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Wilson College of Textiles
Mengmeng Zhu

Research

Overview

Our lab is dedicated to advancing the safety, reliability, operation readiness and assurance of Cyber-Physical Systems (CPS) through a multidisciplinary research approach. We focus on key areas such as energy storage systems (e.g., lithium-ion batteries), human-machine interaction, smart health technologies, and robust software development. Our work integrates theoretical and data-driven methodologies to develop innovative tools and testbeds that address the complex challenges of CPS. This includes ensuring reliable system functionalities, enhancing resilience to both accidental disruptions and intentional cyber-physical threats, and implementing advanced prognostics and health management strategies for early fault detection and mitigation. Our goal is to enable next-generation CPS that are adaptable, secure, and resilient in real-world applications. Our research is sponsored by both NSF and industry partners.

Our current projects include

A Unified Optimization Design Framework for Human and Energy Storage System Grounded in Naturalistic User Behavior and Interaction

Development of Deep Learning Based Nonwoven Uniformity Analysis

Explainable AI based Health Management of Energy Storage Systems

Stochastic Process based Reliability Analysis of Lithium-ion Batteries with Risk Considerations

Human In-the-loop Software Reliability Modeling, Algorithm, and Evaluation

Resilient Engineering System Design with Uncertainty

Publications

(+ denotes graduate students under my supervision, * denotes corresponding author)

Journal Papers

  1. Gao, M.+, Shim, E., & Zhu, M.* (2026). A frequency-domain enhanced YOLO framework for industrial material defect detection. To be submitted in March 2026.
  2. Zhang C.+, & Zhu, M.* (2026). Reinforcement learning for multi-UAV cooperative search under heterogeneous UAV battery degradation. To be submitted in July 2026.
  3. Malaek, M.+, Zhu, M., & Chen, K. (2026). Emotional states and charging behaviors in human-system interaction with battery electric vehicles. Submitted in March 2026.
  4. Lin, H.+, & Zhu, M.* (2025). Reinforcement learning-based charging optimization for lithium-ion batteries using Gamma process degradation and particle filter-enhanced thermal models. To be submitted in July 2025.
  5. Lin, H+ & Zhu, M.* (2025). Damage-resistant CPS reliability modeling considering coupled system resistance effects. Accepted. Reliability Engineering and System Safety, 256, 110757.
  6. Wang, R.+, Zhu, M.* & Zhang, X. (2024). Lifetime prediction and maintenance assessment of Lithium-ion batteries based on combined information of discharge voltage curves and capacity fade. Journal of Energy Storage, 81, 110376.
  7. Choi, J., Zhu, M., Kang, J. & Jeong, M. (2024). Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing. Annals of Operations Research. Accepted.
  8. Hu, Y.+, Zhu, M.* & Lin, H.+ (2023). A nonlinear Wiener process degradation model with damage resistance for reliability analysis. Annals of Operations Research. Accepted May 2025.
  9. Wang, R+, Lin, H.+, Choi, J.,  Hashemi, A., & Zhu, M.* (2025). Novel differential voltage features based machine learning approach to lithium-ion batteries SOH prediction at various current rates. Energy. Accepted July 2025.
  10. Zhu, M.*, & Huang, C. (2025). Structure information based multi-component system resilience assessment framework with application to battery energy storage system design. Annals of Operations Research. Accepted July 2025.
  11. Hu, Y.+, Wang, R.+, Zhu, M.* & Chen, K. B. (2023). Modeling human-machine interaction system reliability with multiple dependent degradation processes and situation awareness. International Journal of Reliability, Quality and Safety Engineering.  https://doi.org/10.1142/S0218539323500146.
  12. Wang, R.+, Zhu, M.*, Zhang, X. & Pham, H. (2023). Lithium-ion battery remaining useful life prediction using a two-phase degradation model with a dynamic change point. Journal of Energy Storage, 59, 106457.
  13. Wang, R.+ & Zhu, M.* (2022). Shock-loading based method for modeling dependent competing risks with degradation processes and random shocks. International Journal of Reliability, Quality and Safety Engineering, 29(03), 2250002.
  14. Zhu, M.*, Huang, X. & Pham, H. (2021). A random field environment-based multidimensional time-dependent resilience modeling of complex systems. IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2021.3083515.
  15. Zhu, M.* (2021). A new framework of complex system reliability with imperfect maintenance policy. Annals of Operations Research. Available online: https://doi.org/10.1007/s10479-020-03852-w.
  16. Zhu, M.* & Pham, H. (2020). A generalized multiple environmental factors software reliability model with stochastic fault detection process. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03732-3.
  17. Zhu, M.* & Pham, H. (2020). An empirical study of factor identification in smart health monitoring wearable device. IEEE Transactions on Computational Social Systems, 7(2), 404-416.
  18. Zhu, M.*& Pham, H. (2019). A novel system reliability modeling of hardware, software, and interactions of hardware and software. Mathematics, 7(11), 1049.
  19. Zhu, M.*& Pham, H. (2018). A software reliability model incorporating martingale process with gamma-distributed environmental factors. Annals of Operations Research. Available online: https://doi.org/10.1007/s10479-018-2951-7.
  20. Zhu, M.*& Pham, H. (2018). A two-phase software reliability modeling involving with software fault dependency and imperfect fault removal. Computer Languages, Systems & Structures, 53, 27-42.
  21. Zhu, M. & Pham, H. (2018). A multi-release software reliability modeling for open source software incorporating dependent fault detection process. Annals of Operations Research, 269(1-2), 773 – 790.
  22. Zhu, M. & Pham, H.(2017). Environmental factors analysis and comparison affecting software reliability in development of multi-release software. Journal of Systems and Software, 132, 72-84.
  23. Zhu, M., Zhang, X. & Pham, H.(2015). A comparison analysis of environmental factors affecting software reliability. Journal of Systems and Software, 109, 150-160.
  24. Zhu, M. & Pham, H. (2016). A software reliability model with time-dependent fault detection and fault removal. Vietnam Journal of Computer Science, 3(2), 71-79.
  25. Fan, S., Ma, Y., Zhu, X., Xiong, R. & Zhu, M. (2008). Entropy-based performance evaluation of operation characteristic curve. Industrial Engineering and Management, China, 13(4), 99-101.

