Book chapter & monograph
[1] Zhongliang LI, Zhixue ZHENG, Fei GAO, Chapter 10: Diagnosis and prognosis of proton exchange membrane fuel cells, Book title: Electrical Systems 2: From Diagnosis to Prognosis, ISBN: 9781786306081, ISTE, March 2020. (Lien du livre : http://www.iste.co.uk/book.php?id=1616 ).
Journal articles
PhD & Postdoc
[1] Zhongliang LI, Rachid OUTBIB, Daniel HISSEL, Stefan GIURGEA, “Data-driven diagnosis of PEM fuel cell: A comparative study”, Control Engineering Practice, vol. 28, pp. 1-12, Jul. 2014.
[2] Zhongliang LI, Stefan GIURGEA, Rachid OUTBIB, Daniel HISSEL, “Online Diagnosis of PEMFC by Combining Support Vector Machine and Fluidic Model”, Fuel Cells, vol. 14, pp. 448–456. Jun. 2014.
[3] Zhongliang LI, Rachid OUTBIB, Stefan GIURGEA et al., “Fault detection and isolation for Polymer Electrolyte Membrane Fuel Cell systems by analyzing cell voltage generated space”, Applied Energy, vol. 148, pp. 260-272, Jun. 2015.
[4] Zhongliang LI, Rachid OUTBIB, Stefan GIURGEA, Daniel HISSEL, “Diagnosis for PEMFC Systems: A Data-Driven Approach with the Capabilities of Online Adaptation and Novel Fault Detection”, IEEE Transactions on Industrial Electronics, vol. 62, no. 8, pp. 5164-5174, Aug. 2015.
[5] Zhongliang LI, Rachid OUTBIB, Stefan GIURGEA et al., “Online implementation of SVM based fault diagnosis strategy for PEMFC systems”, Applied Energy, vol. 164, pp. 284-293, Feb. 2016.
2018
[6] Zhongliang LI, Zhixue ZHENG, Rachid OUTBIB, “A prognostic methodology for power MOSFETs under thermal stress using echo state network and particle filter”, Microelectronics Reliability, vol. 88–90, pp. 350-354, 2018.
[7] Chen LIU, Rui MA, Hao BAI, Zhongliang LI et al., “Hybrid modeling of power electronic system for hardware-in-the-loop application”, Electric Power Systems Research, vol. 163, part A, pp. 502-512, 2018.
[8] Rui MA, Tao YANG, Elena BREAZ, Zhongliang LI et al., “Data-driven proton exchange membrane fuel cell degradation predication through deep learning method”, Applied Energy, vol. 231, pp. 102-115, 2018.
[9] Rui MA, Elena BREAZ, Zhongliang LI et al., “Co-Oxidation Modeling for a Syngas-Supplied Microtubular Solid Oxide Fuel Cell”, IEEE Transactions on Industry Applications, vol. 54, no. 5, pp. 4917-4926, Sept.-Oct. 2018.
[10] Rui MA, Zhongliang LI, Elena BREAZ et al., “Numerical Stiffness Analysis for Solid Oxide Fuel Cell Real-time Simulation Applications”, IEEE Transactions on Energy Conversion, vol. 33, no. 4, pp. 1917-1928, Dec. 2018.
[11] Chen LIU, Xizheng GUO, Rui MA, Zhongliang LI et al., “A System-Level FPGA-Based Hardware-in-the-Loop Test of High-Speed Train”, IEEE Transactions on Transportation Electrification, vol. 4, no. 4, pp. 912-921, Dec. 2018.
2019
[12] Zhongliang LI, Rachid OUTBIB, Stefan GIURGEA, Daniel HISSEL, “Fault diagnosis for PEMFC systems in consideration of dynamic behaviors and spatial inhomogeneity”, IEEE Transactions on Energy Conversion, vol. 34, no. 1, pp. 3-11, March 2019.
[13] Chen LIU, Rui MA, Hao BAI, Zhongliang LI et al., “FPGA-Based Real-time Simulation of High-Power Electronic System with Nonlinear IGBT Characteristics”, IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 1, pp. 41-51, March 2019.
