Most influential feature form for supervised learning in voltage sag source localization

Mohammadi, Younes; Polajžer, Boštjan; Leborgne, Roberto Chouhy; Khodadad, Davood

doi:10.1016/j.engappai.2024.108331

Cited by 5 publications

(1 citation statement)

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“…Second, the trend is to reduce the complexity of the system model, discarding all technical details that are out of the scope of calculations, which would have required significant computational time. In this sense, the implementation of AI is generally valuable in providing an almost real-time response to every query, with some exceptions [19]. The integration of ML is always based on a real dataset that is sufficiently large to allow a reliable training phase for the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Miraftabzadeh,

Longo,

Di Martino

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

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Section: Introductionmentioning

confidence: 99%