Journal of Electrochemical Energy Conversion and Storage最新文献_第2页

Composite structural battery: A review 复合结构电池：综述

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-03-18 DOI: 10.1115/1.4065094

Rui Mao, Zhenkun Lei, Jianyu Di, Yuxiang Shang, R. Bai, Cheng Yan

引用次数: 0

Lithium-ion battery capacity prediction method based on improved extreme learning machine 基于改进型极端学习机的锂离子电池容量预测方法

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-03-18 DOI: 10.1115/1.4065095

Zhengyu Liu, Zaijun Huang, Liandong Tang, Hao Wang

{"title":"Lithium-ion battery capacity prediction method based on improved extreme learning machine","authors":"Zhengyu Liu, Zaijun Huang, Liandong Tang, Hao Wang","doi":"10.1115/1.4065095","DOIUrl":"https://doi.org/10.1115/1.4065095","url":null,"abstract":"\u0000 Currently, research and applications in the field of capacity prediction mainly focus on the use and recycling of batteries, encompassing topics such as SOH estimation, RUL prediction, and echelon use. However, there is scant research and application based on capacity prediction in the battery manufacturing process. Measuring capacity in the grading process is an important step in battery production. The traditional capacity acquisition method consumes considerable time and energy. To address the above issues, this study establishes an improved extreme learning machine (ELM) model for predicting battery capacity in the manufacturing process, which can save approximately 45% of energy and time in the grading process. The study involves the extraction of features from the battery charge–discharge curve that can reflect battery capacity performance and subsequent calculation of the grey correlation between these features and capacity. The feature set comprises features with a high correlation with capacity, which are used as inputs for the ELM model. Kernel functions are used to adjust the ELM model, and Bayesian optimization methods are employed to automatically optimize the hyperparameters to improve the capacity prediction performance of the model. The study uses lithium-ion battery data from an actual manufacturing process to test the predictive effect of the model. The mean absolute percentage error of the capacity prediction results is less than 0.2%, and the root-mean-square error is less than 0.3 Ah.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reviewers Recognition 评审员表彰

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-02-07 DOI: 10.1115/1.4064680

Partha P. Mukherjee

引用次数: 0

Reviewers Recognition 评审员表彰

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-02-07 DOI: 10.1115/1.4064680

Partha P. Mukherjee

引用次数: 0

Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-ion Battery 基于比较-转移学习的锂离子电池健康状态估计

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-02-05 DOI: 10.1115/1.4064656

Wei Liu, Songchen Gao, Wendi Yan

{"title":"Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-ion Battery","authors":"Wei Liu, Songchen Gao, Wendi Yan","doi":"10.1115/1.4064656","DOIUrl":"https://doi.org/10.1115/1.4064656","url":null,"abstract":"\u0000 Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality requirements and poor model generalizability. And this paper proposes a comparison-transfer learning approach with cyclic synchronization to estimate the state of health of lithium-ion batteries. Firstly, a cyclic synchronization method based on the Bezier curve fitting algorithm is introduced to synchronize the data obtained at different charge-discharge cycles of the lithium-ion battery, facilitating input to the model. Secondly, a comparison-transfer network using Pearson correlation coefficient is proposed to transfer knowledge from the source dataset to predict the target dataset under different environmental temperatures. By training a pre-trained model on the source dataset and utilizing the correlation coefficient to analyze the similarity between the source and target datasets, the accumulated knowledge in the source dataset can be effectively utilized to enhance prediction performance on the target dataset. In the experiments, the proposed method is validated using the lithium-ion battery aging public datasets. The experimental results demonstrate that the proposed approach achieves superior prediction performance in the case of small sample sizes, exhibiting higher accuracy and stability compared to traditional deep learning methods.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139865327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-ion Battery 基于比较-转移学习的锂离子电池健康状态估计

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-02-05 DOI: 10.1115/1.4064656

