Hu, Chao

Chao Hu

Associate Professor, School of Mechanical, Aerospace and Manufacturing Engineering

Email chao.hu@uconn.edu
Phone (860) 486-2371
Mailing Address School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, 191 Auditorium Rd. U-3139, Storrs, CT 06269
Campus Storrs
Google Scholar Link

Brief Bio

Dr. Chao Hu received his B.E. degree in Engineering Physics from Tsinghua University in Beijing, China, in 2007 and his Ph.D. degree in Mechanical Engineering from the University of Maryland, College Park in Maryland, in 2011. He worked first as a Senior Reliability Engineer and then as a Principal Scientist at Medtronic in Minnesota from 2011 to 2015; he joined the Department of Mechanical Engineering at Iowa State University in 2015 and worked first as an Assistant Professor and then as an Associate Professor from 2015 to 2022. He is currently an Associate Professor in the Department of Mechanical Engineering at the University of Connecticut.

Dr. Hu’s research interests are engineering design under uncertainty, lifetime prediction of lithium-ion batteries, and prognostics and health management. He has received several awards and recognitions for his research, including two Highly Cited Research Paper Awards (2012-2013 and 2020) in the Journal of Applied Energy, received in 2015 and 2022, respectively; the ASME Design Automation Young Investigator Award in 2018; and the Best Paper Awards at the ASME Design Automation Conference and the IEEE International Conference on Prognostics and Health Management in 2013 and 2012, respectively.

Dr. Hu serves as a Review Editor of Structural and Multidisciplinary Optimization and an Associate Editor of the ASME Journal of Mechanical Design and IEEE Sensors Journal.

  • Engineering Design under Uncertainty,
  • Prognostics and Health Management (PHM),
  • Physics-Informed Machine Learning for PHM,
  • Design for Failure Recovery of Lithium-ion Batteries
A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation
C Hu, BD Youn, J Chung
Applied Energy 92, 694-704
Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries
S Shen, M Sadoughi, M Li, Z Wang, C Hu
Applied Energy 260, 114296
Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
C Hu, BD Youn, P Wang, JT Yoon
Reliability Engineering & System Safety 103, 120-135
Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
C Hu, BD Youn, P Wang
Prognostics and Health Management (PHM), 2011 IEEE Conference on, 1-10
A deep learning method for online capacity estimation of lithium-ion batteries
S Shen, M Sadoughi, X Chen, M Hong, C Hu
Journal of Energy Storage 25, 100817
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ...
Structural and Multidisciplinary Optimization 65 (12), 354
Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery
C Hu, G Jain, P Zhang, C Schmidt, P Gomadam, T Gorka
Applied Energy 129, 49-55
Resilience-Driven System Design of Complex Engineered Systems
BD Youn, C Hu, P Wang
Journal of Mechanical Design 133 (10), 101011
Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems
C Hu, BD Youn
Structural and Multidisciplinary Optimization 43 (3), 419-442
A generic probabilistic framework for structural health prognostics and uncertainty management
P Wang, BD Youn, C Hu
Mechanical Systems and Signal Processing 28, 622-637
A physics-informed deep learning approach for bearing fault detection
S Shen, H Lu, M Sadoughi, C Hu, V Nemani, A Thelen, K Webster, M Darr, ...
Engineering Applications of Artificial Intelligence 103, 104295
Method for estimating capacity and predicting remaining useful life of lithium-ion battery
C Hu, G Jain, P Tamirisa, T Gorka
Applied Energy 126, 182–189
Online estimation of lithium-ion battery capacity using sparse Bayesian learning
C Hu, G Jain, C Schmidt, C Strief, M Sullivan
Journal of Power Sources 289, 105-113
Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings
M Sadoughi, C Hu
IEEE Sensors Journal 19 (11), 4181-4192
An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction
Z Li, D Wu, C Hu, J Terpenny
Reliability Engineering & System Safety 184, 110-122
Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations
Z Li, Y Jiang, Q Guo, C Hu, Z Peng
Renewable Energy 116, 55-73
Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review
Z Li, Y Jiang, C Hu, Z Peng
Measurement 90, 4-19
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
V Nemani, L Biggio, X Huan, Z Hu, O Fink, A Tran, Y Wang, X Zhang, ...
Mechanical Systems and Signal Processing 205, 110796
A generic model-free approach for lithium-ion battery health management
G Bai, P Wang, C Hu, M Pecht
Applied Energy 135, 247-260
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ...
Structural and multidisciplinary optimization 66 (1), 1