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

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

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Neural Network Based Feasible Region Approximation Model for Optimal Operation of Integrated Electricity and Heating System

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CSEE Journal of Power and Energy Systems 2023年第5期9卷 1808-1819页

作者： Xuewei Wu Bin Zhang Mads Pagh Nielsen Zhe Chen aalborg university Aalborg 9220Denmark

This paper proposes a neural network based feasible region approximation model of a district heating system(DHS),and it is intended to be used for optimal operation of integrated electricity and heating system(IEHS)considering privacy *** this model,a neural network is trained to approximate the feasible region of the DHS operation and then is reformulated as a set of mixed-integer linear *** on the received approximation models of DHSs and detailed electricity system model,the electricity operator conducts centralized optimization,and then sends specific heating generation plans back to corresponding heating ***,subsequent optimization is formulated for each DHS to obtain detailed operation strategy based on received heating generation *** this scheme,optimization of the IEHS could be achieved and privacy protection requirement is satisfied since the feasible region approximation model does not contain detailed system *** studies conducted on a small-scale system demonstrate accuracy of the proposed strategy and a large-scale system verify its application possibility.

关键词： Artificial intelligence district heating system integrated energy system machine learning multi-energy systems neural network optimal operation wind power

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Investigation of multi-step fast charging protocol and aging mechanism for commercial NMC/graphite lithium-ion batteries

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Journal of Energy Chemistry 2023年第5期80卷 237-246,I0006页

作者： Yaqi Li Jia Guo Kjeld Pedersen Leonid Gurevich Daniel-Ioan Stroe Department of Materials and Production Aalborg UniversityAalborg 9220Denmark Department of Energy Aalborg UniversityAalborg 9220Denmark

Fast charging is considered a promising protocol for raising the charging efficiency of electric ***,high currents applied to Lithium-ion(Li-ion)batteries inevitably accelerate the degradation and shorten their *** work designs a multi-step fast-charging method to extend the lifetime of LiNi0.5Co0.2Mn0.3O2(NMC)/graphite Li-ion batteries based on the studies of half cells and investigates the aging mechanisms for different charging *** degradation has been studied from both full cell behaviour and materials perspectives through a combination of non-destructive diagnostic methods and post-mortem *** the proposed multi-step charging protocol,the state-of-charge(SOC)profile is subdivided into five ranges,and the charging current is set differently for different SOC *** of the designed multi-step fast charging protocols is shown to allow for a 200 full equivalent cycles longer lifetime as compared to the standard charging method,while the charging time is reduced by 20%.From the incremental capacity analysis and electrical impedance spectroscopy,the loss of active materials and lithium inventory on the electrodes,as well as an increase in internal resistance for the designed multistep constant-current-constant-voltage(MCCCV)protocol have been found to be significantly lower than for the standard charging ***-mortem analysis shows that cells aged by the designed MCCCV fast charging protocol exhibit less graphite exfoliation and crystallization damage,as well as a reduced solid electrolyte interphase(SEI)layer growth on the anode,leading to a lower Rseiresistance and extended lifetime.

关键词： Fast-charging Lithium-ion battery Aging mechanism Electrode

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The development of machine learning-based remaining useful life prediction for lithium-ion batteries

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Journal of Energy Chemistry 2023年第7期82卷 103-121,I0003页

作者： Xingjun Li Dan Yu Vilsen Søren Byg Store Daniel Ioan Department of Energy Aalborg UniversityAalborg 9220Denmark Department of Mathematical Sciences Aalborg UniversityAalborg 9220Denmark

Lithium-ion batteries are the most widely used energy storage devices,for which the accurate prediction of the remaining useful life(RUL)is crucial to their reliable operation and accident *** work thoroughly investigates the developmental trend of RUL prediction with machine learning(ML)algorithms based on the objective screening and statistics of related papers over the past decade to analyze the research core and find future improvement *** possibility of extending lithium-ion battery lifetime using RUL prediction results is also explored in this *** ten most used ML algorithms for RUL prediction are first identified in 380 relevant *** the general flow of RUL prediction and an in-depth introduction to the four most used signal pre-processing techniques in RUL prediction are *** research core of common ML algorithms is given first time in a uniform format in chronological *** algorithms are also compared from aspects of accuracy and characteristics comprehensively,and the novel and general improvement directions or opportunities including improvement in early prediction,local regeneration modeling,physical information fusion,generalized transfer learning,and hardware implementation are further ***,the methods of battery lifetime extension are summarized,and the feasibility of using RUL as an indicator for extending battery lifetime is *** lifetime can be extended by optimizing the charging profile serval times according to the accurate RUL prediction results online in the *** paper aims to give inspiration to the future improvement of ML algorithms in battery RUL prediction and lifetime extension strategy.

