Abstract
Improving fuel efficiency and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel efficiency in laboratory controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This study uses standard driving cycles as a base for comparison, while aiming to develop fuel efficiency estimates based on personalized driving cycles that can inform consumers’ vehicle purchase and use decisions. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: aggressive, normal, or calm. Then the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate personal fuel efficiency for conventional vehicles and HEVs. To optimize personal fuel efficiency estimates, the Equivalent Consumption Minimization Strategy (ECMS) is applied. Depending on the driving style (aggressive, normal, or calm) and the driving scenario (city or highway driving), conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel efficiency was 13% lower in calm city driving, but almost 34% higher for aggressive highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on application of case-based reasoning and used to tune the power distribution in HEVs, their fuel consumption can improve by up to 12% in city driving. Implications and limitations of the findings are discussed in the paper.
