A machine learning-based battery management system for state-of-charge prediction and state-of-health estimation for unmanned aerial vehicles
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A machine learning framework guided by domain expertise produces SOH estimates that are more precise and computationally effective by using physical knowledge of battery degradation processes.
“By using physical knowledge of battery degradation processes to guide the ML model, this technique produces SOH estimates that are more precise and computationally efective.”
From Artificial Intelligence Approaches for SOH Estimation in Lithium-Ion Batteries: A Comprehensive Review of Progress, Critical Evaluations, Challenges, and Toward Future Outlooks (2025) · cites this paper as [182]
The Mathematical model section (p. unavailable) defines SOH using charge-capacity thresholds and says classification reduces computational demand; Results & Discussion (p. unavailable) reports approximately 0.92 accuracy for the SOH classifier. The paper does not say that physical knowledge of battery degradation processes guides the ML model or causes the reported accuracy and efficiency. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe domain-expertise-guided ML framework facilitates accurate capacity prediction without requiring substantial processing resources.
“Trough the integration of domain-specifc energy and power fade indicators, the framework facilitates accurate capacity prediction without requiring substantial processing resources.”
From Artificial Intelligence Approaches for SOH Estimation in Lithium-Ion Batteries: A Comprehensive Review of Progress, Critical Evaluations, Challenges, and Toward Future Outlooks (2025) · cites this paper as [182]
The Related work section says the proposed approach accurately predicts state of charge and uses classification for state-of-health estimation, substantially reducing computational power. The Results & Discussion section describes voltage, current, and temperature inputs, not domain-specific energy and power fade indicators. The paper does not establish accurate capacity prediction by the framework, so those parts of the claim are unsupported. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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Huang, X., Zhang, H., Zhou, S., Xia, S., Du, Q., & Yuan, Y. (2026) · International Conference on Energy, Power and Electrical Technology (ICEPET) · doi:10.1109/icepet70032.2026.11725462
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Chen, Z., Chen, Z., Zhou, D., Xiao, P., Huang, X., Wang, L., Wang, Y., Hu, T., Wu, J., Zhang, Q., et al. (2026) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2026.133748
- Authors
- Shibl, M. M., Ismail, L. S., & Massoud, A. M.
- Affiliations
- Qatar University (QA)
- Corresponding author
- Shibl, Mostafa M.
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 66
- Pages
- 107380
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W4367186878
- Cited by
- 92
- References
- 22
- Citations by year
- 2026: 32 · 2025: 28 · 2024: 23 · 2023: 9
- FWCI
- 6.39
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Journal of Energy Storage (publishedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar Foundation
- Fonds National de la Recherche Luxembourg
- Qatar National Research Fund (10-0130-170286)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- battery management systems, unmanned aerial vehicles, state of charge estimation, state of health estimation, lithium-ion batteries, deep neural network, long short-term memory, random forest, battery voltage and current, ambient temperature, ESP32 microcontroller, Raspberry Pi, cloud-based monitoring
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