关键词： Lithium-ion batteries Remaining useful lifetime prediction Machine learning Lifetime extension

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Risk of pacemaker implantation after femur fracture in patients with and without a history syncope: a Danish nationwide registry-based follow-up study

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Journal of Geriatric Cardiology 2022年第9期19卷 712-718页

作者： Sara LK Clemmensen Kristian Kragholm Bhupendar Tayal Christian Torp-Pedersen Søren Kold Peter Søgaard Sam Riahi Department of Cardiology Aalborg University HospitalAalborgDenmark Unit of Clinical Biostatistics and Epi-demiology Aalborg University HospitalAalborgDenmark Department of Clinical Epidemiology Aalborg University HospitalAalborgDenmark Department of Orthopedics Aalborg University HospitalAalborgDenmark Depart-ment of Clinical Institute Aalborg University HospitalAalborgDenmark

BACKGROUND It has previously been described that fall-associated injuries including fractures are commonly observed among patients with ***,knowledge on the risk of pacemaker implantation after admission due to femur fracture from large population-based epidemiologic data is ***,we investigated the risk of pacemaker implanta-tion following femur fracture in patients with and without a history of previous *** All patients with femur fracture between 2005-2017 were identified using the Danish Nationwide Patient *** these,patients already having a pacemaker were *** outcome was one-year risk of pacemaker implanta-tion and secondary outcome was one-year all-cause *** logistic regression was used to obtain absolute and relative risks of the study endpoint in relation to patients with versus without history of syncope and standardized to the age,sex,selected comorbidity and pharmacotherapy distribution of all *** Of 93,093 patients with femur fracture,5508(5.9%)had a history of syncope within five *** with prior syn-cope were slightly older(84 vs.83 years),more often male(33.6%vs.29.4%),and had more often comorbidities relative to those without history of ***-cause mortality was significantly higher among those with previous history of syncope com-pared to those without previous syncope(29.9%vs.28.6%,P=0.021).The relative mortality risk was 1.05(95%CI:1.01−1.09,P=0.021).A total of 695(0.8%)patients underwent pacemaker implantation within 5 years following femur fracture,and a signific-antly higher proportion of patients with syncope had a pacemaker implanted within one year(1.6%vs.0.7%,P<0.001;relative risk,2.01[95%CI:1.55−2.46]).CONCLUSIONS In patients with femur fracture,a history of syncope was significantly associated with a higher one-year risk of pacemaker implantation.

关键词： patients syncope injuries

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Boosting battery state of health estimation based on self-supervised learning

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Journal of Energy Chemistry 2023年第9期84卷 335-346页

作者： Yunhong Che Yusheng Zheng Xin Sui Remus Teodorescu Department of Energy Aalborg UniversityAalborg 9220Denmark

State of health(SoH) estimation plays a key role in smart battery health prognostic and ***,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH *** this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH *** from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at *** learning is then used to learn the aging characteristics from the unlabeled data through an *** learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation *** proposed method has been validated under different battery chemistries,formats,operating conditions,and *** estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with *** with other pure supervised machine learning methods demonstrate the superiority of the proposed *** simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

关键词： Lithium-ion battery State of health Battery aging Self-supervised learning Prognostics and health management Data-driven estimation

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Modelling, Implementation, and Assessment of Virtual Synchronous Generator in Power Systems

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Journal of Modern Power Systems and Clean Energy 2020年第3期8卷 399-411页

作者： Meng Chen Dao Zhou Frede Blaabjerg Department of Energy Technology Aalborg UniversityAalborg 9220Denmark

As more and more power electronic based generation units are integrated into power systems, the stable operation of power systems has been challenged due to the lack of system inertia. In order to solve this issue, the virtual synchronous generator(VSG), in which the power electronic inverter is controlled to mimic the characteristics of traditional synchronous generators, is a promising strategy. In this paper, the representation of the synchronous generator in power systems is firstly presented as the basis for the VSG. Then the modelling methods of VSG are comprehensively reviewed and *** of the VSG in power systems are summarized as well. Finally, the challenges and future trends of the VSG implementation are discussed.

