Designing a multi-level reverse logistics network for waste batteries of electric vehicles under uncertainty—A case study in the Yangtze River Delta Urban Agglomerations of China

As a breakthrough for alleviating the impact of the energy crisis and improving the environment, the electric vehicle (EV) industry is entering a period of rapid development. The growth of this industry is expected to lead to a dramatic increase in the quantity of end-of-life EV traction batteries in the future, posing significant challenges to environmental pollution and the sustainable development of the EV industry. This study addresses the cascade utilization and uncertainties of waste batteries, by incorporating the development weight of the match degree between urban commercial environments and facility development, proposes a fuzzy chance constrained programming model for the design of multi-level reverse logistics network (RLN) for retired EV batteries. The objective is to minimize network costs and carbon emissions, determining optimal facility locations and transportation distribution schemes. The mathematical model is validated through a real case study in the Yangtze River Delta Urban Agglomerations in China, and sensitivity analysis is conducted to explore the impact of various influencing factors on network design. The results indicate that the development weight significantly influence facility location decisions, while the quantity and quality of battery recycling have a substantial impact on the operational quantities of network facilities at different levels. • A fuzzy programming model is constructed considering multiple uncertain factors. • Considering the commercial environment, cities are assigned development weights. • Facilities selection opens in cities conducive to their sustainable development. • Quantity, quality, uncertainties, and development weights impact network design. • Effective network design boosts sustainable growth in the electric vehicle industry.