[14] Zhongliang LI, Rachid OUTBIB, Stefan GIURGEA et al., “Fault diagnosis for fuel cell systems: A data-driven approach using high-precise voltage sensors”, Renewable Energy, vol. 135, pp. 1435-1444, May 2019.
[15] Zhongliang LI, Catherine CADET, Rachid OUTBIB, “Diagnosis for PEMFC based on magnetic measurements and data-driven approach”, IEEE Transactions on Energy Conversion, vol. 34, no. 2, pp. 964-972, June 2019.
[16] Rui MA, Zhongliang LI, Elena BREAZ et al., “Data-Fusion Prognostics of Proton Exchange Membrane Fuel Cell Degradation”, IEEE Transactions on Industry Applications, vol. 55, no. 4, pp. 4321-4331, July-Aug. 2019.
[17] Zhongliang LI, Zhixue ZHENG, Liangfei XU et Xiaonan LU, “A review of the applications of fuel cells in microgrids: opportunities and challenges”, BMC Energy, vol. 1, pp. 1-23, 2019.
2020
[18] Zhongliang LI, Zhixue ZHENG, Rachid OUTBIB, “Adaptive prognostic of fuel cells by implementing ensemble Echo State Networks in time varying model space”, IEEE Transactions on Industrial Electronics, vol. 67, no. 1, pp. 379-389, Jan. 2020.
[19] Zhigen NIE, Zhongliang LI, Wanqiong WANG et al., “Gain-scheduling control of dynamic lateral lane change for automated and connected vehicles based on multi-points preview”, IET Intelligent Transport Systems, vol. 14, no. 10, pp. 1338–1349, 2020.
[20] Yucen XIE, Jianxiao ZOU, Zhongliang LI et al., “A novel deep belief network and extreme learning machine based performance degradation prediction method for proton exchange membrane fuel cell”, IEEE Access, vol. 8, pp. 176661-176675, 2020.
2022
[21] Chu WANG, Zhongliang LI, Rachid OUTBIB et al., “Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells”, Applied Energy, vol. 305, pp. 117918, 2022.
[22] Meiling YUE, Zhongliang LI, Robin ROCHE et al., “Degradation identification and prognostics of proton exchange membrane fuel cell under dynamic load”, Control Engineering Practice, vol. 118, pp. 104959, 2022.
[23] Wang CHU, Zhongliang LI, Rachid OUTBIB et al., “A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells”, International Journal of Hydrogen Energy, vol. 47(18), pp. 10395-10408, 2022.
[24] Majdi Saidi, Zhongliang LI, Seifeddine BEN ELGHALI, Rachid OUTBIB, “Realization of the optimal sizing of local hybrid photovoltaic and wind energy systems with load scheduling capacity”, International Journal of Energy Research, vol. n/a, pp. 1-15, 2022.
[25] Jian ZUO, Catherine CADET, Zhongliang LI, et al., “Post-prognostics decision-making strategy for load allocation on a stochastically deteriorating multi-stack fuel cell system”, Journal of Risk and Reliability, vol. n/a, pp. n/a, 2022.
[26] Chu Wang, Manfeng Dou, Zhongliang Li, et al., « A fusion prognostics strategy for fuel cells operating under dynamic conditions », eTransportation, vol. 12, 100166, 2022.
[27] Hanqing Wang, Arnaud Gaillard., Zhongliang Li, Robin Roche, & Daniel Hissel, Multiple-Fuel Cell Module Architecture Investigation: A Key to High Efficiency in Heavy-Duty Electric Transportation. IEEE Vehicular Technology Magazine, 17(3), 94-103, 2022.
2023
[28] NIE, Zhigen, LI, Zhongliang, WANG, Wanqiong, et al. ROBUST GAIN-SCHEDULING CONTROL OF DYNAMIC LATERAL OBSTACLE AVOIDANCE FOR CONNECTED AND AUTOMATED VEHICLES. International Journal of Automotive Technology, 2023, vol. 24, no 1, p. 63-78.
