Estimation of state-of-charge of Li-ion batteries in EV using the genetic particle filter

2nd International Conference on Materials Science, Energy Technology and Environmental Engineering

材料科学;能源学;生态环境科学

Bi, Jun^1,2 ; Gao, Hang^2 ; Wang, Yongxing^2 ; Zhao, Xiaomei^2

Beijing Jiaotong University, Weihai, China^1

School of Traffic and Transportation, Beijing Jiaotong University, China^2

关键词: Filter algorithm;    Nonlinear properties;    Particle degeneracy;    Particle filter;    Prediction accuracy;    Safe trip;    Sanitation vehicles;    State of charge;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/81/1/012183/pdf
DOI  :  10.1088/1755-1315/81/1/012183

来源: IOP

【 摘 要 】

Estimating the state of charge (SOC) of electric vehicle (EV) batteries accurately and timely is of great significance to the safe trip of pure EV. Based on the nonlinear properties of the battery, and the standard particle filter (PF) has certain adaptability for this feature, so it can be used to accurately estimate the SOC of the batteries. However, the standard PF has particle degeneracy phenomenon, which will make the accuracy of prediction lower. Therefore, in this paper, the genetic algorithm is applied to the standard PF, and the estimation of SOC is optimized, which makes the improved filter algorithm more accurate. Based on the measured data of Beijing pure electric sanitation vehicle, an experiment is defined to verify the algorithm. The experimental results show that the genetic particle filter (GPF) can increase the diversity of particles and has better prediction accuracy and timeliness than the PF.