The electric vehicle (EV) battery supply chain will face challenges in sourcing scarce, expensive minerals required for manufacturing and in disposing of hazardous retired batteries. Integrating recycling technology into the supply chain has the potential to alleviate these issues; however, players in the battery market must design investment plans for recycling facilities. In this paper, we propose a two-stage stochastic optimization model for computing minimum cost recycling capacity decisions, in which retired batteries are recycled and recovered materials are used to manufacture new batteries. The model is a separable concave minimization subject to linear constraints, a class for which we design a new finitely convergent global optimization algorithm based on piecewise linear approximation that solves up to 10x faster than comparable algorithms. We propose an equivalent reformulation of the model that reduces the total number of variables by introducing integrality constraints. The reformulation can also be solved by our global algorithm with drastically reduced solve times. We detail a cut grouping strategy for Benders’ decomposition in the second stage which improves convergence relative to single-cut and multi-cut implementations. To produce a set of second-stage scenarios, we design an approach for generating time-series projections for new battery demand, retired battery supply, and material costs. Analysis of the optimal solutions shows that effective investment in recycling can reduce battery manufacturing costs by 22% and reduce environmental impacts by up to 7%.