Search Space Reduction-Supported Multi-objective Optimization of Charging System Configuration for Electrified City Bus Transport System

Significant research efforts have been recently devoted to city bus transport system electrification and its integration into the smart city framework. A key challenge is to determine the optimal charging infrastructure regarding (i) the selection of terminals to be equipped with chargers and (ii) the number of chargers to be installed at each terminal while satisfying the buses' schedule and minimizing the investment cost. To address this issue, the paper proposes a charging system configuration optimization method based on a genetic algorithm, which is aimed at minimizing the number of charging terminals, the number of chargers per terminal and charging-related cumulative time delay of the driving missions. Furthermore, each route needs to be covered with at least one charging terminal to meet the transport system charging sustainability condition. To reduce the wide-range search space of the optimization algorithm and facilitate convergence, a search space reduction is conducted by determining the best charging terminal candidates based on a modified greedy set-cover algorithm.