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Route Planning Heuristic Approach Figure 6.8: Heuristic approach. The concept of this approach is to split the driving range circle into two sides split by the angle of 180 degrees. There are considered all stations if, and only if, they are within an angle of 180 degrees to the destination and vice versa. The charging stations which are behind this imaginary line, will be not used. With this approach many queries can be reduced to Google servers which could be equally unnecessary to get the shortest path and because the queries to the remote servers take a while, there will be reduced also the time of running the algorithm. I tried this approach in the already developed algorithm, and it significantly reduced the number of queries to Google servers, but I came across a serious problem in this approach. There can occur several problems. If I consider the start position, then the vehicle’s battery may be discharged, and if there is a station behind the imaginary line which will be not accepted, then this algorithm says that there is no charging station nearby to the start position, and the vehicle must charge the battery at home with the low-current station. The other problem can be that there might be a high-current charging station behind the imaginary line and the algorithm will select another with a lower charging power instead. This problem could be solved if I were to consider all high-current stations as inclusive space behind the line. There can, of course, occur a problem in reaching the destination. The outcome of the algorithm can be that the destination wouldn’t be reached, despite the fact that behind the imaginary line could be a charging station which I could have used to reach the goal. Another problem could be, that in this algorithm I consider the parameter SoC in destination, and if I do not take the route through the high-current station, I will have to charge the battery with the low-current station at the destination. In summary, I decided not to use this approach, although the number of queries would be reduced. We could have used this approach in the situation where the network of stations rises up and we must search in more depth to graph them, but for now the improvement would be at the expense of the accuracy our algorithm. 34PDF Image | Driving Range Estimation and Trajectory Planning
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