This study proposes a robust SOC estimation scheme for lithium-ion batteries in commercialized electric vehicles: pure electric vehicles and hybrid electric vehicles. Development of a nonlinear-model-based robust SOC estimation method and comprehensive performance comparison with the existing SOC estimation methods under the two kinds of operating scenarios are considered. The features of the results of this study are summarized as follows: a) a robust SOC estimator is designed to deal with the model uncertainty and parameter variation, b) an analysis of asymptotic stability of the proposed robust SOC estimator is provided and c) a comprehensive analysis was performed not only on the different aging levels of battery cells but also on the different operational conditions of the electrified vehicles. The effectiveness of the proposed SOC estimation method was experimentally verified using two 18 650 cylindrical battery cells with different degradation levels. The proposed SOC estimation scheme ensures considerable accuracy under both pure electric vehicle operating mode and hybrid vehicle operating mode even if the battery cell is aged.

This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods.

The predictive energy management (PEM) problem for hybrid electric powertrains is challenging to solve in real time, mainly due to the nonconvexity from the battery state of energy (SOE) model, which is nonlinear. This letter proposes a control-oriented battery model consisting of a stochastic linear SOE model and a quadratic power loss model to realize real-time PEM. The stochastic linear model describes the SOE trajectory from an average point of view. The quadratic power loss model describes the nonlinear power loss that the stochastic linear SOE model cannot consider. By replacing the nonlinear SOE model with the control-oriented model, the PEM problem is reformulated into quadratic programming (QP), which can be easily solved in real time by a QP solver. Simulation results obtained using a fuel cell-powered unmanned aerial vehicle (UAV) show that the proposed model predicts the trend of the SOE trajectory well, even for long prediction horizons (maximum of 750 s). In addition, PEM based on the proposed model results in near-optimal performance (0.36% difference from the global solution) with real-time capability (solved within 0.27 s).