Abstract
This paper proposes a novel online condition monitoring algorithm estimating battery states and model parameters. The proposed method includes: 1) an electrical circuit battery model incorporating the hysteresis effect, 2) an extended Kalman Filter-based online parameter identification algorithm for the electrical battery model, and 3) a smooth variable structure filter (SVSF)-based state estimation algorithm for state of charge (SOC) estimation. The proposed method enables an accurate and robust condition monitoring for lithium-ion batteries. Since the proposed hybrid filter further reduces the complexity compared to existing dual extended Kalman filter (DEKF), it is much more suitable for the real-time embedded battery management system (BMS) application. Simulation studies validate the effectiveness of the proposed strategy.
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