Dr Yongzhi Zhang is a Marie Skłodowska-Curie Postdoctoral Fellow in the Electrochemical Science and Engineering group at Imperial College London, working with Dr Huizhi Wang and Professor Gregory Offer. His research addresses a fundamental observability problem in battery science: you cannot see inside a working battery, yet every decision about its safety, lifetime, and performance depends on its hidden internal state. He develops computational methods that combine physics-based electrochemical models with machine learning and statistical inference to diagnose degradation, predict lifetime, and assess safety from operational measurements alone.

Prior to Imperial, Yongzhi held an Associate Professor position at Chongqing University (2021–2025), where he built a research group from scratch, secured national and industrial funding as PI, delivered English-medium postgraduate teaching, and continues to supervise three PhD students. He was among the first to apply deep learning to battery remaining useful life prediction, a contribution recognised with the IEEE VTS Best Vehicular Electronics Paper Award (2021). He received his PhD in Vehicle Engineering from Beijing Institute of Technology (2019), with visiting research periods at the University of Maryland and Mälardalen University, followed by a postdoctoral fellowship at Chalmers University of Technology.

Yongzhi has published over 40 journal articles (h-index: 25; 4,500+ citations) and is recognised in the Stanford/Elsevier World's Top 2% Scientists list (2021–2025). His longer-term research aims to establish the fundamental limits of battery diagnostics from operational data and to develop open tools that make physics-based degradation diagnosis accessible at scale.