Lithium-ion batteries, at the end of their first life (EOFL), are typically sent for recycling despite often retaining 70–90% of their original capacity. This residual capacity presents significant potential for second-life applications, particularly in low-power applications. Realizing this potential requires reliable and efficient assessment of individual cells to determine their suitability for reuse, or recycling (ReX). However, current evaluation methods are often complex, time-intensive, and cost-prohibitive, due to stringent safety, performance, and reliability standards. This study proposes a novel three-stage inspection framework to support robust ReX decision-making for EOFL battery cells. The framework comprises automated visual inspection for surface defect detection, rapid electrical diagnostics using open-circuit voltage (OCV) and internal resistance (IR) measurements and cycling tests with degradation features quantified through differential capacity (dQ/dV) peak analysis. The framework was validated through a case study on retired vacuum cleaner and e-bike batteries and scaled through scenario-based modelling of 10,000 cells, demonstrating improved diagnostic efficiency, reduced cost, and robust classification accuracy across different rejection-rate conditions. The proposed framework offers a transferable methodology for researchers and a scalable solution for industry to enhance throughput, ensure safety, and support circular battery management, thereby advancing regulatory compliance and sustainable second-life deployment.
Chauhan et al. (Thu,) studied this question.