In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types.

与确定到目的地的最短距离或最快路径的常规路线选择系统相比，这项工作通过找到能量最优的解决方案，同时考虑了电池的压力，设计了专门针对电动汽车的路线计划。在找到电动汽车能耗的物理模型（包括供暖，空调和其他额外负载）后，将街道网络建模为具有节点和加权边的网络，以便应用最短路径算法来找到最小的边缘成本。修改了Bellman-Ford算法（Yen算法）的一种变体，以便可以包括电池约束。从而，改进的Yen算法可帮助解决多目标优化问题，它具有三个优化变量，分别代表能量消耗（车辆到达目的地并可能达到最高充电状态），行驶时间和电池的循环寿命（使数量最小化）通过最小化消耗或再生的能量来减少充电/放电周期。对于优化问题，将权重分配给每个变量，以强调一个或另一个。路线规划系统已在奥地利郊区和加利福尼亚州旧金山市进行了测试。地形对能量消耗和电池运行有很大影响，因此对路线的选择也有很大的影响。该算法根据不同的偏好找到不同的结果，将权重放在多目标优化的决策变量上。而且，针对不同的外部温度和天气条件以及不同的车辆类型进行了测试。