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Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems

Author

Listed:
  • Siow Jat Shern

    (Centre for Electric Energy and Automation, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia)

  • Md Tanjil Sarker

    (Centre for Electric Energy and Automation, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia)

  • Mohammed Hussein Saleh Mohammed Haram

    (Centre for Electric Energy and Automation, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia)

  • Gobbi Ramasamy

    (Centre for Electric Energy and Automation, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia)

  • Siva Priya Thiagarajah

    (Centre for Electric Energy and Automation, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia)

  • Fahmid Al Farid

    (Centre for Digital Home, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia)

Abstract

This paper presents an advanced AI-based optimization framework for Electric Vehicle (EV) smart charging systems, focusing on efficient energy distribution to meet dynamic user demand. The study leverages machine learning models such as Random Forest, Support Vector Regression (SVR), Gradient Boosting Regressor, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) to forecast user demand and optimize energy allocation. Among the models, XGBoost demonstrated superior predictive performance, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), making it the most effective for real-time user demand prediction in smart charging scenarios. The framework introduces proportional and priority-based allocation strategies to distribute available energy effectively, with a focus on minimizing energy shortfalls and balancing supply with user demand. Results from the XGBoost model reduced prediction error by 15% compared to other models, significantly improving the station’s ability to meet user demand efficiently. The proposed AI framework enhances charging station operations, supports grid stability, and promotes sustainability in the context of increasing EV adoption.

Suggested Citation

  • Siow Jat Shern & Md Tanjil Sarker & Mohammed Hussein Saleh Mohammed Haram & Gobbi Ramasamy & Siva Priya Thiagarajah & Fahmid Al Farid, 2024. "Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems," Energies, MDPI, vol. 17(22), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5772-:d:1523919
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    References listed on IDEAS

    as
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    2. Md Tanjil Sarker & Mohammed Hussein Saleh Mohammed Haram & Gobbi Ramasamy & Fahmid Al Farid & Sarina Mansor, 2023. "Solar Photovoltaic Home Systems in Malaysia: A Comprehensive Review and Analysis," Energies, MDPI, vol. 16(23), pages 1-23, November.
    3. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    4. Majidpour, Mostafa & Qiu, Charlie & Chu, Peter & Pota, Hemanshu R. & Gadh, Rajit, 2016. "Forecasting the EV charging load based on customer profile or station measurement?," Applied Energy, Elsevier, vol. 163(C), pages 134-141.
    5. Md. Tanjil Sarker & Mohammed Hussein Saleh Mohammed Haram & Siow Jat Shern & Gobbi Ramasamy & Fahmid Al Farid, 2024. "Readiness of Malaysian PV System to Utilize Energy Storage System with Second-Life Electric Vehicle Batteries," Energies, MDPI, vol. 17(16), pages 1-23, August.
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