Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter

ABSTRACT

A novel RUL prediction approach for lithium-ion batteries using quantum particle swarm optimization (QPSO)-based particle filter (PF) is proposed. Compared to particle swarm optimization (PSO)-based PF, QPSO-based PF is proved to have a better performance in global searching and has fewer parameters to control, which makes QPSO-PF easier for applications. Moreover, fewer particles are required by QPSO-PF to accurately track the battery's health status, leading to a reduction of computation complexity. RUL prediction results using real data provided by NASA and compared with benchmark approaches demonstrates the superiority of the proposed approach.

KEYWORDS:

• lithium-ion battery
• particle filter
• prognostics
• quantum particle swarm optimization
• remaining useful life

Additional information

Notes on contributors

Jinsong Yu

Jinsong Yu is an associate professor in the School of Automation Science and Electrical Engineering at Beihang University, Beijing, China. He received his Ph.D. degree from Beihang University in 2004. From 2013–2014, he was a visiting scholar at the University of Canterbury, Christchurch, New Zealand. His research interest includes prognostic and health management technologies, instrumentation, and measurement technologies.

Baohua Mo

Baohua Mo is a Master candidate in the School of Automation Science and Electrical Engineering at Beihang University, Beijing, China. He received his Bachelor's degree from the Ecole Centrale Pekin at Beihang University in 2015. His research interests focus on remaining useful life prediction.

Diyin Tang

Diyin Tang is an assistant professor in the School of Automation Science and Electrical Engineering at Beihang University, Beijing, China. She received her Bachelor and Ph.D. degrees from Beihang University, Beijing, China in 2008 and 2015, respectively. From 2012–2013, she was a visiting Ph.D. student in the Department of Mechanical and Industrial Engineering at University of Toronto, Canada. Her research interests include optimization for condition-based maintenance and degradation-based modeling.

Hao Liu

Hao Liu is currently a postdoctoral researcher in the School of Automation Science and Electrical Engineering at Beihang University, Beijing, China. He received his Ph.D. from Beihang University in 2014. His research interest includes prognostic and health management technologies, instrumentation, and measurement technologies.

Jiuqing Wan

Jiuqing Wan is an associate professor in the School of Automation Science and Electrical Engineering at Beihang University, Beijing, China. He received his Ph.D. from Beihang University in 2003. His research interest covers signal/image/video processing, statistical inference, target detection, tracking and recognition, and fault diagnosis and prognosis of complex system.