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<p>Electric motors, which are recharged through the Charging Station (CS), power the Electric Vehicle (EV). However, the EV charging operation is slowed down by the convoy effect. Thus, by using Second-order Similarity Scaled Parametric Exponential Linear Unit Deep-Long Short-Term Memory (S<sup>3</sup>-PELU-DeepLSTM), an effective priority-based queuing process with a multimodal charging system is proposed. Primarily, by utilizing Single Tuned-filter STATCOM (ST-STATCOM), the power loss during the generated solar power’s transmission to CS is mitigated. Likewise, the CS data is preprocessed and features are extracted. The EV charging modes are classified by the S<sup>3</sup>-PELU-DeepLSTM. The EV will be prioritized and charged if residential charging is obtained. Else, for EV charging, the optimal CS is selected by using Gauss-power mixing distribution-Jellyfish Search Optimization (Gp-JSO). Here, the slot can be reserved by the users for charging. Thus, Gp-JSO attained fitness of 746&#xa0;m in 20 iterations when compared to 985&#xa0;m (JSO), 10200&#xa0;m (CSO), and 13600&#xa0;m (ESO). The minimum distance attained by the proposed Gp-JSO shows better CS selection than existing models. Also, this model is robust (consistent performance) by attaining 98.05% classification accuracy.</p>

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An Effective Priority-Based Queuing and Multimodal Charging System for Electric Vehicles Using S3-PELU-DeepLSTM and TEC3RA

  • 期刊论文

  • 发表日期：

    2025年5月20日

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  • K. Sathiya,
  • V. Krishnakumar,
  • P. Velmurugan

摘要

Electric motors, which are recharged through the Charging Station (CS), power the Electric Vehicle (EV). However, the EV charging operation is slowed down by the convoy effect. Thus, by using Second-order Similarity Scaled Parametric Exponential Linear Unit Deep-Long Short-Term Memory (S3-PELU-DeepLSTM), an effective priority-based queuing process with a multimodal charging system is proposed. Primarily, by utilizing Single Tuned-filter STATCOM (ST-STATCOM), the power loss during the generated solar power’s transmission to CS is mitigated. Likewise, the CS data is preprocessed and features are extracted. The EV charging modes are classified by the S3-PELU-DeepLSTM. The EV will be prioritized and charged if residential charging is obtained. Else, for EV charging, the optimal CS is selected by using Gauss-power mixing distribution-Jellyfish Search Optimization (Gp-JSO). Here, the slot can be reserved by the users for charging. Thus, Gp-JSO attained fitness of 746 m in 20 iterations when compared to 985 m (JSO), 10200 m (CSO), and 13600 m (ESO). The minimum distance attained by the proposed Gp-JSO shows better CS selection than existing models. Also, this model is robust (consistent performance) by attaining 98.05% classification accuracy.