Siwei Guan

Education and Research Experience

Siwei earned a BEng in Engineering from Jiangxi Normal University in 2021 and an MSc in Engineering from Hangzhou Dianzi University in 2023. He completed his PhD in Engineering at Hangzhou Dianzi University in June 2026, developing data-driven methods for lifetime prediction and fault diagnosis in lithium-ion batteries.

From September 2025 to March 2026, he was a Visiting Researcher in Chemistry at Swansea University. Under the supervision of Dr Rui Tan, he worked on redox flow batteries, techno-economic analysis (TEA) and life-cycle assessment (LCA).

Research

Siwei specialises in battery management and data-driven diagnostics. His work covers lifetime prediction from early-life data, degradation analysis, state-of-health estimation, battery-pack fault diagnosis and anomaly detection. He applies deep learning, contrastive learning, transfer learning and domain adaptation to predict battery lifetime across diverse degradation conditions and diagnose multiple faults in electric-vehicle battery packs. He also studies second-life applications for retired batteries and assesses energy storage technologies at the system level using TEA and LCA.

Publications

His publications cover battery lifetime prediction, fault diagnosis and multivariate time-series anomaly detection. They have appeared in IEEE Transactions on Transportation Electrification, Journal of Energy Storage, Computers & Security, ISA Transactions and IEEE Internet of Things Journal.