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Battery impedance spectrum prediction from partial charging voltage curve by machine learning

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Journal of Energy chemistry 2023年第4期79卷 211-221页

作者： Jia Guo yunhong che Kjeld Pedersen Daniel-Ioan Stroe AAU Energy Aalborg UniversityAalborg 9220Denmark Department of Materials and Production Aalborg UniversityAalborg 9220Denmark

Electrochemical impedance spectroscopy(EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery impedance testing during vehicle operation. However, the mechanistic relationship between charging curves and impedance spectrum remains unclear, which hinders the development as well as optimization of EIS-based prediction techniques. In this paper, we predicted the impedance spectrum by the battery charging voltage curve and optimized the input based on electrochemical mechanistic analysis and machine learning. The internal electrochemical relationships between the charging curve,incremental capacity curve, and the impedance spectrum are explored, which improves the physical interpretability for this prediction and helps define the proper partial voltage range for the input for machine learning models. Different machine learning algorithms have been adopted for the verification of the proposed framework based on the sequence-to-sequence predictions. In addition, the predictions with different partial voltage ranges, at different state of charge, and with different training data ratio are evaluated to prove the proposed method have high generalization and robustness. The experimental results show that the proper partial voltage range has high accuracy and converges to the findings of the electrochemical analysis. The predicted errors for impedance spectrum are less than 1.9 mΩ with the proper partial voltage range selected by the corelative analysis of the electrochemical reactions inside the batteries. Even with the voltage range reduced to 3.65–3.75 V, the predictions are still reliable with most RMSEs less than 4 mO.

关键词： Impedance spectrum prediction Lithium-ion battery Machine learning EIS Graphite anode

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

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Chinese Journal of Mechanical Engineering 2022年第1期35卷 192-207页

作者： yunhong che Zhongwei Deng Xiaolin Tang Xianke Lin Xianghong Nie Xiaosong Hu College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China State Key Laboratory of Mechanical Transmissions Chongqing UniversityChongqing 400044China Department of Automotive Mechanical and Manufacturing EngineeringOntario Tech UniversityOshawaON L1G 0C5Canada Powertrain Engineering R&D Institute Chongqing Changan Automotive Co.LtdChongqing 401133China

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe *** paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process *** health indicators are extracted from the partial discharge *** sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation *** future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime ***,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model *** results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging ***,it largely reduces the time and labor for battery pack *** predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.

关键词： Lithium-ion battery packs Lifetime prediction Degradation prognostic Model migration Machine learning

Joint Estimation of Inconsistency and State of Health for Series Battery Packs

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Automotive Innovation 2021年第1期4卷 103-116页

作者： yunhong che Aoife Foley Moustafa El‑Gindy Xianke Lin Xiaosong Hu Michael Pecht Department of Automotive Engineering Chongqing UniversityChongqing 400044China School of Mechanical&Aerospace Engineering Queen’s UniversityBelfast BT95AHUK School of Engineering Trinity College DublinUniversity of DublinCollege GreenDublin 2Ireland Department of Automotive Mechanical and Manufacturing EngineeringOntario Tech University2000 Simcoe St NOshawaON L1G 0C5Canada Center for Advanced Life Cycle Engineering University of MarylandCollege ParkMD 20742USA

Battery packs are applied in various areas(e.g.,electric vehicles,energy storage,space,mining,etc.),which requires the state of health(SOH)to be accurately ***,also known as cell variation,is considered a significant evaluation index that greatly affects the degradation of battery *** paper proposes a novel joint inconsistency and SOH estimation method under cycling,which fills the gap of joint estimation based on the fast-charging process for electric ***,fifteen features are extracted from current change points during the partial charging ***,a joint estimation system is designed,where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency.A wrapper is used to select the optimal feature subset,and Gaussian process regression is implemented to estimate the ***,the estimation performance is assessed by the test *** results show that the inconsistency evaluation can reflect the aging conditions,and the inconsistency does affect the aging *** wrapper selection method improves the accuracy of SOH estimation by about 75.8%compared to the traditional filter method when only 10%of data is used for model *** maximum absolute error and root mean square error are 2.58%and 0.93%,respectively.

关键词： Battery pack inconsistency State of health Fusion weight Feature selection GPR

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基于数据驱动的电池系统泛化SOH估计方法

机械工程学报 2022年第24期58卷 253-263页

作者：车云弘邓忠伟李佳承谢翌胡晓松重庆大学机械与运载工程学院重庆400044 重庆大学机械传动国家重点实验室重庆400044

准确可靠的电池健康状态估计是保证锂离子电池安全运行的关键,同时为失效预警提供参考。提出一种适用于电池单体和电池组的健康状态估计通用方法。首先,提出基于局部充放电数据的电池单体高效健康因子提取方法,保证健康因子和容量的高... 详细信息

准确可靠的电池健康状态估计是保证锂离子电池安全运行的关键,同时为失效预警提供参考。提出一种适用于电池单体和电池组的健康状态估计通用方法。首先,提出基于局部充放电数据的电池单体高效健康因子提取方法,保证健康因子和容量的高相关性和实现健康因子的在线可获取性。其次,提出考虑电池组容量衰减和不一致性的特征生成策略,利用主成分分析获取融合特征,利用双时间尺度滤波和电池组等效电路模型拓宽特征提取方法的应用范围。然后,基于高斯过程回归算法框架,考虑健康因子和容量衰减的整体关系和局部变化提出改进的高斯核函数提高估计精度和可靠性。最后,利用多个试验数据集验证算法在不同应用条件下的泛化能力。估计结果表明,对恒流放电工况的电池单体估计误差小于1.28%,在动态变温条件下电池单体估计误差小于1.82%;串联电池组的验证结果表明在各种应用场景下估计误差均小于1.43%。提高了电池系统健康状态估计的精度以及在广泛应用场景下的适应性。

