A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering framework. (1st February 2017)

• Record Type: Journal Article
• Authors: Lyu, Chao; Lai, Qingzhi; Ge, Tengfei; Yu, Honghai; Wang, Lixin; Ma, Na
• Is Part Of: Energy. Volume 120(2017)
• Page Start: 975
• Page End: 984
• Publication Date: 2017-02-01
• Subjects: Electrochemical modeling -- Remaining useful life prediction -- Particle Filter framework -- Effectiveness; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.12.004
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurate prediction of battery's remaining useful life (RUL) is significant for the reliability and the cost of systems. This paper presents a new Particle Filter (PF) framework for lead-acid battery's RUL prediction by incorporating the battery's electrochemical model. An electrochemical model that simulates the charging and discharging of lead-acid battery is introduced. The effectiveness of both the model and parameter identification is validated through both synthetic and experimental data. In the new PF framework, model parameters that reflect the degradation of battery are seen as state variables, the procedure of capacity simulation and the fitting equations of known state variables are measurement model and process model respectively. Aging experiment is depicted and applied to validate the effectiveness of the method. RUL predictions are made with two different beginning points, the results of which show that the new electrochemical-model-based PF has better state variable stability and prediction accuracy than the traditional data-driven PF. Highlights: A new electrochemical-model-based battery's life prediction method is established. The method fills the research gap between mechanism model and data-driven PF. Electrochemical model offers state variables and system's output observation. The method provides satisfactory results for high quality RUL prediction.