Journal of Electrochemical Energy Conversion and Storage最新文献

Online capacity estimation for lithium-ion batteries in partial intervals considering charging conditions 考虑充电条件的部分间隔锂离子电池在线容量估算

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-08-09 DOI: 10.1115/1.4066190

Jian Wang, Lijun Zhu, Xiaoyu Liu, Yutao Wang, Lujun Wang

引用次数: 0

Characterization of Particulate Emissions from Thermal Runaway of Lithium-ion Cells 锂离子电池热失控产生的微粒排放特征分析

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-07-12 DOI: 10.1115/1.4065938

Vinay Premnath, Mohammad Parhizi, Nicholas Niemiec, Ian Smith, Judith A. Jeevarajan

{"title":"Characterization of Particulate Emissions from Thermal Runaway of Lithium-ion Cells","authors":"Vinay Premnath, Mohammad Parhizi, Nicholas Niemiec, Ian Smith, Judith A. Jeevarajan","doi":"10.1115/1.4065938","DOIUrl":"https://doi.org/10.1115/1.4065938","url":null,"abstract":"\u0000 Over the past decade, there has been a significant acceleration in the adoption of lithium-ion (li-ion) batteries for various applications, ranging from portable electronics to automotive, defense, and aerospace applications. Lithium-ion batteries are the most used energy storage technologies due to their high energy densities and capacities. However, this battery technology is a potential safety hazard under off-nominal conditions, which may result in thermal runaway events. Such events can release toxic gaseous and particulate emissions, posing a severe risk to human health and the environment. Particulate emissions from the failure of two different cell chemistries – lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC) were studied. Experiments were conducted at multiple states of charge (SOC), and three repeats were conducted at each SOC for each cell chemistry to examine the repeatability/variability of these events. Particulate emissions were characterized in terms of particulate matter mass (PM2.5), black carbon, and particle number (PN)/size. Failure of a single cell led to a significant release of particulate emissions, with peak emission levels being higher at the higher SOCs. A high level of variability was observed for a specific SOC for LFP cells, while NMCs exhibited relatively less variability. In general, much higher particulate emissions were observed for NMCs compared to LFPs at each SOC. For NMCs at 100% SOC, peak PN levels were ∼2.5E+09 particles/cc (part/cc), and black carbon levels were ∼60 mg/m3. For LFPs at 100% SOC, peak PN levels were ∼9.0E+08 part/cc, and black carbon levels were 2.5 mg/m3.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"81 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid data-driven method based on data preprocessing to predict the remaining useful life of lithium-ion batteries 基于数据预处理的混合数据驱动法预测锂离子电池的剩余使用寿命

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-07-01 DOI: 10.1115/1.4065862

Weiwei Huo, Aobo Wang, Bing Lu, Yunxu Jia, Chen Li

{"title":"A hybrid data-driven method based on data preprocessing to predict the remaining useful life of lithium-ion batteries","authors":"Weiwei Huo, Aobo Wang, Bing Lu, Yunxu Jia, Chen Li","doi":"10.1115/1.4065862","DOIUrl":"https://doi.org/10.1115/1.4065862","url":null,"abstract":"\u0000 The estimation of remaining useful life (RUL) for lithium-ion batteries is an essential part for battery management system (BMS). A hybrid method is presented which is combining principal component analysis (PCA), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sparrow search algorithm (SSA), Elman neural network (Elman-NN), and gaussian process regression (GPR) to forecast battery RUL. Firstly, in the data preprocessing stage, the PCA+ICEEMDAN algorithm is creatively proposed to extract features of capacity decay and fluctuation. The PCA method is used to reduce the dimensionality of the extracted indirect health indicators (HIs), and then the ICEEMDAN algorithm is introduced to decompose the fused HI sequence and actual capacity data into residuals and multiple Intrinsic mode functions (IMFs). Secondly, in the prediction stage, feature data is corresponded one to-one with the mixed model. The prediction models of SSA-Elman algorithm and GPR algorithm are established, with the SSA-Elman algorithm predicting the capacity decay trend and the GPR algorithm quantifying the uncertainty caused by the capacity regeneration phenomenon. The final prediction results are obtained by superimposing the two sets of prediction data, and the prediction error and RUL are calculated. The effectiveness of the proposed hybrid approach is validated by RUL prediction experiments on three kinds of batteries. The comparative experimental results indicate that the mean absolute error (MAE) and root mean square error (RMSE) of the presented prediction model for lithium-ion battery capacity are less than 0.7% and 1.0%.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"21 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lithium-ion battery health state estimation based on feature reconstruction and optimized least squares support vector machine 基于特征重构和优化最小二乘支持向量机的锂离子电池健康状态估计

