WeChat Mini Program
Old Version Features

Modeling a High-Energy, High-Rate Li/CFx Battery with a Capacity-Contributing Electrolyte

ECS Meeting Abstracts(2024)

Cited 0|Views5
Abstract
The Li/CFx primary cell has the highest specific energy of any commercialized cell, but recent funding programs (e.g., the IARPA RESILIENCE program) seek significant increases in performance beyond that achieved with current cell formulations, including both higher average rates than are typical for Li/CFx (e.g., C/3 for the RESILIENCE program) and high-rate pulses (e.g., 5C/3, also for the RESILIENCE program).1 One method to raise the specific energy beyond that of current commercial cells is to also obtain capacity from the electrolyte (i.e., to use a capacity-contributing electrolyte).2,3 This should occur after the CFx has been mostly consumed. The present talk will briefly introduce experimental results demonstrating the operation of a capacity-contributing electrolyte in a Li/CFx cell, and then focus on our 0-D modeling framework that includes three reactants (CFx, salt, and solvent), a cathode that undergoes volume expansion during discharge (based on the molar volumes of the reactants and products), and the growth of resistive electrolyte products.4 Our base case cell design is for a ~750 Wh/kg cell. We demonstrate the behavior of our model formulation for a C/3 average rate with two 5C/3 one-minute pulses (one at 99% and one at 20% SOC, based on the capacity of the CFx and capacity-contributing electrolyte). We find that there is significant cathode swell during discharge (i.e., 10s of %) that affects the cell polarization, and the electrochemical behavior of the 20% SOC pulse is highly dependent on the thermodynamics, kinetics, and transport properties of the salt and solvent reactants and products. Our modeling framework can be applied to additional chemistries with multiple active materials, significant electrode thickness changes, and resistive electrolyte products. 1. https://www.iarpa.gov/research-programs/resilience 2. J. Am. Chem. Soc. 2014, 136, 19, 6874–6877 3. Adv. Mater., 2015, 27, 3473–3483 4. Journal of The Electrochemical Society, 158 (5) A504-A510 (2011)
More
Translated text
求助PDF
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined