Measurement of State of Charge of LithiumNickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

被引：0

作者：

Divya, R. ^{[1
]}

Karunanithi, K. ^{[1
]}

Ramesh, S. ^{[1
]}

Raja, S. P. ^{[2
]}

机构：

[1] Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T, Sch Elect & Commun Engn, Chennai, Tamil Nadu, India

[2] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India

来源：

INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS | 2024年 / 15卷 / 04期

关键词：

ANN; SoC estimation; FNN algorithm; NARX algorithm; Li-NMC battery; ION BATTERY; SOC ESTIMATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The battery's SoC is a crucial variable since it reflects its performance. An accurate estimation of SoC protects the battery, prevents overcharging or discharge, and extends its life time. Since most of the traditional methods use complex equations, ANN has been implemented to reduce the complications and provide better accuracy. In this research, Li-NMC with capacity rating of 2000mAh is used for the estimation of SoC. In this paper, Feedforward Neural Network (FNN) algorithm and NonlinearAuto-Regressive network with exogenous inputs (NARX) have been used for designing a neural networkmodel. Here, the performance matrixes of both neural network models have been compared and analyzed with the same dataset.

引用

页码：305 / 311

页数：7

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