Email: hante@bit.edu.cn, hant15@tsinghua.org.cn
Dr. Han is currently working at the Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology, and serves as the Assistant Director of the Beijing Laboratory for System Engineering of Carbon Neutrality, led by Prof. Yi-Ming Wei. His research interests include Sustainable Energy, Energy an AI, Diagnostics and Health Management for Energy Equipment, Energy Forecasting and Load Prediction, Optimization and Scheduling of Energy Systems, as well as Machine Learning and Trustworthy AI. He has authored and co-authored more than 80 articles in technical journals and conference proceedings. 14 of his articles have been honored with the “ESI highly cited paper”, and 5 articles has been honored with the “ESI hot paper” in the Web of Science. He has been recognized as one of the World’s Top 2% Scientists by Stanford University consecutively from 2021 to 2024. He is an editor of “ Applied Soft Computing ”, associate editor of “ Knowledge and Information Systems ”. In addition, he has previously served as a guest editor for internationally renowned journals such as “ IEEE Transactions on Industrial Cyber-Physical Systems ”, “ Reliability Engineering & System Safety ”, etc. He is also an active reviewer for over 60 prestigious journals from IEEE, Elsevier, Springer and other publishers. He was awarded the Outstanding Ph.D. Graduate Thesis by Tsinghua University. He was also selected for the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (CAST).
Ph.D.—Energy and Power Engineering—Tsinghua University 8/2015 - 7/2020
Visiting Ph.D.—Mechanical Engineering—University of Alberta 3/2019 - 9/2019
B.S.—Energy and Power Engineering—Tsinghua University 8/2011 - 6/2015
Research Associate Industrial Engineering—Tsinghua University 9/2020 - 2/2023
Associate Professor—School of Management Beijing Institute of Technology 3/2023-present
Associate Professor—Center for Energy and Environmental Policy Research (CEEP) Beijing Institute of Technology 3/2023-present
Assistant Director—Beijing Laboratory for System Engineering of Carbon Neutrality Beijing Institute of Technology 6/2024-present
Google Scholar: https://scholar.google.com/citations?user=""HqIb9_EAAAAJ&hl=en
* Corresponding Author.
[1] Han Te#, Wang Yongzhen#*, Mi Zhifu*, Han Kai*, Tian Jinpeng & Wei Yi-Ming. Designing and regulating clean energy data centres. Nature Reviews Clean Technology , https://doi.org/10.1038/s44359-025-00062-0.
[2] Wu Rui, Tian Jinpeng, Yao Jiachi, Han Te*, Hu Chunsheng. Confidence-aware quantile Transformer for reliable degradation prediction of battery energy storage systems. Reliability Engineering & System Safety , 2025, 260, 111019. (JCR Q1, IF= 9.4)
[3] Han Te, Cong Rong-Gang*, Yu Biying, Tang Baojun, Wei Yi-Ming*. Integrating local knowledge with ChatGPT-like large-scale language models for enhanced societal comprehension of carbon neutrality. Energy and AI , 2024, 18, 100440. (JCR Q1, IF= 9.6)
[4] Chang Zhonghao, Han Te*. Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives. Renewable and Sustainable Energy Reviews , 2024, 205, 114861. (JCR Q1, IF= 16.3)
[5] Chang Zhonghao, Jia Kaiwen, Han Te*, Wei Yi-Ming. Towards More Reliable Photovoltaic Energy Conversion Systems: A Weakly-Supervised Learning Perspective on Anomaly Detection. Energy Conversion and Management , 2024, 316, 118845. (JCR Q1, IF= 9.9)
[6] Han Te, Tian Jinpeng*, Chung C.Y.*, Wei Yi-Ming. Challenges and opportunities for battery health estimation: Bridging laboratory research and real-world applications. Journal of Energy Chemistry , 2024, 89, 434-436. (JCR Q1, IF= 14.0)
[7] Yao Yuantao, Han Te*, Yu Jie, Xie Min. Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems. Energy , 2024, 291, 130419. (JCR Q1, IF= 9.0, ESI Top 1% Highly Cited Paper)
[8] Yao Jiachi, Chang Zhonghao, Han Te*, Tian Jinpeng*. Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems. Energy , 2024, 294, 130882. (JCR Q1, IF= 9.0, ESI Top 1% Highly Cited Paper)
[9] Chang Zhonghao, Zhang An-jun, Wang Huan*, Xu Jiajia, Han Te*. Photovoltaic cell anomaly detection enabled by scale distribution alignment learning and multi-scale linear attention framework. IEEE Internet of Things Journal , 2024, 11(16): 27816-27827. (JCR Q1, IF= 8.2)
[10] Zhang Xiaochen, Wang Chen, Zhou Wei, Xu Jiajia, Han Te*. Trustworthy diagnostics with out-of-distribution detection: A novel max-consistency and min-similarity guided deep ensembles for uncertainty estimation. IEEE Internet of Things Journal , 2024, 11(13): 23055-23067. (JCR Q1, IF= 8.2)
[11] Liu Ruonan, Zhang Quanhu, Han Te*, Yang Boyuan*, Zhang Weidong, Yin Shen and Zhou Donghua. Survey on foundation models for prognostics and health management in industrial cyber-physical systems. IEEE Transactions on Industrial Cyber-Physical Systems , 2024, 2: 264-280.
