Future Batteries最新文献

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Thermorechargeable battery composed of mixed electrodes 由混合电极组成的热充电电池
Future Batteries Pub Date : 2024-07-10 DOI: 10.1016/j.fub.2024.100004
Yuuga Taniguchi , Touya Aiba , Takahiro Kubo , Yutaka Moritomo
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引用次数: 0
Complex polycation redox material interfaced with renewable porous carbon for asymmetric supercapacitors 用于不对称超级电容器的可再生多孔碳界面复合多阳离子氧化还原材料
Future Batteries Pub Date : 2024-06-01 DOI: 10.1016/j.fub.2024.100002
Khang Huynh , Vinod Amar , Bharath Maddipudi , Rajesh Shende
{"title":"Complex polycation redox material interfaced with renewable porous carbon for asymmetric supercapacitors","authors":"Khang Huynh ,&nbsp;Vinod Amar ,&nbsp;Bharath Maddipudi ,&nbsp;Rajesh Shende","doi":"10.1016/j.fub.2024.100002","DOIUrl":"https://doi.org/10.1016/j.fub.2024.100002","url":null,"abstract":"<div><p>Mixed polycation transition metal ferrites are known to exhibit unique and superior characteristics for structural, electrical, magnetic, and optical applications. Although a few binary transition metal ferrites are found to be suitable for electrochemical energy storage application, ternary transition metal ferrites are not investigated for asymmetric supercapacitors (ASCs). Mixed polycation oxides are expected to have increased active sites that can facilitate proton and electron transfer impacting the redox reactions. Specific crystal structure and associated lattice parameters as well as surface and morphological characteristics can also influence the energy storage properties. This study for the first time reports a novel complex polycation redox material, (Cu<sub>p</sub>Mn<sub>q</sub>Zn<sub>r</sub>)<sub>x</sub>Fe<sub>y</sub>O<sub>z</sub> and renewable pinewood (PW) derived porous carbon (POC) as electrodes for ASC. Both (Cu<sub>p</sub>Mn<sub>q</sub>Zn<sub>r</sub>)<sub>x</sub>Fe<sub>y</sub>O<sub>z</sub> and PW-POC are subjected to electrochemical characterization and used in ASC configuration with aqueous KOH electrolyte. It is anticipated that the Faradaic characteristics of (Cu<sub>p</sub>Mn<sub>q</sub>Zn<sub>r</sub>)<sub>x</sub>Fe<sub>y</sub>O<sub>z</sub> will make it to serve as a cathode while PW-POC with capacitive behavior will act as anode in ASCs. Relatively higher specific capacitance of &gt; 200 F/g is observed for the (Cu<sub>p</sub>Mn<sub>q</sub>Zn<sub>r</sub>)<sub>x</sub>Fe<sub>y</sub>O<sub>z</sub> reference electrode and fabricated ASCs. Capacitance retention rate is tested in 10,000 cycles for the working electrodes whereas for ASC, the stability tests are performed over 100 charging-discharging cycles exhibiting relatively higher capacitance retention. (Cu<sub>p</sub>Mn<sub>q</sub>Zn<sub>r</sub>)<sub>x</sub>Fe<sub>y</sub>O<sub>z</sub> appears to be a promising material for a supercapacitor.</p></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"2 ","pages":"Article 100002"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950264024000029/pdfft?md5=ba211f007a84b22777cae39ab7a29952&pid=1-s2.0-S2950264024000029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhancing prediction accuracy of Remaining Useful Life in lithium-ion batteries: A deep learning approach with Bat optimizer 提高锂离子电池剩余使用寿命的预测精度:带有蝙蝠优化器的深度学习方法
Future Batteries Pub Date : 2024-06-01 DOI: 10.1016/j.fub.2024.100003
Shahid A. Hasib , S. Islam , Md F. Ali , Subrata. K. Sarker , Li Li , Md Mehedi Hasan , Dip K. Saha
