Application of Battery Life Prediction Technology in EMUs - DAI Yi; YU Tianjian; CHENG Shu; WU Xun; LIU Jiawen (Kongzhi Yu Xinxi Jishu)
Reliability of electric multiple unit (EMU) batteries is related to the safety of trains. However, due to the unique memory effect of alkaline cadmium-nickel battery, there are few life prediction algorithms suitable for batteries in EMU, and there is currently no practical application in China. To this end, the paper proposes a battery remaining useful life prediction algorithm based on particle filter-extended Kalman filter (PF-EKF). The algorithm is compared to particle filter (PF) and extended Kalman filter (EKF). Experimental results show that the PF-EKF algorithm is simple in structure, easy to implement, and has the highest accuracy of battery life prediction, with an accuracy rate of 96.691%.