[12] Wang Zhe, Ding Yi, Han Te*, Xu Qiang*, Yan Hong and Xie Min. Adaptive Attention-Driven Few-Shot Learning for Robust Fault Diagnosis. IEEE Sensors Journal , 2024, 24(16): 26034-26043. (JCR Q1, IF= 4.3)
[13] Liu Ruonan, Hu Puyuan, Zhao Siheng, Sun Zhijian, Han Te*, Pang Zhibo, Zhang Weidong*. Out-of-Distribution Fault Diagnosis of Industrial Cyber-physical Systems Based on Orthogonal Anchor Clustering with Adaptive Balance. IEEE Transactions on Industrial Cyber-Physical Systems , 2024, 1-13, 10.1109/TICPS.2024.3506872.
[14] Cheng Yongbo, Qv Junheng, Feng Ke, Han Te*. A Bayesian adversarial probsparse Transformer model for long-term remaining useful life prediction. Reliability Engineering & System Safety , 2024, 248, 110188. (JCR Q1, IF= 9.4)
[15] Han Te, Xie Wenzhen*, Pei Zhongyi. Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine. Information Sciences , 2023, 648, 119496. (JCR Q1, IF= 8.1, ESI Top 0.1% Hot Paper, ESI Top 1% Highly Cited Paper)
[16] Yao Jiachi and Han Te*. Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data. Energy , 2023, 271, 127033. (JCR Q1, IF= 9.0, ESI Top 0.1% Hot Paper, ESI Top 1% Highly Cited Paper)
[17] Xie Wenzhen, Han Te*, Pei Zhongyi and Xie Min. A unified out-of-distribution detection framework for trustworthy prognostics and health management in renewable energy systems. Engineering Applications of Artificial Intelligence , 2023, 125, 106707. (JCR Q1, IF= 7.5)
[18] Wang Zhe, Wu Zhiying, Li Xingqiu, Shao Haidong, Han Te*, Xie Min. Attention-aware temporal-spatial graph neural network with multi-sensor information fusion for fault diagnosis. Knowledge-Based Systems , 2023, 278, 110891. (JCR Q1, IF= 7.2)
[19] Meng Huixing, Geng Mengyao and Han Te*. 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. (JCR Q1, IF= 9.4, ESI Top 1% Highly Cited Paper)
[20] Miao Yonghao, Li Chenhui, Shi Huifang and Han Te*. Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis. Mechanical Systems and Signal Processing , 2023, 189: 110110. (JCR Q1, IF= 7.9, ESI Top 1% Highly Cited Paper)
[21] Zhou Taotao, Han Te* and Enrique Lopez Droguett. Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework. Reliability Engineering & System Safety , 2022, 224: 108525. (JCR Q1, IF= 9.4, ESI Top 0.1% Hot Paper, ESI Top 1% Highly Cited Paper)
[22] Han Te and Li Yan-Fu*. Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles. Reliability Engineering & System Safety , 2022, 226, 108648. (JCR Q1, IF= 9.4, ESI Top 0.1% Hot Paper, ESI Top 1% Highly Cited Paper)
[23] Han Te, Wang Zhe* and Meng Huixing. End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation. Journal of Power Sources , 2022, 520: 230823. (JCR Q1, IF= 8.1)
[24] Han Te, Zhou Taotao, Xiang Yongyong* and Jiang Dongxiang. Cross-machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer. Structural Control and Health Monitoring , 2022, 29(3): e2898. (JCR Q1, IF=""4.6)
[25] Han Te, Li Yan-Fu* and Qian Min. A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions. IEEE Transactions on Instrumentation and Measurement , 2021, 70: 3520011. (JCR Q1, IF= 5.6, ESI Top 1% Highly Cited Paper)
[26] Han Te, Liu Chao*, Wu Rui and Jiang Dongxiang. Deep transfer learning with limited data for machinery fault diagnosis. Applied Soft Computing , 2021, 103: 107150. (JCR Q1, IF= 7.2, ESI Top 1% Highly Cited Paper)
[27] Han Te, Liu Chao*, Yang Wenguang and Jiang Dongxiang. Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application. ISA Transactions , 2020, 97: 269-281. (JCR Q1, IF= 6.3, ESI Top 0.1% Hot Paper, ESI Top 1% Highly Cited Paper)
