A robust method for state of charge estimation of lithium-ion batteries using adaptive nonlinear neural observer

To ensure the safety and functionality, it is crucial to evaluate the state of battery system in electric vehicles. Describing battery dynamic characteristic with a combined state space model, this paper presents a novel adaptive nonlinear neural observer based approach for state of charge estimation, which is composed of linear discriminant function, nonlinear neural proportional-integral observer and extended Kalman filter. It incorporates the local linear approximation capability of extended Kalman filter with the nonlinear mapping, selflearning and self-adjusting capabilities of neural proportional-integral observer, which is used to compensate the deviation resulted from the underestimated initial state, process noise and measurement noise. Taking the samples collected from lithium-ion battery test system for example, simulation is carried out to verify the proposed method. Results show that it is capable of evaluating the state of charge of cell with a rapid convergence and an error <2 % while remaining unaffected by the unknown initial cell states and the underestimation of the process noise and the measurement noise.