The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus

As prediction of trip-based electricity consumption has become an prerequisite for the deployment of large-scale EB fleets, this study has established random forest-based models to systematically investigate the impacts of environmental conditions, route characteristics, and dynamic traffic conditions. The models have been performed on real-world data collected from 1024 EBs over five consecutive months in Shenzhen, China. The results show that considering all the influencing variables can significantly enhance the prediction performance, but comparatively speaking, the route characteristics contribute the most among the three categories and involving more variables demonstrates greater advantages within the trip length under 20 km. It is also found that the trip length, the number of bus stops and the number of the traffic lights passed rank the top three most influencing factors, while the wet-dry condition is the least one. In addition, the variations under five operation scenarios show similar trend. The trip length and average travel speed are inversely proportional to the specific electricity consumption, while the number of bus stops visited, traffic lights passed, and ambient temperature exhibit a gentle proportional relationship. Moreover, it is suggested to plan the new bus line over 10 km in terms of reducing electricity consumption per kilometre. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study established random forest-based models to systematically investigate the impacts of environmental conditions, route characteristics, and dynamic traffic conditions on trip-based electricity consumption of electric buses. The results show that considering all influencing variables can significantly enhance prediction performance, with trip length, number of bus stops, and number of traffic lights passed being the most influential factors.