Eco-driving for connected electric vehicle considering air conditioning cooling energy demand in high temperature environments

Conventional energy-efficient speed planning for electric vehicles mainly considers traction energy consumption, whereas the additional energy demand of the air-conditioning system under high-temperature conditions is often neglected. This paper proposes an eco-driving framework that jointly optimizes traction and air-conditioning energy consumption for connected electric vehicles in green-wave traffic scenarios at signalized intersections. Based on the optimized speed trajectory, the air-conditioning system is further coordinated through control. To address the multivariable and highly nonlinear characteristics of automotive air-conditioning systems, a hierarchical model predictive control strategy is developed. In the prediction horizon, dynamic programming is employed to determine the optimal cabin temperature trajectory, while in the control horizon, a genetic algorithm is used to obtain the optimal control sequence of the air-conditioning system. Simulation results show that, with comparable travel time, the proposed method reduces total energy consumption by 15% relative to baseline strategies. In addition, the proposed framework improves computational efficiency, showing potential for real-time implementation under the tested simulation environment. Compared with rule-based air-conditioning control, the proposed hierarchical control strategy further reduces air-conditioning energy consumption by approximately 3%.