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Energetic Map Data Imputation: A Machine Learning Approach

Author

Listed:
  • Tobias Straub

    (Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany)

  • Mandy Nagy

    (Department of Informatics, Technical University of Munich, 85748 Garching, Germany)

  • Maxim Sidorov

    (BMW Group, 80788 Munich, Germany)

  • Leonardo Tonetto

    (Department of Informatics, Technical University of Munich, 85748 Garching, Germany)

  • Michael Frey

    (Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany)

  • Frank Gauterin

    (Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany)

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.

Suggested Citation

  • Tobias Straub & Mandy Nagy & Maxim Sidorov & Leonardo Tonetto & Michael Frey & Frank Gauterin, 2020. "Energetic Map Data Imputation: A Machine Learning Approach," Energies, MDPI, vol. 13(4), pages 1-23, February.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:982-:d:323920
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    References listed on IDEAS

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    1. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    2. Qingyou Yan & Guangyu Qin & Meijuan Zhang & Bowen Xiao, 2019. "Research on Real Purchasing Behavior Analysis of Electric Cars in Beijing Based on Structural Equation Modeling and Multinomial Logit Model," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
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