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Cost-Efficient Battery Management System with Predictive Analysis

Publication Type : Conference Paper

Publisher : IEEE

Source : 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2024, pp. 1502-1510, doi: 10.1109/IDCIoT59759.2024.10467945.

Url : https://ieeexplore.ieee.org/document/10467945

Campus : Bengaluru

School : School of Engineering

Department : Electrical and Electronics

Year : 2024

Abstract : As the current world is inclined towards sustainability, steps are being taken to create an eco-friendly environment. Integration of several renewable energy sources such as solar, wind, and hydro-power has grown into momentum. The application of energy storage and conversion devices such as Batteries and Converters play a vital role in the generation and storage of electricity. The efficiency of storage devices such as Li-Ion Batteries can be improved by constantly monitoring and regulating parameters such as SOC, SOH, and temperature of the device. This is where the proposed model comes into the picture in providing an effective and efficient Battery Management System [BMS], which includes both hardware and software implementations. Adaptation of such an Innovative BMS model provides an efficient and cost-effective solution and is suitable for a wide range of energy storage applications.

Cite this Research Publication : Sanjeev Krishna R; Pooja Naidu Tammisetti; Harshit Grandhi; Lekshmi S; Manitha P V, "Cost-Efficient Battery Management System with Predictive Analysis," 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2024, pp. 1502-1510, doi: 10.1109/IDCIoT59759.2024.10467945.

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