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

Houzhi Li, Qingwen Han, Xueyuan Bai, Li Zhang, Wen Wang (), Wenjia Chen and Lin Xiang
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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

Energies, 2024, vol. 17, issue 21, 1-17

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.

Keywords: electric vehicle charging station recommendation; user charging preference; user comment data; LightGBM; singular value decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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