State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

State of charge (SOC) estimation is of vital importance for the battery management system in electric vehicles. This paper proposes a new fuzzy logic sliding mode observer for SOC estimation. The second-order resistor-capacitor equivalent circuit model is used to describe the discharging/charging behavior of the battery. The exponential fitting method is applied to determine the parameters of the model. The fuzzy logic controller is introduced to improve the performance of sliding mode observer forming the fuzzy logic sliding mode observer (FLSMO). The Federal Urban Driving Schedule (FUDS), the West Virginia Suburban Driving Schedule (WUBSUB), and the New European Driving Cycle (NEDC) schedule test results show that the average SOC estimation error of FLSMO algorithm is less than 1%. When the initial SOC estimation error is 20%, the FLSMO algorithm can converge to 3% error boundary within 2400 s. Comparison test results show that the FLSMO algorithm has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against measurement noise and parameter disturbances.

Abstract licence: CC BY

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