检索结果-南通市图书馆

Towards Long Lifetime battery:AI-Based manufacturing and Management

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1139-1165页

作者： Kailong Liu Zhongbao Wei Chenghui Zhang Yunlong Shang Remus Teodorescu Qing-Long Han Warwick manufacturing Group University of WarwickCoventry CV47ALUnited Kingdom National Engineering Laboratory for Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China School of Control Science and Engineering Shandong UniversityJinan 250061China Department of Energy Technology Aalborg UniversityAalborg 9220Denmark School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia IEEE

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply *** battery inevitably ages with time,losing its capacity to store charge and deliver it *** directly affects battery safety and efficiency,making related health management *** advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management *** paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime ***,AI-based battery manufacturing and smart battery to benefit battery health are *** the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and *** through designing suitable AI solutions to enhance battery longevity are also ***,the main challenges involved and potential strategies in this field are *** work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.

关键词： Artificial intelligence battery health management battery life diagnostic battery manufacturing smart battery

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Explainable Neural Network for Sensitivity Analysis of Lithium-ion battery Smart

引用

IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1944-1953页

作者： Kailong Liu Qiao Peng Yuhang Liu Naxin Cui Chenghui Zhang School of Control Science and Engineering Shandong UniversityJinan 250061China Information Technology Analytics&Operations groupQueen’s University BelfastBelfast BT71NNUK

battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery *** battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently *** paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven *** be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and *** experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating *** addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural *** to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

关键词： battery management battery manufacturing data science explainable artificial intelligence sensitivity analysis

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence

引用

能源与人工智能(英文) 2022年第1期7卷 75-90页

作者： Mona Faraji Niri Kailong Liu Geanina Apachitei Luis A.A Román-Ramírez Michael Lain Dhammika Widanage James Marco Warwick manufacturing Group University of WarwickCV47AL CoventryUK The Faraday Institution Quad OneHarwell Science and Innovation CampusDidcotUK

Li-ion battery is one of the key players in energy storage technology empowering electrified and clean transportation ***,it is still associated with high costs due to the expensive material as well as high fluctuations of the manufacturing *** production processes involving mechanical,chemical,and electrical operations makes the predictability of the manufacturing process a challenge,hence the process is optimised through trial and error rather systematic *** establish an in-depth understanding of the interconnected processes and manufacturing parameters,this paper combines data-mining techniques and real production to offer a method for the systematic analysis,understanding and improving the Li-ion battery electrode manufacturing *** novelty of this research is that unlike most of the existing research that are focused on cathode manufacturing only,it covers both of the cathode and anode case ***,it is based on real manufacturing data,proposes a systematic design of experiment method for generating high quality and representative data,and leverages the artificial intelligence techniques to identify the dependencies in between the manufacturing parameters and the key quality factors of the *** this study,machine learning models are developed to quantify the predictability of electrode and cell properties given the coating process control ***,the manufacturing parameters are ranked and their contribution to the electrode and cell characteristics are quantified by *** systematic data acquisition approach as well as the quantified interdependencies are expected to assist the manufacturer when moving towards an improved battery production chain.

关键词： Li-ion battery electrodes Anode Cathode battery manufacturing Machine learning Artificial intelligence

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Revisiting the electrode manufacturing: A look into electrode rheology and active material microenvironment

引用

Journal of Energy Chemistry 2022年第9期31卷 41-55,I0002页

作者： Yan He Lei Jing Yuan Ji Zhiwei Zhu Lanxiang Feng Xuewei Fu Yu Wang Cllege of Polymer Science and Engineering State Key Laboratory of Polymer Materials EngineeringSichuan UniversityChengdu 610065SichuanChina

The microstructures on electrode level are crucial for battery performance, but the ambiguous understanding of both electrode microstructures and their structuring process causes critical challenges in controlling and evaluating the electrode quality during fabrication. In this review, analogous to the cell microenvironment well-known in biology, we introduce the concept of ‘‘active material microenvironment”(ME@AM)that is built by the ion/electron transport structures surrounding the AMs, for better understanding the significance of the electrode microstructures. Further, the scientific significance of electrode processing for electrode quality control is highlighted by its strong links to the structuring and quality control of ME@AM. Meanwhile, the roles of electrode rheology in both electrode structuring and structural characterizations involved in the entire electrode manufacturing process(i.e., slurry preparation, coating/printing/extrusion, drying and calendering) are specifically detailed. The advantages of electrode rheology testing on in-situ characterizations of the electrode qualities/structures are emphasized. This review provides a glimpse of the electrode rheology engaged in electrode manufacturing process and new insights into the understanding and effective regulation of electrode microstructures for future high-performance batteries.

关键词： Active material microenvironment Electrode microstructures and rheology battery manufacturing High energy and power density Fast charging and discharging

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：