Faculty Publications

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Publications by NITK Faculty

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Subject: search.filters.subject.Battery Management

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    Enhanced electric vehicle battery management system employing bat algorithm with chaotic diversification strategies
    (John Wiley and Sons Inc, 2024) Vani, B.V.; Kishan, D.; Ahmad, Md.W.; Reddy, C.R.P.
    As the demand for electric vehicles (EV) continues to increase, the need for effective charging and switching of battery systems becomes more important. This article presents a method using the Bat Algorithm (BA) improved by chaotic diversification as well as social education to optimize the power source replacement and the electric vehicle charging procedure. The plan is intended to solve the issues of payment delay and battery management failure. The algorithm searches for better positions by combining chaotic diversity, while social learning supports the coordination of battery stations. Thanks to extensive simulation and real-world testing, our approach shows significant improvements in optimization and a reduced payback period. The results show that the suggested approach outperforms the current algorithms in terms of rotation speed and good solution. This research supports the development of efficient transportation by providing practical solutions to increase the efficiency of electric vehicle transfer and payment and ultimately encourage greater effort. © 2024 The Author(s). IET Power Electronics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
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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.