[29] Chu Wang, Manfeng Dou, Zhongliang Li, et al., Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load, Reliability Engineering & System Safety, 2023 (In press). https://www.sciencedirect.com/science/article/pii/S0951832023000388
Conference papers
[1] Zhongliang LI, Xudong SUN, Jianyun CHAI, “Proportional power sharing control for parallel-connected inverters”, In Proc. Electric Utility Deregulation and Restructuring and Power Technologies (DRPT 2011), pp.1647-1651, Qingdao, July 2011. (5 citations)
[2] Zhongliang LI, Rachid OUTBIB, Stefan GIURGEA, Daniel HISSEL, “Diagnosis of PEMFC by using statistical analysis”, In Proc. Integrated Modeling and Analysis in Applied Control and Automation (IMAACA 2012), pp.191-198, Vienna, 2012. (0 citation)
[3] Zhongliang LI, Stefan GIURGEA, Rachid OUTBIB, Daniel HISSEL, “Online Diagnosis of PEMFC by Combining Support Vector Machine and Fluidic Model”, In Proc. 6th International Conference on Fundamentals and Developments of Fuel Cells (FDFC’2013), pp. 069, Karlsruhe, 2013. (0 citation)
[4] Zhongliang LI, Rachid OUTBIB, Daniel HISSEL, Stefan GIURGEA, “Online diagnosis of PEMFC by analyzing individual cell voltages”, In Proc. European Control Conference (ECC2013), pp. 2439-2444, Zurich, Jul. 2013. (13 citation)
[5] Zhongliang LI, Rachid OUTBIB, Daniel HISSEL, Stefan GIURGEA, “Diagnosis of PEMFC by using data-driven parity space strategy”, In Proc. European Control Conference (ECC 2014), pp.1268-1273, Strasbourg, Jun. 2014. (4 citations)
[6] Zhongliang LI, Stefan GIURGEA, Rachid OUTBIB, Daniel HISSEL, “Fault diagnosis and novel fault type detection for PEMFC system based on Spherical-Shaped Multiple-class Support Vector Machine”, In Proc. Advanced Intelligent Mechatronics (AIM 2014), pp.1628-1633, Besançon, Jul. 2014. (6 citations)
[7] Zhongliang LI, Stefan GIURGEA, Rachid OUTBIB, Daniel HISSEL, “Fault detection and isolation of PEMFC system: a classification approach”, In Proc. International Discussion on Hydrogen Energy and Applications (IDHEA 2014), pp.49, Nantes, 2014. (0 citation)
[8] Zhongliang LI, Rachid OUTBIB, Stefan GIURGEA, Daniel HISSEL, Samir JEMEI, Alain GIRAUD, Sébastien ROSINI, “Online implementation of SVM based fault diagnosis strategy for PEMFC systems”, In Proc. 6th International Conference on Fundamentals and Developments of Fuel Cells (FDFC’2015), pp. 048, Toulouse, Feb. 2015. (0 citation)
[9] Zhongliang LI, Samir JEMEI, Rafael Gouriveau et al., “Remaining useful life estimation for PEMFC in dynamic operating conditions”, In Proc. Vehicular Power and Propulsion Conference (VPPC 2016), pp. 1-6, Hangzhou, 2016. (23 citations)
[10] Zhongliang LI, Rachid OUTBIB, “PHM oriented behavior modeling for PEM fuel cell systems via Delayed Feedback Reservoir Computing Model”, In Proc. Integrated Modeling and Analysis in Applied Control and Automation (IMAACA 2017), Barcelona, 2017. (0 citation)
[11] Zhongliang LI, Seifeddine BEN ELGHALI, Rachid OUTBIB, “Energy management for hybrid energy storage systems: a comparison of current tracking control methods”, In Proc. 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), pp. 7128-7133, Beijing, 2017. (2 citation)
[12] Majdi SAIDI, Zhongliang LI, Rachid OUTBIB, et al., “Realizing optimum design of a hybrid renewable energy system using multi-objective evolutionary algorithm”, In Proc. Integrated Modeling and Analysis in Applied Control and Automation (IMAACA 2018), Budapest, 2018. (0 citation)
