Planning a circular economy system for electric vehicles using network simulation

The fast-growing market for electric vehicles (EVs) offers great potential for reducing the carbon footprint of the transportation sector but is heavily reliant on electric motors and batteries, which are in turn dependent on the availability of critical materials, e.g., rare earth elements and lithium. Such critical materials are subject to substantial supply chain risk, indicating a potential lack of source materials to meet EV production. To ameliorate this supply chain uncertainty that may compromise the economic competitiveness of EVs, it is essential to establish an end-of-life (EoL) infrastructure that is focused on closing the material loops within the EV life cycle, thus achieving a circular economy. However, there is a lack of research devoted to enabling technologies and supporting tools to design the reverse logistic chain for EVs that considers EoL processing of components in a holistic manner. To close this knowledge gap, this study develops a stochastic activity network to simulate an EV renewal system (EVRS) that has the goal of rebuilding used EVs and extracting maximum value from EoL EV components and materials. Based on the simulation outputs, the process flow and economic performance of the proposed system are enhanced through operation optimization and pricing optimization. The results indicate the technical viability of the EVRS from an operational standpoint and suggest its promising economic potential in industrial practice. As industries around the globe transition to a circular economy, the proposed network simulation framework has enormous potential to be adapted to other products and scenarios to provide decision support in system planning.