Data-driven lithium-ion battery remaining life prediction on actual operating vehicles

Abstract

The development of new energy vehicles with electric vehicles as the main force is essential to the emission reduction and energy security of China. Lithium-ion batteries are the core components of electric vehicles. Effective health assessment and life prediction are the key to ensuring the endurance and safe and reliable operation of electric vehicles. Unlike most previous studies, this paper provides a remaining life prediction method of lithium-ion battery based on the operating data of electric vehicles under actual operating conditions by combining amper-hour integral method and machine learning algorithms. The data-driven method performs well in both health status estimation and remaining life prediction, thus can provide articulate monitoring of the safety and reliability of the lithium-ion battery system in its whole life cycle and relieve range anxiety.