{"title":"Enhancing prediction accuracy of Remaining Useful Life in lithium-ion batteries: A deep learning approach with Bat optimizer","authors":"Shahid A. Hasib ,&nbsp;S. Islam ,&nbsp;Md F. Ali ,&nbsp;Subrata. K. Sarker ,&nbsp;Li Li ,&nbsp;Md Mehedi Hasan ,&nbsp;Dip K. Saha","doi":"10.1016/j.fub.2024.100003","DOIUrl":"https://doi.org/10.1016/j.fub.2024.100003","url":null,"abstract":"<div><p>Remaining Useful Life (RUL) prediction in lithium-ion batteries is crucial for assessing battery performance. Despite the popularity of deep learning methods for RUL prediction, their complex architectures often pose challenges in interpretation and resource consumption. We propose a novel approach that combines the interpretability of a convolutional neural network (CNN) with the efficiency of a bat-based optimizer. CNN extracts battery data features and characterizes degradation kinetics, while the optimizer refines CNN parameters. Tested on NASA PCoE data, our method achieves exceptional results with minimal computational burden and fewer parameters. It outperforms traditional approaches, yielding an <strong>R2-score</strong> of <strong>0.9987120</strong>, an <strong>MAE</strong> of <strong>0.004397067 Ah</strong>, and a low <strong>RMSE</strong> of <strong>0.00656 Ah</strong>. The proposed model outperforms traditional deep learning models, as confirmed by comparative analysis.</p></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"2 ","pages":"Article 100003"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950264024000030/pdfft?md5=be1fc3a71e46f09dd45d43a2d6ef742b&pid=1-s2.0-S2950264024000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Influence of temperature, state of charge and state of health on the thermal parameters of lithium-ion cells: Exploring thermal behavior and enabling fast-charging 温度、充电状态和健康状态对锂离子电池热参数的影响:探索热行为并实现快速充电
Future Batteries Pub Date : 2024-04-01 DOI: 10.1016/j.fub.2024.100001
Luca Tendera , Hendrik Pegel , Carlos Gonzalez , Dominik Wycisk , Alexander Fill , Kai Peter Birke
{"title":"Influence of temperature, state of charge and state of health on the thermal parameters of lithium-ion cells: Exploring thermal behavior and enabling fast-charging","authors":"Luca Tendera ,&nbsp;Hendrik Pegel ,&nbsp;Carlos Gonzalez ,&nbsp;Dominik Wycisk ,&nbsp;Alexander Fill ,&nbsp;Kai Peter Birke","doi":"10.1016/j.fub.2024.100001","DOIUrl":"https://doi.org/10.1016/j.fub.2024.100001","url":null,"abstract":"<div><p>The precise input of thermal parameters is essential for thermal simulation. Although constant thermal parameters are commonly used for parametrizing thermal modeling frameworks, extensive measurements indicate a significant dependence of thermal parameters on temperature, SOC and SOH. Therefore, this work summarizes experimental data and integrates determined operating point dependencies into a validated thermal-electrical-electrochemical modeling framework. Exploring the effect of variable thermal parameters, detailed effects on fast-charging and corresponding charging times are assessed.</p><p>It is found that the strong reduction in through-plane thermal conductivity due to aging can notably increase thermal inhomogeneity. Thus, heat dissipation is reduced and the thermal management has to be revised to prevent an increase in charging time of up to 3%. However, the operating point-dependent through-plane thermal conductivity has no significant effect on fast-charging for the analyzed pristine cylindrical lithium-ion cell. Furthermore, a temperature-dependent specific heat capacity definition considerably affects the thermal behavior of lithium-ion cells at extreme temperatures. While enabling a faster heating at low temperatures, a temperature-related current derating at high temperatures is delayed. Thus, a variable thermal parameter definition can lead to an increase in fast-charging capability of up to 3% due to the more precise modeling of the physical behavior of the cell.</p></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"1 ","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950264024000017/pdfft?md5=9e9fc4915a5f835d6ccde23bd1e82ecd&pid=1-s2.0-S2950264024000017-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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