2012 Prognostics & System Health Management Conference
(PHM-2012 Beijing)

State of Charge Estimation for Electric Vehicle Batteries under an Adaptive Filtering Framework

Wei He, Nicholas Williard, Chaochao Chen & Michael Pecht

Center for Advanced Life Cycle Engineering (CALCE)
University of Maryland
College Park, MD 20742



Electric vehicles (EVs), which are powered by lithium batteries, will penetrate the automobile market within the next few years. This is mainly due to the increasing concerns of global warming and fossil fuel depletion. However, there are still some challenges for EVs that remain to be solved. The most notable one is the state of charge (SOC) estimation and prediction for relieving EV drivers' range anxiety. To address this problem, an equivalent circuit model is built to simulate battery behavior under dynamical loading conditions. The parameters of the model should be tuned on-line in order to handle the prediction uncertainty arising from unit to unit variations and loading condition changes. This paper proposed an Unscented Kalman filtering-based method to self-adjust the model parameters and provide the SOC estimation. The performance of the proposed method is demonstrated using data collected from LiFePO4 batteries cycled with two dynamical discharge profiles.

Keywords:State of Charge Estimation; Lithium-ion Battery;Unscented Kalman Filter; styling; Electric Vechicles

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