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Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

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Journal of Energy Chemistry 2024年第5期92卷 591-604页

作者： Xingjun Li Dan Yu Søren Byg Vilsen daniel ioan stroe Department of Energy Aalborg UniversityAalborg 9220Denmark Department of Mathematical Sciences Aalborg UniversityAalborg 9220Denmark Walker Department of Mechanical Engineering The University of Texas at AustinAustinTX 78712USA

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and *** of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real *** work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is *** features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding ***,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset *** linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation *** results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR *** was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher *** work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

关键词： Feature engineering Dynamic forklift aging profile State of health comparison Machine learning Lithium-ion batteries

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Alternating current heating techniques for lithium-ion batteries in electric vehicles:Recent advances and perspectives

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Journal of Energy Chemistry 2024年第9期96卷 679-697页

作者： Xinrong Huang Jinhao Meng Wei Jiang Wenjie Liu Kailong Liu Yipu Zhang daniel-ioan stroe Remus Teodorescu School of Energy and Electrical Engineering Chang’an UniversityXi’an 710018ShaanxiChina School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina School of Highway Chang’an UniversityXi’an 710018ShaanxiChina School of Automation Northwestern Polytechnical UniversityXi’an 710072ShaanxiChina School of Control Science and Engineering Shandong UniversityJinan 250100ShandongChina Department of Energy Aalborg UniversityAalborg 9220Denmark

The significant decrease in battery performance at low temperatures is one of the critical challenges that electric vehicles(EVs)face,thereby affecting the penetration rate in cold *** current(AC)heating has attracted widespread attention due to its low energy consumption and uniform heating *** paper introduces the recent advances in AC heating from the perspective of practical EV ***,the performance degradation of EVs in low-temperature environments is introduced *** concept of AC heating and its research methods are ***,the effects of various AC heating methods on battery heating performance are *** on existing studies,the main factors that affect AC heating performance are ***,various heating circuits based on EVs are categorized,and their cost,size,complexity,efficiency,reliability,and heating rate are elaborated and *** evolution of AC heaters is presented,and the heaters used in brand vehicles are sorted ***,the perspectives and challenges of AC heating are *** paper can guide the selection of heater implementation methods and the optimization of heating effects for future EV applications.

关键词： Lithium-ion battery Low temperature Alternating current heating Heater Electric vehicle

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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

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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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Analysis of 3.4 Ah lithium-sulfur pouch cells by electrochemical impedance spectroscopy

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Journal of Energy Chemistry 2022年第9期31卷 318-325,I0009页

作者： Dominika Capkova Vaclav Knap Andrea Strakova Fedorkova daniel-ioan stroe Department of Physical Chemistry Pavol Jozef Safarik University in KosiceKosice 04154Slovak Republic Faculty of Electrical Engineering Czech Technical University in PraguePrague 16627Czech Republic Department of Energy Technology Aalborg UniversityAalborg East 9220Denmark

Despite great progress in lithium-sulfur(Li-S) batteries, the electrochemical reactions in the cell are not yet fully understood. Electrode processes, complex interfaces and internal resistance may be characterized by electrochemical impedance spectroscopy(EIS). EIS is a non-destructive technique and easy to apply, though there are challenges in ensuring the reproducibility of measurements and the interpretation of impedance data. Here, we present the impedance behavior of a 3.4 Ah Li-S pouch cell characterized by EIS. The impedance changes were analyzed over the entire depth-of-discharge, depth-of-charge,and at various temperatures. Based on the formation of intermediates during(dis)charging, the changes of resistances are observed. Overall, the increase in temperature causes a decrease in electrolyte viscosity,lowering the surface energy which can improve the penetration of the electrolyte into the electrode pores. Moreover, the effect of superimposed AC current during EIS measurement was analyzed, and the results show the dependence of the charge transfer resistance on superimposed AC current which was lower compared to steady-state conditions and consents with theory.

关键词： Lithium-sulfur battery Impedance Equivalent circuit Temperature dependencies Charging/discharging differences Dynamic electrochemical impedance spectroscopy

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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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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

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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 *** 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 *** 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 *** the incremental capacity and differential voltage(IC-DV)analysis,we concluded that the increased capacity in a full cell originates from the graphite ***,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 ***-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

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Highly luminescent europium and terbium complexes based on succinimide and N-hydroxysuccinimide

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Journal of Rare Earths 2012年第5期30卷 401-407页

作者： Corneliu Sergiu Stan ioan Rosca daniel Sutiman Marius Sebastian Secula Faculty of Chemical Engineering and Environmental Protection Gheorghe Asachi Technical University of Iasi73 D.Mangeron700050IasiRomania

The preparation of two highly luminescent europium（Ill） and terbium（Ill） complexes with succinimide （SI） and N-hydroxysuccinimide （NHSI） were reported, which were further investigated through elemental chemical analysis, thermal analysis, FT-IR, powder X-ray diffraction, SEM and fluorescence spectroscopy. Data collected through these analysis revealed the formation of the Eu（III） and Th（III） complexes with the above mentioned ligands at a metal to ligand （M：L） molar ratio of 1：3. Interesting conclusions regarding the effi- cient sensitization through the coordination site of the lanthanide luminescent centers were retained from the photoluminescent spectra inves- tigation. Strong luminescence emission was observed in case of Eu（III）-SI and Tb（III）-NHSI complexes while the corresponding Eu（III）-NHSI and Tb（III）-SI complexes exhibited no photo luminescent properties. Newly obtained luminescent lanthanide complexes may be of particular interest for various applications in optoelectronics.

关键词： lanthanide complex succinimide N-hydroxysuccinimide luminescence rare earths

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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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