Technoeconomic Analysis of Domestically Manufactured L(M)FPmaterials and Packs
Bibliographic record
Abstract
There is an increased focus on offering low-cost, locally manufactured batteries for electric vehicle and grid storage applications. Lithium-ion battery manufacturing in the United States is currently dominated by nickel-based oxide chemistries partly because they have high energy densities, which lead to long range electric vehicles and lightweight consumer electronics. The one downside of nickel-containing materials is a high cost due to the price of nickel, cobalt (which is used in most materials), and lithium (where only up to 80% is active). Batteries made of phosphate-based cathode active materials – i.e., lithium iron phosphate (LiFePO 4 , LFP) and lithium iron/manganese phosphate (LiMn x Fe 1-x PO 4 , LMFP) – can potentially meet cost demands due to the low price of iron and manganese and their near-100% utilization of lithium. However, the cost and opportunities for domestically manufactured, phosphate-based materials and batteries are difficult to assess since these materials are typically manufactured overseas. This work seeks to estimate the cost of U.S. manufactured L(M)FP cathode active materials using two different production pathways: carbothermal and hydrothermal. Two pathways are investigated to provide insight into the undeveloped L(M)FP manufacturing sector in the U.S. and to provide sensitivities and ranges for the expected costs. Both pathways are modeled using a suite of technoeconomic models at Roland Berger and Argonne National Laboratory to translate the price of raw materials into cathode active material (CAM) costs when manufactured at scale within the United States. These costs are used to study the tradeoffs and opportunities for L(M)FP battery packs when compared to the leading competitive chemistry: lithium nickel manganese cobalt oxide (NMC). Case studies are conducted using the Battery Performance and Cost (BatPaC) Model at Argonne National Laboratory to translate the CAM costs into pack costs. The case studies assume electric vehicle packs with fixed volumes and demonstrate the tradeoffs and challenges of adopting phosphate active materials. This study provides insight into the main cost drivers and design considerations that increase the viability of U.S. manufactured L(M)FP packs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".