TY - JOUR
T1 - Cooperative optimization of velocity planning and energy management for connected plug-in hybrid electric vehicles
AU - Liu, Yonggang
AU - Huang, Zhenzhen
AU - Li, Jie
AU - Ye, Ming
AU - Zhang, Yuanjian
AU - Chen, Zheng
PY - 2021/3/2
Y1 - 2021/3/2
N2 - In this paper, a cooperative optimization strategy is proposed for velocity planning and energy management of intelligent connected plug-in hybrid electric vehicles. Based on the established vehicle model, a mathematical analytical method is investigated to convert the driving cycles from the original time based profiles to the driving distance based speed values. Then, the iterative dynamic programming is exploited to achieve the synergistic optimization in terms of speed planning and power allocation of the vehicle with the consideration of gear shifting limits and speed fluctuation. To meet the requirement of trip duration limitation which may be violated due to autonomous speed planning, the terminal driving time is constrained by adding a time adjustment factor to the cost function. The simulation results suggest that the proposed strategy attains the collaborative optimization with high efficiency in terms of speed planning and driving power distribution. In addition, the proposed strategy leads to significant reduction of the energy consumption cost under the constraints of allowed speed variation ranges.
AB - In this paper, a cooperative optimization strategy is proposed for velocity planning and energy management of intelligent connected plug-in hybrid electric vehicles. Based on the established vehicle model, a mathematical analytical method is investigated to convert the driving cycles from the original time based profiles to the driving distance based speed values. Then, the iterative dynamic programming is exploited to achieve the synergistic optimization in terms of speed planning and power allocation of the vehicle with the consideration of gear shifting limits and speed fluctuation. To meet the requirement of trip duration limitation which may be violated due to autonomous speed planning, the terminal driving time is constrained by adding a time adjustment factor to the cost function. The simulation results suggest that the proposed strategy attains the collaborative optimization with high efficiency in terms of speed planning and driving power distribution. In addition, the proposed strategy leads to significant reduction of the energy consumption cost under the constraints of allowed speed variation ranges.
U2 - 10.1016/j.apm.2021.02.033
DO - 10.1016/j.apm.2021.02.033
M3 - Article
VL - 95
JO - Applied Mathematical Modelling
JF - Applied Mathematical Modelling
SN - 0307-904X
ER -