Route planning and energy consumption estimation for electric commercial vehicles

Licentiate thesis, 2017

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 impacts 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 precisely estimate their energy consumption. This thesis gives an overall background and state of the art review in the introductory chapters. The main contributions are presented in the second part. The first paper describes a time-dependent electric vehicle routing problem. It also analyses the different factors that affect energy consumption and routing for electric vehicles. The second paper introduces the Two-stage Electric Vehicle Routing Problem (2sEVRP), with a precise energy consumption estimation model, a first stage to find the best paths between the nodes to be visited and the second stage to find the route considering time-windows and planning charging when necessary. The paper shows numerical experiments with the road network from Gothenburg-Sweden and a high-fidelity vehicle simulation. The results indicate higher precision in energy estimation and savings while routing when comparing to existing approaches from the literature.