Optimal charge scheduling and on-board control of an urban electrified BRT fleet considering synthetic representative driving cycles.

This paper presents a comprehensive approach for optimal charge scheduling and on-board vehicular control of electrified fleets based on synthetic driving cycles. The proposed approach is conducted within a real case-study in Cairo, Egypt, whereto a representative distance-based driving cycle has been synthesized using K-means clustering over a sliding horizon of gathered data-sets. Two multi-objective problems defining optimal charge scheduling and vehicular control have been formulated to achieve minimal energy consumption and operating cost of the fleet . Non-dominant genetic algorithm (NSGA-II) has been implemented to solve the optimization problems jointly considering fluctuating electricity cost of the grid. The comparative evaluation of results reveals an improvement of 19% and 28% in energy consumption and retention of on-board energy accordingly, with less than 2% mitigation of driveability. Moreover, a reduction of 40.8%, 20%, and 21.9% in fleet size, required charging stations, and annual recharging cost respectively has been realized. The main innovation of this work can be put forward as the ability to address the above-mentioned quadrilateral objectives of electrified fleets in a single comprehensive approach, considering synthetic driving cycles and electricity prices to yield a customized-optimal solution.