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Wang Y, Mu A, Wang W, Yang B, Wang J. A Review of Capacity Decay Studies of All-vanadium Redox Flow Batteries: Mechanism and State Estimation. CHEMSUSCHEM 2024; 17:e202301787. [PMID: 38440928 DOI: 10.1002/cssc.202301787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 03/06/2024]
Abstract
As a promising large-scale energy storage technology, all-vanadium redox flow battery has garnered considerable attention. However, the issue of capacity decay significantly hinders its further development, and thus the problem remains to be systematically sorted out and further explored. This review provides comprehensive insights into the multiple factors contributing to capacity decay, encompassing vanadium cross-over, self-discharge reactions, water molecules migration, gas evolution reactions, and vanadium precipitation. Subsequently, it analyzes the impact of various battery parameters on capacity. Based on this foundation, the article expounds upon the significance of battery internal state estimation technology. Additionally, the review also summarizes domestic and international mathematical models utilized for simulating capacity decay, serving as a valuable reference for future research endeavors. Finally, through the comparison of traditional experimental methods and mathematical modeling methods, this article offers effective guidance for the future development direction of battery state monitoring. This review generally overview the problems related to the capacity attenuation of all-vanadium flow batteries, which is of great significance for understanding the mechanism behind capacity decay and state monitoring technology of all-vanadium redox flow battery.
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Affiliation(s)
- Yupeng Wang
- School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, P. R. China
| | - Anle Mu
- School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, P. R. China
| | - Wuyang Wang
- School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, P. R. China
| | - Bin Yang
- School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, P. R. China
| | - Jiahui Wang
- School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, P. R. China
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Ji S, Zhu J, Yang Y, Dos Reis G, Zhang Z. Data-Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects. SMALL METHODS 2024; 8:e2301021. [PMID: 38213008 DOI: 10.1002/smtd.202301021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/11/2023] [Indexed: 01/13/2024]
Abstract
Battery characterization and prognosis are essential for analyzing underlying electrochemical mechanisms and ensuring safe operation, especially with the assistance of superior data-driven artificial intelligence systems. This review provides a unique perspective on recent progress in data-driven battery characterization and prognosis methods. First, recent informative image characterization and impedance spectrum as well as high-throughput screening approaches on revealing battery electrochemical mechanisms at multiple scales are summarized. Thereafter, battery prognosis tasks and strategies are described, with the comparison of various physics-informed modeling strategies. Considering unlocking mechanisms from tremendous battery data, the dominant role of physics-informed interpretable learning in accelerating energy device development is presented. Finally, challenges and prospects on data-driven characterization and prognosis are discussed toward accelerating energy device development with much-enhanced electrochemical transparency and generalization. This review is hoped to supply new ideas and inspirations to the next-generation battery development.
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Affiliation(s)
- Shanling Ji
- School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China
| | - Jianxiong Zhu
- School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Yaxin Yang
- School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China
| | - Gonçalo Dos Reis
- School of Mathematics, University of Edinburgh, JCMB, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, UK
| | - Zhisheng Zhang
- School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China
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Noh J, Doan HA, Job H, Robertson LA, Zhang L, Assary RS, Mueller K, Murugesan V, Liang Y. An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations. Nat Commun 2024; 15:2757. [PMID: 38553488 PMCID: PMC10980761 DOI: 10.1038/s41467-024-47070-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.
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Affiliation(s)
- Juran Noh
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Hieu A Doan
- Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA.
| | - Heather Job
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Lily A Robertson
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Lu Zhang
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Rajeev S Assary
- Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Karl Mueller
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Vijayakumar Murugesan
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
| | - Yangang Liang
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
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Fedorov R, Gryn’ova G. Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks. J Chem Theory Comput 2023; 19:4796-4814. [PMID: 37463673 PMCID: PMC10414033 DOI: 10.1021/acs.jctc.3c00355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Indexed: 07/20/2023]
Abstract
Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.
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Affiliation(s)
- Rostislav Fedorov
- Heidelberg
Institute for Theoretical Studies (HITS gGmbH), 69118 Heidelberg, Germany
- Interdisciplinary
Center for Scientific Computing, Heidelberg
University, 69120 Heidelberg, Germany
| | - Ganna Gryn’ova
- Heidelberg
Institute for Theoretical Studies (HITS gGmbH), 69118 Heidelberg, Germany
- Interdisciplinary
Center for Scientific Computing, Heidelberg
University, 69120 Heidelberg, Germany
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