Jian Wang, Lijun Zhu, Xiaoyu Liu, Yutao Wang, Lujun Wang
Employed extensively for lithium-ion battery health assessment and capacity estimation, Incremental Capacity Analysis (ICA) traditionally requires substantial time investment under standard charge and discharge conditions. However, in practical usage, Li-ion batteries rarely undergo full cycles. This study introduces aging temperature cycles within different partial intervals of the battery, integrating local ICA curves, peak range analysis, and Incremental Slope (IS) as an auxiliary feature. The extracted partial incremental capacity curves serve as features for State of Health (SOH) estimation. The proposed temperature-rate-based SOH estimation method relies on a mechanistic function, analyzing relationships between temperature, different partial intervals, aging rate, and aging. Experimental tests on FCB21700 batteries demonstrate accurate SOH estimation using only partial charge curves, with an average error below 2.82%. By manipulating charging and discharging ranges, the method significantly extends battery lifespan, offering promising widespread applications.
{"title":"Online capacity estimation for lithium-ion batteries in partial intervals considering charging conditions","authors":"Jian Wang, Lijun Zhu, Xiaoyu Liu, Yutao Wang, Lujun Wang","doi":"10.1115/1.4066190","DOIUrl":"https://doi.org/10.1115/1.4066190","url":null,"abstract":"\u0000 Employed extensively for lithium-ion battery health assessment and capacity estimation, Incremental Capacity Analysis (ICA) traditionally requires substantial time investment under standard charge and discharge conditions. However, in practical usage, Li-ion batteries rarely undergo full cycles. This study introduces aging temperature cycles within different partial intervals of the battery, integrating local ICA curves, peak range analysis, and Incremental Slope (IS) as an auxiliary feature. The extracted partial incremental capacity curves serve as features for State of Health (SOH) estimation. The proposed temperature-rate-based SOH estimation method relies on a mechanistic function, analyzing relationships between temperature, different partial intervals, aging rate, and aging. Experimental tests on FCB21700 batteries demonstrate accurate SOH estimation using only partial charge curves, with an average error below 2.82%. By manipulating charging and discharging ranges, the method significantly extends battery lifespan, offering promising widespread applications.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"52 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922945","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}
Vinay Premnath, Mohammad Parhizi, Nicholas Niemiec, Ian Smith, Judith A. Jeevarajan
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.
{"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}
Weiwei Huo, Aobo Wang, Bing Lu, Yunxu Jia, Chen Li
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%.
{"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}
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.
电池的健康状态（SOH）是衡量电池寿命的主要指标。为了提高 SOH 估算的准确性，本文提出了一种利用特征重构和改进的最小二乘支持向量机进行锂离子电池健康状态估算的模型框架。首先，通过主成分分析（PCA）处理从充电和放电阶段提取的多个健康特征，去除多个特征之间的信息冗余，从而得到间接健康特征（HF）；然后利用变异模态分解（VMD）得到多个不同频率的平滑分量子序列，有效捕捉数据的整体下降趋势和再生波动。然后利用麻雀搜索算法（SSA）优化最小二乘支持向量机（LSSVM）建立估计模型，并预测和叠加多个特征子序列的重构融合特征，再利用重构 HI 与 SOH 之间的映射关系进行估计。美国国家航空航天局（NASA）和马里兰大学（CACLE）的电池数据集（CACLE）用于对不同循环间隔的多个电池进行验证测试。结果表明，平均绝对误差（MAE）和均方根误差（RMSE）均小于 1%，该方法具有较高的估计精度和鲁棒性。
{"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}
Juan Wang, Yonggang Ye, Minghui Wu, Fan Zhang, Ye Cao, Zetao Zhang, Ming Chen, Jing Tang
To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on temporal convolutional autoencoder (TCAE) that can quickly and accurately identify abnormal power battery data was proposed. Its encoder utilized a temporal convolutional network (TCN) structure with residuals to parallelly process data while capturing time dependencies. A novel TCN structure with an effect–cause relationship was developed for the decoder. The same-time-scale connection was established between the encoder and decoder to improve the model performance. The validity of the proposed model was confirmed using a real-world car dataset. Compared with the GRU-AE model, the proposed approach reduced the parameter count and mean square error by 19.5% and 71.9%, respectively. This study provides insights into the intelligent battery pack abnormality detection technology.
{"title":"Unsupervised anomaly detection for power batteries: A temporal convolution autoencoder framework","authors":"Juan Wang, Yonggang Ye, Minghui Wu, Fan Zhang, Ye Cao, Zetao Zhang, Ming Chen, Jing Tang","doi":"10.1115/1.4065445","DOIUrl":"https://doi.org/10.1115/1.4065445","url":null,"abstract":"\u0000 To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on temporal convolutional autoencoder (TCAE) that can quickly and accurately identify abnormal power battery data was proposed. Its encoder utilized a temporal convolutional network (TCN) structure with residuals to parallelly process data while capturing time dependencies. A novel TCN structure with an effect–cause relationship was developed for the decoder. The same-time-scale connection was established between the encoder and decoder to improve the model performance. The validity of the proposed model was confirmed using a real-world car dataset. Compared with the GRU-AE model, the proposed approach reduced the parameter count and mean square error by 19.5% and 71.9%, respectively. This study provides insights into the intelligent battery pack abnormality detection technology.","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":"58 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016690","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}
Lujun Wang, Xiankai Zeng, Long Chen, Lu Lv, Li Liao, Jiuchun Jiang
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.
{"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}
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.
{"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}
Lin Chen, Mingsi Zhao, Manping He, Deqian Chen, Yunhui Ding, H. Pan
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.
{"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}
Xiangyu Yin, Zhen Liu, Xinyi Li, Meili Qi, Ming Hu, Xin Mu
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.
由于具有出色的理论比容量，过渡金属氧化物 Fe2O3 因其作为阴极材料的潜力而备受关注。然而，Fe2O3 仍然存在导电率低和在充放电过程中体积膨胀大的缺点。在本实验中，我们报道了一种通过水热法在碳布（Fe2O3@CC）上生长 Fe2O3 纳米线阵列的简便策略。制备的 Fe2O3@CC 复合材料可用作 LIB 和超级电容器的电极。在此，我们利用上述支撑在碳布上的独特的 Fe2O3@CC 纳米线阵列复合材料作为具有高比表面积的重复性和定向性阳极电极复合材料。在电流密度为 200 mA g-1 时，超级电容器在循环 500 次后显示出 221.19 F g-1 的比电容。将 Fe2O3@CC 纳米线复合材料用于 LIB 中，在 500 mA g-1 的高电流密度下显示出 240.7 mAh g-1 的超高速率容量，以及在 100 mA g-1 下循环 100 次后 514.1 mAh g-1 的高可逆容量。
{"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}
{"title":"Reviewer’s Recognition","authors":"","doi":"10.1115/1.4046425","DOIUrl":"https://doi.org/10.1115/1.4046425","url":null,"abstract":"","PeriodicalId":508445,"journal":{"name":"Journal of Electrochemical Energy Conversion and Storage","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141223334","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}
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