Wei Liu, Songchen Gao, Wendi Yan

{"title":"Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-ion Battery","authors":"Wei Liu, Songchen Gao, Wendi Yan","doi":"10.1115/1.4064656","DOIUrl":"https://doi.org/10.1115/1.4064656","url":null,"abstract":"\u0000 Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality requirements and poor model generalizability. And this paper proposes a comparison-transfer learning approach with cyclic synchronization to estimate the state of health of lithium-ion batteries. Firstly, a cyclic synchronization method based on the Bezier curve fitting algorithm is introduced to synchronize the data obtained at different charge-discharge cycles of the lithium-ion battery, facilitating input to the model. Secondly, a comparison-transfer network using Pearson correlation coefficient is proposed to transfer knowledge from the source dataset to predict the target dataset under different environmental temperatures. By training a pre-trained model on the source dataset and utilizing the correlation coefficient to analyze the similarity between the source and target datasets, the accumulated knowledge in the source dataset can be effectively utilized to enhance prediction performance on the target dataset. In the experiments, the proposed method is validated using the lithium-ion battery aging public datasets. The experimental results demonstrate that the proposed approach achieves superior prediction performance in the case of small sample sizes, exhibiting higher accuracy and stability compared to traditional deep learning methods.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139805429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Black-Fe2O3 Polyhedron-Assembled 3D Film Electrode with Enhanced Conductivity and Energy Density for Aqueous Solid-State Energy Storage 黑色-Fe2O3 多面体组装三维薄膜电极具有更强的导电性和能量密度，可用于水体固态储能

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2023-12-29 DOI: 10.1115/1.4064380

Yi Xing, Xiaoyu Sun, Wentian Chen, Xiaoqing Ma, Zirui Huang, Minglian Li, Wenfeng Guo, Yuqian Fan

{"title":"Black-Fe2O3 Polyhedron-Assembled 3D Film Electrode with Enhanced Conductivity and Energy Density for Aqueous Solid-State Energy Storage","authors":"Yi Xing, Xiaoyu Sun, Wentian Chen, Xiaoqing Ma, Zirui Huang, Minglian Li, Wenfeng Guo, Yuqian Fan","doi":"10.1115/1.4064380","DOIUrl":"https://doi.org/10.1115/1.4064380","url":null,"abstract":"The construction of advanced Fe2O3 materials with high energy density for energy storage faces challenges due to the defects of conventional widely-known red-brown Fe2O3 such as poor electronic conductivity and insufficient physical/chemical stability. Unlike previous work, we successfully synthesize a novel black Fe2O3 (B-Fe2O3) thin film electrode by adopting simple hydrothermal strategy. Physical characterizations indicate that the as-made B-Fe2O3 product is composed of polyhedrons (mainly exhibit 4-8 sides) with a micrometer grade size range. Besides, the Fe-based thin film electrode with this 3D structure has stronger affinity and high electronic conductivity. As anode of aqueous solid-state energy storage devices, the as-synthesized B-Fe2O3 film electrode exhibits excellent volume energy density of 14.349 kWh m−3 at power density of 1609 kW m−3, which is much higher than the best result of previous works (∼8 kWh m−3). This study may provide new insights into the development of the Fe2O3 series on developing high-efficiency Fe-based anode materials for solid-state energy storage.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Critical Review of Hydrogen Production via Seawater Electrolysis and Desalination: Evaluating Current Practices 海水电解和脱盐制氢批判性评论：评估当前做法

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2023-12-29 DOI: 10.1115/1.4064381

