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Energetic Map Data Imputation: A Machine Learning Approach

Straub, Tobias; Nagy, Mandy; Sidorov, Maxim; Tonetto, Leonardo; Frey, Michael; Gauterin, Frank

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. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000118920
Veröffentlicht am 04.05.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1996-1073
KITopen-ID: 1000118920
Erschienen in Energies
Band 13
Heft 4
Seiten Art. Nr.: 982
Vorab online veröffentlicht am 22.02.2020
Schlagwörter electric mobility, big data, artificial intelligence, supervised machine learning, regression, classification, missing data imputation
Nachgewiesen in Web of Science
Scopus
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