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-06-04 DOI: 10.1115/1.4065666

Tiezhou Wu, Jian Kang, Junchao Zhu, Te Tu

{"title":"Lithium-ion battery health state estimation based on feature reconstruction and optimized least squares support vector machine","authors":"Tiezhou Wu, Jian Kang, Junchao Zhu, Te Tu","doi":"10.1115/1.4065666","DOIUrl":"https://doi.org/10.1115/1.4065666","url":null,"abstract":"\u0000 The state of health (SOH) of a battery is the main indicator of battery life. In order to improve the SOH estimation accuracy, a model framework for lithium-ion battery health state estimation with feature reconstruction and improved least squares support vector machine is proposed. Firstly, the indirect health features (HF) are obtained by processing multiple health features extracted from the charging and discharging phases through principal component analysis (PCA) to remove the information redundancy among multiple features; then multiple smooth component subsequences of different frequencies are obtained by using variational modal decomposition (VMD) to efficiently capture the overall downtrend and regeneration fluctuations of the data. Then we use the Sparrow Search Algorithm (SSA) to optimize the Least Squares Support Vector Machine (LSSVM) to build an estimation model, and then predict and superimpose the reconstructed fusion features of multiple feature subsequences, and then use the mapping relationship between the reconstructed HI and the SOH for the estimation . The NASA and University of Maryland (CACLE) battery dataset(CACLE) is used to perform validation tests on multiple batteries with different cycle intervals. The results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are less than 1% and the method has high estimation accuracy and robustness.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised anomaly detection for power batteries: A temporal convolution autoencoder framework 动力电池的无监督异常检测：时间卷积自动编码器框架

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-05-03 DOI: 10.1115/1.4065445

Juan Wang, Yonggang Ye, Minghui Wu, Fan Zhang, Ye Cao, Zetao Zhang, Ming Chen, Jing Tang

引用次数: 0

An Active Equalization Method for Cascade Utilization Lithium Battery Pack With Online Measurement of Electrochemical Impedance Spectroscopy 利用电化学阻抗光谱在线测量级联利用锂电池组的主动均衡方法

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-03-28 DOI: 10.1115/1.4065196

Lujun Wang, Xiankai Zeng, Long Chen, Lu Lv, Li Liao, Jiuchun Jiang

{"title":"An Active Equalization Method for Cascade Utilization Lithium Battery Pack With Online Measurement of Electrochemical Impedance Spectroscopy","authors":"Lujun Wang, Xiankai Zeng, Long Chen, Lu Lv, Li Liao, Jiuchun Jiang","doi":"10.1115/1.4065196","DOIUrl":"https://doi.org/10.1115/1.4065196","url":null,"abstract":"\u0000 With the rapid development of new energy vehicles, a large number of lithium batteries have been produced, used and retired. The full utilization and safe use of the whole life cycle of the batteries have become a hot topic in the research field. Compared to brand new batteries, retired power batteries exhibit significant inconsistency and safety risks due to aging, thus necessitating effective battery equalization and safety monitoring methods. In this article, an active equalization method for cascade utilization lithium battery pack with online measurement of electrochemical impedance spectroscopy is proposed to actively equalize the retired battery pack and alleviate the inconsistency of the battery pack. Besides, the electrochemical impedance spectrum of the single battery is measured online without adding additional hardware circuit, so as to realize real-time safety monitoring and solve the safety problem of the battery. Finally, in order to verify the feasibility of the active equalization and electrochemical impedance spectrum monitoring scheme designed in this article, a simulation model is built based on MATLAB-Simulink platform. The simulation results show that the six batteries in the proposed scheme model complete the active equalization in about 710s, 850s and 740s respectively in the balance mode, charge mode and discharge mode, and the electrochemical impedance spectrum in the frequency range of 1-20KHz can be successfully measured.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"24 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analytical modelling of water droplet behavior at the gas channel corner for proton exchange membrane fuel cells 质子交换膜燃料电池气体通道拐角处水滴行为的分析建模

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-02-27 DOI: 10.1115/1.4064848