[28] Han Te, Liu Chao*, Yang Wenguang and Jiang Dongxiang. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowledge-Based Systems , 2019, 165: 474-487. (JCR Q1, IF= 7.2, ESI Top 1% Highly Cited Paper)
[29] Han Te, Liu Chao*, Wu Linjiang, Sarkar Soumik and Jiang Dongxiang. An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems. Mechanical Systems and Signal Processing , 2019, 117: 170-187. (JCR Q1, IF= 7.9, ESI Top 1% Highly Cited Paper)
[30] Han Te, Liu Chao*, Yang Wenguang and Jiang Dongxiang. Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions. ISA Transactions , 2019, 93: 341-353. (JCR Q1, IF= 6.3)
[31] Han Te, Jiang Dongxiang*, Sun Yankui, Wang Nanfei and Yang Yizhou. Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification. Measurement , 2018, 118: 181-193. (JCR Q1, IF= 5.2)
[32] Han Te*, Jiang Dongxiang, Zhao Qi, Wang Lei and Yin Kai. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control , 2018, 40(8): 2681-2693. (JCR Q3, IF= 1.7, ESI Top 1% Highly Cited Paper)
[33] Li Chenxi, Wang Huan* and Han Te. Dynamic Subdomain Pseudolabel Correction and Adaptation Framework for Multiscenario Mechanical Fault Diagnosis. IEEE Transactions on Reliability , 2024, 10.1109/TR.2024.3397913. (JCR Q1, IF= 5.0)
[34] Zhao Xiaoyu, Wang Zuolu*, Han Te, Yang Wenxian, Gu Fengshou and Ball Andrew David. A Meta-Learning Method for Few-Shot Multi-Domain State of Health Estimation of Lithium-ion Batteries. IEEE Transactions on Transportation Electrification , 2024, 10.1109/TTE.2024.3470551. (JCR Q1, IF= 7.2)
[35] Luo Xiaoyu, Wang Huan*, Han Te and Zhang Ying. FFT-Trans: Enhancing Robustness in Mechanical Fault Diagnosis With Fourier Transform-Based Transformer Under Noisy Conditions. IEEE Transactions on Instrumentation and Measurement , 2024, 73: 2515112. (JCR Q1, IF= 5.6)
[36] Zhu Ning, Wang Jing, Zhang Ying, Wang Huan* and Han Te. An Adversarial Training Framework Based on Unsupervised Feature Reconstruction Constraints for Crystalline Silicon Solar Cells Anomaly Detection. IEEE Transactions on Instrumentation and Measurement , 2024, 10.1109/TIM.2024.3462989. (JCR Q1, IF= 5.6)
[37] Pei Shicheng, Wang Huan* and Han Te. Time-Efficient Neural Architecture Search for Autonomous Underwater Vehicle Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement , 2023, 72: 3536211 (JCR Q1, IF= 5.6)
[38] Zhou Taotao, Jiang Shan*, Han Te, Zhu Shun-Peng, Cai Yinan. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network. International Journal of Fatigue , 2023, 166, 107234. (JCR Q1, IF= 5.7, ESI Top 1% Highly Cited Paper)
[39] Deng Zhenyu, Han Te, Zheng Hao, Zhi Fengyao, Jiang Jiajia and Duan, Fajie*. Critical concurrent feature selection and enhanced heterogeneous ensemble learning approach for fault detection in industrial processes. IEEE Sensors Journal , 2022, 22(8): 7931-7943. (JCR Q1, IF= 4.3)
[40] Wang Zhe, Han Te, Wang Shen, Zhan Zaifu, Zhao Wei and Huang Songling*. Health monitoring of plate structures based on tomography with combination of guided wave transmission and reflection. IEEE Sensors Journal , 2022, 22(11): 10850-10860. (JCR Q1, IF= 4.3)
[41] Si Jin, Shi Hongmei*, Han Te, Chen Jingcheng and Zheng Changchang. Learn generalized features via multi-source domain adaptation: Intelligent diagnosis under variable/constant machine conditions. IEEE Sensors Journal , 2022, 22(1): 510-519. (JCR Q1, IF= 4.3)
[42] Qian Min, Li Yan-Fu*, Han Te. Positive-unlabeled learning-based hybrid deep network for intelligent fault detection. IEEE Transactions on Industrial Informatics , 2021, 18(7): 4510-4519. (JCR Q1, IF=""11.7)
