Energy-Efficient Adaptive Cruise Control for BEVs in Urban Scenarios with Traffic Lights Negotiation

Optimizing energy consumption for small electric vehicles in urban areas is crucial for enhancing travel efficiency and environmental sustainability. This study presents a longitudinal vehicle dynamics controller based on Model Predictive Control (MPC) that integrates vehicle following and traffic light information to minimize energy consumption and optimize comfort. The proposed MPC dynamically adjusts the headway distance to the preceding vehicle, achieving efficient energy management in urban traffic conditions by leveraging the knowledge of the electric motor map. The impact of knowing the lead vehicle trajectory through Vehicle-to-Vehicle (V2V) communication versus its estimation is discussed. The integration of traffic light status through Vehicle-to-Infrastructure (V2I) communication is also introduced. The Signal Phasing and Timing (SPaT) information is assumed to be available, enabling the vehicle to anticipate traffic signals and adjust speed proactively. This paper details the design principles, algorithm implementation, and simulation results. The experiments demonstrate that the proposed controller reduces the energy consumption of a battery electric vehicle (BEV) during urban operation, providing a safe and reliable driving experience.