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

Siyang Wang and Xianke Lin

Applied Energy, 2020, vol. 271, issue C, No S0306261920307455

Abstract: 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.

Keywords: Eco-driving; Energy management strategy; Connected and automated vehicle; Hybrid electric vehicle; Intelligent transportation system; Model predictive control (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (22)

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DOI: 10.1016/j.apenergy.2020.115233

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