Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios

• Published in: Applied energy 2020-08, Vol.271, p.115233, Article 115233
• Main Authors: Wang, Siyang, Lin, Xianke
• Format: Article
• Language: English
• Subjects: Connected and automated vehicle; Eco-driving; Energy management strategy; Hybrid electric vehicle; Intelligent transportation system; Model predictive control
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2020.115233

•A bi-level MPC-based eco-driving strategy for CAHEVs is proposed.•The control strategy uses real-time traffic information via V2V and V2I.•The driving scenario classifier is designed for driving in mixed driving scenarios.•The strategy is tested in a realistic traffic simulation environment.•The results are compared to the conventional rule-based strategy. This paper proposes a bi-level eco-driving control strategy for connected and automated hybrid electric vehicles (CAHEVs) under mixed driving scenarios. First, the hybrid electric vehicle powertrain is modelled, and the communications via Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) are introduced as the main data sources for the decision-making of the control system. Next, the problem is divided into three objectives, namely, (1) safe driving, (2) energy management, and (3) exhaust emission reduction. Based on the real-time road information, the driving scenario classifier (DSC) works towards determining the corresponding vehicle mode on which the cost function can be adjusted accordingly. The simulation is carried out in a realistic urban traffic simulation environment in SUMO. The results show that with the proposed model predictive control (MPC)-based strategy applied, safe driving in a trip involving a mixture of driving scenarios can be guaranteed throughout the entire driving. In addition, in comparison to the rule-based benchmark strategy, the proposed strategy can reduce the fuel consumption by 34.10% with battery kept in a healthy state of charge range, and the exhaust emissions (HC, CO, and NOx) are reduced by 25.36%, 72.30%, and 30.39%, respectively, which demonstrates the effectiveness and robustness of the proposed MPC-based strategy for CAHEVs.