Automotive Engineering
NOTICE OF SEMINAR PRESENTATION
CANDIDATE: Emma Pellattiero
DEGREE SOUGHT: MASc
DATE: 7/18/2025
TIME: 11:30am
PLACE: Room 1102
TITLE: Prediction of Remaining Useful Life of Second-Life Lithium-ion Batteries Considering Cell-to-Cell Variation
Abstract
The electrification of the transportation industry requires increasing attention to battery life-cycle management. In particular, Lithium-ion (Li-ion) batteries are often retired from Electric Vehicles (EVs) when they still have significant residual capacity. Therefore, they can be repurposed in less-demanding applications, where they are referred to as Second-Life Batteries (SLBs). A key challenge to the reliable use of SLBs is the accurate prediction of their Remaining Useful Life (RUL). This work proposes a Gaussian Process Regression (GPR) model for RUL prediction that explicitly accounts for Cell-to-Cell variation while requiring minimal prior knowledge on the specific cell, thereby offering a practical solution for SLB management. The model is validated on both experimental and synthetic data.
Due to the strong influence of input data on data-driven models, a comprehensive sensitivity analysis has been conducted to evaluate the impact of different input features and data quantity on prediction accuracy. Among the various features considered, only normalized capacity and test duration consistently provided robust and reliable performance. The model has then been adapted to module configuration using features extracted from half of the discharge test curve. This modified approach better suits real-world applications, where EV batteries are rarely dismantled to cell level, while maintaining high accuracy. The feasibility of using synthetic data for training and experimental data for validation has also been assessed.