Energy consumption prediction and routing for electric commercial vehicles
Doctoral thesis, 2021
This thesis focuses on the development of energy consumption prediction and routing methods for electric commercial vehicles. The first part presents an overall background and short state of the art review. The main contributions are presented in the second part. The included articles are a step-by-step development of the methods, each covering different aspects of the problem. The first paper presents a deterministic energy prediction model integrated into routing models. The second paper proposes a probabilistic energy estimation method based on Bayesian machine learning and adds chance-constraints into the routing problem in order to plan charging within a confidence interval. The third paper covers routing with dynamic customers and stochastic energy consumption, proposing a solution method based on Safe Reinforcement Learning to minimize the risk of battery depletion by planning charging in an anticipative way. All papers are validated with realistic simulations as well as logged data. The results indicate that it is possible to save energy and reduce the risk of running out of energy while en route.
Chalmers, Electrical Engineering, Systems and control, Automatic Control
Electric Vehicle Routing Problem with Machine Learning for Energy Prediction
Transportation Research Part B: Methodological,; Vol. 145(2021)p. 24-55
Energy consumption estimation integrated into the Electric Vehicle Routing Problem
Transportation Research Part D: Transport and Environment,; Vol. 69(2019)p. 141-167
Dynamic Stochastic Electric Vehicle Routing Problem with Safe Reinforcement Learning
This thesis introduces energy consumption prediction and routing methods for electric commercial vehicles based on probabilistic machine learning. The included articles present a systematic development, each covering different aspects of the problem. The objective is to minimize energy consumption and minimize the risk of battery depletion while the vehicles are driving, by planning routes and charging in real-time and considering uncertainty. Consequently, these methods can help increase confidence in these vehicles, enabling their integration in current transport operations.
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 Systems and Logistics
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4902
Chalmers University of Technology
Landahlsrummet (room 7430), Hörsalsvägen 11, Göteborg
Opponent: Professor Jesus Gonzalez Feliu, Department of Project Purchasing and Supply Chain, La Rochelle Business School, France