Review of Battery-supercapacitor Hybrid Energy Storage Systems for Electric Vehicles

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103598 - 103598

Published: Dec. 1, 2024

Language: Английский

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Unveiling the impacts of climate change on the resilience of renewable energy and power systems: Factors, technological advancements, policies, challenges, and solutions

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: 493, P. 144933 - 144933

Published: Feb. 1, 2025

Language: Английский

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Is renewable energy storage sustainable? A review

Green Technologies and Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100176 - 100176

Published: Feb. 1, 2025

Language: Английский

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Accelerating Analysis and Prediction of Lithium‐Ion Battery State of Health

Energy Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

With the increase in green power generation and construction of energy storage systems, demand for lithium‐ion batteries has also increased rapidly. Many are expected to be recycled future due aging decay, then being sorted reused according their status. However, accuracy current equivalent circuit model predictions screening classifying still needs improvement ensure precise battery classification. This study develops a novel by measuring electrochemical impedance inside fit impedance. Subsequently, trend line is established based on fitting results finally used predict verify state health (SOH) other identical retired batteries. approach makes process obsolete faster more precise. Through prediction SOH, constructed herein as high 99.38%. The average overall error 2.57%, highest only 3.58%. bridges gap between experimental data theoretical analysis, contributes advancing understanding degradation mechanisms management systems.

Language: Английский

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A Novel Method for Estimating the State of Health of Lithium-Ion Batteries Based on Physics-Informed Neural Network

Batteries, Journal Year: 2025, Volume and Issue: 11(2), P. 49 - 49

Published: Jan. 26, 2025

An accurate state of health (SOH) assessment lithium-ion batteries is essential for ensuring the reliability and safety electric vehicles (EVs). Data-driven SOH estimation methods have shown promise but face challenges in generalizing across diverse battery types variable operating conditions. To address this, this study integrates physical information into data-driven approaches, enabling physically consistent inferences a rapid adaptation to different chemistries usage scenarios. Specifically, features correlated with degradation, such as link between incremental capacity (IC) peaks SOH, are used constraints guide model learning. A fully connected layer within back-propagation neural network (BPNN) employed capture aging dynamics effectively. Experimental results on two datasets show that proposed outperforms traditional networks, reducing RMSE by at least 1.1% demonstrating strong generalizability both single-dataset transfer learning tasks.

Language: Английский

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Small-Sample Battery Capacity Prediction Using a Multi-Feature Transfer Learning Framework

Batteries, Journal Year: 2025, Volume and Issue: 11(2), P. 62 - 62

Published: Feb. 7, 2025

The accurate prediction of lithium-ion battery capacity is crucial for the safe and efficient operation systems. Although data-driven approaches have demonstrated effectiveness in lifetime prediction, acquisition lifecycle data long-life lithium batteries remains a significant challenge, limiting accuracy. Additionally, varying degradation trends under different operating conditions further hinder generalizability existing methods. To address these challenges, we propose Multi-feature Transfer Learning Framework (MF-TLF) predicting small-sample scenarios across diverse (different temperatures C-rates). First, introduce multi-feature analysis method to extract comprehensive features that characterize aging. Second, develop transfer learning-based framework, which leverages pre-trained models trained on large datasets achieve strong performance data-scarce scenarios. Finally, proposed validated using both experimental open-access datasets. When small sample dataset, predicted RMSE error consistently stays within 0.05 Ah. results highlight MF-TLF achieving high accuracy, even with limited data.

Language: Английский

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Advancements in Vibration Testing: Effects on Thermal Performance and Degradation of Modern Batteries

Batteries, Journal Year: 2025, Volume and Issue: 11(2), P. 82 - 82

Published: Feb. 19, 2025

Lithium-ion cells are increasingly being used as central power storage systems for modern applications, i.e., e-bikes, electric vehicles (EVs), satellites, and spacecraft, they face significant constant vibrations. This review examines how these vibrations affect the batteries’ mechanical, thermal, electrical properties. Vibrations can cause structural issues, such separation of electrodes deformation separators. These problems raise internal resistance lead to localized heat generation. As a result, thermal management becomes more complicated, battery aging accelerates, safety risks arise, including short circuits runaways. To tackle challenges, we need realistic testing protocols that consider combined effects vibrations, temperature, mechanical stress. Improving (TMSs) using advanced cooling techniques materials, e.g., phase change solutions, help alleviate problems. It is also essential design batteries with vibration-resistant materials enhanced integrity boost their durability. Moreover, play role in various degradation mechanisms, dendrite formation, self-discharge, lithium plating, all which reduce capacity lifespan. Our current research builds on insights multiscale physics-based modeling approach investigate interact behavior contribute degradation. By combining computational models experimental data, aim develop strategies tools enhance lithium-ion safety, reliability, longevity challenging environments.

Language: Английский

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Supercapacitor-Assisted Low-Frequency Converters for DC Microgrids, DC Homes, and DC Appliances for Increased End-to-End Efficiency: Implementation Example of a DC-Converted Refrigerator

Energy storage and applications, Journal Year: 2025, Volume and Issue: 2(1), P. 3 - 3

Published: Feb. 20, 2025

More recently, researchers and the industrial community have started researching DC appliances microgrids as a means of increasing end-to-end efficiency systems. Given fluctuating nature renewable resources, energy storage becomes mandatory in powering households with minimal AC grid supply, rechargeable battery packs maximum power point tracking controllers inverters are used. However, this approach is not most efficient due to losses converters used supply path, while short life environmental concerns also come into play. With rapid development commercial super-capacitors, longer life, higher density wider operational temperature range, device family can be at center new era, for homes appliances. The protection systems known supercapacitor-assisted techniques unique minimize or eliminate batteries improving ETEE. These SCA based on theoretical concept now published loss management theory. In paper, we will demonstrate how extend SCALoM theory develop whiteware, example DC-converted double-door refrigerator implementation details.

Language: Английский

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Artificial Intelligence in Gas Sensing: A Review

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

The role of artificial intelligence (AI), machine learning (ML), and deep (DL) in enhancing automating gas sensing methods the implications these technologies for emergent sensor systems is reviewed. Applications AI-based intelligent sensors include environmental monitoring, industrial safety, remote sensing, medical diagnostics. AI, ML, DL can process interpret complex data, allowing improved accuracy, sensitivity, selectivity, enabling rapid detection quantitative concentration measurements based on sophisticated multiband, multispecies systems. These discern subtle patterns signals, to readily distinguish between gases with similar signatures, adaptable, cross-sensitive multigas under various conditions. Integrating AI technology represents a paradigm shift, achieve unprecedented performance, adaptability. This review describes while highlighting approaches AI–sensor integration.

Language: Английский

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Economic analysis of solar power plant and battery energy storage: Case study of Binh Phuoc province, Vietnam

Cleaner Engineering and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100937 - 100937

Published: March 1, 2025

Language: Английский

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