Electric Vehicle Routing Problem with Machine Learning for Energy Prediction
Artikel i vetenskaplig tidskrift, 2020

Routing electric commercial vehicles requires taking into account their limited driving range, which is affected by several uncertain factors such as traffic conditions. This paper presents the time-dependent Electric Vehicle Routing Problem with Chance- Constraints (EVRP-CC) and partial recharging. The routing method is divided into two stages, where the first finds the best paths and the second optimizes the routes. A probabilistic Bayesian machine learning approach is proposed for predicting the expected energy consumption and variance for the road links, paths and routes. Hence it is possible to consider the uncertainty in energy demand by planning charging within a confidence interval. The energy estimation is validated with data from electric buses driving a public transport route in Gothenburg-Sweden as well as with realistic simulations for 24 hours traffic in the city of Luxembourg connected to a high fidelity vehicle model. Routing solutions are compared with a deterministic formulation of the problem similar to the ones found in the literature. The results indicate high accuracy for the energy prediction as well as energy savings and more reliability for the routes.

Machine Learning

Bayesian inference

Electric vehicles

Eco-routing

Vehicle Routing

Energy consumption

Green logistics

Författare

Rafael Basso

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Ivan Sanchez-Diaz

Chalmers, Teknikens ekonomi och organisation, Service Management and Logistics

Transportation Research Part B: Methodological

0191-2615 (ISSN)

EL FORT - El Flottor Optimering i Real-Tid

VINNOVA, 2014-07-01 -- 2017-06-30.

EL FORT - Optimering av elfordonsflotta i Real-Tid - (Fas 2)

VINNOVA, 2018-03-01 -- 2019-12-31.

Styrkeområden

Transport

Ämneskategorier

Transportteknik och logistik

Infrastrukturteknik

Annan samhällsbyggnadsteknik

Mer information

Skapat

2020-12-28