Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning  
Artikel i vetenskaplig tidskrift, 2022

Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefits

Reinforcement Learning

Approximate Dynamic Programming

Energy Consumption

Electric Vehicles

Vehicle Routing

Green Logistics


Rafael Basso

Chalmers, Elektroteknik, System- och reglerteknik

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik

Ivan Sanchez-Diaz

Chalmers, Teknikens ekonomi och organisation, Service Management and Logistics

Xiaobo Qu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Transportation Research Part E: Logistics and Transportation Review

1366-5545 (ISSN)

Vol. 157 157 102496

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