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Operation-area-constrained Adaptive Primary Frequency Support Strategy for Electric Vehicle Clusters

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1982-1994页

作者： Tianqi Liu Pengyu Wang Qiao Peng Min Zhang Tengxin Wang jinhao meng College of Electrical Engineering Sichuan UniversityChengdu 610065China Electric Power Research Institute State Grid Shanxi Electric Power CompanyTaiyuan 030001China

Due to their fast response and strong short-term power throughput capacity, electric vehicles(EVs) are promising for providing primary frequency support to power grids. However, due to the complicated charging demands of drivers, it is challenging to efficiently utilize the regulation capacity of EV clusters for providing stable primary frequency support to the power grid. Accordingly, this paper proposes an adaptive primary frequency support strategy for EV clusters constrained by the charging-behavior-defined operation area. First, the forced charging boundary of the EV is determined according to the driver's charging behavior, and based on this, the operation area is defined. This ensures full utilization of the available frequency support capacity of the EV. An adaptive primary frequency support strategy of EV clusters is then proposed. The output power of EV is adaptively regulated according to the real-time distance from the EV operating point to the forced charging boundary. With the proposed strategy, when the EV approaches the forced charging boundary, its output power is gradually reduced to zero. Then, the rapid state-of-charge declines of EVs and sudden output power reductions in EV clusters caused by forced charging to meet the driver's charging demands can be effectively avoided. EV clusters can then provide sustainable frequency support to the power grid without violating the driver's charging demands. Simulation results validate the proposed operation-area-constrained adaptive primary frequency support strategy, which outperforms the average strategy in terms of stable output maintenance and the optimal utilization of regulation capacities of EV clusters.

关键词： Primary frequency control frequency support electric vehicle vehicle-to-grid(V2G) operation area charging behavior

Understanding the mechanism of capacity increase during early cycling of commercial NMC/graphite lithium-ion batteries

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Journal of Energy Chemistry 2022年第11期31卷 34-44,I0003页

作者： Jia Guo Yaqi Li jinhao meng Kjeld Pedersen Leonid Gurevich Daniel-Ioan Stroe AAU Energy Aalborg UniversityAalborg9220Denmark Department of Materials and Production Aalborg UniversityAalborg 9220Denmark College of Electrical Engineering Sichuan UniversityChengdu 610065SichuanChina

A capacity increase is often observed in the early stage of Li-ion battery cycling.This study explores the phenomena involved in the capacity increase from the full cell,electrodes,and materials perspective through a combination of non-destructive diagnostic methods in a full cell and post-mortem analysis in a coin cell.The results show an increase of 1%initial capacity for the battery aged at 100%depth of discharge(DOD)and 45℃.Furthermore,large DODs or high temperatures accelerate the capacity increase.From the incremental capacity and differential voltage(IC-DV)analysis,we concluded that the increased capacity in a full cell originates from the graphite anode.Furthermore,graphite/Li coin cells show an increased capacity for larger DODs and a decreased capacity for lower DODs,thus in agreement with the full cell results.Post-mortem analysis results show that a larger DOD enlarges the graphite dspace and separates the graphite layer structure,facilitating the Li+diffusion,hence increasing the battery capacity.

关键词： Capacity increasing Lithium-ion battery Full cell Coin cell Graphite anode

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

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Journal of Energy Chemistry 2023年第10期85卷 534-546,I0013页

作者： Mingqiang Lin Yuqiang You jinhao meng Wei Wang Ji Wu Daniel-Ioan Stroe School of Mechanical and Electrical Engineering Fujian Agriculture and Forestry UniversityFuzhou 350100FujianChina Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesJinjiang 362200FujianChina School of Electrical Engineering Xi’an Jiaotong UniversityXi'an 710049ShaanxiChina School of Management Xi'an Jiaotong UniversityXi'an 710049ShaanxiChina School of Automotive and Transportation Engineering Hefei University of TechnologyHefei 230009AnhuiChina AAU Energy Aalborg UniversityAalborg 9220Denmark

Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

关键词： Lithium-ion battery Degradation trajectory Long-term prediction Transferred convolutional neural network

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

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Chinese Journal of Mechanical Engineering 2023年第1期36卷 305-314页

作者： Xinghao Du jinhao meng Kailong Liu Yingmin Zhang Shunli Wang Jichang Peng Tianqi Liu College of Electrical Engineering Sichuan UniversityChengdu610044China School of Electrical Engineering Xi’an Jiaotong University710049Xi’anChina Warwick Manufacturing Group University of WarwickCoventryUK Southwest University of Science and Technology Mianyang621010China Nanjing Institute of Technology Nanjing211103China

Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.

