A modal-based approach for estimating electric vehicle energy consumption in transportation networks

摘要: Abstract Electric vehicles (EVs) will play a central role in future energy-efficient and sustainable transportation systems. Compared to the operation of conventional vehicles, EVs provide significantly reduced energy consumption lower operating costs. With fuel use ties directly instantaneous required motive power wheels. Predicting can be much more complex for with hybridized powertrains because onboard vehicle systems are trying balance provision wheels as well manage state charge (SOC) battery pack. Traditional modeling methodologies estimating real-world either depend on numerical analysis laboratory or on-road test data full-system simulation tools. Unfortunately, tools suffer from scaling problems context large network, necessitating development approaches that support network projections modal EV operations applicable rates (energy various modes operation) predict consumption. In this study, new modal-based approach is proposed. The considers variance conditions supports estimation large-scale networks. Department Energy’s tool known Autonomie® used generate specific simulations conditions. A sample models was first developed simulate wide range selected EVs. Classification regression tree (CART) then applied output under distinct conditions, represented by combinations speed, acceleration rate, (SOC). regional travel demand performed Atlanta, GA metropolitan area, integrating variety market share scenarios. CART-derived model-predicted link-by-link traffic attributes estimate fleet framework employs MOVES-embedded driving cycles represent average speed random initial SOC start levels model inputs. results suggest 50% PHEV achieve 30% savings without adding electricity load. This assess network-level wide-variety modeled studies, such evaluating improvement plans, assessing net impact electric grid, forecasting potential benefits electrifying shared-autonomous