Editorial Positions and Conference Committees
Associate Editor, IEEE Internet of Things Journal (SCI, JCR Q1), 2025 to present.
Associate Editor, Knowledge and Information Systems (SCI, JCR Q2), 2025 to present.
Editor, Editorial Board, Applied soft Computing (SCI, JCR Q1), 2024 to present.
Associate Editor, IEEE Sensors Journal (SCI, JCR Q1), 2023 to present.
Leading Guest Editor, Reliability Engineering & System Safety (SCI, JCR Q1), Special Issue on Scientific Machine Learning for Enhancing Reliability and Safety of AI-powered Systems (2023).
Guest Editor, IEEE Transactions on Industrial Cyber-Physical Systems , Special Issue on Machine Learning for Prognostics and Health Management of Industrial Cyber-physical Systems (2023).
Guest Editor, Journal of Risk and Reliability (SCI, JCR Q2), Special Issue on Domain-Knowledge Guided Machine Learning in Safety-Critical Applications (2022).
Guest Editor, Measurement Science and Technology (SCI, JCR Q1), Special Issue on AI-Enabled Industrial Equipment Monitoring, Diagnosis and Health Management (2022).
Session Chair, The 2025 IEEE Global Reliability & Prognostics and Health Management Conference (IEEE GlobalRel & PHM 2025)
Session Chair, The 2024 IEEE Global Reliability & Prognostics and Health Management Conference (IEEE GlobalRel & PHM 2024)
Session Chair, The 2023 IEEE Global Reliability & Prognostics and Health Management Conference (IEEE GlobalRel & PHM 2023)
Session Chair, The 2024 International Conference on Artificial Intelligence and Autonomous Transportation (AIAT 2024)
Session Chair, The 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT 2023)
Session Organizer, The Fourth International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2023)
Organizing Committee, The International Conference on Aerospace Structural Dynamics (ICASD 2023)
Session Organizer, The Fourth International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2023)
Session Organizer, The Third International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2022)
Reviewer for over 60 prestigious journals, such as
Energy and AI
Applied Energy
Solar Energy
Energy Technology
International Journal of Green Energy
International Journal of Energy Research
Knowledge-Based Systems
Engineering Applications of Artificial Intelligence
Reliability Engineering & System Safety
IEEE Internet of Things Journal
IEEE Sensors Journal
IEEE Transactions on Industrial Informatics
IEEE Transactions on Systems, Man, and Cybernetics: Systems
IEEE Transactions on Cybernetics
IEEE Transactions on Industrial Electronics
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Reliability
IEEE/ASME Transactions on Mechatronics
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Neural Networks and Learning Systems
Mechanical Systems and Signal Processing
ISA Transactions
Neurocomputing
Measurement
Automatika
International Journal of Production Research
IET Signal Processing
IET Generation, Transmission & Distribution
Journal of Risk and Reliability
Journal of Mechanical Engineering Science
Journal of Process Mechanical Engineering
Measurement Science and Technology
Transactions of the Institute of Measurement and Control
2024, World's Top 2% Scientists by Stanford University
2024, Second Prize of the Invention and Entrepreneurship Award presented by the China Association of Inventions
2023, Selected for the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (CAST)
2023, World's Top 2% Scientists by Stanford University
