Enhancing Battery Management Systems in the Industrial Internet of Things (IIoT): An In-depth Exploration of Energy Efficiency, Predictive Analytics, and Intelligent Maintenance Approaches

Battery management systems (BMS) are essential in the fast developing IIoT domain for guaranteeing the efficiency, lifetime, and dependability of energy storage solutions. With the proliferation of IIoT devices across many industries, there is a growing need for sophisticated BMS systems that can optimise energy usage, anticipate battery performance, and enable intelligent maintenance. Improvements in energy efficiency, predictive analytics, and intelligent maintenance approaches are the primary foci of this research as it pertains to the IIoT ecosystem and BMS. Our first step is to investigate ways that IIoT devices can be made more energy efficient. This includes things like optimising the charging and discharging operations of batteries and using adaptive power management techniques. We want to keep IIoT devices running longer and lessen their impact on the environment by making the most of BMS’s energy efficiency. Our second topic is the function of predictive analytics in predicting the state of a battery’s health and output. Models are created using machine learning algorithms and big data analytics to forecast battery deterioration patterns, identify impending failures, and plan proactive maintenance to reduce maintenance expenses and downtime. We conclude by exploring intelligent maintenance approaches that can deliver BMS valuable insights through the use of real-time data and advanced analytics. A combination of these methods—which include automated maintenance procedures, remote monitoring, and self-diagnostic devices—improves the performance and dependability of IIoT battery systems. This paper examines the present and future of BMS in the IIoT through a thorough literature analysis, case studies, and experimental validation. The insights it provides are quite significant. In light of these results, data-driven methods have the ability to transform battery management, guaranteeing that IIoT devices can continue to withstand the increasing demands of industry while being environmentally friendly, efficient, and robust.

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Published
2024-11-07
DOI
https://doi.org/10.1109/teeccon64024.2024.10939734
Primary Topic
Advanced Battery Technologies Research
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article
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article

Enhancing Battery Management Systems in the Industrial Internet of Things (IIoT): An In-depth Exploration of Energy Efficiency, Predictive Analytics, and Intelligent Maintenance Approaches

M. Maheswari, J. Lakshmi Priya, V. Samuthira Pandi, Neeraj Kumar et al.
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Advanced Battery Technologies Research
article

Enhancing Battery Management Systems in the Industrial Internet of Things (IIoT): An In-depth Exploration of Energy Efficiency, Predictive Analytics, and Intelligent Maintenance Approaches

M. Maheswari, J. Lakshmi Priya, V. Samuthira Pandi, Neeraj Kumar, S. Sujatha, Deepak Arumugam
article en

Abstract

Battery management systems (BMS) are essential in the fast developing IIoT domain for guaranteeing the efficiency, lifetime, and dependability of energy storage solutions. With the proliferation of IIoT devices across many industries, there is a growing need for sophisticated BMS systems that can optimise energy usage, anticipate battery performance, and enable intelligent maintenance. Improvements in energy efficiency, predictive analytics, and intelligent maintenance approaches are the primary foci of this research as it pertains to the IIoT ecosystem and BMS. Our first step is to investigate ways that IIoT devices can be made more energy efficient. This includes things like optimising the charging and discharging operations of batteries and using adaptive power management techniques. We want to keep IIoT devices running longer and lessen their impact on the environment by making the most of BMS’s energy efficiency. Our second topic is the function of predictive analytics in predicting the state of a battery’s health and output. Models are created using machine learning algorithms and big data analytics to forecast battery deterioration patterns, identify impending failures, and plan proactive maintenance to reduce maintenance expenses and downtime. We conclude by exploring intelligent maintenance approaches that can deliver BMS valuable insights through the use of real-time data and advanced analytics. A combination of these methods—which include automated maintenance procedures, remote monitoring, and self-diagnostic devices—improves the performance and dependability of IIoT battery systems. This paper examines the present and future of BMS in the IIoT through a thorough literature analysis, case studies, and experimental validation. The insights it provides are quite significant. In light of these results, data-driven methods have the ability to transform battery management, guaranteeing that IIoT devices can continue to withstand the increasing demands of industry while being environmentally friendly, efficient, and robust.

Salem College (US), Saveetha University (IN)
Affordable and clean energy
Openalex Percentile: Top 69%
Advanced Battery Technologies Research
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