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Author

Kannan Thirugnanam

Bio: Kannan Thirugnanam is an academic researcher from Indian Institute of Technology Guwahati. The author has contributed to research in topics: Battery (electricity) & Energy management. The author has an hindex of 9, co-authored 21 publications receiving 439 citations. Previous affiliations of Kannan Thirugnanam include Khalifa University & Singapore University of Technology and Design.

Papers
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Journal ArticleDOI
TL;DR: Through numerical simulation case studies, it is demonstrated that the proposed BEMS is capable of achieving the following: reduction in DGs’ operating hours, reduction in PV power fluctuations, and concurrent management of multiple batteries of different characteristics and extension of battery lifetime by controlling battery charge and discharge rate.
Abstract: Using solar photovoltaics (PV) to help a microgrid (MG) operator for cost reduction may not be a straightforward problem due to the intermittent nature of PV power generation and unpredictable load demands. One potential way to address this challenge is to use batteries that can store the surplus PV energy whenever possible and supply the energy back to the MG when needed. In this context, this paper proposes a battery energy management system (BEMS) for an MG, in which PVs and diesel generators (DGs) are the primary sources of electricity. The novelty of the proposed BEMS lies within the energy management of multiple types of batteries’ characteristics and the reduction of DGs’ operating hours simultaneously. Furthermore, the proposed BEMS also takes into account different characteristics of the batteries when controlling the charging and discharging decision to extend the battery lifetime. Real-world data of MG load and PV power generations are used to verify the effectiveness of the proposed BEMS. Through numerical simulation case studies, it is demonstrated that the proposed BEMS is capable of achieving the following: reduction in DGs’ operating hours, reduction in PV power fluctuations, and concurrent management of multiple batteries of different characteristics and extension of battery lifetime by controlling battery charge and discharge rate.

130 citations

Journal ArticleDOI
TL;DR: In this paper, an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications were presented, where the circuit parameters of the battery model (BM) were extracted using genetic algorithm-based optimization method.
Abstract: This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers’ data. The battery profiles of different manufacturers’ like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers’ catalogue) characteristics.

100 citations

Journal ArticleDOI
TL;DR: In this model the economic analysis has been done in such a way that the battery related liabilities do not become a financial burden to EV owners and the optimal cost of electricity is determined such that the grid, EV owners, and consumers are benefitted.
Abstract: The objective of this work is to develop a mathematical model for the integration of electric vehicle (EVs) to the grid. Integrating the EV with the grid would help in simultaneous charging of numerous EVs and provide peak hour energy to the grid (from EV). This bi-directional exchange of energy between the grid and EV results in a complex financial calculations. So a simple model has been proposed. The energy provided by the EV to the grid depends on the battery capacity. Battery capacity is affected by capacity losses (CL). The model includes the possible cases of CL, such as CL due to battery usage (discharge during vehicle transportation) and CL due to the grid interaction. The main cause for a higher per kilometer (Km) transportation cost in EV, when compared to conventional vehicle, is the high cost of the battery and its maintenance. In this model, the economic analysis has been done in such a way that the battery related liabilities do not become a financial burden to EV owners. The above scenario has been evaluated for different combinations of charge rate (Cr) and discharge rate (Dr) ranging from 1Cr-1Dr to 3Cr-3Dr. Finally the optimal cost of electricity is determined such that the grid, EV owners, and consumers (EV users) are benefitted.

65 citations

Journal ArticleDOI
TL;DR: The main focus of this paper is to coordinate the EVs, present at the CSs, and support the grid by peak shaving and valley filling.
Abstract: In this paper, the charging stations (CSs) of electric vehicles (EVs) and their coordination at the substation level are presented. It is considered that the EVs of a particular area arrive at the CS in their idle time to charge their batteries. Fuzzy logic controllers (FLCs) have been designed at the substation and the CS level. The FLC at the substation level decides the amount of power to be compensated by the entire CSs, and the FLC at the CS level determines the power to be exchanged by individual CS. The aggregator at the substation level will distribute the power among the CSs connected to different subfeeders. Also, every subfeeder has an aggregator which distributes the power among different CSs connected to the same subfeeder. Batteries of EVs have been modeled which can handle the capacity loss at different charging/discharging rates $(C_{\rm rate})$ . The $C_{\rm rate} $ of the battery is controlled to achieve the desired rate of power flow between the grid and the EV battery. The main focus of this paper is to coordinate the EVs, present at the CSs, and support the grid by peak shaving and valley filling.

