Optimising the Regeneration Process of Spent Lithium-Ion Battery Cathode Through a Performance Analysis Model

Abstract

The urgent global demand for sustainable energy storage materials has amplified interest in efficient recycling and regeneration methods for lithium-ion batteries (LIBs), particularly in response to the increasing volume of spent batteries generated by electric vehicles and portable electronics. This thesis investigates the potential of machine learning (ML) to optimise the regeneration process of spent LIB cathode materials, aiming to enhance performance recovery while reducing time, cost, and experimental workload. The research focuses on direct regeneration methods, which restore the electrochemical activity of cathode materials by repairing their crystal structure, rather than decomposing them into elemental components, making them highly promising for sustainable battery reuse. While ML has been applied to predict the performance of fresh LIBs, its application to regenerated cathode materials remains unexplored. Unlike fresh batteries, regenerated materials may exhibit residual impurities that affect their electrochemical behaviour, highlighting the need for specialised data-driven approaches tailored to these conditions. The study developed and validated an ML framework that integrated experimental data and predictive modelling to enable the optimisation of regeneration processes of three widely used cathode chemistries: lithium cobalt oxide (LCO), lithium iron phosphate (LFP), and nickel-manganese-cobalt oxide (NMC). A total of eight ML algorithms were evaluated, including Classification and Regression Trees, Support Vector Machine, K-Nearest Neighbours, Random Forest, and Artificial Neural Networks (ANN), to model battery performance and optimise regeneration conditions. Each case study demonstrated how ML can predict the discharge capacity of regenerated materials based on key parameters of the direct regeneration method, including regeneration temperature, duration, and the ratio and amount of added lithium salt. Results show that ANN provides the highest prediction accuracy, with R2 values exceeding 0.99 across all case studies. The ANN model was then employed to identify optimal regeneration conditions, with findings indicating that ML-guided approaches outperform traditional empirical methods in restoring battery performance. This thesis demonstrates the transformative potential of ML in the regeneration of spent LIB cathodes, presenting an accurate and sustainable approach to improving circularity in battery materials.