Abstract
Electric vehicles are becoming more important in our society. Using them in a fleet to minimize energy cost is, therefore, a compelling opportunity for taxi companies. It is crucial to develop accurate models that estimate energy consumption for traveling from one point to another. Consumption can be estimated using a physical model, but such a model fails to fit real-world data, especially in taxi-driving conditions. We compare different approaches to learn from historical data in order to correct/improve the physical model. Similar techniques can be used to estimate consumption for a new vehicle model, which can be useful for companies that want to add a new vehicle model for which they do not have historical data.
Highlights
TEO Taxi (Taxelco Inc., Montreal, QC, Canada) is a company that runs a fleet of 100% electric taxis
Since official ratings lack precision especially in winter conditions [2,3,4], the need for an accurate energy consumption prediction model is a real preoccupation for the company
We propose a new hybrid model that uses interaction terms and historical data to further enhance its accuracy
Summary
TEO Taxi (Taxelco Inc., Montreal, QC, Canada) is a company that runs a fleet of 100% electric taxis (approximately 170 cars). This leads to cost reduction and a reduction of greenhouse gas emissions [1]. Since official ratings lack precision especially in winter conditions [2,3,4], the need for an accurate energy consumption prediction model is a real preoccupation for the company. This is mandatory to allow optimized usage of each owned vehicle as well as for future acquisitions. TEO Taxi wants to be able to use the developed model to predict more accurately the consumption for new/unknown vehicle models early on
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