Energy consumption prediction and routing for electric commercial vehicles

Doctoral thesis, 2021

With the recent growing interest for electric vehicles as one of the initiatives to help tackle pollution and climate change, several opportunities and challenges emerge. This kind of vehicle releases no tailpipe emissions, is quieter, more energy efficient in terms of tank-to-wheels and simpler, which can lead to less maintenance. On the other hand, their battery is still the main limitation in terms of energy capacity, time to recharge, weight and cost. One of the main consequences is a limitation in driving range, which especially affects commercial vehicles. In order to adopt electric trucks for urban distribution of goods, there is a need to improve and adapt current planning tools to take into account their constraints. To plan the routes and charging for these vehicles it is necessary to estimate their energy consumption accurately.

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.