Research On Remaining Useful Life Prediction Method Of Lithium-ion Battery Based On Particle Filtet Framework

Lithium-ion batteries are characterized by high energy density,high power density,long life cycles and low self-discharge rate,they are widely used in portable electronic devices,electric vehicles,energy storage systems,aerospace and other fields.However,with the increase of charging and discharging cycles,a series of irreversible chemical and physical reactions will occur in lithium-ion batteries,which will lead to the loss of electrode active materials,and consequently lead to degradation of performance and shortening of life,which may cause unstable operation of the system,failure or even catastrophic safety accidents.In this paper,the prediction methods of remaining useful life(RUL)of lithium-ion batteries are studied to increase the prediction accuracy and uncertainty expression accuracy,then improve the security of systems which powered by lithium-ion batteries,and to provide suggestions for timely maintenance and replacement of batteries.In the research of RUL prediction method for lithium-ion batteries,not only improving the prediction accuracy is necessary,but also getting the uncertainty expression of the prediction results,so that the prediction results are more scientific and have higher practical value.For this purpose,the research emphasis are battery degradation model and prediction algorithm.Therefore,the RUL prediction of lithium-ion batteries in the framework of particle filter is studied from two aspects:building an more appropriate degradation model of lithium-ion batteries and choosing an more appropriate particle filter(PF)algorithm.The main research contents as followings:(1)The working principle and degradation mechanism of lithium-ion batteries were studied.This part is the theoretical basis for the study of lithium-ion batteries.The analysis results show that the chemical and physical reactions inside the batteries are complex,and the coupling is strong.The feasibility of establishing the degradation model of lithium-ion batteries from the perspective of mechanism and equivalent circuit is low.Therefore,this paper establishes the empirical degradation model of battery capacity from the perspective of data.(2)The degradation model of battery capacity was established.Four empirical degradation models were compared and analyzed,and the model with the strongest ability to describe the degradation trend of batteries was selected.The comparison results show that the new capacity degradation model has better description ability for the degradation trend of lithium-ion batteries,so the prediction in this paper will be based on the new capacity degradation model.(3)A RUL prediction method for lithium-ion batteries based on the new degradation model and the standard PF algorithm is proposed.This method can give an uncertainty expression of prediction results in the way of probability density function(PDF)and Frequency Distribution Histogram,but the accuracy of prediction and uncertainty expression is low.(4)A RUL prediction method based on new degenerate model and weighted selected particle filter(WSPF)is proposed.By comparing the performance of WSPF algorithm and Sequential Importance Resampling Particle Filter(SIR-PF)algorithm,it can be concluded that WSPF algorithm has higher performance and can be used for RUL prediction of lithium-ion batteries under the premise of reasonable number of candidate particles.Then,Based on the new degradation model and WSPF algorithm,a prediction method based new degradation model and WSPF algorithm are proposed.The experimental results show that the method improves the prediction accuracy and uncertainty expression accuracy,and has a convergence to some extent.In this paper,the prediction method based on the new degradation model and WSPF algorithm can improve the accuracy of RUL prediction and uncertainty expression,help to ensure the safety of lithium-ion batteries and achieve condition-based maintenance,and provide suggestions for formulating the replacement strategy of lithium-ion batteries.