关键词： Li-ion battery Equivalent circuit model Recursive least squares Recursive total least squares

An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

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Automotive Innovation 2022年第2期5卷 134-145页

作者： Huanyang Huang jinhao meng Yuhong Wang Lei Cai Jichang Peng Ji Wu Qian Xiao Tianqi Liu Remus Teodorescu College of Electrical Engineering Sichuan UniversityChengduChina Faculty of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security TechnologyXi’an University of TechnologyXi’anChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjingChina Department of Automation University of Science and Technology of ChinaHefeiChina Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjinChina Department of Energy Technology Aalborg University9220 AalborgDenmark

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management system.This paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized features.The Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the GPR.As for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency.In the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing efficiency.Advantages of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

关键词： Li-ion battery State of health Gaussian process regression Kernel function Feature optimization

Purification and anti-inflammatory effect of selenium-containing protein fraction from selenium-enriched Spirulina platensis

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Food Bioscience 2022年第1期45卷 197-203页

作者： Pingyingzi Jiang jinhao meng Lifei Zhang Li Huang Lulu Wei Yunxia Bai Xiaoling Liu Shubo Li College of Light Industry and Food Engineering Guangxi UniversityNanning530004China

Spirulina platensis is an excellent carrier of Se,and thus widely used in medical fields and as well as in the food industry.However,there is little information about the characteristics and bioactivity of selenium-containing S.platensis proteins(Se-SP).In this study,Se-SP with different molecular weights were isolated from selenium-enriched S.platensis,and the bioactivities(such as antioxidant and anti-inflammatory activities)of Se-SP were investigated.Se-SP3(with a molecular weight range of 20-48 kDa)showed better free radical scavenging ability(ABTS)than the other Se-SPs.In addition,Se-SP3 suppressed inflammatory cytokines,in which decreased by 74% interleukin 6(IL-6),42.28% tumor necrosis factor-α(TNF-α),69.07% content of malondialdehyde(MDA),40.45% interleukin-1β(IL-1β)relative to the LPS group.Moreover,Se-SP3 decreased the nitric oxide(NO)production by 64.84% compared with the LPS group and increased the activities of superoxide dismutase(SOD)and glutathione peroxidase(GSH-Px),indicating that Se-SP3 has excellent antioxidant and anti-inflammatory activities,and could be used as a functional food ingredient or natural medicine.

关键词： Selenium Spirulina platensis protein Purification Bioactivities Inflammatory cytokines

基于多新息最小二乘和多新息扩展卡尔曼滤波算法的锂电池SOC估计

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华南理工大学学报（自然科学版） 2024年第2期52卷 74-83页

作者：巫春玲付俊成徐先峰孟锦豪郑克军胡雯博长安大学能源与电气工程学院陕西西安710064 西安交通大学电气工程学院陕西西安710049

针对现有SOC(荷电状态)估计方法中电池模型参数恒定,没有考虑电池模型参数的动态变化,导致SOC的估计不够精准的问题,文中提出了一种基于电池模型参数在线辨识与SOC估计联合的算法。在二阶RC等效电路模型基础上,采用多新息最小二乘(Multi... 详细信息

针对现有SOC(荷电状态)估计方法中电池模型参数恒定,没有考虑电池模型参数的动态变化,导致SOC的估计不够精准的问题,文中提出了一种基于电池模型参数在线辨识与SOC估计联合的算法。在二阶RC等效电路模型基础上,采用多新息最小二乘(Multi Innovation Least Squares,MILS)算法对锂离子电池模型中的参数进行在线辨识,从而对电池模型进行实时修正;同时基于修正后的电池模型,采用多新息扩展卡尔曼滤波(Multi Innovation Extended Kalman Filter,MIEKF)算法对电池荷电状态进行估计。MILS算法可以解决在线参数辨识过程中的初始误差累积问题,能够实现模型参数的在线精准辨识,MIEKF算法融合了多新息理论和卡尔曼滤波理论,加入了遗忘因子以削弱历史数据并修正权重,解决了数据过饱和问题,具有较高的准确性和收敛性。实验结果表明,在对电池模型进行参数辨识时,MILS算法、RLS算法辨识的均方根误差分别为1.4、1.9 mV,MILS算法相比RLS算法的估计精度提高了26.3%;对于参数辨识后SOC的估计,MIEKF算法估计的均方根误差为0.0037,EKF算法、AEKF算法估计的均方根误差分别为0.0073、0.0052,MIEKF算法比EKF算法的估计精度提高了49.31%,比AEKF算法的估计精度提高了28.84%;并且在给定SOC初值错误的情况下,文中所提出算法在电池开始工作后30 s左右就能够收敛到真实值,是一种精度高而且鲁棒性好的有效估计方法。

