Towards autonomous triaging for end-of-life EV components: a critical review and diagnostic-to-decision framework

Abstract

The rapid adoption of electric vehicle (EV) has intensified the challenge of managing End-of-Life (EoL) components, particularly the batteries and motors of the EV, which contain both valuable resources and environmentally hazardous materials. This review synthesises emerging tools for the autonomous triaging of the EoL EV components. We critically examine the disconnect between established diagnostic methods (fault diagnosis / condition assessment) and Circular Economy (CE) decision-making. Key findings indicate that while fault detection accuracy is high (over 90% for data-driven methods), the industry lacks standardised thresholds to map these diagnostics to specific CE pathways. To address this, we propose a unified ‘Diagnostic-to-CE Decision Framework’ and a novel autonomy taxonomy to guide the transition from manual inspection to fully automated triaging systems.