parameters M Please refer to the value setting in the book , Then delete this line of code ')M=200;  Future_Cycle=100; if N>260    N=260;   end % Process noise covariance Qcita=1e-4;wa=0.000001;wb=0.01;wc=0.1;wd=0.0001;Q=cita*diag([wa,wb,wc,wd]); % Drive matrix F=eye(4); % Observation noise covariance R=0.001; a=-0.0000083499;b=0.055237;c=0.90097;d=-0.00088543;​end % Predict the future trend of capacitance start=N-Future_Cycle;for k=start:N    Zf(1,k-start+1)=feval('hfun',Xpf(:,start),k);    Xf(1,k-start+1)=k;end​Xreal=[a*ones(1,M);b*ones(1,M);c*ones(1,M);d*ones(1,M)];figuresubplot(2,2,1);hold on;box on;plot(Xpf(1,:),'-r.');plot(Xreal(1,:),'-b.')legend(' After particle filter a',' Average a')subplot(2,2,2);hold on;box on;plot(Xpf(2,:),'-r.');plot(Xreal(2,:),'-b.')legend(' After particle filter b',' Average b')subplot(2,2,3);hold on;box on;plot(Xpf(3,:),'-r.');plot(Xreal(3,:),'-b.')legend(' After particle filter c',' Average c')subplot(2,2,4);hold on;box on;plot(Xpf(4,:),'-r.');plot(Xreal(4,:),'-b.')legend(' After particle filter d',' Average d')​figurehold on;box on;plot(Z,'-b.') plot(Zpf,'-r.')plot(Xf,Zf,'-g.') bar(start,1,'y')legend(' Experimental measurement data ',' Filter estimated data ',' Natural prediction data ')%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%​

3 Running results

4 reference

[1] Peng Fei . Research and implementation of lithium ion battery state estimation method [D]. University of electronic technology .

[2] Luo Yue . Research on residual life prediction method of lithium ion battery based on particle filter [D]. Harbin Institute of Technology , 2012.

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