62 citations

Journal ArticleDOI
TL;DR: Simulation studies show that the CS could effectively perform controlled charging/discharging based on the grid condition and EVs' batteries constraints.
Abstract: This paper proposes the modeling and control of contactless based Charging Station (CS) in Vehicle-to-Grid (V2G) scenario. Various charging points, also called multi-point is present in a CS. The CS is a place where Electric Vehicles (EVs) of particular area will come to charge as well as to participate for the grid support. A multi-point bidirectional contactless based CS with its control structure has been modeled in this work. The smart control algorithm is developed for the CS, which has the ability to decide the power flow between EVs and grid. The control algorithm modulates the charging/discharging rates of individual EV batteries by updating the power requirement and realizes a fast and synchronized response among multiple EVs. Each charging points is designed for a maximum peak power handling capacity of 50 kW. The performance of the CS with its control system is investigated with multiple EVs of different battery ratings connected under a single power distribution node of a grid. Simulation studies show that the CS could effectively perform controlled charging/discharging based on the grid condition and EVs' batteries constraints.

56 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the authors reviewed the technologies in the wireless power transfer (WPT) area applicable to electric vehicle (EV) wireless charging, and the obstacles of charging time, range, and cost can be easily mitigated.
Abstract: Wireless power transfer (WPT) using magnetic resonance is the technology which could set human free from the annoying wires. In fact, the WPT adopts the same basic theory which has already been developed for at least 30 years with the term inductive power transfer. WPT technology is developing rapidly in recent years. At kilowatts power level, the transfer distance increases from several millimeters to several hundred millimeters with a grid to load efficiency above 90%. The advances make the WPT very attractive to the electric vehicle (EV) charging applications in both stationary and dynamic charging scenarios. This paper reviewed the technologies in the WPT area applicable to EV wireless charging. By introducing WPT in EVs, the obstacles of charging time, range, and cost can be easily mitigated. Battery technology is no longer relevant in the mass market penetration of EVs. It is hoped that researchers could be encouraged by the state-of-the-art achievements, and push forward the further development of WPT as well as the expansion of EV.

1,603 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the possibility of charging battery electric vehicles at workplace in Netherlands using solar energy and proposed a priority mechanism to facilitate the charging of multiple EVs from a single EV-PV charger.

330 citations

Journal ArticleDOI
TL;DR: A heuristic operation strategy for commercial building microgrids that can be utilized in embedded systems for real-time allocation of EV charging rate and designed to operate without forecasting on photovoltaic output or EV charging demand.
Abstract: Commercial building microgrids will play an important role in the smart energy city. Stochastic and uncoordinated electric vehicle (EV) charging activities, which may cause performance degradations and overloads, have put great stress on the distribution system. In order to improve the self-consumption of PV energy and reduce the impact on the power grid, a heuristic operation strategy for commercial building microgrids is proposed. The strategy is composed of three parts: the model of EV feasible charging region, the mechanism of dynamical event triggering, and the algorithm of real-time power allocation for EVs. Furthermore, in order to lower the cost of computation resource, the strategy is designed to operate without forecasting on photovoltaic output or EV charging demand. A comprehensive result obtained from simulation tests has shown that the proposed strategy has both satisfactory results and high efficiency, which can be utilized in embedded systems for real-time allocation of EV charging rate.

216 citations

Journal ArticleDOI
TL;DR: The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.
Abstract: The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.

194 citations

Journal ArticleDOI
TL;DR: In this article, a multi-perspectives classification of the data fusion to evaluate the smart city applications is presented, where the proposed classification is applied to evaluate selected applications in each domain of smart city and the potential future direction and challenges of data fusion integration are discussed.

174 citations