Faculty Publications
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Item A Technological Research on Electric Vehicles Charging Approaches and Optimization Methods(Institute of Electrical and Electronics Engineers Inc., 2022) Vani, B.V.; Kishan, D.; Ahmad, M.W.; Hanumanthakari, S.; Reddy, B.N.K.In Present day various countries all throughout the world have taken on Electric Vehicles (EVs) to diminish air pollution and fuel consumption. In coming years, Electric Vehicles are bound to become crucial in the transport field. Subsequently, the charging approaches are in the same line. This paper presents an outline of the current and proposed EV charging approaches and optimization methods. Especially the various EV charging methods like battery exchange, conductive charging and wireless charging are presented. Next, some of the EV charging/discharging optimization methods are examined. On the basis of investigation, a few proposals are put forward for future research. © 2022 IEEE.Item Bat Optimization Model for Electric Vehicle Route Optimization Under Time-of-Use Electricity Pricing(Springer, 2023) Vani, B.; Kishan, D.; Ahmad, M.W.; Naresh Kumar Reddy, B.In the framework of fuel reduction and energy conservation, the electric vehicles (EV’s) has been identified as a promising option in contrast to fuel-driven vehicles. EV’s battery limits to require visiting a greater number of times to the recharging stations, which must be viewed as in the route planning to keep away from inefficient vehicle routes with lengthy diversions. These problems have to consider, we propose an Efficient Electric Vehicle Route Optimization with Time-of-Use Electricity Pricing using Bat algorithm. Which can reduce the used vehicles as well as electricity-cost and total travel distance. Additionally, functional model and collective models are used to minimize the objectives: distance and cost. The computational assessment in light of the notable benchmarking test instances exhibits, proposed optimization algorithm electricity cost conservation on average 12.17% with Learnable Partheno-Genetic Algorithm (Yang et al. in IEEE Trans Smart Grid 6:657–666, 2015) 8.45% with VNS/TS Algorithm (Lin et al. in Trans Res Part-C 130:103285, 2021) and 5.15% with Mixed Integer Programming model (Ham and Park in IEEE Access 9:37220–37228, 2021). © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item 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