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All Journal International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Raharya, Naufan
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Computer Science & IT Electrical & Electronics Engineering Materials Science & Nanotechnology Medicine & Pharmacology

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An Implementation of Quasi-Newton Algorithm for Fast-charging Lithium-Ion Battery (LIB) Optimization in Electric Vehicle Application Anjarani, Mahmudda Mitra Anjarani; Raharya, Naufan
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 2 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i2.54

Abstract

Lithium-Ion Battery (LIB) is still an effective alternative technology in maximizing the efficiency of electric vehicles (EV). The application of EVs has had a significant impact in order to reduce the issue of global problems - reducing carbon gas emissions. The LIB charging mechanism with the fast-charging method is an alternative to the application of EVs on a more massive scale. However, the dynamics of the battery where the battery work function can decrease over time will affect battery performance. In addition, fast-charging efforts at LIB with maximum speed have the impact of increasing the risk of battery temperature and the existence of a larger gap in battery degradation. This paper proposes the application of Limited-Memory-Broyden-Fletcher-Goldfarb-ShannoBound Constrained (L-BFGS-B) algorithm for Lithium-Ion Battery (LIB) fast-charging optimization as an innovative solution approach in dealing with the complex LIB fastcharging dynamics. The results show that this approach is able to improve fast-charging speed and efficiency.
Anomaly Detection in Imbalance Secure Water Treatment Dataset Using LSTM-DC-Wasserstein Generative Adversarial Network with Gradient Penalty Kevin, Jonathan Marshell; Raharya, Naufan
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 3 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i3.56

Abstract

In modern industrial systems, particularly with the advancement of the Internet of Things (IoT), industry players can record machine and system data for comprehensive analysis. This capability is crucial for detecting anomalies and taking necessary corrective actions.However, it is common for manufacturers to lack recorded anomaly datasets, especially for newly operational systems. In this paper, we develop a model to detect anomalies in an imbalanced dataset from the Secure Water Treatment (SWaT) system. The performance of the proposed model is compared with previous works, demonstrating significant improvements in anomaly detection capabilities where it achieves accuracy of 0.9546, precision of 0.9086, recall of 0.6654, and F1 score of 0.7681
Co-Authors Anjarani, Mahmudda Mitra Anjarani Kevin, Jonathan Marshell