Performance analysis of Na-ion batteries by machine learning

Oral, Burcu; Tekin, Burak; Eroğlu, Damla; Yıldırım, Ramazan

doi:10.1016/j.jpowsour.2022.232126

Cited by 19 publications

(5 citation statements)

References 32 publications

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“…With the rapid developments of computer science and big data technology, machine learning has shown its value in effectively guiding the design and preparation of high-performance battery materials. − As mentioned in the previous section, the construction of WT-SIBs strongly relies on the design and optimization of each battery component. Based on the accumulated research data, combining the density functional theory (DFT) calculations with the machine learning methods to screen for suitable materials and predict the electrochemical performances can be expected to accelerate the development of WT-SIBs.…”

Section: Discussionmentioning

confidence: 99%

Emerging Chemistry for Wide-Temperature Sodium-Ion Batteries

Zhang,

He,

Xin

et al. 2024

Chem. Rev.

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The shortage of resources such as lithium and cobalt has promoted the development of novel battery systems with low cost, abundance, high performance, and efficient environmental adaptability. Due to the abundance and low cost of sodium, sodium-ion battery chemistry has drawn worldwide attention in energy storage systems. It is widely considered that wide-temperature tolerance sodium-ion batteries (WT-SIBs) can be rapidly developed due to their unique electrochemical and chemical properties. However, WT-SIBs, especially for their electrode materials and electrolyte systems, still face various challenges in harsh-temperature conditions. In this review, we focus on the achievements, failure mechanisms, fundamental chemistry, and scientific challenges of WT-SIBs. The insights of their design principles, current research, and safety issues are presented. Moreover, the possible future research directions on the battery materials for WT-SIBs are deeply discussed. Progress toward a comprehensive understanding of the emerging chemistry for WT-SIBs comprehensively discussed in this review will accelerate the practical applications of wide-temperature tolerance rechargeable batteries. CONTENTSApplications 4.3.1. Cathode Electrolyte Interface 4.3.2. Solid Electrolyte Interface 5. Summary and Outlook 5.1. Conclusion on the Current Progress of WT-SIBs 5.2. Discussion on the Further Development of WT-SIBs

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Section: Discussionmentioning

confidence: 99%

Emerging Chemistry for Wide-Temperature Sodium-Ion Batteries

Zhang,

He,

Xin

et al. 2024

Chem. Rev.

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“…Gradient boosting, support vector machines, Random forest, (for regression), and decision tree (for classification) methods have been used for the data analysis.They have analyzed that anode material and the preparation methods are highly effective for cycle stability. 270 Chen et al 271 describes over 160 NASICON materials through Random forest (RF) and neural network (NN) models. They implied suitable NASICON electrolyte design for SIBs.…”

Section: Machine Learning On Sodium Batterymentioning

confidence: 99%

“…They analyzed that the anode material and preparation methods are highly effective in determining the cycle stability. 270 Chen et al 271 described over 160 NASICON materials through random forest (RF) and neural network (NN) models. They implied suitable NASICON electrolyte design for SIBs.…”

Section: Machine Learning On Sodium Batterymentioning

confidence: 99%

“…272 Yildirim and co-workers also collected 1227 data points from 335 published papers and applied random forest (for discharge capacity prediction) and decision tree (classification). 271 Chao et al described different carbon-based materials as anodes for sodium batteries. They correlated the structure and synthesis of different carbon materials with their discharge capacity.…”

Section: Machine Learning On Sodium Batterymentioning

confidence: 99%

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The energy storage application of core-/yolk–shell structures in sodium batteries

Maiti,

Biswal,

Debnath

et al. 2024

Energy Adv.

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“…For more complex EESC systems, such as fuel cells, lithium metal batteries (LMBs), lithium-sulfur (Li-S) batteries, and sodium-ion batteries (SIBs), ML affords the similar key effects. [131][132][133][134][135] Proton exchange membrane fuel cell (PEMFC) is susceptible to the impurities in H 2 and operating conditions, which generally results in deteriorated performance over time. Thus, the degradation prediction is crucial in evaluating the reliability of the PEMFC system.…”

Section: Performance Predictionmentioning

confidence: 99%

Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning

Yang

Zhang

et al. 2023

EcoMat

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For a "Carbon Neutrality" society, electrochemical energy storage and conversion (EESC) devices are urgently needed to facilitate the smooth utilization of renewable and sustainable energy where the electrode materials and catalysts play a decisive role. However, the efficiency of the current trial-and-error research paradigm largely lags behind the imminent demands of EESC requiring increasingly improved performance. The emerged machine learning (ML), a subfield of artificial intelligence, is capable of evaluating and analyzing big data for hidden rules. In this regard, the relationships between the structure and performance of the key materials can be more efficiently revealed, which fundamentally revolutionizes the material research manner of the current EESC devices. In this review, the typical ML algorithms utilized in EESC development are first introduced. Then, focused attention has been paid to multiple aspects of applying ML to reshape the materials research for EESC. In addition to highlighting the emerging prospect, the challenges which are still hindering the further development of this emerging field are also discussed.

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Performance analysis of Na-ion batteries by machine learning

Cited by 19 publications

References 32 publications

Emerging Chemistry for Wide-Temperature Sodium-Ion Batteries

Emerging Chemistry for Wide-Temperature Sodium-Ion Batteries

The energy storage application of core-/yolk–shell structures in sodium batteries

Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning

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