Electric Vehicle Routing Problem with Machine Learning for Energy Prediction
Journal article, 2021

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.

Energy consumption

Bayesian inference

Eco-routing

Machine Learning

Electric vehicles

Green logistics

Vehicle Routing

Author

Rafael Basso

Volvo Group

Balázs Adam Kulcsár

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Ivan Sanchez-Diaz

Chalmers, Technology Management and Economics, Service Management and Logistics

Transportation Research Part B: Methodological

0191-2615 (ISSN)

Vol. 145 24-55

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

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

EL FORT - El Flottor Optimering i Real-Tid

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

Areas of Advance

Transport

Subject Categories

Transport Systems and Logistics

Infrastructure Engineering

Other Civil Engineering

DOI

10.1016/j.trb.2020.12.007

More information

Latest update

2/11/2021