Cooperative eco-driving for mixed platoons at signalized intersections with wireless charging lanes

Guosheng Xiao, Zhihong Yao, Shimiao Zhang and Yangsheng Jiang

Energy, 2024, vol. 313, issue C

Abstract: Vehicle electrification is an important way to reduce traffic pollution, but mileage anxiety has been one of the key obstacles constraining the promotion of electric vehicles (EVs). To address this issue, this paper proposes eco-driving cooperation for mixed platoons at signalized intersections considering wireless charging lanes (WCLs). First, a Mixed Space-Time-State Network, composed of a space-time-state network and a stochastic cellular automata model, is proposed to represent the movements of connected autonomous and human-driven vehicles within a unified network. Second, a 0–1 programming model is developed to solve eco-driving within WCLs, which converts nonlinear trajectory optimization into a shortest-path search problem. Then, a specific dynamic programming algorithm is proposed to improve the solving efficiency by reducing the number of feasible state spaces. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed eco-driving approach in different scenarios. The results indicate that (1) the eco-driving of EV and internal combustion engine vehicles is distinctly different when considering the WCL; (2) the proposed cooperative eco-driving can balance the energy composition of the mixed platoon to smooth the trajectory, resulting in a 62 % energy reduction; (3) the longer the WCL, the more pronounced the energy-saving effect. However, the optimal deployment position of the WCL is contingent upon the speed that vehicles pass through the intersection. The results can provide theoretical support for future deployment of WCLs and cooperative eco-driving of mixed platoons.

Keywords: Electric vehicle; Eco-driving; Wireless charging lanes; Mixed platoons; Mixed space-time-state network; Dynamic programming (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224038969

DOI: 10.1016/j.energy.2024.134118

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