%X 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. %O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. %I Elsevier %J Energy %K Eco-driving; Dynamic programming; Model predictive control; Electric vehicles; Energy efficiency %L discovery10183141 %D 2024 %T A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes %A Bingbing Li %A Weichao Zhuang %A Hao Zhang %A Ruixuan Zhao %A Haoji Liu %A Linghu Qu %A Jianrun Zhang %A Boli Chen %V 289