A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization

Md Rayhan Tanvir

doi:doi:10.11648/j.jeee.20251305.12

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A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization

Md Rayhan Tanvir^*

Published in Journal of Electrical and Electronic Engineering (Volume 13, Issue 5)

Received: 11 October 2025 Accepted: 23 October 2025 Published: 26 November 2025

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Abstract

Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditional Battery Management Systems (BMS) that are usually on rule-based or physics-based models can hardly adapt to various chemistries, operation conditions, and fault scenarios. Approaches based on Artificial Intelligence (AI), such as machine learning (ML) and deep learning (DL), have recently been proposed to provide more promising data-driven options for fault diagnosis, state of health (SoH) estimation, and predictive optimization. This work provides a cutting-edge review of AI deployment in BESSs, which is conceptually classified into three fundamental categories: fault detection and classification, health monitoring and degradation prognosis, and intelligent control and optimization. We discuss the merits and limitations of different AI models, including supervised / unsupervised / hybrid / reinforcement learning, deployment feasibility, interpretability, and data requirements. An analysis of novel techniques, including digital twin modeling, explainable AI, and secure learning frameworks, is also included. Using comparative analysis, taxonomy visualizations, and performance summaries, this review points out the current limitations, standardization issues, and future research directions that are required for industrial-scale deployment. Through capturing the cutting-edge advancements in this field, we hope this work will serve as a guiding reference for the researchers and industry participants to design robust, scalable, and dependable AI-supported battery management systems.

Published in	Journal of Electrical and Electronic Engineering (Volume 13, Issue 5)
DOI	10.11648/j.jeee.20251305.12
Page(s)	226-241
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Battery Energy Storage Systems (BESS), Artificial Intelligence (AI), Fault Diagnosis, State of Health (SoH) Estimation, Predictive Optimization, Machine Learning in Energy Systems

References

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Tanvir, M. R. (2025). A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization. Journal of Electrical and Electronic Engineering, 13(5), 226-241. https://doi.org/10.11648/j.jeee.20251305.12

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Tanvir, M. R. A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization. J. Electr. Electron. Eng. 2025, 13(5), 226-241. doi: 10.11648/j.jeee.20251305.12

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AMA Style

Tanvir MR. A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization. J Electr Electron Eng. 2025;13(5):226-241. doi: 10.11648/j.jeee.20251305.12

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@article{10.11648/j.jeee.20251305.12,
  author = {Md Rayhan Tanvir},
  title = {A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization
},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {13},
  number = {5},
  pages = {226-241},
  doi = {10.11648/j.jeee.20251305.12},
  url = {https://doi.org/10.11648/j.jeee.20251305.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20251305.12},
  abstract = {Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditional Battery Management Systems (BMS) that are usually on rule-based or physics-based models can hardly adapt to various chemistries, operation conditions, and fault scenarios. Approaches based on Artificial Intelligence (AI), such as machine learning (ML) and deep learning (DL), have recently been proposed to provide more promising data-driven options for fault diagnosis, state of health (SoH) estimation, and predictive optimization. This work provides a cutting-edge review of AI deployment in BESSs, which is conceptually classified into three fundamental categories: fault detection and classification, health monitoring and degradation prognosis, and intelligent control and optimization. We discuss the merits and limitations of different AI models, including supervised / unsupervised / hybrid / reinforcement learning, deployment feasibility, interpretability, and data requirements. An analysis of novel techniques, including digital twin modeling, explainable AI, and secure learning frameworks, is also included. Using comparative analysis, taxonomy visualizations, and performance summaries, this review points out the current limitations, standardization issues, and future research directions that are required for industrial-scale deployment. Through capturing the cutting-edge advancements in this field, we hope this work will serve as a guiding reference for the researchers and industry participants to design robust, scalable, and dependable AI-supported battery management systems.
},
 year = {2025}
}

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TY  - JOUR
T1  - A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization

AU  - Md Rayhan Tanvir
Y1  - 2025/11/26
PY  - 2025
N1  - https://doi.org/10.11648/j.jeee.20251305.12
DO  - 10.11648/j.jeee.20251305.12
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 226
EP  - 241
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20251305.12
AB  - Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditional Battery Management Systems (BMS) that are usually on rule-based or physics-based models can hardly adapt to various chemistries, operation conditions, and fault scenarios. Approaches based on Artificial Intelligence (AI), such as machine learning (ML) and deep learning (DL), have recently been proposed to provide more promising data-driven options for fault diagnosis, state of health (SoH) estimation, and predictive optimization. This work provides a cutting-edge review of AI deployment in BESSs, which is conceptually classified into three fundamental categories: fault detection and classification, health monitoring and degradation prognosis, and intelligent control and optimization. We discuss the merits and limitations of different AI models, including supervised / unsupervised / hybrid / reinforcement learning, deployment feasibility, interpretability, and data requirements. An analysis of novel techniques, including digital twin modeling, explainable AI, and secure learning frameworks, is also included. Using comparative analysis, taxonomy visualizations, and performance summaries, this review points out the current limitations, standardization issues, and future research directions that are required for industrial-scale deployment. Through capturing the cutting-edge advancements in this field, we hope this work will serve as a guiding reference for the researchers and industry participants to design robust, scalable, and dependable AI-supported battery management systems.

VL  - 13
IS  - 5
ER  -

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Md Rayhan Tanvir

School of Electronic and Electrical Engineering, East China University of Technology, Nanchang, China

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http://orcid.org/0000-0002-8263-0916

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Tanvir, M. R. (2025). A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization. Journal of Electrical and Electronic Engineering, 13(5), 226-241. https://doi.org/10.11648/j.jeee.20251305.12

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ACS Style

Tanvir, M. R. A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization. J. Electr. Electron. Eng. 2025, 13(5), 226-241. doi: 10.11648/j.jeee.20251305.12

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AMA Style

Tanvir MR. A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization. J Electr Electron Eng. 2025;13(5):226-241. doi: 10.11648/j.jeee.20251305.12

Copy | Download

@article{10.11648/j.jeee.20251305.12,
  author = {Md Rayhan Tanvir},
  title = {A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization
},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {13},
  number = {5},
  pages = {226-241},
  doi = {10.11648/j.jeee.20251305.12},
  url = {https://doi.org/10.11648/j.jeee.20251305.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20251305.12},
  abstract = {Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditional Battery Management Systems (BMS) that are usually on rule-based or physics-based models can hardly adapt to various chemistries, operation conditions, and fault scenarios. Approaches based on Artificial Intelligence (AI), such as machine learning (ML) and deep learning (DL), have recently been proposed to provide more promising data-driven options for fault diagnosis, state of health (SoH) estimation, and predictive optimization. This work provides a cutting-edge review of AI deployment in BESSs, which is conceptually classified into three fundamental categories: fault detection and classification, health monitoring and degradation prognosis, and intelligent control and optimization. We discuss the merits and limitations of different AI models, including supervised / unsupervised / hybrid / reinforcement learning, deployment feasibility, interpretability, and data requirements. An analysis of novel techniques, including digital twin modeling, explainable AI, and secure learning frameworks, is also included. Using comparative analysis, taxonomy visualizations, and performance summaries, this review points out the current limitations, standardization issues, and future research directions that are required for industrial-scale deployment. Through capturing the cutting-edge advancements in this field, we hope this work will serve as a guiding reference for the researchers and industry participants to design robust, scalable, and dependable AI-supported battery management systems.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization

AU  - Md Rayhan Tanvir
Y1  - 2025/11/26
PY  - 2025
N1  - https://doi.org/10.11648/j.jeee.20251305.12
DO  - 10.11648/j.jeee.20251305.12
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 226
EP  - 241
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20251305.12
AB  - Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditional Battery Management Systems (BMS) that are usually on rule-based or physics-based models can hardly adapt to various chemistries, operation conditions, and fault scenarios. Approaches based on Artificial Intelligence (AI), such as machine learning (ML) and deep learning (DL), have recently been proposed to provide more promising data-driven options for fault diagnosis, state of health (SoH) estimation, and predictive optimization. This work provides a cutting-edge review of AI deployment in BESSs, which is conceptually classified into three fundamental categories: fault detection and classification, health monitoring and degradation prognosis, and intelligent control and optimization. We discuss the merits and limitations of different AI models, including supervised / unsupervised / hybrid / reinforcement learning, deployment feasibility, interpretability, and data requirements. An analysis of novel techniques, including digital twin modeling, explainable AI, and secure learning frameworks, is also included. Using comparative analysis, taxonomy visualizations, and performance summaries, this review points out the current limitations, standardization issues, and future research directions that are required for industrial-scale deployment. Through capturing the cutting-edge advancements in this field, we hope this work will serve as a guiding reference for the researchers and industry participants to design robust, scalable, and dependable AI-supported battery management systems.

VL  - 13
IS  - 5
ER  -

Copy | Download