[13] Zhongliang LI, Zheng ZHENG, Rachid OUTBIB, “A Prognostic Methodology for Power MOSFETs Under Thermal Stress Using Echo State Network and Particle Filter”, In Proc. 29th European Symposium on Reliability of Electron Devices, Failure Physics and Analysis (ESREF 2018), Aalborg, 2018. (11 citations)
[14] Majdi SAIDI, Seifeddine BENELEGHALI, Zhongliang LI, Rachid OUTBIB, et al., “Optimal Sizing of Mobile Hybrid Off-Grid Multi-Sources Installation”, IECON 2019 – 45th Annual Conference of the IEEE Industrial Electronics Society, pp. 2434-2439, Lisbon, Portugal, 2019. (0 citation)
[15] Majdi SAIDI, Zhongliang LI, Seifeddine BENELEGHALI, Rachid OUTBIB, “Optimal Sizing of hybrid grid-connected energy system with demand side scheduling”, 45th Annual Conference of the IEEE Industrial Electronics Society (IECON 2019), pp. 2209-2214, Lisbon, Portugal, 2019. (1 citation)
[16] Jian ZUO, Catherine CADET, Zhongliang LI et al., “Post-prognostics decision making for multi-stacks fuel cell system based on a load-dependent degradation model”, Fifth European Conference of the Prognostics and Health Management Society 2020 (PHMe 2020), Online, 2020. (3 citations)
[17] Meiling YUE, Zhongliang LI, Robin ROCHE et al., “A Feature-based Prognostics Strategy For PEM Fuel Cell Operated Under Dynamic Conditions”, 2020 Prognostics and Health Management Conference (PHM 2020), pp. 122-127, Besançon, 2020. (2 citations)
[18] Chu WANG, Zhongliang LI, Rachid OUTBIB et al., “Proton Exchange Membrane Fuel Cells Prognostic Strategy Based on Navigation Sequence Driven Long Short-term Memory Networks”. 46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020), pp.3969-3974, Singapour, Oct 2020. (1 citation)
[19] Chu WANG, Zhongliang LI, Rachid OUTBIB et al., “A Hybrid Prognostics Approach for Proton Exchange Membrane Fuel Cells under Dynamic Operating Conditions”, 34th International Electric Vehicle Symposium and Exhibition (EVS34), Nanjing, June 2021. (0 citation)
[20] Jian ZUO, Catherine CADET, Zhongliang LI et al., “Post-Prognostics Decision Making Strategy to Manage the Economic Lifetime of a Two-Stack PEMFC System”, 2021 Annual Reliability and Maintainability Symposium (RAMS 2021), pp. 1-7, Online, 2021. (0 citation)
[21] Liang GUO, Zhongliang LI and Rachid OUTBIB, “Reinforcement Learning based Energy Management for Fuel Cell Hybrid Electric Vehicles”, 47th Annual Conference of the IEEE Industrial Electronics Society (IECON 2021), pp. 1-6, Online, 2021. (0 citation)
[22] Jian ZUO, Catherine CADET, Zhongliang LI et al., “A dynamic load allocation strategy for a stochastically deteriorating multi-stack fuel cell system”, European Safety and Reliability Association 2022.
[23] J. Zuo, C. Cadet, Z. Li, C. Berenguer and R. Outbib, « Fuel Cell Stochastic Deterioration Modeling for Energy Management in a Multi-stack System, » 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS), Kowloon, Hong Kong, 2022, pp. 104-108, doi: 10.1109/ICRMS55680.2022.9944566.
[24] W. Touil, Z. Li, R. Outbib, D. Hissel and S. Jemei, « Model predictive control energy management strategy of fuel cell hybrid electric vehicle, » IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, 2022, pp. 1-8, doi: 10.1109/IECON49645.2022.9968429.
