Adaptive extended Kalman filter based state of charge determination for lithium-ion batteries

The accurate online estimation of cell state of charge in electric vehicles is challenging due to extensive computational requirement, measurement noise and convergence issues. This paper proposes an adaptive extended Kalman filter based state of charge determination method, which employs improved simulated annealing method to access the uncertain states and adaptive switch mechanism to adjust the estimation algorithm. It combines the advantages of the local linear approximation and noise reduction capability from extended Kalman filter with the global optimization from improved simulated annealing. The method is verified by the samples collected from battery test system. Results illustrate that the proposed method estimates the cell remained capacity with great performance despite a range of common errors from initialization, current disturbance and measurement noise. (C) 2018 Elsevier Ltd. All rights reserved.