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Lei Zhang, Yingjun Ji, Xiaohui Li, Zhijia Huang, Dingsong Cui, Haibo Chen, Jingyu Gong, Fabian Breer, Mark Junker, Dirk Uwe Sauer,
Multi-objective charging scheduling for electric vehicles at charging stations with renewable energy generation,
Green Energy and Intelligent Transportation, Volume 4, Issue 4, 2025, 100283, ISSN 2773-1537,

https://doi.org/10.1016/j.geits.2025.100283.

Abstract: The rapid adoption of electric vehicles (EVs) in recent years has posed significant challenges to the safe operation of local grids, particularly due to massive charging operations at public charging stations. This paper proposes a real-time charging scheduling scheme to enable efficient Vehicle-to-Grid (V2G) interactions and facilitate renewable energy integration at public charging stations while accounting for real-world EV charging behaviors. First, an EV charging/discharging behavior database is developed to capture the temporal uncertainty and charging characteristics of both fast- and slow-charging operations on weekdays and weekends. Then a charging pile allocation mechanism is introduced to optimize the charging power distribution for each EV to maximize the operational efficiency of the studied charging station. A micro-grid system model is developed by incorporating efficient V2G interactions and renewable energy integration. Finally, a comprehensive charging scheduling scheme is proposed to achieve a balanced optimization of multiple objectives. Extensive simulation studies are conducted to evaluate the performance of the proposed scheduling method. The results demonstrate that the proposed scheme achieves strong performance across all three selected indicators.

Keywords: Electric vehicles; Charging stations; Micro-grid; V2G; Charging scheduling

Yitong Shang, Wen-Long Shang, Dingsong Cui, Peng Liu, Haibo Chen, Dongdong Zhang, Runsen Zhang, Chengcheng Xu, Ye Liu, Chenxi Wang, Mohannad Alhazmi, Spatio-temporal data fusion framework based on large language model for enhanced prediction of electric vehicle charging demand in smart grid management, Information Fusion, Volume 126, Part B, 2026, 103692, ISSN 1566-2535,

https://doi.org/10.1016/j.inffus.2025.103692.

Abstract: Accurate prediction of electric vehicle (EV) charging demand is pivotal for effective smart grid management and renewable energy integration. However, predicting spatio-temporal EV charging patterns remains challenging due to complex data fusion requirements arising from heterogeneous temporal, spatial, and contextual features, as well as difficulties in effectively integrating multiple modeling approaches. This paper introduces EV-STLLM, a novel spatio-temporal data fusion framework based on Large Language Model explicitly designed for accurate short-term EV charging demand forecasting through innovative integration of data-level and model-level fusion techniques. At the data level, a multi-source embedding module is developed to seamlessly fuse temporal features (e.g., time slots, weekdays), spatial heterogeneity (e.g., geographical location), and contextual charging behaviors into a unified representation via embedding convolutional network. At the model level, a large language model (LLM) is employed to capture global spatiotemporal dependencies, enhanced with Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning, substantially reducing computational costs while maintaining prediction robustness. Using a comprehensive real-world dataset comprising over 830,000 EV charging records across 16 districts and 331 subdistricts in Beijing, we validate EV-STLLM across multiple forecasting scenarios (district and subdistrict levels, one-step and two-step ahead predictions). Extensive comparative evaluations demonstrate that EV-STLLM consistently outperforms classical, graph-based, and deep learning baselines. Specifically, in one-step ahead district-level forecasting, EV-STLLM achieves up to a 15.41% reduction in MAE and a 53.51% reduction in MAPE compared to the leading baseline, underscoring its potential to significantly enhance data-driven smart grid operations.

Keywords: Electric vehicle; Charging demand prediction; Spatiotemporal data fusion; Large language models; Model fusion; Low-rank adaptation