Cooperative optimization of velocity planning and energy management for connected plug-in hybrid electric vehicles

Abstract

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.

Cooperative Ecological Adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on Approximate Dynamic Programming

“…We firstly analyze the sensitivity of the proposed method with different cooperation horizons. Referring to the parameter setting of the common prediction-based ACC approaches [22,42], six cooperation horizon samples are discretely chosen from 1 s to 10 s, i.e., [1,2,3,5,8,10] . In this test, the proposed method is compared with the PID-ACC method.…”

“…Eco-driving control mainly aims at planning velocity trajectories to reduce energy consumption, and has attracted much research attention [9,10]. The studies of eco-driving control can be divided into three categories based on different driving scenarios, i.e., single-vehicle, car-following and multivehicle [11].…”

Rule-based energy management of hybrid electric vehicles focus on load following strategy

“…Fuzzy logic energy management (Singh et al (2021)) or "Equivalent Consumption Management Strategy" (Li and Jiao (2019))) are also more and more developed. A lead of development of these laws now concerns predictive energy management laws based on the knowledge of future driving conditions (speed, trac slopes ...) (Liu et al (2021)). Neural networks (often linked with charge depleting operation) and predictive energy management are also a recent lead of development (Zhou et al (2021)).…”

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

“…The optimization results were solved and accelerated by PMP considering the vehicle speed, the power distribution, and the engine working point. In Liu et al (2021), an ITS-based EMS was proposed based on IoV and real-time traffic information. The speed limit curve was proposed and transformed into a spatial domain problem.…”