Z. Ning (Zhansheng)

Researcher
EEMCS-EE-PE
z.ning@utwente.nl
+31534899680
Business card

Zhansheng Ning received a Bachelor’s and Master’s degree in Mechatronic Engineering from the University of Science and Technology Beijing, China, in 2015 and 2018, respectively. From 2018 to 2022, he was a battery management systems (BMS) software engineer at Gotion High-tech Company, Hefei, China. From 2019 to 2020, he was also a Research Associate at Gotion-NTU Smart Energy Labratory at Nanyang Technological University, Singapore.

He is currently working toward the Ph.D. degree with Power Electronics group, University of Twente, Enschede, The Netherlands. His research interests include the state estimation, modeling, and life prediction of Lithium-ion batteries.

Expertise

  • Engineering

    • State of Charge
    • Battery Capacity
    • Time Domain
    • Frequency Domain
    • Battery System
    • Equivalent Circuit Model

  • Computer Science

    • Convolutional Neural Network
    • Feature Extraction

Organisations

Since September 2022, Ning has been working as a Ph.D. candidate at the University of Twente on the 'Modelling and Testing for Enhanced Battery Lifetime' project. His research interests include state estimation, modeling, and life prediction of lithium-ion power batteries. For his doctoral thesis, Ning aims to develop an AI-based algorithm for early diagnostics and prognostics of batteries, using Electrochemical Impedance Spectroscopy, with the goal of extending battery life.

Publications

2026

Impedance-Based State Estimation for Battery Systems (2026)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Ning, Z.https://doi.org/10.3990/1.9789036573405Combined Time- and Frequency-Domain Approach for Fast and Accurate Battery SOC Estimation (2026)IEEE Transactions on Industrial Electronics, 73(8), 11625-11637. Ning, Z., Venugopal, P., Soeiro, T. B. & Rietveld, G.https://doi.org/10.1109/TIE.2026.3670254Enhancing Battery Equivalent Circuit Model Performance through Hybrid-Domain-Informed and Current-Adaptive Parameter Identification (2026)IEEE Transactions on Industrial Electronics, 73(6), 8628-8639. Article 11355863. Ning, Z., Venugopal, P., Chiang, C., Ho, K.-C., Soeiro, T. B. & Rietveld, G.https://doi.org/10.1109/TIE.2025.3645416

2025

Multi-step prediction of battery state of health based on self-supervised pre-training and transfer learning using the xPatch model (2025)Energy, 341. Article 139410. Yuan, Z., Deng, Z., He, Y., Ning, Z. & Liu, J.https://doi.org/10.1016/j.energy.2025.139410Modeling Battery Cells with Different Chemistries Based on EIS (2025)In 2025 Energy Conversion Congress & Expo Europe (ECCE Europe): Proceedings (pp. 1-6). Article 11238792 (European Conference on Power Electronics and Applications; Vol. 2025). IEEE Advancing Technology for Humanity. Ning, Z., Venugopal, P., Batista Soeiro, T. & Rietveld, G.https://doi.org/10.1109/ECCE-Europe62795.2025.11238792Partial-Range SOC-Insensitive Model With EIS Change Pattern Recognition Model for Battery Aging Estimation (2025)IEEE Transactions on Industrial Electronics, 72(7), 7005-7016. Ning, Z., Deng, J., Venugopal, P., Soeiro, T. B. & Rietveld, G.https://doi.org/10.1109/TIE.2024.3511086Computation-light AI models for Robust Battery Capacity Estimation based on Electrochemical Impedance Spectroscopy (2025)IEEE Transactions on Transportation Electrification, 11(1), 3146-3158. Ning, Z., Venugopal, P., Batista Soeiro, T. & Rietveld, G.https://doi.org/10.1109/TTE.2024.3435455

2024

High-Frequency Core Loss Modeling Based on Knowledge-Aware Artificial Neural Network (2024)IEEE transactions on power electronics, 39(2), 1968-1973. Deng, J., Wang, W., Ning, Z., Venugopal, P., Popovic, J. & Rietveld, G.https://doi.org/10.1109/TPEL.2023.3332025

2023

Data-Driven Methods for Robust Battery Capacity Estimation based on Electrochemical Impedance Spectroscopy (2023)In 2023 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe. IEEE. Ning, Z., Venugopal, P., Rietveld, G. & Soeiro, T. B.https://doi.org/10.23919/EPE23ECCEEurope58414.2023.10264480Battery Dynamics Exploration: Insights and Implications of Relaxation Time in Electrochemical Impedance Spectroscopy (2023)In 2023 IEEE 8th Southern Power Electronics Conference and 17th Brazilian Power Electronics Conference (SPEC/COBEP) (IEEE 8th Southern Power Electronics Conference and Brazilian Power Electronics Conference (SPEC/COBEP); Vol. 2023). IEEE. Azizighalehsari, S., Ning, Z., Breazu, B., Venugopal, P., Rietveld, G. & Soeiro, T. B.https://doi.org/10.1109/SPEC56436.2023.10407778

Research profiles

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