Development of E-rickshaw driving cycle (ERDC) based on micro-trip segments using random selection and K-means clustering techniques

Abstract In India, auto rickshaws are the most convenient and cheapest mode of near-to-door transport in both rural and urban areas. Such vehicles powered with internal combustion engines (ICEs) are one of the main sources of pollutant on urban corridors. One way to minimize emissions is to use electric motors in place of ICE. To evaluate the vehicle performance, energy consumption, driving behavior, optimal design and management of such electric vehicles, driving cycle is an important tool. So far, only limited studies exist on the development of a driving cycle for e-rickshaw. Moreover, these studies are concentrated in urban traffic environment and research accounting rural and urban environment together remain unexplored. In this study, real world driving data for 100 trips of e-rickshaw are collected on a road stretch passing through an rural and urban setting. A high-end GPS data logger was used to collect vehicle kinematics such as continuous speed profile, acceleration/deceleration, heading, and vehicle position coordinates. Nine different driving characteristics representing actual traffic condition are identified and used for developing e-rickshaw driving cycle (ERDC). Two approaches, random selection and k-means clustering are explored to arrive at best representative ERDC using micro-trips technique. The analysis results revealed that k-means clustering outperforms the random selection method with additional benefit of accounting traffic conditions systematically. The insights from this study can be used to understand and model the performance of e-rickshaw, in terms of energy consumption and driving characteristics, compared to other fossil-fuel driven automobiles.