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Roadmap for rechargeable batteries:present and beyond

Science China Chemistry 2024年第1期67卷 13-42页

作者： Sen Xin Xu Zhang Lin Wang Haijun Yu Xin Chang Yu-Ming Zhao Qinghai Meng Pan Xu Chen-Zi Zhao Jiahang Chen Huichao Lu Xirui Kong Jiulin Wang Kai Chen Gang Huang Xinbo Zhang Yu Su Yao Xiao Shu-Lei Chou Shilin Zhang Zaiping Guo Aobing Du Guanglei Cui Gaojing Yang Qing Zhao Liubing Dong Dong Zhou Feiyu Kang Hu Hong Chunyi Zhi Zhizhang Yuan Xianfeng Li Yifei Mo Yizhou Zhu Dongfang Yu Xincheng Lei Jianxiong Zhao Jiayi Wang Dong Su Yu-Guo Guo Qiang Zhang Jun Chen Li-Jun Wan CAS key Laboratory of Molecular Nanostructure and Nanotechnology CAS Research/Education Center for Excellence in Molecular SciencesBeijing National Laboratory for Molecular Sciences(BNLMS)Institute of ChemistryChinese Academy of Sciences(CAS)Beijing 100190China University of Chinese Academy of Sciences.Beijing 100049 China Institute of Advanced Battery Materials and Devices Faculty of Materials and ManufacturingBeijing University of TechnologyBeijing 100124China key Laboratory of Advanced Functional Materials Ministry of EducationBeijing University of TechnologyBeijing 100124China Beijing key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical EngineeringTsinghua UniversityBeijing 100084China State key Laboratory of Chemistry and Utilization of Carbon-Based Energy Resources College of ChemistryXinjiang UniversityUrumqi 830046China Department of Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China State key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryChangchun 130022China Institute for Carbon Neutralization College of Chemistry and Materials EngineeringWenzhou UniversityWenzhou 325035China School of Chemical Engineering and Advanced Materials The University of AdelaideAdelaide 5000Australia Qingdao Industrial Energy Storage research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101China Frontiers Science Center for New Organic Matter Key Laboratory of Advanced Energy Materials Chemistry(Ministry of Education)College of ChemistryNankai UniversityTianjin 300071China College of Chemistry and Materials Science Jinan UniversityGuangzhou 511443China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Department of Materials Science and Engineering City University of Hong KongHong Kong 999077China Division of Energy Storage Dalian National Laboratory for Clean Energy(DNL)Dalian Institute of Chem

Rechargeable batteries currently hold the largest share of the electrochemical energy storage market,and they play a major role in the sustainable energy transition and industrial decarbonization to respond to global climate *** to the increased popularity of consumer electronics and electric vehicles,lithium-ion batteries have quickly become the most successful rechargeable batteries in the past three decades,yet growing demands in diversified application scenarios call for new types of rechargeable *** efforts are made to developing the next-generation post-Li-ion rechargeable batteries,which include,but are not limited to solid-state batteries,lithium–sulfur batteries,sodium-/potassium-ion batteries,organic batteries,magnesium-/zinc-ion batteries,aqueous batteries and flow *** the great achievements,challenges persist in precise understandings about the electrochemical reaction and charge transfer process,and optimal design of key materials and interfaces in a *** roadmap tends to provide an overview about the current research progress,key challenges and future prospects of various types of rechargeable *** computational methods for materials development,and characterization techniques will also be discussed as they play an important role in battery research.

关键词： energy storage rechargeable batteries battery materials electrochemistry

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A method for weighing broiler chickens using improved amplitude-limiting filtering algorithm and BP neural networks

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Information Processing in Agriculture 2021年第2期8卷 299-309页

作者： Weihong Ma Qifeng Li Jiawei Li Luyu Ding Qinyang Yu Beijing research Center for Information Technology in Agriculture Beijing 100097China National Engineering research Center for Information Technology in Agriculture Beijing 100097China key Laboratory of Agri-informatics Ministry of AgricultureBeijing 100097China Beijing Engineering research Center of Agriculture Internet of Things Beijing 100097China

Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and *** chickens usually exhibit stress-related behavior during *** 3D camera-based weighing system for broiler chickens can only weigh the broiler chicken in the monitoring ***,it makes poor weight prediction due to poor segmentation especially when the broiler chicken is flapping its *** solve these issues,we developed one simple and low-cost weighing system with high stability and accuracy.A validity value extraction method from dynamic weighing was ***,an improved amplitude-limiting filtering algorithm and a BP neural networks model were developed to avoid accidental *** BP neural networks model used daily weight gain,day-age,average velocity,and the weight data after filtering algorithm as the input *** weighing system was tested in a commercial Beijing Fatty Chickens house with Beijing Fatty *** tested thirteen groups of Beijing Fatty Chickens of different weights,from 500 g to 1800 g in intervals of 100 g,using the three different methods:no filtering algorithm or BP neural networks,only the improved amplitude-limiting filtering algorithm and a hybrid of the improved amplitude-limiting filtering algorithm and BP neural *** results showed that the hybrid algorithm had a better performance in minimizing the error,lowering from the original 6%down to 3%.The accurate weight data was transmitted to the remote service platform for further decision-making,such as activity analysis,feeding management,and health alerts.

关键词： Weighing of broiler chickens Improved amplitude-limiting filtering algorithm BP neural networks Dynamic weighing

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