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    Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations
    (Mdpi, 2024) Aksoz, Ahmet; Asal, Burcak; Bicer, Emre; Oyucu, Saadin; Gencturk, Merve; Golestan, Saeed
    Concerns over fossil fuel depletion, fluctuating fuel prices, and CO2 emissions have accelerated the development of electric vehicle (EV) technologies. This article reviews advancements in EV fast charging technology and explores the development of battery-assisted DC fast charging stations to address the limitations of traditional chargers. Our proposed approach integrates battery storage, allowing chargers to operate independently of the electric grid by storing electrical energy during off-peak hours and releasing it during peak times. This reduces dependence on grid power and enhances grid stability. Moreover, the transformer-less, modular design of the proposed solution offers greater flexibility, scalability, and reduced installation costs. Additionally, the use of smart energy management systems, incorporating artificial intelligence and machine learning techniques to dynamically adjust charging rates, will be discussed to optimize efficiency and cost-effectiveness.
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    Ensemble Learning in Li-Ion Battery Management Systems: Focus on Voting Regression for Capacity Estimation
    (Institute of Electrical and Electronics Engineers Inc., 2024) Asal, Burcak; Oyucu, Saadin; Aksoz, Ahmet
    Accurate estimation of discharge capacity in lithium-ion batteries is very important for optimizing their performance and longevity and directly affects the efficiency of battery management systems (BMS). Traditional models frequently encounter challenges in handling the non-linearities and complex interdependencies inherent in battery behavior. This study introduces a robust voting regression-based approach that combines multiple regression models to improve prediction accuracy and reliability. We employ four different regression configurations, Multi-Layer Perceptron (MLP), Random Forest (RF), Linear Regression and K-Nearest Neighbor (KNN) to form a combined estimator through voting mechanisms. The robustness of our voting-based approach is further validated through extensive experimentation on two real-world battery datasets including various operational conditions. Our results show that the voting regression approach we propose provides stable and accurate performance, making it a viable tool for different BMS applications. We also discuss the potential practical implications of our proposed voting approach and suggest directions for future research to further refine and adapt this approach to different types of battery technologies and configurations. © 2024 IEEE.