Enhancing Battery Health in Electric Vehicles: AI-Enhanced BMS for Accurate SoC, SoH, and Fault Diagnosis

Ratnababu Mamidi; D. Obulesu; Prajna K. B; Sushama Rani Dutta; P Uma Maheshwara  Rao; R. Senthamil Selvan; M. Prabha

doi:10.63278/10.63278/mme.v31.1

Authors

Ratnababu Mamidi Department of Artificial intelligence & Machine Learning, Vishnu Institute of Technology, India
D. Obulesu Associate professor, CVR college of engineering college, India
Prajna K. B Department of ECE, Assistant professor, Nitte Meenakshi Institute of technology, India
Sushama Rani Dutta Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, India
P Uma Maheshwara Rao
R. Senthamil Selvan Department of ECE, Annamacharya Institute of Technology and Sciences, India
M. Prabha Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India

DOI:

https://doi.org/10.63278/10.63278/mme.v31.1

Keywords:

Electric Vehicles, Battery Management System, SoC, State of health, Artificial Intelligence.

Abstract

Electric vehicles (EVs) are the only way to solve both harmful fuel emissions and other environmental issues. Safety, efficiency, and lifetime of electric vehicles depend on their battery management system (BMS), which is thus essential. Since internal resistance causes the capacity of the battery to drop with age, the BMS must continuously check its state. Reliable State of Health (SoH) as well as State of Charge (SoC) predictions call for more complex algorithms considering charging time, current, and capacity. Artificial intelligence (AI) driven methods increase the precision of diagnostics, the speed of issue identification, and the regulation of thermal management—all of which help to ensure the performance and safety of batteries. A malfunction diagnostic system offers further more protections. By means of successful use of these BMS algorithms, Energy Storage Systems (ESS) achieves effective control of battery capacity and long-term viability of operations of electric vehicles.

References

Farman, Md Khaja, et al. "AI-Enhanced Battery Management Systems for Electric Vehicles: Advancing Safety, Performance, and Longevity." E3S Web of Conferences. Vol. 591. EDP Sciences, 2024.

Omojola, Ayogoke Felix, et al. "Artificial Intelligence-Driven Strategies for Advancing Lithium-Ion Battery Performance and Safety."

Miraftabzadeh, Seyed Mahdi, et al. "Exploring the synergy of artificial intelligence in energy storage systems for electric vehicles." Electronics 13.10 (2024): 1973.

Madani, Seyed Saeed, et al. "Recent progress of deep learning methods for health monitoring of lithium-ion batteries." Batteries 10.6 (2024): 204.

Shi, Dapai, et al. "Cloud-based artificial intelligence framework for battery management system." Energies 16.11 (2023): 4403.

Villa Avila, Edisson Andres. "A Systematic Review on the Integration of Artificial Intelligence into Energy Management Systems for Electric Vehicles: Recent Advances and Future Perspectives." (2024).

Kumar, T. Rajasanthosh, G. Laxmaiah, and S. Solomon Raj. "A Framework of Intelligent Manufacturing Process by Integrating Various Function." AI-Driven IoT Systems for Industry 4.0. CRC Press 241-254.

Arévalo, Paul, Danny Ochoa-Correa, and Edisson Villa-Ávila. "A systematic review on the integration of artificial intelligence into energy management systems for electric vehicles: Recent advances and future perspectives." World Electric Vehicle Journal 15.8 (2024): 364.

Mewada, Shivlal, et al. "Smart diagnostic expert system for defect in forging process by using machine learning process." Journal of Nanomaterials 2022.1 (2022): 2567194.

Qu, Xudong, et al. "Insights and reviews on battery lifetime prediction from research to practice." Journal of Energy Chemistry (2024).

Zhao, Jingyuan, et al. "Battery safety: Fault diagnosis from laboratory to real world." J. Power Sources 598.234111 (2024): 10-1016.

Mittal, Amit, et al. "Smart energy: artificial intelligence (AI) in charging and battery management systems." 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 2024.

Safari, Ashkan, Mohammadreza Daneshvar, and Amjad Anvari-Moghaddam. "Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management." Applied Sciences 14.23 (2024): 11112.

Kabir, Md Rafiul, Dipal Halder, and Sandip Ray. "Digital Twins for IoT-Driven Energy Systems: A Survey." IEEE Access (2024).

Fichtner, Maximilian, et al. "Rechargeable batteries of the future—the state of the art from a BATTERY 2030+ perspective." Advanced Energy Materials 12.17 (2022): 2102904.

Minchala-Ávila, Camila, Paul Arévalo, and Danny Ochoa-Correa. "A Systematic Review of Model Predictive Control for Robust and Efficient Energy Management in Electric Vehicle Integration and V2G Applications." Modelling 6.1 (2025): 20.

Stincescu, Tudor. Modelling Energy Use in Commercial Electric Vehicle Fleets. Diss. University of Sheffield, 2023.

Patro, Pramoda, et al. "A hybrid approach estimates the real-time health state of a bearing by accelerated degradation tests, Machine learning." 2021 Second International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE, 2021

Chen, Chen, Jun Lai, and Minyuan Guan. "A Collaborative Design and Modularized Assembly for Prefabricated Cabin Type Energy Storage System with Effective Safety Management." Frontiers in Energy Research 10 (2022): 846741.

Singh, Chaitanya, et al. "Applied machine tool data condition to predictive smart maintenance by using artificial intelligence." International Conference on Emerging Technologies in Computer Engineering. Cham: Springer International Publishing, 2022.