Work

Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement

Year: 2024

Type: article

Abstract: Eco-driving control techniques have shown significant potential in reducing energy consumption in urban scenarios. The presence of slow-moving vehicles typically disrupts ecological velocity planning, leading to an increase in energy consumption. To solve it, this study proposes a hierarchical eco-driving control strategy, that integrates speed optimization and lane change decision-making in urban scenarios, to not only ensure traffic efficiency, but also save the energy consumption. Firstly, a data-driven energy model is leveraged in the upper level to estimate the energy consumption of candidate lanes and generate ecological lane change decisions. Then, in the lower level, the preceding vehicles and traffic lights are considered to plan an ecological velocity profile via deep reinforcement learning algorithm after transitions to the target driving lane, thereby enhancing the fuel economy and travel efficiency. A virtual driving environment model is established to verify the proposed method through numerous simulation cases. The results indicate that the proposed method effectively reduces energy consumption while maintaining favorable travel efficiency, compared with conventional benchmarks. Furthermore, the notable improvements are observed particularly in free traffic conditions.

Source: Energy

DOI: 10.1016/j.energy.2024.133294

Authors: Jie Li, Yonggang Liu, Jun Cheng, Abbas Fotouhi, Zheng Chen

Institutions: Guangxi University, Chongqing University, State Key Laboratory of Mechanical Transmission, Cranfield University, Kunming University of Science and Technology

Language: English

Topic: Electric and Hybrid Vehicle Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Affordable and clean energy, Climate action

Open Access status: hybrid

Estimated APC paid: $4,140