TR2014-041

State of charge estimation based on a realtime battery model and iterative smooth variable structure filter



This paper proposes a novel real-time model-based state of charge (SOC) estimation method for lithium-ion batteries. The proposed method includes: 1) an electrical circuit battery model incorporating the hysteresis effect, 2) a fast upper-triangular and D-diagonal recursive least square (FUDRLS)-based online parameter identification algorithm for the electrical battery model, and 3) an iterated smooth variable structure filter (ISVSF) for SOC estimation. The proposed method enables an accurate and robust condition monitoring for lithium-ion batteries. Due to its low complexity, the proposed method is suitable for the real-time embedded battery management system (BMS) application. Simulation and experiment are performed to validate the proposed method for a polymer lithium-ion cell.