Open AccessState-of-health estimation and remaining useful life prediction of lithium-ion batteries based on extreme learning machine
Author(s) -
Meng Wei,
Min Ye,
Qiao Wang,
Changzhi Wu,
Yuchuan Ma
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1983/1/012058
Subject(s) - extreme learning machine , state of health , particle swarm optimization , computer science , artificial neural network , mean squared error , machine learning , artificial intelligence , reliability engineering , engineering , statistics , power (physics) , battery (electricity) , mathematics , physics , quantum mechanics
Lithium-ion batteries have been widely applied in electric vehicles, accurate health state prediction of batteries is one of the key technologies to obtain optimal operation and health management. To achieve the highly accurate state of health (SOH) estimation and remaining useful life (RUL) prediction, a framework based on extreme learning machine (ELM) is proposed. Firstly, the indirect health indicators are extracted from discharge data. Then, the ELM model is proposed to estimate SOH and predict RUL. Finally, the propagation neural network based on particle swarm optimization (BPNN-PSO) is compared with the ELM method. The results show that proposed method hits lower average root mean square error for SOH and RUL.