TY - JOUR N1 - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions. VL - 289 KW - Eco-driving; Dynamic programming; Model predictive control; Electric vehicles; Energy efficiency PB - Elsevier A1 - Li, Bingbing A1 - Zhuang, Weichao A1 - Zhang, Hao A1 - Zhao, Ruixuan A1 - Liu, Haoji A1 - Qu, Linghu A1 - Zhang, Jianrun A1 - Chen, Boli JF - Energy AV - public TI - A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes SN - 0360-5442 Y1 - 2024/02/15/ ID - discovery10183141 UR - https://doi.org/10.1016/j.energy.2023.129916 N2 - This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. ER -