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World Journal of Engineering Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

An Official Publication of Society for Advance Healthcare Research (Reg. No. : 01/01/01/31674/16)

ISSN 2454-695X

Impact Factor : 8.067

ICV : 79.45

WJERT Citation

  All Since 2020
 Citation  172  110
 h-index  7  5
 i10-index  1  0

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Abstract

MULTI-OBJECTIVE OPTIMIZATION OF HYBRID BATTERY-SUPERCAPACITOR ENERGY STORAGE SYSTEMS: MACHINE LEARNING-ENHANCED PERFORMANCE FOR FAST-CHARGING ELECTRIC VEHICLE INFRASTRUCTURE

Adel Elgammal*

ABSTRACT

With the rapid growth of the global electric vehicle (EV) stock, the large-scale deployment of fast-charging infrastructure is facing an increasingly urgent need for compatible energy storage adaptation. All traditional single-type energy storage technologies have inherent, irreconcilable flaws: the power density of pure battery energy storage is insufficient to withstand the peak load shocks of fast-charging stations, while pure supercapacitor energy storage has low energy density and high full-lifecycle costs, which makes it unable to independently meet the operational requirements of long-duration fast-charging services. To address this issue, this study proposes a new multi-objective optimized battery-supercapacitor hybrid energy storage system (HESS) tailored for EV fast-charging stations. The study adopts the non-dominated sorting genetic algorithm II (NSGA-II) to complete multi-objective parameter optimization, introduces deep reinforcement learning (DRL) to realize real-time energy scheduling, and conducts simulation verification across multiple operational scenarios using measured EV charging data from 12 real fast-charging stations. The results show that compared with pure battery energy storage systems, the optimized HESS achieves a 23.7% increase in energy efficiency, a 41.2% reduction in grid peak load, and a 34.6% extension in system service life. The DRL module attains a 94.3% charging demand prediction accuracy, cuts operational costs by 18.9%, reduces grid harmonics by 27.4%, and can help fast-charging stations lower capital infrastructure investment by 15%-25%. This study still has two limitations: the training of the machine learning model relies on a large volume of historical data, and the computational complexity of the real-time optimization algorithm is relatively high, which requires iteration and optimization in follow-up research.

[Full Text Article] [Download Certificate] https://doi.org/10.5281/zenodo.21062015