Faculty Publications

Now showing 1 - 3 of 3

An efficient battery swapping and charging mechanism for electric vehicles using bat algorithm
(Elsevier Ltd, 2024) Vani, B.V.; Kishan, D.; Ahmad, M.W.; Naresh Kumar Reddy, B.N.K.
The recent surge in electric vehicle (EV) adoption has presented various challenges, notably in the charging and discharging processes of EV batteries, each characterized by unique traits. While conventional charging stations remain popular, battery swap stations (BSS) offer a compelling alternative, addressing issues like prolonged waiting times and potential battery degradation from fast charging. BSS, with its extensive array of battery systems, ensures efficient services for EVs. However, meticulous planning for the charging and discharging operations is imperative for both BSS and the overall grid to guarantee optimal functionality. This paper proposes an efficient approach to enhance the efficiency of battery swapping and charging mechanisms (BSCM) for electric vehicles, leveraging the bat algorithm. The BSCM is conceived as a system that incorporates both the battery swapping mechanism (BSM) and the battery charging mechanism (BCM). The key contribution lies in designing an effective BSCM where the BSM functions as a manager, handling battery swapping requests from EV users, while the BCM acts as a supporter, interfacing with the grid to regulate battery charging and discharging power. To efficiently address the mixed-integer nonlinear program (MINLP) inherent in this system, a Bat algorithm is developed. The results clearly demonstrate the effectiveness of the proposed algorithm in efficiently addressing large-scale problems, producing solutions that closely approach optimality. It promptly achieves a substantial reduction in battery swapping energy by 30% and 24%, respectively, and significantly enhances charging station utilization by 25% and 21% compared to the LSTM-Based Rolling Horizon Approach and Bilevel Optimization Approach. Additionally, the algorithm showcases remarkable improvements in battery swapping performance, boasting a 25% and 19% enhancement, and noteworthy increases in charging station utilization by 20% and 17% compared to the aforementioned approaches. This enhancement in the energy exchange with grid and regulation contributes to the overall efficiency and sustainability of electric vehicle operations. © 2024 Elsevier Ltd
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.
Meta-heuristic algorithm based optimization studies in cryogenic and conventional milling of magnesium alloy AZ91
(Elsevier B.V., 2025) Marakini, V.; P, S.P.; D'Mello, G.; Bhat K, U.; Thakur, D.; Achar, B.P.
The surface finish of a machined product is essential for assessing its quality and other attributes. Modeling the surface roughness and hardness of a machined component is challenging for several reasons. The present study examines the effectiveness of four meta-heuristic algorithms in optimizing surface characteristics like roughness (Ra) and hardness (HV) in the machining of magnesium alloy AZ91. Experiments with uncoated carbide inserts have been conducted under dry and cryogenic conditions. The study's input parameters are the depth of cut, feed rate, and cutting speed. Modeling and prediction studies have been conducted using Multi Layered Perceptron (MLP) Neural Network, and the output of this model has been considered as the objective function for the optimization algorithms. Algorithms, namely Particle Swarm Optimization (PSO), Bat Algorithm (BA), and recently developed algorithms, namely Jaya Algorithm (JAYA) and Fruit Fly Optimization Algorithm (FOA), have been tested. The optimization accuracy of FOA has been found to be superior to that of the other algorithms. As per the knowledge of the authors, this work probably presents a first attempt in applying the JAYA and FOA metaheuristic algorithms in the machining studies of an AZ series magnesium alloy. © 2025 The Authors

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