BattOpt: Optimal Facility Planning for Electric Vehicle Battery Recycling

The electric vehicle (EV) battery supply chain will face challenges in sourcing scarce and 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 multistage 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. We transform EverBatt, a leading evaluation framework for battery recycling cost and environmental impact, into a prescriptive decision-making tool for determining optimal investment strategies. Our model is a separable concave minimization subject to linear constraints, a class for which we design a new finitely convergent global optimization algorithm that solves up to 14x faster than comparable algorithms. We propose an equivalent reformulation of the model that reduces the total number of variables by introducing integrality constraints, drastically reducing solve times. We detail a cut grouping strategy for a Benders' decomposition which improves convergence relative to single-cut and multi-cut implementations. To produce a set of operational scenarios, we design an approach for generating time-series projections for new battery demand, retired battery supply, and material costs, leveraging state-of-the-art econometric models. We apply our model to compute optimal investment plans and quantify the impact of optimal decision-making: effective investment in recycling can reduce battery manufacturing costs by 22% and environmental impacts by up to 7%. Finally, we analyze the impact of policy instruments to reveal key insights on the efficacy of capacity grants and production credits.