2023, Outstanding Member of the Youth Editorial Board, Journal of Dynamics, Monitoring and Diagnostics
2022, World's Top 2% Scientists by Stanford University
2021, World's Top 2% Scientists by Stanford University
2020, Selected for the "Shuimu Scholars" at Tsinghua University
2020, Outstanding Ph.D. Graduate Thesis of Tsinghua University (Top 5% students)
2019, National Scholarship for Doctoral Students, Ministry of Education (Top 10% students)
2019, ISA Transactions, Outstanding Reviewer Status Award (Top 10%)
2018, Outstanding Paper Award in Equipment Monitoring and Diagnosis and Maintenance Conference
2018, Tsinghua University First-Class Scholarship for Comprehensive Excellence (Top 5% students)
2017, Tsinghua University Second-Class Scholarship for Comprehensive Excellence (Top 10% students)
2016, Mitsubishi Heavy Industries Scholarship
Principle Investigator. Research on Health State Characterization and Intelligent Domain Generalization Diagnosis for Wind Turbine Systems Under Limited Data, National Natural Science Foundation of China, 2023/01-2025/12
Principal Investigator. “Explainable and Generalizable” Scientific Machine Learning-Enabled Intelligent Operation and Maintenance of Complex Systems, China Association for Science and Technology’s Young Elite Scientists Sponsorship Program, 2023/12-2025/12
Principal Investigator. Mechanism- and Data-Driven Smart Energy Management and Control Methods for Railway Logistics Parks, Beijing Natural Science Foundation, 2024/10-2027/09
Principal Investigator. Research on Energy Efficiency Optimization and Digital Carbon Management for Enterprises Facing New Industrialization, China’s Ministry of Industry and Information Technology Guiding Soft Science Project, 2024/05-2024/12
Sub-project Principal Investigator. Key Technologies for Low-Carbon and Efficient Utilization of Waste Heat from Cement Kilns, Anhui Provincial Science and Technology Innovation Project, 2024/09-2027/09
Sub-project Principal Investigator. Research on Health Diagnosis and Safety Assurance Technologies for Electrochemical Liquid Cooling Energy Storage Systems, Ningxia Hui Autonomous Region Key R&D Program, 2024/06-2027/05
Sub-project Principal Investigator. Research on Multimodal Intelligent Perception and Cognitive Decision-Making Technologies for the Industrial Internet, Anhui Provincial Major Science and Technology Project, 2023/05-2025/12
Principle Investigator. Research on Data-driven Intelligent Fault Diagnosis and Root Cause Analysis Method for High-speed Train Transmission System, China Postdoctoral Science Foundation, 2021/05-2022/09
Principle Investigator. Research on Sub-Health State Identification Technology for Electromechanical Equipment and Function Demonstration, China Shipbuilding Group System Engineering Research Institute, 2023/06-2024/06
Keynote Speech at The 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation, 20 Oct., 2023, Organized by China Electrotechnical Society
Title: Trustworthy Prognosis and Health Management for Complex Systems
Invited Presenter: Dr. Te Han
Workshop in Intelligent Maintenance and Health Management for Large Equipment, 30 Oct., 2021, Organized by Tianjin University
Title: AI-based Machinery Fault Diagnosis
Invited Presenter: Dr. Te Han
Workshop in Development of Intelligent Manufacturing Equipment for Lithium Battery, 6 Nov., 2020, Organized by GEESUN Co., Ltd.
Title: Prognosis and Health Management of Energy Storage Systems
Invited Presenter: Dr. Te Han
Updated in Apr., 2025
