Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter

Dong Wang, Fangfang Yang, Kwok Leung Tsui, Qiang Zhou, Suk Joo Bae

Research output: Contribution to journalArticlepeer-review

222 Scopus citations


Lithium-ion batteries are critical components to provide power sources for commercial products. To ensure a high reliability of lithium-ion batteries, prognostic actions for lithium-ion batteries should be prepared. In this paper, a prognostic method is proposed to predict the remaining useful life (RUL) of lithium-ion batteries. A state-space model for the lithium-ion battery capacity is first constructed to assess capacity degradation. Then, a spherical cubature particle filter (SCPF) is introduced to solve the state-space model. The major idea of the SCPF is to adapt a spherical cubature integration-based Kalman filter to provide an importance function of a standard particle filter (PF). Once the state-space model is determined, the extrapolations of the state-space model to a specified failure threshold are performed to infer the RUL of the lithium-ion batteries. Degradation data of 26 lithium-ion battery capacities were analyzed to validate the effectiveness of the proposed prognostic method. The analytical results show that the proposed prognostic method is more effective in the prediction of RUL of lithium-ion batteries, compared with an existing PF-based prognostic method.

Original languageEnglish (US)
Article number7432024
Pages (from-to)1282-1291
Number of pages10
JournalIEEE Transactions on Instrumentation and Measurement
Issue number6
StatePublished - Jun 2016
Externally publishedYes


  • Battery management systems (BMSs)
  • Prognostics and health management
  • electric vehicles (EVs)
  • lithium batteries
  • particle filters (PFs)

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter'. Together they form a unique fingerprint.

Cite this