A Torque Demand Model Predictive Control Approach for Driving Energy Optimization of Battery Electric Vehicle

In this paper, based on model predictive control algorithm, a torque demand control approach is proposed to optimize driving energy consumption of battery electric vehicle, which consists of demand control approach and model predictive controller. The demand control approach is developed to compute the driving mode and the desired vehicle speed. For the design of control law, a novel driving dynamics model of battery electric vehicle is formulated into a set of differential equations by vehicle speed, the front and rear wheel speed. A model predictive control law is designed to compute the optimized torque of electric motor. The torque demand model predictive control algorithm is downloaded into vehicle control unit, which is equipped on the battery electric vehicle for experimental validation. The New European Driving Cycle is utilized to test the control law on the real road. The experimental results indicate that the proposed model predictive controller has a preferable performance in reducing energy consumption, which can improve 1.81% over the original control strategy in the urban road cycle and 1.67% in the city highway condition. It can be considered that the torque demand model predictive control approach is a good candidate for driving energy optimization of battery electric vehicle.