Economic Adaptive Cruise Control for a Power Split Hybrid Electric Vehicle

• Published in: IEEE transactions on intelligent transportation systems Vol. 21; no. 10; pp. 4161 - 4170
• Main Authors: Liu, Haiou, Miao, Chengsheng, Zhu, Guoming G.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Computer simulation; Controllers; Cruise control; Economic adaptive cruise control; Economics; Electric power distribution; Electric vehicles; Energy efficiency; Engines; Feedback control; Feedforward control; Fuel consumption; Fuel economy; Fuels; global power distribution; hybrid electric vehicle; Hybrid electric vehicles; linear quadratic tracking; Local optimization; Mechanical power transmission; Motion planning; Optimization; Power consumption; Powertrain; Strategy; Traffic congestion; Traffic jams; Traffic models; Traffic speed; vehicle macroscopic motion planning
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2938923

An extended adaptive cruise control (ACC), called economic adaptive cruise control, is proposed to improve the fuel economy for power split hybrid electric vehicles (HEV) by optimizing the vehicle route, speed, and powertrain control simultaneously. The economic route and speed for a given origin-destination pair with the given expected trip time are optimized by a proposed vehicle macroscopic motion planning method, where the powertrain is optimized by a proposed global power distribution (GPD) strategy. The HEV powertrain controller, consisting of feedforward and feedback control schemes, is developed for real-time cruise control, where the former is based on the GPD strategy and the latter is based on the receding horizon linear quadratic tracking method. In addition, a mode-switch local optimization method is used to modify the reference speed for passing, traffic jam and light crossing. A co-simulation model, combining the SUMO traffic model and Simulink hybrid powertrain model, is developed and used for validating the proposed EACC strategy. The co-simulation results indicate that the proposed EACC is able to decrease the fuel consumption by more than 30% comparing with the power follower strategy adopting the fastest route. Note that even with the same powertrain controller, the economic route and speed stratgey is able to improve the fuel economy by 14.21%, comparing with the fastest route without optimization.