University of Technology Sydney. Faculty of Engineering and Information Technology.A comprehensive research on the development process of an extended range logistics vehicle (ERLV) is conducted in the automobile theory and vehicle control points of view. The research priority of the study consists of three parts: the background review and the vehicle structure design, the consumption and cost analysis and the innovation on its control strategy. Firstly, a comprehensive background investigation and literature review of the energy management strategy is discussed. Secondly, an extended range mathematical platform for a logistics van is proposed. It presents a thorough energy consumption and Total cost of ownership analysis for an ERLV. Dynamic programming (DP) algorithm is adopted in the energy management strategy optimization to reveal the optimal energy consumption. Thirdly, a novel auxiliary power unit charging strategy with multi-object optimization is proposed using Reinforcement learning algorithms on the ERLV platform to achieve high fuel conversion efficiency while maintaining battery charging health. The comparative results show that the Soft Actor-Critic (SAC) had a 36% faster convergence speed than a traditional algorithm while providing a smoother and more stable action space. The fuel consumption with SAC also outplays by around 3%, which achieves almost 90% of the DP results
