Performance evaluation of an extended kalman filter for state estimation of a pseudo-2D thermal-electrochemical lithium-ion battery model

Conference item

Fast and accurate state estimation is one of the major challenges for designing an advanced battery management system based on high-fidelity physics-based model. This paper evaluates the performance of a modified extended Kalman filter (EKF) for on-line state estimation of a pseudo-2D thermal-electrochemical model of a lithium-ion battery under a highly dynamic load with 16C peak current. The EKF estimation on the full model is shown to be significantly more accurate (< 1% error on SOC) than that on the single-particle model (10% error on SOC). The efficiency of the EKF can be improved by reducing the order of the discretised model while maintaining a high level of accuracy. It is also shown that low noise level in the voltage measurement is critical for accurate state estimation.

Authors: Zhao, S; Bizeray, A; Duncan, S; Howey, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Society of Mechanical Engineers
• Host title: ASME 2015 Dynamic Systems and Control Conference (DSCC2015) , Columbus, Ohio, 2015.
• Journal: ASME 2015 Dynamic Systems and Control Conference
• Series: ASME Proceedings
• Publication date: 2015-01-01
• Acceptance date: 2015-05-14
• DOI: 10.1115/DSCC2015-9836
• ISBN: 9780791857243