A state-space-based prognostics model for lithium-ion battery degradation. (March 2017)

• Record Type: Journal Article
• Authors: Xu, Xin; Chen, Nan
• Is Part Of: Reliability engineering & system safety. Volume 159(2017)
• Page Start: 47
• Page End: 57
• Publication Date: 2017-03
• Subjects: Degradation -- PHM -- State-space model -- EKF -- EM -- RUC; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2016.10.026
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: This paper proposes to analyze the degradation of lithium-ion batteries with the sequentially observed discharging profiles. A general state-space model is developed in which the observation model is used to approximate the discharging profile of each cycle, the corresponding parameter vector is treated as the hidden state, and the state-transition model is used to track the evolution of the parameter vector as the battery ages. The EM and EKF algorithms are adopted to estimate and update the model parameters and states jointly. Based on this model, we construct prediction on the end of discharge times for unobserved cycles and the remaining useful cycles before the battery failure. The effectiveness of the proposed model is demonstrated using a real lithium-ion battery degradation data set. Abstract : Highlights: Unifying model for Li-Ion battery SOC and SOH estimation. Extended Kalman filter based efficient inference algorithm. Using voltage curves in discharging to have wide validity.