PhD Defence Zhansheng Ning | Impedance-Based State Estimation for Battery Systems

Impedance-Based State Estimation for Battery Systems

Zhansheng Ning is a PhD student in the department Power Electronics. (Co)Promotors are prof.dr.ir. G. Rietveld, prof.dr. T. Batista Soeiro & dr.ir. P. Venugopal from the faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente.

The transition towards sustainable transportation requires advanced battery technologies with higher reliability, safety, and lifetime. Lithium-ion batteries are currently the key technology enabling the electrification of vehicles, ships, and aircraft. However, battery degradation during operation remains a major challenge, creating a strong need for accurate monitoring, diagnosis, and prediction methods.

This PhD research focuses on the development of advanced battery management methods using electrochemical impedance spectroscopy (EIS). Unlike conventional measurements based mainly on voltage, current, and temperature, EIS provides detailed information about internal electrochemical processes and can reveal changes in battery condition.

In this thesis, EIS-based methods are developed for battery modeling, state-of-charge (SOC) estimation, and state-of-health (SOH) estimation at both cell and module levels. Different lithium-ion battery chemistries, including NMC, LFP, and LTO, are investigated. A reliable EIS measurement framework is first established by analyzing measurement uncertainty and aging sensitivity. Based on this foundation, advanced impedance-based models and estimation methods are proposed by combining equivalent circuit models with data-driven approaches.

The developed methods improve the accuracy and robustness of SOC and SOH estimation, including under uncertain operating conditions. Furthermore, the approaches are extended from individual cells to battery modules, where practical factors such as busbar impedance and cell inconsistency are considered. Experimental validation demonstrates that reliable battery health assessment can be achieved using impedance measurements.

The outcomes of this research contribute to the development of intelligent battery management systems and support the safe, efficient, and sustainable deployment of electrified transportation.