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

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