Diankai Qiu, Zhutian Xu, H. Shao, Linfa Peng

{"title":"Analytical modelling of water droplet behavior at the gas channel corner for proton exchange membrane fuel cells","authors":"Diankai Qiu, Zhutian Xu, H. Shao, Linfa Peng","doi":"10.1115/1.4064848","DOIUrl":"https://doi.org/10.1115/1.4064848","url":null,"abstract":"\u0000 Water management is of significant importance to achieving high performance of proton exchange membrane fuel cells. In recent years, droplets emerged from the rib surface and accumulated at the channel corner have been found to be a crucial part of water flooding. In this study, an analytical model is first proposed to quantitatively estimate the variation in the morphology and dynamic behavior of growing droplets with consideration of the channel sidewall interaction. In order to predict the water geometry, the flow channel with compressed gas diffusion layer (GDL) is described mathematically, and water behavior at steady state and dynamic state are both evaluated through the geometric and force analysis. The model results indicate that the droplet profile transforms from concave to convex when its size grows, in which process contact angles and channel shape play an important role. Compared with the graphite channel, the droplet in the metallic channel is more inclined to be adsorbed on the sidewall and GDL, resulting in a higher adhesion force and a lower gas shear force. The critical gas velocities for the detachment of various droplets are quantitatively predicted to avoid water flooding. The model is helpful to understand the droplet behavior in the presence of channel sidewall interaction.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"27 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An electrochemical-thermal coupling model based on two-factor parameter modification for Li-ion battery 基于双因子参数修正的锂离子电池电化学-热耦合模型

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-02-27 DOI: 10.1115/1.4064847

Lin Chen, Mingsi Zhao, Manping He, Deqian Chen, Yunhui Ding, H. Pan

{"title":"An electrochemical-thermal coupling model based on two-factor parameter modification for Li-ion battery","authors":"Lin Chen, Mingsi Zhao, Manping He, Deqian Chen, Yunhui Ding, H. Pan","doi":"10.1115/1.4064847","DOIUrl":"https://doi.org/10.1115/1.4064847","url":null,"abstract":"\u0000 The accurate establishment of battery model can improve the design reliability and reduce the design risk, which provides an important basis for the research of battery. Firstly, the key parameters of the Li-ion battery model were identified by the least square method based on the full-cell equivalent circuit model of single-particle impedance spectrum, and the diffusion coefficient and exchange current density under different temperature and SOC conditions were calculated. At the same time, the one-dimension thermal rate model is used as the heat source of the three-dimensional model, and the mean temperature T of the three-dimensional model is calculated by using Fourier's law, and T is fed back to the one-dimensional model as the key parameter to modify the conductivity, diffusion coefficient and exchange current density, and a semi-empirical electrochemical-thermal coupling model with two-factor parameter modification is established. Finally, the model is verified by the temperature field distribution and discharge voltage curve at different discharge rates. The maximum temperature difference is less than 3.1 °C, and the maximum voltage difference error is less than 0.131V. The results show that the improved model can accurately reflect the influence of temperature on the model parameters, and has high accuracy in the estimation of battery terminal voltage and SOC.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"63 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart construction of Fe2O3 nanowire arrays on carbon cloth for enhanced supercapacitor and lithium-ion battery 在碳布上智能构建用于增强超级电容器和锂离子电池的 Fe2O3 纳米线阵列

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2024-01-30 DOI: 10.1115/1.4064603

Xiangyu Yin, Zhen Liu, Xinyi Li, Meili Qi, Ming Hu, Xin Mu

{"title":"Smart construction of Fe2O3 nanowire arrays on carbon cloth for enhanced supercapacitor and lithium-ion battery","authors":"Xiangyu Yin, Zhen Liu, Xinyi Li, Meili Qi, Ming Hu, Xin Mu","doi":"10.1115/1.4064603","DOIUrl":"https://doi.org/10.1115/1.4064603","url":null,"abstract":"\u0000 Due to its excellent theoretical specific capacity, the transition metal oxide Fe2O3 has garnered significant attention due to its potential as a cathode material. Nevertheless, Fe2O3 remains the drawback of low the electrical conductivity and significant volume expansion in the charge and discharge process. In this experiment, we have reported a facile strategy for Fe2O3 nanowire array grown on carbon cloth (Fe2O3@CC) by hydrothermal method. The prepared Fe2O3@CC composite was served as an electrode for LIBs and supercapacitors. Herein, we utilized above-mentioned unique composite of Fe2O3@CC nanowire array supported on carbon cloth as repetitive and directional composite of anode electrode composite with high specific surface area. The supercapacitors exhibited a specific capacitance of 221.19 F g−1 after 500 cycles at a current density of 200 mA g−1. Fe2O3@CC nanowire composite was utilized in LIBs, demonstrating exceptional rate capacity of 240.7 mAh g−1 at a high current density of 500 mA g−1, as well as a high reversible capacity of 514.1 mAh g−1 after 100 cycles at 100 mA g−1.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"144 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140482109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reviewer’s Recognition 评审员表彰

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2020-03-11 DOI: 10.1115/1.4046425

引用次数: 0