Human-like Eco-driving Strategy of Electric Vehicle Considering Vehicle-to-everything Communication

Vehicle-to-everything (V2X) technology has shown great potential in energy management of electric vehicles (EVs) via optimizing speed profiles. To cope with the benchmark problem challenge offered by the 2023 CVCI Committee, this paper develops powertrain and A/C controllers using Model Predictive Control (MPC), which consider traffic signal timing and phase information via V2X in optimizing energy consumption, traffic efficiency and also cabin temperature. As for the powertrain controller, the upper layer plans optimal speed for EVs based on the V2X information, while the lower layer outputs motor torques according to an offline-calculated distribution table. Regarding the A/C system, a predictive model is established through non-linear autoregression, based on which a Nonlinear MPC (NMPC) controller is designed. Comparing with two classical car-following algorithms, i.e. the Intelligent Driver Model (IDM) and MPC based car-following, our algorithm can achieve significant reductions in energy consumption, 30.4% and 13.5%, respectively, while there is only a minor delay in traffic efficiency.