Nuuraan Risqi Amaliah

Nuuraan Risqi Amaliah is a PhD student at Coventry University, affiliated with the Centre for E-Mobility and Clean Growth. Her current research focuses on the use of impedance-based features for remaining useful life prediction of used lithium-ion batteries, with the aim of supporting decision-making in second-life battery allocation. Prior to commencing her PhD, she was enrolled as a Master by Research student, where her research explored the role of human-in-the-loop explainable artificial intelligence (HITL-XAI) in improving maintenance decision-making.

She received her Bachelor’s degree from Politeknik Negeri Ujung Pandang, Makassar, in 2024. Her undergraduate thesis and related projects focused on the development of virtual reality simulators for industrial training in the smelting industry, including a tapping furnace simulator, a hot metal crane simulator, and a calcine transfer system simulator. During her undergraduate studies, she received several awards, including the Indonesian International Student Mobility Awards (IISMA), which enabled her to study at Coventry University for one semester, as well as a full scholarship for her bachelor’s degree.

Supervised by Professor Benny Tjahjono and Dr Haijun Ruan

Impedance-Based Health Indicators and Remaining Useful Life Prediction for Second-Life Lithium-Ion Battery Allocation

The rapid growth of electric vehicles will result in a significant number of retired lithium-ion batteries that still retain usable capacity for second-life applications. However, determining whether a battery is suitable for repurposing requires reliable diagnostics and prediction of its remaining useful life (RUL). This research investigates the use of impedance-derived health indicators for estimating battery degradation and predicting RUL to support second-life allocation decisions. Using publicly available degradation datasets (NASA dataset), impedance parameters such as electrolyte resistance and charge transfer resistance are analysed to construct health indicators that capture internal ageing behaviour beyond conventional state-of-health metrics. These indicators are then used to develop data-driven models that estimate the remaining lifetime of batteries and assess their suitability for different second-life duty cycles. The proposed framework aims to support scalable battery repurposing by providing a diagnostic and predictive tool for matching retired batteries with appropriate second-life applications.