Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background

• Published in: Journal of power sources Vol. 134; no. 2; pp. 252 - 261
• Main Author: Plett, Gregory L.
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 12.08.2004; Elsevier Sequoia
• Subjects: Applied sciences; Battery management system (BMS); Direct energy conversion and energy accumulation; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Energy; Energy. Thermal use of fuels; Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc; Exact sciences and technology; Extended Kalman filter (EKF); Fuel cells; Hybrid electric vehicle (HEV); Lithium-ion polymer battery (LiPB); State of charge (SOC); State of health (SOH); Extended Kalman filter (EKF); Hybrid electric vehicle (HEV); Lithium-ion polymer battery (LiPB); State of health (SOH); Battery management system (BMS); State of charge (SOC); Hybrid vehicle; Electric vehicle; Parallel; Electric batteries; Modeling; Operating conditions; Hybrid system; Lithium battery; Lifetime; State of charge; Comparative study
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2004.02.031

Battery management systems (BMS) in hybrid-electric-vehicle (HEV) battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium-ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. This first paper investigates the estimation requirements for HEV BMS in some detail, in parallel to the requirements for other battery-powered applications. The comparison leads us to understand that the HEV environment is very challenging on batteries and the BMS, and that precise estimation of some parameters will improve performance and robustness, and will ultimately lengthen the useful lifetime of the pack. This conclusion motivates the use of more complex algorithms than might be used in other applications. Our premise is that EKF then becomes a very attractive approach. This paper introduces the basic method, gives some intuitive feel to the necessary computational steps, and concludes by presenting an illustrative example as to the type of results that may be obtained using EKF.