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Electric Vehicle Charging Station Recommendations Considering User Charging Preferences Based on Comment Data

Author

Listed:
  • Houzhi Li

    (State Grid Smart Internet of Vehicles Co., Ltd., Beijing 100052, China)

  • Qingwen Han

    (State Grid Smart Internet of Vehicles Co., Ltd., Beijing 100052, China)

  • Xueyuan Bai

    (School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China)

  • Li Zhang

    (State Grid Smart Internet of Vehicles Co., Ltd., Beijing 100052, China)

  • Wen Wang

    (School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China)

  • Wenjia Chen

    (School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China)

  • Lin Xiang

    (School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China)

Abstract

User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations.

Suggested Citation

  • Houzhi Li & Qingwen Han & Xueyuan Bai & Li Zhang & Wen Wang & Wenjia Chen & Lin Xiang, 2024. "Electric Vehicle Charging Station Recommendations Considering User Charging Preferences Based on Comment Data," Energies, MDPI, vol. 17(21), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5514-:d:1513903
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    References listed on IDEAS

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    1. Algafri, Mohammed & Alghazi, Anas & Almoghathawi, Yasser & Saleh, Haitham & Al-Shareef, Khaled, 2024. "Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming," Applied Energy, Elsevier, vol. 372(C).
    2. Fathabadi, Hassan, 2020. "Novel stand-alone, completely autonomous and renewable energy based charging station for charging plug-in hybrid electric vehicles (PHEVs)," Applied Energy, Elsevier, vol. 260(C).
    3. Helferich, Marvin & Tröger, Josephine & Stephan, Annegret & Preuß, Sabine & Pelka, Sabine & Stute, Judith & Plötz, Patrick, 2024. "Tariff option preferences for smart and bidirectional charging: Evidence from battery electric vehicle users in Germany," Energy Policy, Elsevier, vol. 192(C).
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