%0 Journal Article
%@ 0360-5442
%A Li, Bingbing
%A Zhuang, Weichao
%A Zhang, Hao
%A Zhao, Ruixuan
%A Liu, Haoji
%A Qu, Linghu
%A Zhang, Jianrun
%A Chen, Boli
%D 2024
%F discovery:10183141
%I Elsevier
%J Energy
%K Eco-driving; Dynamic programming; Model predictive control; Electric vehicles; Energy efficiency
%T A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes
%U https://discovery.ucl.ac.uk/id/eprint/10183141/
%V 289
%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.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.