Nonlinear model predictive control of battery electric vehicle with slope information

This paper introduces a model predictive control approach for the energy management problem of a battery electric vehicle (BEV) system with slope information. The features of this study are as follows. The BEV physical constraints and the battery state of charge (SOC) are addressed in the cost function of optimal control problem with a model of the battery electric vehicle system. Nonlinear real-time optimal control problem in the BEV system is solved using numerical computation method: continuation and generalized minimum residual method. This approach in the BEV system uses terrain information from digital maps to calculate the desired SOC for better recuperation of free braking energy. We conclude that the model predictive control approach is effective for the application of battery management systems for BEV and has the potential for real-time implementation. The effectiveness of the proposed algorithm in the energy management of BEVs is compared with a proportional-integral control method approach.