Compressed air energy storage based on variable-volume air storage: A review

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 110, P. 115361 - 115361

Published: Jan. 9, 2025

Language: Английский

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Lithium Battery Degradation and Failure Mechanisms: A State-of-the-Art Review

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 342 - 342

Published: Jan. 14, 2025

This paper provides a comprehensive analysis of the lithium battery degradation mechanisms and failure modes. It discusses these issues in general context then focuses on various families or material types used batteries, particularly anodes cathodes. The begins with overview batteries their operations. explains fundamental principles electrochemical reaction that occurs battery, as well key components such anode, cathode, electrolyte. explores also processes modes batteries. examines main factors contributing to issues, including operating temperature current. highlights specific associated each type material, whether it is graphite, silicon, metallic lithium, cobalt, nickel, manganese oxides electrodes. Some degradations are due current waveforms. Then, importance thermal management emphasized throughout paper. negative effects overheating, excessive current, inappropriate voltage stability lifespan underscores significance systems (BMS) monitoring controlling parameters minimize risk failure. work summary valuable insight into development BMS. emphasizes understanding different critical influence quality. Rational efficient can enhance performance, reliability,

Language: Английский

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High-Throughput Screening of 6858 Compounds for Zinc-Ion Battery Cathodes via Hybrid Machine Learning Optimization

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

This work presents a machine-learning framework to explore cathode materials for zinc-ion batteries from data set of 6858 zinc-containing compounds. Utilizing the extensive Materials Project (MP) database, we employed two-step machine learning (ML) approach that uses transfer compensate missing electrochemical properties. Initially, random forest regressor was used fill in features zinc compounds, harnessing full battery explorer predictions. Two hybrid models were then developed: sparrow search algorithm-light gradient boosting (SSA-LGBM), and Harris Hawk optimization-deep neural networks (HHO-DNN). The contains 107 feature vectors, which minimized through principal component analysis. These include descriptors related structural, chemical, electronic Both trained using 4351 known compounds MP predict key properties such as average voltage gravimetric capacity. After initial prediction 62 potential electrodes, further screening criteria applied identify 18 promising electrodes based on their voltage, specific capacity, conductivity, safety, stability, cost, abundance. validation our carried out by applying materials, verifying accuracy innovative significantly accelerates discovery efficient stable batteries, paving way more sustainable high-performance energy storage solutions. method also provides robust future exploration across various technologies.

Language: Английский

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Assessment of E-mode GaN technology, practical power loss, and efficiency modelling of iL2C resonant DC-DC converter for xEV charging applications

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 91, P. 112008 - 112008

Published: May 23, 2024

Language: Английский

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Enhancing Lithium-ion Battery Monitoring: A Critical Review of Diverse Sensing Approaches

eTransportation, Journal Year: 2024, Volume and Issue: 22, P. 100360 - 100360

Published: Aug. 30, 2024

Language: Английский

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Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Batteries, Journal Year: 2024, Volume and Issue: 10(6), P. 204 - 204

Published: June 13, 2024

In recent years, the rapid evolution of transportation electrification has been propelled by widespread adoption lithium-ion batteries (LIBs) as primary energy storage solution. The critical need to ensure safe and efficient operation these LIBs positioned battery management systems (BMS) pivotal components in this landscape. Among various BMS functions, state temperature monitoring emerge paramount for intelligent LIB management. This review focuses on two key aspects health management: accurate prediction (SOH) estimation remaining useful life (RUL). Achieving precise SOH predictions not only extends lifespan but also offers invaluable insights optimizing usage. Additionally, RUL is essential estimation, especially demand electric vehicles continues surge. highlights significance machine learning (ML) techniques enhancing while simultaneously reducing computational complexity. By delving into current research field, aims elucidate promising future avenues leveraging ML context LIBs. Notably, it underscores increasing necessity advanced their role addressing challenges associated with burgeoning vehicles. comprehensive identifies existing proposes a structured framework overcome obstacles, emphasizing development machine-learning applications tailored specifically rechargeable integration artificial intelligence (AI) technologies endeavor pivotal, researchers aspire expedite advancements performance present limitations adopting symmetrical approach, harmonizes management, contributing significantly sustainable progress electrification. study provides concise overview literature, offering state, prospects, utilizing monitoring.

Language: Английский

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Review of vehicle to grid integration to support power grid security

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 2786 - 2800

Published: Sept. 2, 2024

Language: Английский

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Development of low-cost and efficient catalysts: Application of nitrogen-doped multi-walled carbon nanotubes loaded with tungsten nitride in zinc-air batteries

Journal of Industrial and Engineering Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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A Joint Prediction of the State of Health and Remaining Useful Life of Lithium-Ion Batteries Based on Gaussian Process Regression and Long Short-Term Memory

Processes, Journal Year: 2025, Volume and Issue: 13(1), P. 239 - 239

Published: Jan. 15, 2025

To comprehensively evaluate the current and future aging states of lithium-ion batteries, namely their State Health (SOH) Remaining Useful Life (RUL), this paper proposes a joint prediction method based on Gaussian Process Regression (GPR) Long Short-Term Memory (LSTM) networks. First, health features (HFs) are extracted from partial charging data. Subsequently, these fed into GPR model for SOH estimation, generating predictions. Finally, estimated values initial cycle to start point (SP) input LSTM network in order predict trajectory, identify End (EOL), infer RUL. Validation Oxford Battery Degradation Dataset demonstrates that achieves high accuracy both estimation RUL prediction. Furthermore, proposed approach can directly utilize one or more without requiring dimensionality reduction feature fusion. It also enables at early stages battery’s lifecycle, providing an efficient reliable solution battery management. However, study is data small-capacity batteries does not yet encompass applications large-capacity high-temperature scenarios. Future work will focus expanding scope validating model’s performance real-world systems, driving its application practical engineering

Language: Английский

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A review of Bayesian-filtering-based techniques in RUL prediction for Lithium-Ion batteries

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 111, P. 115371 - 115371

Published: Jan. 18, 2025

Language: Английский

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