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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19444
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
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| Paper Title: | AI-Enhanced Battery Management Systems: A Comprehensive Review of Intelligent Monitoring, Fault Diagnosis, and Optimization Techniques |
| Authors Name: | Deeksha Kushwah , Ambika Sharma |
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IJRTI_205610
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| Published Paper Id: | IJRTI2508044 |
| Published In: | Volume 10 Issue 8, August-2025 |
| DOI: | |
| Abstract: | The increasing deployment of lithium-ion batteries in critical infrastructure like electric vehicles and renewable energy storage necessitates advanced Battery Management Systems (BMS) that ensure operational efficiency, safety, and longevity. Conventional BMS face limitations in handling the nonlinear dynamics and environmental variability inherent to battery behavior. This review examines the application of Artificial Intelligence (AI) and Machine Learning (ML) methodologies to enhance the precision of State of Charge (SoC) and State of Health (SoH) estimations, facilitate early fault diagnosis, optimize thermal regulation, and enable predictive maintenance frameworks. The synergistic integration of Internet of Things (IoT) technologies supports real-time data acquisition and distributed analytics, rendering BMS more intelligent and scalable. Emerging research trends also emphasize AI-based strategies for managing second-life batteries in grid-scale storage applications, promoting sustainability and cost-efficiency. This comprehensive analysis synthesizes recent advancements, underscoring the transformative impact of AI-enhanced BMS architectures on energy management systems. |
| Keywords: | Battery Management system (BMS), Machine Learning (ML), State of Charge (SoC), State of Health (SoH), Artificial Intelligence (AI), Fault Detection, Thermal Management, Internet of Things (IoT), Predictive Modelling |
| Cite Article: | "AI-Enhanced Battery Management Systems: A Comprehensive Review of Intelligent Monitoring, Fault Diagnosis, and Optimization Techniques", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.a359-a363, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508044.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2508044
Registration ID:205610
Published In: Volume 10 Issue 8, August-2025
DOI (Digital Object Identifier):
Page No: a359-a363 Country: Bhopal, Madhya Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2508044 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2508044 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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