The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Prospects for reducing the processing cost of lithium ion batteries
Open accessAbstract
A detailed processing cost breakdown is given for lithium-ion battery (LIB) electrodes, which focuses on: 1) elimination of toxic, costly N -methylpyrrolidone (NMP) dispersion chemistry; 2) doubling the thicknesses of the anode and cathode to raise energy density; and 3) reduction of the anode electrolyte wetting and SEI-layer formation time. These processing cost reduction technologies generically adaptable to any anode or cathode cell chemistry and are being implemented at ORNL. This paper shows step by step how these cost savings can be realized in existing or new LIB manufacturing plants using a baseline case of thin (power) electrodes produced with NMP processing and a standard 10–14-day wetting and formation process. In particular, it is shown that aqueous electrode processing can cut the electrode processing cost and energy consumption by an order of magnitude. Doubling the thickness of the electrodes allows for using half of the inactive current collectors and separators, contributing even further to the processing cost savings. Finally wetting and SEI-layer formation cost savings are discussed in the context of a protocol with significantly reduced time. These three benefits collectively offer the possibility of reducing LIB pack cost from $502.8 kW h −1 -usable to $370.3 kW h −1 -usable, a savings of $132.5/kWh (or 26.4%). • A comprehensive cost study on lithium-ion electrode processing is reported. • Advanced electrode processing can save up to $111/kWh-usable. • Reduced SEI-layer formation time can save an additional $22/kWh-usable. • These processing technologies are amenable to any anode or cathode chemistry. • Capital cost savings realized and cell processing bottlenecks addressed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Ageing mechanisms in lithium-ion batteries
Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K.-C., Besenhard, J. O., Winter, M., Wohlfahrt-Mehrens, M., Vogler, C., & Hammouche, A. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.01.006 · Cited by 3,987
Capacity Fade Mechanisms and Side Reactions in Lithium‐Ion Batteries
Arora, P., White, R. E., & Doyle, M. (1998) · Journal of The Electrochemical Society · doi:10.1149/1.1838857 · Cited by 1,408
Pinson, M. B., & Bazant, M. Z. (2013) · Journal of The Electrochemical Society · doi:10.1149/2.044302jes · Cited by 946
Cited by
Review—SEI: Past, Present and Future
Peled, E., & Menkin, S. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.1441707jes · Cited by 2,182
An, S. J., Li, J., Daniel, C., Mohanty, D., Nagpure, S., & Wood, D. L. (2016) · Carbon · doi:10.1016/j.carbon.2016.04.008 · Cited by 2,121
Self-citationA review on the key issues of the lithium ion battery degradation among the whole life cycle
Han, X., Lu, L., Zheng, Y., Feng, X., Li, Z., Li, J., & Ouyang, M. (2019) · eTransportation · doi:10.1016/j.etran.2019.100005 · Cited by 1,745
Self-citationClosed-loop optimization of fast-charging protocols for batteries with machine learning
Attia, P. M., Grover, A., Jin, N., Severson, K. A., Markov, T. M., Liao, Y.-H., Chen, M. H., Cheong, B., Perkins, N., Yang, Z., et al. (2020) · Nature · doi:10.1038/s41586-020-1994-5 · Cited by 1,180
Placke, T., Kloepsch, R., Dühnen, S., & Winter, M. (2017) · Journal of Solid State Electrochemistry · doi:10.1007/s10008-017-3610-7 · Cited by 1,117
Metadata
- Authors
- Wood, D. L., Li, J., & Daniel, C.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2015-02
- Volume
- 275
- Pages
- 234-242
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2052909548
- Cited by
- 827
- References
- 3
- FWCI
- 20.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Prospects for reducing the processing cost of lithium ion batteries. https://app.cochat.ai/papers/10.1016/j.jpowsour.2014.11.019
- The paper
- Wood, D. L., Li, J., & Daniel, C. (2015). Prospects for reducing the processing cost of lithium ion batteries. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2014.11.019
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.