BattOpt: Optimal Facility Planning for Electric Vehicle Battery Recycling

[2407.00864] BattOpt: Optimal Facility Planning for Electric Vehicle Battery Recycling Skip to main content Search Submit Donate Log in Mathematics > Optimization and Control arXiv:2407.00864 (math) [Submitted on 1 Jul 2024 ( v1 ), last revised 28 Aug 2026 (this version, v3)] Title: BattOpt: Optimal Facility Planning for Electric Vehicle Battery Recycling Authors: Matthew Brun , Xu Andy Sun View PDF HTML (experimental) Abstract: 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 describe a finitely convergent global optimization algorithm based on piecewise linear approximation that solves up to 14x faster than comparable algorithms. We propose an equivalent reformulation of the model that eliminates variables by introducing integrality constraints, drastically reducing solve times. To produce a set of operational scenarios, we generate 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: optimal investment can reduce battery manufacturing costs by 22% and environmental impacts by 26%. We measure the operational benefits of different recycling technologies and demonstrate how high uncertainty motivates greater investment in recycling capacity. Finally, we analyze the impact of policy instruments to reveal key insights on the efficacy of capacity grants and production credits. Subjects: Optimization and Control (math.OC) MSC classes: 90B99, 90C26, 90C15 Cite as: arXiv:2407.00864 [math.OC] (or arXiv:2407.00864v3 [math.OC] for this version) https://doi.org/10.48550/arXiv.2407.00864 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Matthew Brun [ view email ] [v1] Mon, 1 Jul 2024 00:17:12 UTC (743 KB) [v2] Tue, 1 Jul 2025 03:19:29 UTC (1,101 KB) [v3] Fri, 28 Aug 2026 02:02:35 UTC (1,342 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: math.OC < prev | next > new | recent | 2024-07 Change to browse by: math References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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