SOC Estimation for Aged Lithium-Ion Batteries Using Model Adaptive Extended Kalman Filter

被引：0
|
作者：
Sepasi, Saeed [1 ]
Ghorbani, Reza [1 ]
Liaw, Bor Yann [1 ]
机构：
[1] Univ Hawaii Manoa, Honolulu, HI 96822 USA
来源：
2013 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE AND EXPO (ITEC) | 2013年
关键词：
EV; HEV; smart grid; battery management system; SOC; extended Kalman filter; aged cell; LiFePO4; SOH; STATE-OF-CHARGE; MANAGEMENT-SYSTEMS; PACKS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Rechargeable batteries as an energy source in electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids are receiving more attention with the worldwide demand for reduction of greenhouse gas emission. In all of these applications for secondary batteries, the battery management system (BMS) needs to have an accurate inline estimation of state of charge (SOC) of each individual cell in the battery pack. Yet, this estimation is still difficult, especially after substantial aging of batteries. This paper presents a model adaptive extended Kalman filter (MAEKF) method to estimate SOC of Li-ion batteries. This method uses an optimization algorithm to update the EKF model parameters during a discharge period. State of health (SOH) information would be updated while the battery is charged/discharged, (aged). The effectiveness of the proposed method has been verified based on data acquired from a LiFePO4 battery.
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页数：6
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