Research On The Power Lithium Battery State Based On Genetic Particle Filter Algorithm

Due to the impact of the energy crisis and environmental pollution,battery electric vehicles have gradually become a hot topic in academia and industry.Power lithium-ion batteries have attracted the attention of many researchers with their merits of high energy density,low discharge rate,and other advantages,and have been widely employed as the power source of battery electric vehicles.How to estimate the state parameters of the power battery under complex working conditions accurately is of great significance for further development and promotion of battery electric vehicles.In response to this problem,based on the research of the traditional particle filter algorithm,this paper researches the estimation of three vital parameters in lithium-ion batteries and proposes some improved particle filter algorithms.The main work and research contents are listed as below:1)The state of charge(SOC)is a significant parameter reflecting changes in lithiumion batteries’ capacity.The state of power(SOP)reflects the ability of the battery to receive and release the maximum power.Aiming at the shortcomings of the traditional particle filter in estimating SOC that there is a lack of particle diversity and part of the improved algorithm has a large amount of calculation,complicated steps,and affects the efficiency of SOP estimation,the genetic algorithm is used to optimize the particle filter algorithm and improve it,proposing improved genetic particle filter(IGPF)to estimate SOC.Subsequently,this article uses the estimated SOC as a parameter to estimate the SOP and employs multiple constraints to limit the current to estimate the battery’s SOP.The results show that the method can accurately estimate SOC in a short calculation time,and the changing trend of SOP is consistent with the actual situation,which verifies the reliability of the IGPF-based SOP estimation method.2)The state of energy(SOE)is an important parameter reflecting the energy of power lithium batteries.Since the voltage change of the battery is taken into account,it can better reflect the true state of the battery.To accurately estimate the SOE,from the perspective of optimizing particle filter resampling and reducing system noise,this paper uses the ant colony algorithm as the resampling of the particle filter,and combines the adaptive algorithm with it,proposing an ant colony adaptive particle filter algorithm(AAPF)to estimate the SOE of the battery.According to the simulation results of the experimental data of the vehicle working conditions,this algorithm can accurately estimate the SOE.