Abstract

Modeling the global markets is complicated due to the existence of uncertainty in the information available. In addition, the lithium supply chain presents a complex network due to interconnections that it presents and the interdependencies among its elements. This complex supply chain has one large market, electric vehicles (EVs). EV production is increasing the global demand for lithium; in terms of the lithium supply chain, an EV requires lithium-ion batteries, and lithium-ion batteries require lithium carbonate and lithium hydroxide. Realistically, the mass balance in the global lithium supply chain involves more elements and more markets, and together with the assortment of databases in the literature, make the modeling through deterministic models difficult. Modeling the global supply chain under uncertainty could facilitate an assessment of the lithium supply chain between production and demand, and therefore could help to determine the distribution of materials for identifying the variables with the highest importance in an undersupply scenario. In the literature, deterministic models are commonly used to model the lithium supply chain but do not simultaneously consider the variation of data among databases for the lithium supply chain. This study performs stochastic modeling of the lithium supply chain by combining a material flow analysis with an uncertainty analysis and global sensitivity analysis. The combination of these methods evaluates an undersupply scenario. The stochastic model simulations allow a comparison between the known demand and the supply calculated under uncertainty, in order to identify the most important variables affecting lithium distribution. The dynamic simulations show that the most probable scenario is one where supply does not cover the increasing demand, and the stochastic modeling classifies the variables by their importance and sensibility. In conclusion, the most important variables in a scenario of EV undersupply are the lithium hydroxide produced from lithium carbonate, the lithium hydroxide produced from solid rock, and the production of traditional batteries. The global sensitivity analysis indicates that the critical variables which affect the uncertainty in EV production change with time.

Highlights

  • Lithium has become a strategic material since it plays an essential role in the development of a low-carbon economy [1]

  • The results are divided into the following six parts: (1) the mathematical representation of the supply chain as an material flow analysis (MFA); (2) the range of uncertainty based on data collection; (3) the uncertainty of the supply chain due to the distribution variables; (4) in addition to the uncertainty considering the distribution variables, we provide the uncertainty considering national production; (5) the sensitivity indices are presented for future estimations of production. (6) and the classification of the Monte Carlo filtering, which compares the simulations of future production with an estimation of demand, is used to present the most important variables in a scenario of undersupply

  • The following variables experience minimum variation in their sensitivity indices: the production of Argentina, the lithium hydroxide produced from solid rock, the lithium hydroxide produced from lithium carbonate, and the lubricants produced from lithium hydroxide

Read more

Summary

Introduction

Lithium has become a strategic material since it plays an essential role in the development of a low-carbon economy [1]. The growth of EV participation in the battery market is apparent, but the magnitude of how much this participation has grown can vary depending on the databases used as a result of how their corresponding calculations have been completed. The use of exact data in the calculation of EV production makes the results vary depending on the database. This means that if the model starts with database A and database B, it will always obtain result C, with A and B representing the reports in the literature and C being EV production

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.