Circular intelligence: Mapping EV Battery Sustainability with AI and ISO Standards

This thesis presents an AI-enabled frameworks for evaluating the circularity performance of electric vehicle (EV) batteries, aligned with the ISO 59020:2024 standard. The Study integrates artificial intelligence tools, such as predictive modeling, natural language processing, and scenario simulations, to enhance the accuracy, scalability, and transparency of circularity assessments. By applying this framework at a macro scale, the research analyzes key indicators including recycled content, reuse rates, renewable energy use, and water circularity. The methodology combines secondary data, policy benchmarks, and dynamic assumptions to simulate regional trends from 2025 to 2050. Result shows that while Europe leads in circularity metrics, other regions show varying progress. The study highlights the potential of AI to support data harmonizations, automated benchmarking and policy optimization, while also addressing challenges such as data quality and model interpretability. This work contributes to the development of standardized, data driven tools for sustainable battery value chains and supports the transition to a circular economy in the EV sector.