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Electric Vehicle (EV) battery capacity is limited, so EV routing must trade off travel time for energy consumption, which grows quad-ratically with speed. Current multi-parameter EV routing methods assume accurate estimates of time and energy consumed, but current models for obtaining these estimates cannot capture this time-energy tradeoff in a sufficiently flexible way. We present a new approach to EV modeling that addresses such shortcomings. Conventional wisdom holds that models operating at finer time granularities yield better energy consumption estimates. We first show that such is not necessarily the case, by defining a new structuring abstraction for vehicle speed profiles called phases, which models energy consumption accurately at lower temporal granularity. We also address the challenge of generating speed profiles for planned trips with realistic variance in travel times and energy consumed. Our method combines the phase abstraction with Markov chains and kernel density estimation to learn these variations, and construct realistic vehicle speed profiles for real-world routes. Using 52 hours of driving data collected on a Nissan Leaf, we show that our model achieves a per-trip accuracy better than even that of current microscopic models and generates speed proiles that accurately model time and energy consumption at the trip level.

The Phase Abstraction for Estimating Energy Consumption and Travel Times for Electric Vehicle Route Planning