Abstract

Despite a rapid increase of public interest for electric mobility, several factors still impede Battery Electric Vehicles’ (BEVs) acceptance. These factors include their limited range and inconvenient charging. For mitigating these limitations to users, certain BEV-specific services are required. Therefore, such services provide a reliable range prediction and routing, including charging-stop planning. The basis of these services is a precise and reliable Energy Demand (ED) prediction. For that matter, aggregated fleet-vehicle data combined with map-specific data (e.g., road slope) form an energetic map, which can serve for precise ED predictions. However, data coverage is paramount for these predictions, more specifically regarding gapless energetic maps. This work aims to eliminate the energetic map’s gaps using two Machine Learning (ML) approaches: regression and classification. The proposed ML solution builds upon the synergy between map-information and crowdsourced driving profiles of 4.6 million kilometres of training and test traces. For evaluation, two test-scenarios capture the models’ performance for the analysed problem in two perspectives. First, we evaluate our ML models, followed by the problem-specific energetic evaluation perspective for better interpretability. From the latter, the results indicate energetic map data imputation performs promisingly better when using the regression instead of the classification model.

Highlights

  • Environmental awareness in both society and legislation demand a reduction of greenhouse gas emissions

  • This chapter depicts the results from the ML perspective (MLP) followed by the problem-specific energetic perspective (EP)

  • There are direct, Machine Learning (ML)-based and analytical energy demand prediction models in the literature. These models source their inputs for prediction from map data or vehicle data

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Summary

Introduction

Environmental awareness in both society and legislation demand a reduction of greenhouse gas emissions. This desire fosters interest in electrified personal mobility. The limited range is among the statistically significant (p < 0.01) factors decreasing the acceptance of electric vehicles, besides high prices and issues related to charging [1,2] All these factors inhibit potential buyers from purchasing electric vehicles [1]. An alternative solution is required to lower the impact of the named impediments Such services as precise range estimation and BEV route planning can achieve the required acceptance gain [2].

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