Integrating traffic simulation and propulsion modeling to estimate EV range for drivers
31 August 2013
A team at Ford Research and Advanced Engineering has explored the integration of traffic simulation and vehicle propulsion modeling to estimate energy consumption—and hence range—for battery electric vehicles (BEV) to better assist BEV owners in route planning. A paper on their work, initially presented at the SIMULTECH 2011 conference in Noordwijkerhout, The Netherlands, is newly published in the book Simulation and Modeling Methodologies, Technologies and Applications.
To alleviate range anxiety, new vehicle electronics features are needed to help vehicle operators make diving choices that avoid discharged battery situations, extend vehicle range, and combine charging with other good uses of time. Development of these features requires practical meta-models that can accurately predict energy consumption on public roads.
Building meta-models from field-test data requires statistical regression of public-road vehicle data (PRVD) over very large geographic areas. At present there are not enough production test vehicles available to collect a sufficient amount of data, noise factors are not well controlled, and data collection is too time consuming to support product launch. As a results modeling and simulation are essential tools in analysis of BEV performance.
In this work we propose implementation of traffic simulation combined with propulsion modeling for determining electric vehicle energy consumption. We use traffic micro-simulation to create surrogate PRVD data that has many of the properties of actual PRVD data, specifically capturing the stochastic nature of vehicles moving through roads with traffic. The surrogate data is analyzed using propulsion simulation to estimate the amount of energy the vehicles will consume in a specific driving maneuver to derive statistical information.—MacNeille et al.
MacNeille, Perry; Gusikhin, Oleg; Jennings, Mark; Soto, Ciro; Rapolu, Sujith. “Integration of Traffic Simulation and Propulsion Modeling to Estimate Energy Consumption for Battery Electric Vehicles” in N. Pina et al. (Eds.) Simulation and Modeling Methodologies, Technologies & Appl., Springer-Verlag Berlin Heidelberg (2013) doi: 10.1007/978-3-642-34336-0_1
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