The rapid global adoption of electric vehicles (EVs) is generating a fast-growing wave of end-of-life (EOL) traction batteries that retain significant residual value and embody scarce critical raw materials. Determining whether each retired battery should be routed to second-life reuse or to material recovery, and moving it efficiently through a reverse-logistics network, is a complex and data-intensive operations problem of rising strategic importance. This paper proposes a digital-twin reverse-logistics optimization framework that ingests battery-passport-style condition data to support recycle-versus-reuse routing decisions for EOL EV batteries. A mixed-integer linear programming (MILP) model minimizes total network cost net of recovered economic and material value, subject to state-of-health (SOH) thresholds, facility capacities, transport limits, and the recycled-content obligations introduced by EU Regulation 2023/1542. A digital-twin layer maintains a virtual representation of each battery and facility, updated continuously from passport attributes, and re-optimizes routing as conditions change. The framework is evaluated across four constructed demand and policy scenarios and benchmarked against a myopic routing rule, demonstrating net value improvements of 4.7%–11.3%. Sensitivity analysis confirms robustness across SOH threshold, recycled-content quota, transport cost, and second-life price variations. To the best of the author’s knowledge, this is among the first studies integrating battery-passport data, digital twins, reverse logistics optimization, and EU recycled-content obligations within a unified MILP framework.

Keywords

Digital Twin; Reverse Logistics; Electric Vehicle Battery Recycling; EU Battery Passport; Circular Economy.