Exploration of the disposal network model with collaborative strategies for electric vehicle battery

The rapid expansion of electric vehicle brings a massive wave of retired batteries, posing significant environmental risks if not managed properly. To capture residual value, these batteries can be processed through remanufacturing, repurposing, or recycling (3R). However, existing disposal models usually rely on fixed State of health (SOH) classification rules and therefore cannot fully adapt to heterogeneous battery conditions and uncertain return flows. Moreover, SOH is often used only for static sorting, while its effects on remaining useful life (RUL) and pathway-specific value are not fully embedded into disposal decisions. To address this issue, this study develops a multi-period stochastic optimization model for an integrated 3R disposal network for retired electric vehicle batteries. The model treats the SOH classification thresholds as decision variables rather than fixed technical inputs, and embeds an SOH-RUL-value mapping into the network optimization framework. The results show that the integrated 3R network increases profit by 4.87% to 7.59% compared with dual-strategy networks, and that optimizing the SOH thresholds yields a further 40.65% profit increase. The findings suggest that SOH thresholds are not fixed technical cutoffs, but coordination variables that balance product quality, processing volume, and pathway allocation. This study provides a clearer basis for battery sorting and recovery planning in retired battery disposal networks.