Adoption of electric drive vehicles (EDVs) presents an opportunity for reduction of greenhouse gas (GHG) emissions. From an individual vehicle standpoint however, the GHG reduction can vary significantly depending on the type of driving that the vehicle is used for. This is primarily due to conventional vehicles (CVs) having poor energy efficiency in stop-and-go city-like driving compared to their performance in steady highway-like driving. This study attempts to examine the magnitude of the differential in GHG reduction benefit for real driving behaviors obtained from California Household Travel Survey (CHTS-2013). Recorded vehicles speed traces are analyzed via a fuel economy simulator then a hybrid support vector clustering (SVC) technique is applied to form groups of vehicle samples with similar driving behaviors. Unlike many clustering techniques, SVC does not impose a pre-dictated number of clusters, but has a number of parameters that must be tuned in order to obtain meaningful results. Tuning of the parameters is performed via a multi-objective evolutionary algorithm (SPEA2) after formulating the cluster tuning as a two-objective problem that seeks to maximize: i) differential benefit in GHG reduction, and ii) fraction of the population that groups of vehicles represent. Results show that replacing a CV with its equivalent hybrid (HEV) can reduce GHG emissions per mile of driving by 2 to 2.5 times more for a group of vehicles (best opportune for an EDV) compared to the less opportune group.

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