ESTIMATE STATE OF CHARGE OF POWER LITHIUM-ION BATTERIES USED ON FUEL CELL HYBRID VEHICLE WITH METHOD BASED ON EXTENDED KALMAN FILTERING

DAI Haifeng WEI Xuezhe SUN Zechang

(School of Automotive,Tongji University,Shanghai 201804 )

Abstract: One of the most important tasks of the power lithium-ion battery management systems used on the fuel cell hybrid vehicles (FCHVs) is to precisely estimate the current state of charge (SOC) of the batteries online. A simple physical model and a complex physical model of the battery pack used on FCHVs are proposed, which describe the dynamic behavior of the battery pack by different equivalent circuits respectively, and the relationships among different physic variables and state variables of the two models are expressed by the discrete-time state-space functions. Then, based on the Extended Kalman Filter and these two models, the state of charge for the battery pack is estimated by using data such as current and voltage which are sampled when the vehicle is running,and the differences between the two results estimated are also analyzed by using two different models. Our results show that, model based Kalman filter SOC estimation for batteries used on FCHVs is effective. It is also shown that estimating accuracy depends highly on the selected model, and a better model can provide a higher accuracy.

Key words: Extended Kalman filtering Fuel cell hybrid vehicle Lithium-ion battery State of charge

CLC No: TM912.1

国家863计划资助项目(2003AA501033). Received 20060511, received in revised form 20060911