Transportation is a major source of global greenhouse gas emissions. To mitigate this problem and its climate impacts, electric vehicles (EVs) have been proposed as a key solution in the transportation sector. However, battery electric vehicles (BEVs) face significant challenges, mainly related to their supply chain. The limited availability and uneven distribution of mineral resources, especially lithium, create vulnerabilities in the supply chain. At the same time, used BEVs have a high potential for recycling. Therefore, a closed-loop supply chain aims to optimize resource use by integrating raw material sourcing with waste recycling. However, the existing literature has some gaps: the lack of uncertainty handling in BEVs’ supply chain models and the lack of sustainable models that consider both forward and reverse flows. This study aims to fill these gaps by developing a sustainable multi-objective fuzzy model considering all supply chain elements under uncertainty. The model uses fuzzy parameters and variables, such as BEV demand, EV demand, costs, BEV return rate, energy consumption, facilities capacity, and BEV and mineral batteries order amount. The model is solved using the GAMS software and the results show the trade-offs between sustainability aspects, the optimal material flows, and the sensitivity analysis of the model. This study can provide useful insights for the decision makers and the stakeholders of the BEVs supply chain.