Network modeling and structural optimization for Shenzhen end-of-life electric vehicle batteries: A complex systems approach

This research presents a systems analysis and modeling approach to optimize a profit-maximizing reverse logistics network (RLN) for end-of-life (EOL) electric vehicle (EV) batteries in Shenzhen, China, addressing urgent recycling needs driven by the rapidly growing EV fleet. The model integrates uncertainties in battery quantity and quality with network node availability, costs and revenues to design a sustainable circular economy system. Leveraging historical data (2013–2024) and leveraging the Stanford and Weibull Distribution Models, the research identifies critical system-level challenges: a mismatch between scrap quantities and collection site distribution (the collection site density in Futian and Pingshan districts is 4.92 and 83.16[Formula: see text]km 2 /site, respectively), overcapacity in some processing nodes, a lack of collection sites in Yantian and Dapeng, the absence of waste processing facilities within Shenzhen, high acquisition costs for collection sites without revenue generation and over 52[Formula: see text]000 tons of critical materials remaining unrecycled from 2013 to 2024. The model projects a potential profit of 50.98 million CNY. Our predictions show that future EOL battery volumes will sharply increase, resulting in large processing volumes at nodes and significant differences in processing volumes across nodes. Recommendations focus on redesigning the RLN by optimizing collection, incentivizing formal recycling and developing local processing capabilities to build a sustainable, closed-loop system.