End of Life Pathways for Battery Energy Storage Systems(BESS): Designing a Decision Support Tool for Optimal Route Selection

The global acceleration of electrification and renewable energy integration has placed lithium-ion batteries (LIBs) at the centre of sustainable energy systems. However, as large volumes of batteries from electric vehicles and stationary storage approach the end of their operational life, their end-of-life (EoL) management has become a critical technical, economic, and environmental challenge. The growing diversity of recycling and second-life options has made the decision of whether a battery should be reused, repurposed, or recycled increasingly complex, with limited standardised guidance to support such choices. This thesis aims to address this gap by developing a transparent and adaptable decision-support framework for optimal EoL pathway selection for Battery Energy Storage Systems (BESS). A multi-criteria decision-making (MCDM) approach, based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is applied in combination with fuzzy logic to handle uncertainty and variability in stakeholder perspectives. Key Performance Indicators (KPIs) influencing EoL decisions are identified from literature and grouped into three dimensions: technical (State of Health(SoH), Internal Resistance(IR)), economic (Refurbishment Cost, Scrap Value, Chemistry/Material Value), and environmental (CO$_2$ Savings, Hazard). Weights for each criterion are derived from three representative seed papers, reflecting technical, economic, and sustainability-oriented priorities to ensure a literature-grounded, replicable methodology. The model is then demonstrated through a comparative analysis of four representative battery packs with industry demonstrated but hypothetical data. Results highlight how shifts in weighting priorities influence optimal decisions, showing that higher SoH and lower resistance favour reuse scenarios, while higher material value and hazard levels tilt the decision towards recycling. These findings underline the importance of context-sensitive decision frameworks and reveal how trade-offs between technical performance, cost efficiency, and environmental impact shape EoL outcomes. Although this study provides a structured foundation for EoL decision-making, it represents an early-stage framework that relies solely on secondary data and literature-based weights. Future research should incorporate expert interviews, empirical datasets, and dynamic weighting schemes to capture real-world complexity. Ultimately, this thesis contributes a replicable, transparent, and evidence-based approach that can guide policymakers, manufacturers, and recyclers in advancing battery circularity and supporting the broader energy transition.