Predictive model for energy consumption of battery electric vehicle with consideration of self-uncertainty route factors

摘要

Greenhouse gas emissions caused by urban road mobility have reached unsustainable levels, being partly responsible for both, the current global warming alarming situation and the pollutant emissions risk to the health of citizens. Transport electrification represents an encouraging solution to this problem. The development and improvement of electric vehicle technology combined with transportation management strategies have become relevant research topics, not only in the area of mobility of people, but also to large companies whose supply chain may involve hundreds of commercial vehicles. The costs from running these vehicles represent a significant impact on their finance and controlling fuel usage is often a key contributor to keep running costs under control across their fleet and supply chain. In this context, traffic regulation elements evaluation, considering its uncertainty, could represent a significant influence on the vehicle consumption and, consequently, on its indirect greenhouse gas emissions. For this reason, traffic regulation system analysis may represent a step forward for developing solutions that collaborate with both, pollution and global warming, abatement. The present study proposes a predictive methodology for determining battery electric vehicle consumption variations when modifying traffic regulation elements. It uses stochastic speed profiles for neutralizing human intervention in consumption and multiple linear regressions to predict the energy consumed by the electric vehicle as a function of a set of factors that represent the traffic regulations. The research does not aim to provide particular energy consumption data, but to expose the variations in consumption and emissions caused by urban planning modifications. Model accuracy and achieved conclusions are illustrated through the development of a case study. The methodology could be a help for urban planners, fleet managers, logistics and supply chain decision makers environmentally concerned. (C) 2020 Elsevier Ltd. All rights reserved.