Mathematical modeling of Li-ion battery for charge/discharge rate and capacity fading characteristics using genetic algorithm approach

Thirugnanam, Kannan; Saini, Himanshu; Kumar, Praveen

doi:10.1109/itec.2012.6243431

Cited by 41 publications

(22 citation statements)

References 13 publications

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“…The simple EEC has been used in this work which have internal resistance (R 1 ), capacitance (C) and open-circuit voltage (V 0 ) [21]. The EEC based Simulink model of EV battery is given in Fig.…”

Section: B Evs Battery Modellingmentioning

confidence: 99%

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Fuzzy based active and reactive power support to the distribution network using electric vehicles

Thirugnanam

Kumar

2015

2015 IEEE International Transportation Electrification Conference (ITEC)

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Due to increasing peak power demand and decreasing quality of power delivery to consumers, the penetration of Electric Vehicles (EVs) are highly acceptable for grid support. If the EVs are exchange active and reactive power to grid during parking hours, then the peak power demand has reduced and quality of power delivery has improved. The EVs are discharged at a place to be called as a Grid Supporting Station (GSS). The GSS have multiple Discharging Units (DUs) which transfers active and reactive power from EVs' batteries to grid. In this paper, a fuzzy based control method is described for EVs which regulate the active and reactive power for grid support. The active and reactive power flow from EVs to grid based on the node voltage and amount of energy available in the GSS. Each DU is designed for a maximum power handling capacity of 80VA. The performance of the GSS is analyzed with five set of EVs which is connected through point of Common Coupling (PCC) of distribution node. Simulation studies show that the GSS could effectively exchange the power for grid support during the peak hours.Index Terms-Active and reactive power, battery, electric vehicles, grid connected inverter, fuzzy logic controller, vehicle to-grid.

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Section: B Evs Battery Modellingmentioning

confidence: 99%

“…This model is charge or discharge based on the reference current (I ). The battery terminal voltage for charging and discharging scenario has been given in [21]. The EVs battery model neither charge nor discharge based on user specified limits.…”

Section: B Evs Battery Modellingmentioning

confidence: 99%

Fuzzy based active and reactive power support to the distribution network using electric vehicles

Thirugnanam

Kumar

2015

2015 IEEE International Transportation Electrification Conference (ITEC)

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“…This linearized model yields accurate results and also represents the behavior of the battery, but has some considerations such as: (i) the internal resistance is supposed constant during the process, (ii) the temperature doesn't affect the model's behavior, (iii) the self-discharge of the battery is not represented, and (iv) the battery has no memory effect. There are battery models reported in the literature with other considerations [18], but is not the scope of this paper.…”

Section: A Emulation Of Bmsmentioning

confidence: 99%

Charging/discharging process for electric vehicles: Proposal and emulation

Barnola-Sampera

Heredero-Peris

Villafáfila‐Robles

et al. 2014

2014 IEEE International Electric Vehicle Conference (IEVC)

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This paper proposes a charging/discharging process for electric vehicles according to mode 4, specifically based on the CHAdeMO protocol. These initiatives will enable acceptably fast charging of EVs. The proposal has been designed and implemented within the Endesa Novare Vehicle to Microgrid project. As a first stage of the project, the charge/discharge process has been emulated before it will be developed in a real device. The emulation consists of two main parts: (i) an electric vehicle and its battery management system emulated by a microcontroller, and (ii) a charging/discharging device emulated by a computer application. Both parts communicate via CAN bus as it is done in existing protocols. The proposed control flow for charging and discharging and the results of the emulation are presented. Moreover, in order to be able to manage charge and discharge processes according to power network operator, a data model for both electrical vehicle and charging/discharging device according to standard IEC 61850-7-420 is also proposed.

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“…SOC and SOH and the remaining runtime cannot be directly measured being indirectly estimated on the basis of observable signals, in many cases with the aid of mathematical models [10]- [12]. Crucial to the correct performance of BMS, battery models must reproduce the nonlinear behavior of the battery, including the following effects [13], [14]:…”

Section: Introductionmentioning

confidence: 99%

A Variable Effective Capacity Model for <inline-formula> <tex-math notation="TeX">$\hbox{LiFePO}_{4}$</tex-math></inline-formula> Traction Batteries Using Computational Intelligence Techniques

Sánchez

Blanco

Anton

et al. 2015

IEEE Trans. Ind. Electron.

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Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO 4 batteries using rule-based systems. In this paper, rulebased systems are not directly fitted to data, but comprise constructive blocks in a differential-equation-based dynamical model that is numerically integrated to infer battery voltage, charge, and temperature. The design methodology has been validated with three different LiFePO 4 batteries, and the results were found to be more accurate than those of a selection of statistical models and state-of-the-art artificial intelligence techniques. Index Terms-Computational intelligence, equivalent circuit model, LiFePO 4 batteries, semiphysical intelligent systems.0278-0046

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Mathematical modeling of Li-ion battery for charge/discharge rate and capacity fading characteristics using genetic algorithm approach

Cited by 41 publications

References 13 publications

Fuzzy based active and reactive power support to the distribution network using electric vehicles

Fuzzy based active and reactive power support to the distribution network using electric vehicles

Charging/discharging process for electric vehicles: Proposal and emulation

A Variable Effective Capacity Model for <inline-formula> <tex-math notation="TeX">$\hbox{LiFePO}_{4}$</tex-math></inline-formula> Traction Batteries Using Computational Intelligence Techniques

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