Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Bharath, Y.K.Has files: No

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    Modelling of PEM fuel cells : A Parameterized Approach
    (Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, S.; Bharath, Y.K.; Mishra, S.; Vinatha Urundady, U.
    PEM fuel cells are encouraging energy conversion technologies that offer low emissions and high efficiency for various applications, auto motives in particular. In order to create flexible and adaptive models that accurately depict the complex electrochemical, transport, and thermodynamic processes within these systems, a parameterized technique for modelling PEM fuel cells is essential. This work focuses on investigating the profound impact of load and temperature variations on the steady-state behavior of PEM fuel cells. The parameterized approach is then employed to develop a dynamic model of PEM fuel cells. The dynamic model captures the intricate dynamics of the cell under varying load conditions, considering the transient behavior of the cell components and the interactions between the different physical phenomena. © 2023 IEEE.
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    Intelligent Rush Hour Management in Metro Station
    (Institute of Electrical and Electronics Engineers Inc., 2024) Anandu, V.P.; Vinatha Urundady, U.; Bharath, Y.K.; Neethu, V.S.
    Addressing the issue of high crowd density in metro stations during rush hours is indeed a significant challenge, but innovative solutions can help enhance passenger experience and streamline the boarding process. The goal is to implement a Smart Crowd Management System that provides real-time information about congestion levels in metro stations and estimates the time required for passengers to board trains during peak hours. The implementation of a Smart Crowd Management System can significantly improve the passenger experience in metro stations, making the commute more efficient and less stressful during rush hours. This proposal outlines a holistic approach combining sensor technology, machine learning, digital communication, and mobile applications to address the challenges of crowd density in metropolitan cities like Delhi. In this work, an intelligent system is developed with MATLAB/Simulink interface having fuzzy logic and neural network classifier to indicate expected time of departure and degree of congestion in the station. The outputs are displayed in TFT screen, LEDs and ThingSpeak-IoT platform. © 2024 IEEE.
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    Long-Term Estimation of SoH Using Cascaded LSTM-RNN for Lithium Batteries Subjected to Aging and Accelerated Degradation
    (John Wiley and Sons Inc, 2024) Bharath, Y.K.; Anandu, V.P.; Vinatha Urundady, U.; Sudeep, S.
    Accurate estimation of state of health (SoH) of the battery over long-term is a critical challenge for the battery management systems in electric vehicles. This is due to the challenges in accurately modeling the accelerated aging and degradation phenomena caused by diverse operating conditions of the battery. This paper presents a cascaded recurrent neural networks (RNN) with long short-term memory (LSTM) to estimate the internal resistance and SoH, taking account of various abnormal operating conditions of the battery. A datasheet-based degradation model of the battery is developed using fade equations. The training and validation data set for LSTM-RNN are generated by subjecting the battery model to various factors that cause accelerated degradation, such as fast charging, varying operating temperatures, overutilization, and cell imbalance. The cascaded LSTM-RNN is trained to estimate SoH only once after the completion of every charge–discharge cycle. The training error index parameters of the proposed SoH estimator are well within 1%, demonstrating the reliability and robustness of the estimator to diverse operating conditions of the battery. © 2024 John Wiley & Sons Ltd.
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    Supercapacitor Assisted Self-Reconfigurable Battery System for Enhanced Cell Balancing and Voltage Stability
    (John Wiley and Sons Inc, 2025) Bharath, Y.K.; Anandu, V.P.; Vinatha Urundady, U.
    Conventional battery systems with fixed configurations often suffer from cell imbalance arising from variations in cell voltages and capacity mismatches. These imbalances lead to overcharging, over-discharging, and under-utilization of individual cells, ultimately accelerating battery capacity degradation. To overcome these challenges, this article presents a self-reconfigurable battery system that dynamically balances cell voltages while maintaining a stable terminal voltage. The proposed system features a highly reconfigurable switching circuit designed with a minimal number of switches, ensuring optimal cost, space, and weight. A supercapacitor is integrated into the system to ensure voltage stability during reconfiguration events and enhance the overall dynamic response of the system. The effectiveness of the proposed approach is validated through the development of a 24 V self-reconfigurable battery prototype. Experimental results demonstrate the system's capability to mitigate cell imbalance, completely utilize the available battery capacity, and maintain voltage stability. © 2025 John Wiley & Sons Ltd.