关键词：锂离子电池健康状态估计健康因子高斯过程回归状态估计

Recent Progress in Atmospheric chemistry Research in China: Establishing a Theoretical Framework for the “Air Pollution Complex”

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Advances in Atmospheric Sciences 2023年第8期40卷 1339-1361页

作者： Tong ZHU Mingjin TANG Meng GAO Xinhui BI Junji CAO Huizheng che Jianmin cheN Aijun DING Pingqing FU Jian GAO Yang GAO Maofa GE Xinlei GE Zhiwei HAN Hong HE Ru-Jin HUANG Xin HUANG Hong LIAO cheng LIU Huan LIU Jianguo LIU Shaw chen LIU Keding LU Qingxin MA Wei NIE Min SHAO Yu SONG Yele SUN Xiao TANG Tao WANG Tijian WANG Weigang WANG Xuemei WANG Zifa WANG Yan YIN Qiang ZHANG Weijun ZHANG Yanlin ZHANG yunhong ZHANG Yu ZHAO Mei ZHENG Bin ZHU Jiang ZHU Peking University Beijing 100871China Guangzhou Institute of Geochemistry Chinese Academy of SciencesGuangzhou 510640China Hong Kong Baptist University Hong Kong SARChina Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Chinese Academy of Meteorological Sciences Beijing 100081China Fudan University Shanghai 200438China Nanjing University Nanjing 210023China Tianjin University Tianjin 300072China Chinese Research Academy of Environmental Sciences Beijing 100012China Ocean University of China Qingdao 266100China Institute of chemistry Chinese Academy of SciencesBeijing 100190China Nanjing University of Information Science and Technology Nanjing 210044China Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China Institute of Earth Environment Chinese Academy of SciencesXi’an 710061China University of Science and Technology of China Hefei 230026China Tsinghua University Beijing 100084China Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Jinan University Guangzhou 510632China Hong Kong Polytechnic University Hong Kong SARChina Beijing Institute of Technology Beijing 100081China

Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the“air pollution complex”was first proposed by Professor Xiaoyan TANG in *** papers published in 2021 on air pollution(only papers included in the Web of Science Core Collection database were considered),more than 24000 papers were authored or co-authored by scientists working in *** this paper,we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years,including studies on(1)sources and emission inventories,(2)atmospheric chemical processes,(3)interactions of air pollution with meteorology,weather and climate,(4)interactions between the biosphere and atmosphere,and(5)data *** intention was not to provide a complete review of all progress made in the last few years,but rather to serve as a starting point for learning more about atmospheric chemistry research in *** advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established,provided robust scientific support to highly successful air pollution control policies in China,and created great opportunities in education,training,and career development for many graduate students and young *** paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances,whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China,to hopefully be addressed over the next few decades.

关键词： atmospheric chemistry air pollution complex theoretical framework recent progress

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硼镁肥料及其国家标准编制进展

化肥工业 2017年第3期44卷 21-25页

作者：杨云洪王敏房朋张荣阳车太龙张翠玲李长龙田晓美营口菱镁化工集团有限公司辽宁营口115100 中国科学院青海盐湖研究所青海西宁810008 上海化工研究院有限公司上海200062

硼镁肥料是一种新型中微量元素肥料,可为作物提供硼、镁等营养元素,在提高作物产量和改善作物品质方面发挥了重要作用。概述了硼镁肥料的功能及主要生产工艺,介绍了硼镁肥料国家标准的主要内容及编制进展情况。

关键词：硼镁肥料国家标准编制进展

基于白云石矿采用化学合成法制备硫基钙镁肥料工艺研究及其在苹果上的应用效果

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生态产业科学与磷氟工程 2024年第12期39卷 29-34页

作者：李红杨云洪王新李长龙李晓燕车太龙营口菱镁化工集团有限公司辽宁省镁素营养专业技术创新中心

以煅烧白云石矿粉和硫酸为原料，采用化学合成法直接制备硫基钙镁肥料，研究硫酸用量、硫酸浓度、煅烧白云石矿粉细度、反应温度、熟化时间、搅拌转速与时间对化学合成过程钙、镁转化率的影响，并采用X射线衍射仪对产物进行表征。结果... 详细信息

以煅烧白云石矿粉和硫酸为原料，采用化学合成法直接制备硫基钙镁肥料，研究硫酸用量、硫酸浓度、煅烧白云石矿粉细度、反应温度、熟化时间、搅拌转速与时间对化学合成过程钙、镁转化率的影响，并采用X射线衍射仪对产物进行表征。结果表明，最佳的化学合成工艺条件为：硫酸用量为白云石矿粉质量的120%～140%、硫酸w（H2SO4） 64%～68%、煅烧白云石矿粉细度0.125～0.150 mm（通过率≥95%）、反应温度（120±5）℃、熟化时间≥150 min、搅拌转速150 r/min、搅拌时间3 min，在此工艺条件下，实现钙、镁转化率达90%以上。田间试验结果表明，在常规施肥的基础上，施硫基钙镁肥料400～500 g/株，苹果的果实品质、产量及经济效益明显提高。

关键词：白云石矿化学合成法硫基钙镁肥料钙、镁转化率应用效果