Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

Haochen Xu, Niaona Zhang (), Zonghao Li, Zichang Zhuo, Ye Zhang, Yilei Zhang and Haitao Ding
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Haochen Xu: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Niaona Zhang: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Zonghao Li: State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130025, China
Zichang Zhuo: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Ye Zhang: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Yilei Zhang: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Haitao Ding: State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130025, China

Sustainability, 2023, vol. 15, issue 22, 1-16

Abstract: Eco-driving is a driving vehicle strategy aimed at minimizing energy consumption; that is, it is a method to improve vehicle efficiency by optimizing driving behavior without making any hardware changes, especially for autonomous vehicles. To enhance energy efficiency across various driving scenarios, including road slopes, car following scenarios, and traffic signal interactions, this research introduces an energy-conserving speed planning approach for self-driving electric vehicles employing reinforcement learning. This strategy leverages vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to acquire real-time data regarding traffic signal timing, leading vehicle speeds, and other pertinent driving conditions. In the framework of rolling horizon reinforcement learning (RHRL), predictions are made in each window using a rolling time domain approach. In the evaluation stage, Q-learning is used to obtain the optimal evaluation value, so that the vehicle can reach a reasonable speed. In conclusion, the algorithm’s efficacy is confirmed through vehicle simulation, with the results demonstrating that reinforcement learning adeptly modulates vehicle speed to minimize energy consumption, all while taking into account factors like road grade and maintaining a secure following distance from the preceding vehicle. Compared with the results of traditional adaptive cruise control (ACC), the algorithm can save 11.66% and 30.67% of energy under two working conditions.

Keywords: electric vehicle; eco-driving; energy consumption; RHRL; optimal control (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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