Energy-optimal routing for electric vehicles using deep reinforcement learning with transformer

Mengcheng Tang, Weichao Zhuang, Bingbing Li, Haoji Liu, Ziyou Song and Guodong Yin

Applied Energy, 2023, vol. 350, issue C, No S0306261923010759

Abstract: This paper presents an end-to-end deep reinforcement learning (DRL) approach aimed at efficiently determining energy-optimal routes for a group of electric logistic vehicles, with the objective of minimizing operating costs. First, an Energy-Minimization Electric Vehicle Routing Problem (EM-EVRP) is formulated with an energy consumption model for electric vehicles, rather than Distance Minimization EVRP commonly favored in the literature. The energy consumption model incorporates several factors such as vehicle dynamics, road information, and charging losses. Then, the problem is reformulated based on the Markov decision process and solved using the transformer-based DRL method. The policy network is designed following the Transformer structure, including an encoder, a feature embedding module, and a decoder, where the feature embedding module is added to provide contextual information. Finally, extensive experiments demonstrate the superior of the proposed DRL method over existing learning-based methods and conventional methods, in solving both EM-EVRP and DM-EVRP. Notably, the formulated EM-EVRP achieves greater cost reduction than the traditional DM-EVRP.

Keywords: Electric vehicle routing problem; Minimize energy consumption; Deep reinforcement learning; Transformer (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261923010759
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:350:y:2023:i:c:s0306261923010759

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2023.121711

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().