关键词： SOC估计多新息参数辨识扩展卡尔曼滤波

Potential industrial applications of photo/electrocatalysis: Recent progress and future challenges

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Green Energy & Environment 2024年第5期9卷 859-876页

作者： jinhao Li Jing Ren Shaoquan Li Guangchao Li Molly meng-Jung Li Rengui Li Young Soo Kang Xiaoxin Zou Yong Luo Bin Liu Yufei Zhao State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical TechnologyBeijing100029China Quzhou Institute for Innovation in Resource Chemical Engineering Quzhou324000China State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical TechnologyBeijing100029China Department of Applied Physics The Hong Kong Polytechnic UniversityHong Kong999077China State Key Laboratory of Catalysis Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian National Laboratory for Clean EnergyZhongshan Road 457Dalian116023China Environmental and Climate Technology Track of Korea Institute of Energy Korea Institute of Energy TechnologyNaju-siJeollanam do58217Republic of Korea State Key Laboratory of Inorganic Synthesis and Preparative Chemistry College of ChemistryJilin UniversityChangchun130012China

Nowadays,the rapid development of the social economy inevitably leads to global energy and environmental crisis.For this reason,more and more scholars focus on the development of photocatalysis and/or electrocatalysis technology for the advantage in the sustainable production of high-value-added products,and the high efﬁciency in pollutants remediation.Although there is plenty of outstanding research has been put forward continuously,most of them focuses on catalysis performance and reaction mechanisms in laboratory conditions.Realizing industrial application of photo/electrocatalytic processes is still a challenge that needs to be overcome by social demand.In this regard,this review comprehensively summarized several explorations in theﬁeld of photo/electrocatalytic reduction towards potential industrial applications in recent years.Special attention is paid to the successful attempts and the current status of photo/electrocatalytic water splitting,carbon dioxide conversion,resource utilization from waste,etc.,by using advanced reactors.The key problems and challenges of photo/electrocatalysis in future industrial practice are also discussed,and the possible development directions are also pointed out from the industry view.

关键词： Photocatalysis Electrocatalysis Industrial applications H2 economy

Rockburst criterion and evaluation method for potential rockburst pit depth considering excavation damage effect

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第5期16卷 1649-1666页

作者： jinhao Dai Fengqiang Gong Lei Xu Engineering Research Center of Safety and Protection of Explosion & Impact of Ministry of Education (ERCSPEIME) Southeast UniversityNanjing211189China School of Civil Engineering Southeast UniversityNanjing211189China

Excavation-induced disturbances in deep tunnels will lead to deterioration of rock properties and formation of excavation damaged zone(EDZ).This excavation damage effect may affect the potential rockburst pit depth.Taking two diversion tunnels of Jinping II hydropower station for example,the relationship between rockburst pit depth and excavation damage effect is first surveyed.The results indicate that the rockburst pit depth in tunnels with severe damage to rock masses is relatively large.Subsequently,the excavation-induced damage effect is characterized by disturbance factor D based on the Hoek-Brown criterion and wave velocity method.It is found that the EDZ could be further divided into a high-damage zone(HDZ)with D=1 and weak-damage zone(WDZ),and D decays from one to zero linearly.For this,a quantitative evaluation method for potential rockburst pit depth is established by presenting a three-element rockburst criterion considering rock strength,geostress and disturbance factor.The evaluation results obtained by this method match well with actual observations.In addition,the weakening of rock mass strength promotes the formation and expansion of potential rockburst pits.The potential rockburst pit depth is positively correlated with HDZ and WDZ depths,and the HDZ depth has a significant contribution to the potential rockburst pit depth.

关键词： Deep tunnel Rockburst Rockburst pit Excavation damage effect Hoek-Brown criterion