Master Thesis MSTR-2022-57

BibliographyPrakash, Neha: Matrix-based Route Planning for Battery Electric Vehicles.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 57 (2022).
59 pages, english.

Driving a Battery Electric Vehicle (BEV) is an action-packed experience and a more prominent alternative to internal-combustion-engine vehicles. The major barrier to widespread Battery Electric Vehicle adoption, however, continues to be range anxiety. In the era of battery electric vehicles, particularly longer trips are a problem because of limited range, the necessity to charge, and consequently higher travel time. This calls for breakthroughs that increase the popularity of battery electric vehicles. There have been significant developments for determining the best battery-powered routes. The distribution of specific routing techniques, the choice of charging locations, and the amount of energy incorporated into the high voltage storage system at discrete charging sites are all included in the planning of these routes. These globally optimal algorithms, however, are NP-hard and have high computational costs. The real-time applicability of routing algorithms and the associated strategies is thus greatly influenced by heuristics in generating semi-optimal routes. In this work, the search for a charging station is purely geometric and distance-based. A graph with a sizable number of connected nodes, each of which is a potential charging station, is provided as part of the process. A Least Cost Branch and Bound route planning algorithm is then applied to the graph with the calculated weighting factors. The weighting factors are computed for each connection between the nodes and are based on vehicle-specific parameters. The charging and energy consumption model are considered for realistic route calculation. The start and destination location along with the battery charge at the start of the trip is considered for the calculation. The developed matrix routing (MR) approach is then evaluated by bench-marking it to the existing industrial solution provided by HERE Technologies. Total travel time, charging duration analysis, drive time analysis, number of recharging stops, total distance covered, and the energy consumption of a planned route are taken into account when quantifying the matrix route approach. As a result, we get to the conclusion that 91% of the time, the matrix routing approach generates a route plan that is preferable to the conventional HERE routing approach in terms of overall journey time.

Department(s)University of Stuttgart, Institute of Architecture of Application Systems
Superviser(s)Aiello, Prof. Marco; Georgievski, Dr. Ilche; Mertens, Maximilian
Entry dateNovember 29, 2022
   Publ. Computer Science