Xiaosong Hu 1, Le Xu 1, Xianke Lin 2, Shaojiang Dong 3, and Michael G. Pecht 3
1 State Key Laboratory of Mechanical Transmissions, Department of Automotive Engineering, Chongqing University, Chongqing 400044, China
2 Department of Automotive, Mechanical and Manufacturing Engineering, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada
3 CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.