Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm

In recent years, electric ride-hailing has increased considerably in the taxi industry with the development of battery electric vehicles (BEVs) and implementation of greenhouse gas (GHG) emission regulations. Private BEVs are charged mostly in private garages, while electric ride-hailing services require public charging stations (CSs). This work uses an improved genetic algorithm (GA) to locate public CSs by considering the investment of CS operators and the travel costs of BEV owners. A case study is presented with large-scale order data collected from the ride-hailing fleet of the city of Haikou and charging data from the electric ride-hailing fleet of the city of Shanghai. The elastic demand for electric ride-hailing is also considered by incorporating feedback between congestion at the CS and the geographical area. The proposed methodology uses the multipopulation genetic algorithm (MPGA) to provide more feasible allocations for public CSs and could reduce the total cost by 7.6%.

Automated summary

The study uses an improved genetic algorithm to determine the location of public charging stations, considering the investment of charging station operators and the travel costs of BEV owners, reducing the total cost by 7.6%.