(672f) A Mixed-Integer Nonlinear Programming Model for Optimizing the EV Battery Supply Chain with Unconventional Domestic Lithium Resources

The demand for electric vehicles (EVs) and grid storage has grown significantly in response to global efforts toward building a more sustainable ecosystem, with the electrification of the transportation sector leading this transition. Both heavily rely on lithium-ion batteries, making them one of the key technologies of the 21st century [1,2]. As a result, the demand for lithium-ion batteries surged by forty times, increasing from 11 GWh in 2015 to 460 GWh in 2023 [3]. Despite lithium's strategic importance, comprehensive analyses of optimal U.S.-based supply chain configurations - from raw material extraction to battery production and EV assembling - remain limited. Furthermore, emerging unconventional domestic feedstocks, such as geothermal brines and produced waters, are becoming increasingly relevant in the U.S., highlighting the need to develop a supply chain network model to assess their impact on the EV production ecosystem.

To build a thorough EV supply chain network, we initially gathered state-level EV demand forecasts from the U.S. Department of Energy's TransAltas database [5]. We then identified more than 200 strategically suitable sites in the United States, including possible locations for EV assembly plants and battery production facilities. Potential lithium recovery sites were selected based on Salt Lake and geothermal resource data, serving as the primary domestic feedstocks available in the U.S. For exact transportation modeling, we utilized the Open-Source Routing Machine (OSRM) API which creates a comprehensive distance matrix calculating actual driving distances between all potential facility locations, including state centers and 16 major American ports that use for imports of EVs and raw materials. This optimization problem is formulated as Mixed Integer Nonlinear Programming (MINLP) that minimizes the total costs, including production cost, and transportation expenses across all supply chain components while ensuring the satisfaction of projected EV demand across all states. The study assesses several scenarios to find optimal facility configuration under different circumstances, including total domestic production in contrast to reliance on imports. This work helps to identify cost-effective and resilient supply chain configurations that improve America's energy security, and lower reliance on foreign suppliers.

References:

1. Barman, Pranjal, Lachit Dutta, and Brian Azzopardi. "Electric vehicle battery supply chain and critical materials: a brief survey of state of the art." Energies16, no. 8 (2023): 3369.

2. Aakash Arora, William Acker, Brian Collie, Danny Kennedy, David Roberts, Ian Roddy, James Greenberger, et al., “How Lithium Batteries Can Power the US Economy,” BCG, last modified February 24, 2023, https://www.bcg.com/publications/2023/building-resilienceus-lithium-battery-supply-chain

3. Melin, Hans Eric. "The lithium-ion battery life cycle report." Circular Energy Storage: London, UK(2021).

4. Granholm, Jennifer M. "National blueprint for lithium batteries 2021–2030." US Department of Energy: Washington, DC, USA (2021).

5. U.S Department of Energy, "TransAtlas-Alternative Fuels Data Center." 2023, https://afdc.energy.gov/transatlas#/