Giorgos Varras, Michail Chalaris

{"title":"Critical Review of Hydrogen Production via Seawater Electrolysis and Desalination: Evaluating Current Practices","authors":"Giorgos Varras, Michail Chalaris","doi":"10.1115/1.4064381","DOIUrl":"https://doi.org/10.1115/1.4064381","url":null,"abstract":"The pursuit of sustainable and clean energy solutions has led to increased interest in hydrogen as an efficient energy carrier. This paper presents a comprehensive analysis of state-of-the-art technologies for hydrogen production through seawater electrolysis and desalination, addressing the critical need for clean energy generation and sustainable water supply. It emphasizes the importance of hydrogen as a versatile and environmentally friendly energy source, as well as the significance of seawater desalination. The analysis includes a comparison of three electrolysis technologies: solid oxide electrolysis (SOE), alkaline electrolyzers (AE), and proton exchange membrane (PEM) electrolysis. Factors such as energy requirements, capital and maintenance costs, and offshore suitability are considered, facilitating an informed evaluation of the most suitable electrolysis method for seawater hydrogen production. Additionally, three desalination technologies are evaluated: reverse osmosis (RO), thermal desalination, and membrane desalination. The assessment takes into account investment and operation costs, energy demand, and environmental impact, providing insights into the feasibility and sustainability of integrating hydrogen production with seawater desalination. This condensed review provides a holistic perspective on the techno-economic viability, energy efficiency, and environmental sustainability of various technologies, enabling informed decision-making towards a more sustainable and resilient energy-water nexus. Overall, this study contributes to the growing body of knowledge on hydrogen production and seawater desalination, offering insights that can inform strategic planning, policy development, and technological advancements in achieving a greener and more sustainable future.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Internal temperature estimation of lithium-ion battery based on improved electro-thermal coupling model and ANFIS 基于改进电热耦合模型和 ANFIS 的锂离子电池内部温度估算

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2023-12-28 DOI: 10.1115/1.4064353

Jianping Wen, Zhensheng Li, Haodong Zhang, Chuanwei Zhang

{"title":"Internal temperature estimation of lithium-ion battery based on improved electro-thermal coupling model and ANFIS","authors":"Jianping Wen, Zhensheng Li, Haodong Zhang, Chuanwei Zhang","doi":"10.1115/1.4064353","DOIUrl":"https://doi.org/10.1115/1.4064353","url":null,"abstract":"Accurate estimation of the internal temperature of lithium-ion batteries plays an important role in the development of a suitable battery thermal management system, safeguarding the healthy and safe operation of batteries, and improving battery performance. In order to accurately estimate the internal temperature of the battery, this paper proposes a method for estimating the internal temperature of lithium-ion batteries based on an improved electro-thermal coupling model and an Adaptive Network-based Fuzzy Inference System (ANFIS). First, a parameterization method of the electrical model is proposed, and an electrical model whose parameters are affected by temperature and SOC is established. Second, to overcome the complex nonlinear modeling problem of lithium-ion batteries, the ANFIS thermal model is established. Then, an improved electro-thermal coupling model for lithium-ion batteries is established by combining the proposed electrical model and the ANFIS thermal model to improve the accuracy of estimating the internal temperature of the battery. Finally, the effectiveness of the proposed method is verified by simulation and experiment.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A High Ceramic Loading LATP-PVDF-Al2O3 Composite Film for Lithium-ion Batteries with Favorable Porous Microstructure and Enhanced Thermal Stability 一种用于锂离子电池的高陶瓷负载 LATP-PVDF-Al2O3 复合薄膜，具有良好的多孔微结构和更高的热稳定性

IF 2.5 4区工程技术

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2023-12-21 DOI: 10.1115/1.4064352

Yu Gu, Chris Yuan

{"title":"A High Ceramic Loading LATP-PVDF-Al2O3 Composite Film for Lithium-ion Batteries with Favorable Porous Microstructure and Enhanced Thermal Stability","authors":"Yu Gu, Chris Yuan","doi":"10.1115/1.4064352","DOIUrl":"https://doi.org/10.1115/1.4064352","url":null,"abstract":"\u0000 A separator plays a crucial role in a Li-ion battery to carry liquid electrolytes while preventing short-circuiting between electrodes. Nevertheless, conventional commercial separators often exhibit poor wettability and are prone to shrink at elevated temperatures due to their limited thermal stability. Herein, we report a heat-resistant LATP-PVDF-Al2O3 composite film with outstanding wetting performance. The thin film was prepared using ball-mill mixing and tape-casting processes. Two solvents NMP and glycerol were applied to prepare the slurry and a favorable microstructure in the film was created after drying. The ionic conductivity of the film was tested at 1.39 mS cm−1 when paired with liquid electrolyte, almost double that of the commercial counterpart. The high ceramic loading of 70% improved both the thermal shrinkage resistance and dendrite inhibition of the membrane. When assembled in an NMC half-cell, the cycling capacity retentions of 92.8% and 92.1% are achieved after 50 cycles at 0.5 C and 1 C, demonstrating its capability to be used in Li-ion batteries.","PeriodicalId":15579,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0