Book Chapters

  1. Hu, Y.+, & Zhu, M.* (2023). System reliability models with random shocks and uncertainty: A state-of-the-art review. Springer. (Invited book chapter).
  2. Zhu, M.*, & Pham, H. (2022). Software reliability modeling and methods: A state of the art review. Optimization Problems in Software Reliability, Springer.

    Conference Papers

    1. Zhang, C.+, & Zhu, M.* (2026). Multi-UAV path planning considering stochastic energy distribution. 2026 American Control Conference (ACC). Accepted.
    2. Zhang, C.+, Chen, K., & Zhu, M.* (2026). Cumulative prospect theory-based framework for modeling and predicting EV charging behavior integrating battery degradation. Submitted in March 2026.
    3. Malaek, M.+, Zhu, M., & Chen, K. (2026). Path analysis of EV charging sessions: Relationships among battery state, charging context, and emotions. Submitted in February 2026.
    4. Wang, R.+, Zhu, M.* & Choi, J. (2026). Deep Learning-based review prediction for smart health-monitoring wearable device. To be Submitted in May 2026.   
    5. Gao, M.+, Shim, E., & Zhu, M.* (2026). SDDC-YOLO: A diagnostic framework for fault detection in industrial materials. In 2026 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1-6). IEEE.
    6. Lin, H.+, & Zhu, M.* (2025). MDP based charging control for lithium-ion batteries based on stochastic capacity degradation and temperature variation. To be submitted by September 2025.
    7. Malaek, M.+, Zhu, M., & Chen, K. (2025). Understanding Emotional States and Charging Behaviors for Decision Making and Battery Preservation in Electric Vehicles. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (p. 10711813251358248). Sage CA: Los Angeles, CA: SAGE Publications.
    8. Lin, H+ & Zhu, M.* (2024). Function based damage resistance modeling for multi-phase CPS system reliability. Proceedings of the 29th ISSAT International Conference on Reliability and Quality in Design, Miami, FL, 200-204.
    9. Wang, R+ & Zhu, M.* (2023). Experimental analysis of Lithium-ion battery degradation with varying discharge rates. Proceedings of the 28th ISSAT International Conference on Reliability and Quality in Design, San Francisco, CA, 37-41.
    10. Hu, Y.+, & Zhu, M.* (2022). Reliability modeling for shock-degradation-dependent process considering damage resistance. Proceedings of the 27th ISSAT International Conference on Reliability and Quality in Design (Virtual), 281-284.
    11. Zhu, M.* (2021). Probabilistic-based general framework of modeling engineering system resilience. Proceedings of the 26th ISSAT International Conference on Reliability and Quality in Design (Virtual), 284-288.
    12. Zhu, M.* & Pham, H. (2019). System reliability modeling of hardware, software, and interactions of hardware and software. Proceedings of the 25th ISSAT International Conference on Reliability and Quality in Design, Las Vegas, NV, 73-77.
    13. Zhu, M.* & Pham, H. (2018). A generalized martingale-based software reliability model considering multiple environmental factors. Proceedings of the 24th ISSAT International Conference on Reliability and Quality in Design, Toronto, Canada, 36-40.
    14. Zhu, M. & Pham, H. (2017). Software reliability modeling with considerations of two-phase imperfect debugging and fault removal. Proceedings of the 23rd ISSAT International Conference on Reliability and Quality in Design, Chicago, IL, 69-73.
    15. Zhu, M. & Pham, H. (2016). Multi-release software reliability modeling and analysis incorporating dependent software fault detection process. Proceedings of the 22nd ISSAT International Conference on Reliability and Quality in Design, Los Angeles, CA, 122-126.