Development of a one-day driving cycle for electric ride-hailing vehicles

Developing electric ride-hailing vehicles (ERVs) is an integral part of promoting green transportation. To understand how ERVs operate, and in turn to improve their battery systems, we have developed a comprehensive, methodological framework to construct their utilization patterns and driving cycles. The methodology is based on a multi-state model (MSM). It builds one-day activity patterns of electric vehicles that belong in an ERV fleet. The driving patterns outline when (time of occurrence), for how long (duration), and under what battery conditions (state of charge) a driver typically decides to either operate or charge a vehicle. Two driving patterns, pointing toward two typical types of drivers, have been identified. By utilizing operational data in Xiamen, China, obtained from Didi Chuxing, we propose a novel method to build the first driving cycle series of ERVs, which can delineate how ERV drivers drive under the operating state. The cycle series consists of four driving cycles, synthesized by the micro-trip-based method using operational data from four time slices. Analysis against real data has shown that our method significantly outperforms existing methods, demonstrating the representativeness of the cycles.