关键词： Inertia virtual synchronous generator(VSG) frequency control renewable energy source(RES) inverter

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Overview of multi-stage charging strategies for Li-ion batteries

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Journal of Energy Chemistry 2023年第9期84卷 228-241页

作者： Muhammad Usman Tahir Ariya Sangwongwanich Daniel-Ioan Stroe Frede Blaabjerg AAU Energy Aalborg UniversityAalborg DK-9220Denmark

To reduce the carbon footprint in the transportation sector and improve overall vehicle efficiency,a large number of electric vehicles are being *** is due to the fact that environmental concerns and the depletion of fossil fuels have become significant global ***-ion batteries(LIBs)have been distinguished themselves from alternative energy storage technologies for electric vehicles(EVs) due to superior qualities like high energy and power density,extended cycle life,and low maintenance cost to a competitive ***,there are still certain challenges to be solved,like EV fast charging,longer lifetime,and reduced *** fast charging,the multi-stage constant current(MSCC) charging technique is an emerging solution to improve charging efficiency,reduce temperature rise during charging,increase charging/discharging capacities,shorten charging time,and extend the cycle ***,there are large variations in the implementation of the number of stages,stage transition criterion,and C-rate selection for each *** paper provides a review of these problems by compiling information from the *** overview of the impact of different design parameters(number of stages,stage transition,and C-rate) that the MSCC charging techniques have had on the LIB performance and cycle life is described in detail and *** impact of design parameters on lifetime,charging efficiency,charging and discharging capacity,charging speed,and rising temperature during charging is presented,and this review provides guidelines for designing advanced fast charging strategies and determining future research gaps.

关键词： Multi-stage constant current(MSCC)charging Electric vehicles(EVs) Li-ion batteries(LIBs) Fast charging strategies

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Dynamic Protective Control Strategy for Distributed Generation System with Fixed-speed Wind Turbines

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电力系统自动化 2012年第8期36卷 178-184页

作者： Mu WEI Zhe CHEN Department of Energy Technology Aalborg UniversityAalborg 9220Denmark

The characteristics of induction generator based fixed-speed wind turbines(FSWT)are *** impacts of different execution time in protective operations are studied under different fault duration and various wind velocity situations,e.g.,FSWT stabilities of load shedding in distribution *** on this research,a dynamic protective control strategy for a distributed generation system(DGS)with FSWT is ***,simulation results demonstrate the effectiveness of the strategy.

关键词：分布式发电系统控制策略动态保护风力发电机组速度执行时间风力涡轮机异步发电机

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Concurrent multi-scale design optimization of composite frame structures using the Heaviside penalization of discrete material model

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Acta Mechanica Sinica 2016年第3期32卷 430-441页

作者： Jun Yan Zunyi Duan Erik Lund Guozhong Zhao Dalian university of Technology Dalian 116024 China aalborg university 9220 Aalborg Denmark

This paper deals with the concurrent multi-scale optimization design of frame structure composed of glass or carbon fiber reinforced polymer laminates. In the composite frame structure, the fiber winding angle at the micro-material scale and the geometrical parameter of components of the frame in the macro-structural scale are introduced as the independent variables on the two geometrical scales. Considering manufacturing requirements, discrete fiber winding angles are specified for the micro design variable. The improved Heaviside penalization discrete material optimization interpolation scheme has been applied to achieve the discrete optimization design of the fiber winding angle. An optimization model based on the minimum structural compliance and the specified fiber material volume constraint has been established. The sensitivity information about the two geometrical scales design variables are also deduced considering the characteristics of discrete fiber winding angles. The optimization results of the fiber winding angle or the macro structural topology on the two single geometrical scales, together with the concurrent two-scale optimization, is separately studied and compared in the paper. Numerical examples in the paper show that the concurrent multi-scale optimization can further explore the coupling effect between the macro-structure and micro-material of the composite to achieve an ultralight design of the composite frame structure. The novel two geometrical scales optimization model provides a new opportunity for the design of composite structure in aerospace and other industries.

关键词： Composite frame structure Multi-scale optimization Topology optimization Fiber winding angle Structural compliance

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