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

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