[25] L. Guo, Z. Li and R. Outbib, « Fuzzy Rule Value Reinforcement Learning based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles, » IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, 2022, pp. 1-7, doi: 10.1109/IECON49645.2022.9968966.
[26] C. Wang, Z. Li, R. Outbib and M. Dou, « A hybrid method for remaining useful life prediction of fuel cells under variable loads, » 2022 10th International Conference on Systems and Control (ICSC), Marseille, France, 2022, pp. 174-177, doi: 10.1109/ICSC57768.2022.9993918.
[27] L. Guo, Z. Li and R. Outbib, « A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning, » 2022 10th International Conference on Systems and Control (ICSC), Marseille, France, 2022, pp. 161-167, doi: 10.1109/ICSC57768.2022.9993916.
[28] L. Guo, Z. Li and R. Outbib, « Fuzzy REINFORCE: A Fuzzy Policy Gradient Reinforcement Learning based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles », 3rd International Conference on Energy and AI, Belfort, France, 2022 (Best presentation prize).
Patents
[1] Sebastien FAIVRE, Fréderic GUSTIN, Daniel HISSEL, Fabien HAHEL, Zhongliang LI, “Method and system for diagnosing the operating state of an electrochemical system in real-time, and electrochemical system incorporating this diagnostic system”, no. FR3067124A1; WO2018220115A1, 2018. (https://patents.google.com/patent/FR3067124A1/fr)
PhD thesis
Data-driven fault diagnosis for PEMFC systems. (Download Here)
Master thesis
Research on the control strategy of parallel-connected inverters in microgrid. (Download Here)
Invited Talks
[1] “Data-driven diagnosis and control methodologies and applications in fuel cells”, International excellent young-scholar WEISHI forum, Invited by Beihang University, 05/2017, Beijing, China.
[2] “Control of fuel cell systems: issues and solutions”, Seminar in School of Astronautics, Harbin Institute of Technology, Invited by Prof. Ligang WU, 10/2017, Harbin, China.
[3] “Control of fuel cell systems: issues and solutions”, International excellent young-scholar TELI forum, Invited by Beijing Institute of Technology, 11/2017, Beijing, China.
[4] “Data-driven fault diagnosis and control of fuel cell systems”, Beijing Jiaotong University, Invited by Prof. Zhongsheng HOU, 11/2017, Beijing, China.
[5] “Modeling and control of fuel cell systems”, Seminar in Northwest Polytechnical University, Invited by Prof. Dongdong ZHAO, 04/2019, Xi’an, China.
[6] “Data-driven fault diagnosis and prognosis of fuel cell systems”, Seminar in South China University of Technology, Invited by Department of Electrical Engineering, 04/2019, Guangzhou, China.
[7] “Data-driven fault diagnosis and prognosis of fuel cell systems”, Seminar in State Key Lab of Industrial Control Tech., Zhejiang University, Invited by Prof. Jian CHEN, 04/2019, Hangzhou, China.
[8] “Diagnosis and prognosis of complex systems”, Seminar in Laboratory of Health Services and Reliability of Mechatronic Systems in Beihang University, Invited by Prof. Shaoping WANG, 12/2019, Beijing, Chine.
[9] “Energy management strategy for hybrid electric vehicles”, Webinar in Zhengzhou University, Invited by Prof. Chen LIU, 08/2020, Online.
[10] “System-level fuel research on fuel cell durability”, Seminar in lab MADIREL of Aix-Marseille University, Invited by Prof. Phillipe KNAUTH, 01/2020, Marseille France.
[11] “Diagnosis, prognosis and fault tolerant control for fuel cell systems”, Tutorial (4 hours) in the 45th Annual Conf. of the IEEE IES, 10/2020, Online.
[12] “AI-guided energy management for fuel cell micro-CHPs”, 2021 edition of the CNRS energy symposium, Invited by CNRS Energy cell, 02/2022, Paris, France.
[13] “AI-assisted prognostics and health management for fuel cells”, Seminar in CNRS, Invited by Dr. Liang LIU from LCPME, 30/09/2022, Nancy, France.