School of Mechanical，Electronic and Industrial Engineering，University of Electronic Science and Technology of China，Chengdu 611731，China 在期刊界中查找 在百度中查找 在本站中查找
School of Mechanical，Electronic and Industrial Engineering，University of Electronic Science and Technology of China，Chengdu 611731，China 在期刊界中查找 在百度中查找 在本站中查找
School of Mechanical，Electronic and Industrial Engineering，University of Electronic Science and Technology of China，Chengdu 611731，China 在期刊界中查找 在百度中查找 在本站中查找
School of Mechanical，Electronic and Industrial Engineering，University of Electronic Science and Technology of China，Chengdu 611731，China 在期刊界中查找 在百度中查找 在本站中查找
A particle filtering algorithm for predicting the remaining useful life of the lithium-ion battery is presented. First，the concepts and steps of the proposed method are introduced. Then，the particle filtering based method is used to predict the remaining useful life of the lithium-ion battery with experimental data. Comparison study with the extended Kalman filtering based prediction technique is conducted to evaluate the performance of the particle filtering algorithm. The results show that the particle filtering algorithm is more accurate and can predict the actual failure time with an error less than 5%.
