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  -