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Journal : Jurnal INFOTEL

Chattering reduction effect on power efficiency of ifoc based induction motor Dedid Cahya Happyanto; Angga Wahyu Aditya
JURNAL INFOTEL Vol 14 No 2 (2022): May 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i2.753

Abstract

Nowadays, the strategies to control Induction Motor (IM) is growing fast. The vector control strategies give better performance than the scalar control to control IM. IFOC is one of the vector control strategies which more realistic to apply in industry, military, and transportation. However, IFOC requires Sliding Mode Control (SMC) with the Lyapunov function to ensure robustness and stability. The first-order SMC or ordinary SMC uses boundary layers technique such as the saturation function and the tangent-hyperbolic function to overcome the chattering phenomenon. The performance of boundary layer is analyzed in rotor speed response, stator current response in dq0 frame and power performance. In rotor speed response, the SMC with and without boundary layer has error steady-state less than 2%. In stator current response with dq0 frame, the boundary layer with tangent-hyperbolic function has the best performance. The power analysis shows that the boundary layer with saturation function has an active power loss of 39.16%, reactive power loss of 23.37% and apparent power loss of 30.30%. The boundary layer with tangent-hyperbolic functions has the best performance in reducing power consumption with active power loss of 41.24%, reactive power loss of 24.78% and apparent power loss of 31.96%.
Early Warning Safety System Development for Electric Vehicle Batteries to Prevent Fires and Accidents: Implementation in Urban Public Transportation Happyanto, Dedid Cahya; Anita, Jelia; Hendriawan, Akhmad
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1383

Abstract

The increasing adoption of electric vehicles (EVs) in urban public transportation has raised significant safety concerns, particularly regarding thermal runaway incidents that may lead to catastrophic fires. Existing battery monitoring systems often provide inadequate warning times and lack predictive capabilities to mitigate failures before they reach critical conditions. This study proposes an intelligent early warning system for EV battery safety in public transportation fleets by employing predictive analytics. The system integrates a distributed Internet of Things (IoT) sensor network that monitors temperature, voltage, current, and gas emissions, combined with machine learning algorithms—specifically, Random Forest and Support Vector Machine—to analyze battery performance patterns. The proposed architecture incorporates edge computing for real-time data processing and cloud infrastructure for centralised fleet monitoring. Field validation involving 50 electric buses operating under Jakarta's TransJakarta network over a twelve-month period achieved a prediction accuracy of 94.7% for thermal runaway events, with an average warning time of 8.3 minutes. The system successfully prevented 23 potential battery failures while maintaining a false alarm rate below 2.1%. An economic analysis further indicated a favourable cost-benefit ratio of 1:7.4. The proposed solution demonstrates significant potential in enhancing EV battery safety through predictive analytics and automated emergency response, offering a scalable model for broader industry adoption.