Multi-criteria electric vehicle routing planning

EV route planning are widely studied recent days, the key problem is always how to deal with the different criteria. Drivers may choose the shortest travel time or prefer to charge at a cheaper charging station, or even worse (for the planner), at first half of the journey choosing the shortest travel time and at the other half, when almost run out of battery, when to the cheapest charging stations nearby. This requires the planner to not only fully consider each criterion, but also be able to quickly make plans. A common approach is to use a Dijkstra's algorithm with a Pareto set. Obviously, as the size of the map increases and the criteria considered increase, the resulting Pareto set will become unmanageable. Therefore, how to deal with the Pareto set becomes the key to the path planning problem of electric vehicles. For example, use a comprehensive criterion instead of multiple criteria, such as using the 'cost' criterion to integrate energy consumption and charging price. In this thesis, a classic Dijkstra's algorithm with Pareto sets is constructed in Matlab. It is used to compare the impact on the final output when different numbers and types of criteria are used. At the same time, according to the comparison results, a more appropriate Pareto set setting is proposed under different requirements (for example, it is more desirable to reduce travel time).