A Driver-Centric Long-Trip Schedule Optimizer for Battery Electric Vehicles

As the technology of battery electric vehicles (BEVs) becomes more mature, the number of BEVs in the commercial market has been rapidly growing. Despite significant improvements in BEV driving ranges over the past decade, longer charging times and a limited public charging infrastructure still result in extended travel times for BEV users compared to users of gasoline vehicles. To address this challenge, the fixed route vehicle charging problem (FRVCP) has been extensively studied to reduce travel time by strategically planning the visit to charging stations and optimizing the charging duration. However, the solution of FRVCP primarily focuses on the charging schedule, without considering driver's social activities during the trip (e.g., dining, lodging, and visits to places of attraction). Therefore, this paper develops a driver-centric schedule optimizer by integrating the driver's social activities into FRVCP. The driver's needs of dining and lodging are measured by a temporal first-order human energy model, and the problem is formulated into a mixed-integer programming problem. The proposed optimizer is evaluated through Monte Carlo simulation across 6 routes, demonstrating up to 17.19
^%
savings on travel time compared with a human-like heuristic optimizer. It also shows better capability of enhancing driver's comfort.