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

Summary: 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.

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  • Topics: Reliability
  • Keywords: Lithium-ion battery, Particle filter, Prognostics, Quantum particle swarm optimization, Remaining useful life
  • Author: Yu, Jinsong; Mo, Baohua; Tang, Diyin; Liu, Hao; Wan, Jiuqing
  • Journal: Quality Engineering