Abstract
Reliance solely on vehicle-specific information, while neglecting multi-source information such as traffic flow and traffic light status, results in difficulties in optimizing energy allocation based on complex road conditions. To achieve the application of multi-source traffic information and enhance the timeliness in multi-objective optimization (MOO) for connected automated range-extended electric vehicles (CAR-EEV), this paper proposes an intelligent energy management strategy (EMS) from a multi-objective perspective. Firstly, a joint simulation platform for traffic scenarios is established based on SUMO and MATLAB, and a data-driven model of CAR-EEV is constructed using collected data, serving as the data foundation and operational platform for subsequent research and development of EMSs. Then, leveraging an image-like representation of traffic flow information based on grid grayscale maps, multi-source traffic information is materialized into a two-dimensional matrix. The Euclidean distance between consecutive traffic scenario matrices is used as a basis for similarity to optimize speed and predict future vehicle speeds. Moreover, a multi-objective intelligent EMS based on deep reinforcement learning (DRL) is employed, utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm to comprehensively consider vehicle dynamics, energy consumption economy, and the degradation of batteries. This establishes an end-to-end intelligent EMS framework for CAR-EEV and accelerates training convergence through prioritized experience replay. Finally, simulations and bench tests demonstrate that this intelligent EMS significantly improves vehicle dynamics and battery life, with notably enhancing real-time performance and effectiveness.