Biography

Ms. Mengyao Geng is currently a PhD student at The Hong Kong Polytechnic University. She received her Master's Degree from Beijing Institute of Technology (2024) and Bachelor's Degree frome China University of Geosiences (2021). Her research fields include state-of-health estimation, fault diagnosis and early warning of Lithium-ion battery.

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Background of Education

• 2024 - Present, Ph.D., Dept. of Building Environment and Energy Engineering, The Hong Kong Polytechnic University.

• 2021-2024, M. Eng. Safety engineering, Beijing Institute of Technology.

• 2017-2021, B. Eng. Safety engineering, China University of Geosiences.

 

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Research Areas

AI-driven state estimation and fault diagnosis of Battery energy storage systems.

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Prizes and Awards

• 2024 Outstanding Graduates of Beijing

• 2023 National Scholarship for Postgraduate Students

• 2023 Outstanding Postgraduate Students in the Field of Emergency Management in Beijing

• 2022 National Scholarship for Postgraduate Students​​​

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Journal Publications

1. M. Geng, Y. Zhou, Y. Su, L. Zhang, Y. Jiang, C. Liu, Z. Wang, S. Liu, X. Huang. A review on AI-powered safety management of battery energy storage system. Renewable and Sustainable Energy Reviews, 2026, 240:117165.

2. M. Geng, Y. Su, C. Liu, L. Chen, X. Huang. Interpretable deep learning with uncertainty quantification for lithium-ion battery SOH estimation. Energy, 2025, 335:138027.

3. Y. Su, L. Zhang, Y. Zhou, M. Geng, S. Liu, L. Chen, X. Huang. Thermal management performance of flat heat pipe in prismatic battery module. Applied Thermal Engineering, 2025, 279:127747.

4. H. Meng, M. Geng, T. Han. Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis. Reliability Engineering & System Safety, 2023, 236:109288.

5. Q. Yang, C. Xu, M. Geng, H. Meng. A review on models to prevent and control lithium-ion battery failures: From diagnostic and prognostic modeling to systematic risk analysis. Journal of Energy Storage, 2023, 74(4):109230.

6. H. Meng, M. Geng, J. Xing, E. Zio. A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena. Energy, 2022, 261:125278.​

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Conference Communications

1. M. Geng, X. Huang. AI-driven digital twin framework for multi-level safety assessment of energy storage systems. Applied Energy Symposium: AIXEnergy 2026, Ningbo, China, May. 8-11, 2026. (Oral)

2. M. Geng, X. Huang. Artificial Intelligence in safety management of battery energy storage systems. 8th International Conference on Energy Storage and Intelligent Vehicles, Singapore, Dec. 5-7, 2025. (Oral)

3. M. Geng, X. Huang. Lithium-ion battery state of health prediction based on multi-source battery datasets and interpretable deep learning. International Conference on Sustainable Building Energy Engineering, Hong Kong, China, Aug. 6-8, 2025. (Oral)

4. M. Geng, H. Meng, W. Yao, X. Liu. Reliability analysis of metalorganic chemical vapor deposition device, 69th Annual Reliability and Maintainability Symposium. Orlando, Florida, USA, Jan. 23-26, 2023. (Oral)

5. M. Geng, H. Meng, T. Han, Q. Yang. State of health estimation of lithium-ion batteries based on incremental capacity curves. 33rd European Safety and Reliability Conference, University of Southampton, UK, Sep. 3-7, 2023. (Oral)

6. M. Geng, H. Meng, X. An, J. Xing. Degradation Prognostics of Lithium-ion Batteries Based on Partial Features and Long Short-term Memory Network. 13th International Conference on Reliability, Maintainability, and Safety, Hong Kong, China, Aug. 21-24, 2022. (Oral)

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​ Awards of competitions

• 2025 Outstanding Award, Innovation Track, China Digital Automotive Competition

       Team Members: C. Li,  X. Huang

• 2024 Third Prize, University Division, New Energy Vehicle Power Battery Safety Early Warning Algorithm Competition

       Team Members: X. Huang, , Z. Wang, C. Li, L. Zhang, Y. Liu, C. Ye

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Patents

1. X. Huang, Y. Zhou, L. Zhang, M. Geng, Y. Su. A method and device for thermal runaway assessment of cathode materials [P], CN120847323A, 2025-07-23. (Published)

2. H. Meng, M. Geng, C. Wang, J. Xing. Method and device for capacity degradation prediction of lithium-ion battery [P], US-11959970-B2, Application date: 2023-04-13. (Granted)

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（Last updated on 10/08/2026)

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