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Journal of Energy Chemistry 2025年第2期101卷 87-98页

作者： Xinghao Du Jinhao Meng Yassine Amirat Fei Gao Mohamed Benbouzid UMR CNRS 6027 IRDL University of Brest School of Electrical Engineering Xi'an Jiaotong University LbISEN ISEN Yncréa Ouest School of Energy and Computer Science University of Technology of Belfort-Montbeliard

Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric *** impedance spectroscopy(EIS) offers valuable insights into battery degradation analysis and ***,previous studies have not adequately addressed the impedance uncertainties,particularly during battery operating conditions,which can substantially impact the robustness and accuracy of state of health(SOH) *** by this,this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation *** utilizing metrics such as impedance residuals and correlation coefficients,the proposed method effectively filters out invalid and insignificant impedance data,thereby enhancing the reliability of the input ***,the extreme gradient boosting(XGBoost) modeling framework is constructed for estimating the battery degradation *** XGBoost model incorporates a diverse range of hyperparameters,optimized by a genetic algorithm to improve its adaptability and generalization *** validation confirms the effectiveness of the proposed feature optimization scheme,demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.

关键词： Lithium-ion battery state of health Electrochemical impedance spectroscopy Extreme gradient boosting

Method for Estimating the state of health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network

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Energy Engineering 2025年第1期122卷 203-220页

作者： Yu Zhang Daoyu Zhang TiezhouWu School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China

Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery ***,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity ***,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model *** address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural ***,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity ***,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network *** validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging *** experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.

关键词： Lithium-ion battery state of health differential thermal voltammetry Sparrow Search Algorithm

state of health estimation for lithium-ion batteries in real-world electric vehicles

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Science China(Technological Sciences) 2023年第1期66卷 47-56页

作者： WU Ji FANG LeiChao DONG GuangZhong LIN MingQiang School of Automotive and Transportation Engineering Hefei University of TechnologyHefei 2300o9China Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province Hefei 230009China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen 518055China Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesJinjiang362200China

The state of health(SOH) plays a significant role in the mileage and safety of an electric vehicle(EV). In recent years, many methods based on data-driven analysis and laboratory measurements have been developed for SOH estimation. However, most of these proposed methods cannot be applied to real-world EVs. Here, we present a method for SOH estimation based on realworld EV data. A battery-aging evaluation health index(HI) with a strong correlation to the SOH is retrieved from battery-aging data and then modified with thermal factors to depict the former SOH. Afterward, a local weighted linear-regression algorithm is used to qualitatively characterize the declining trend of the HI, which eliminates the local HI fluctuation caused by data ***, a series of features-of-interest(FOIs) is extracted according to the battery consistency, cell-voltage extrema, and cumulative mileage, and validated using the grey relational analysis. Finally, a battery-degradation model is built using the extreme gradient-boosting algorithm with the selected FOIs. The experimental results from real-world data indicate that the proposed method has high estimation accuracy and generalization, and the maximum error is around 2% for batteries in realworld EVs.

关键词： electric vehicle state of health extreme gradient boosting battery consistency

state of health Estimation of LiFePO_(4) Batteries for Battery Management Systems

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Computers, Materials & Continua 2022年第11期73卷 3149-3164页

作者： Areeb Khalid Syed Abdul Rahman Kashif Noor Ul Ain Ali Nasir Department of Electrical Engineering University of Engineering and TechnologyLahorePakistan Department of Electrical Engineering University of Central PunjabLahorePakistan

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the *** optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the *** is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery *** major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)*** model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the *** the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell *** validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical *** parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual ***-adaptive characteristic map using a lookup table was also *** nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

关键词： Aging model state of health lithium-ion cells battery management system state of charge battery modeling

state of health based battery reconfiguration for improved energy efficiency

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Control Theory and Technology 2022年第4期20卷 443-455页

作者： Le Yi Wang George Yin Yi Ding Caiping Zhang Department of Electrical and Computer Engineering Wayne State UniversityDetroitMI 48202USA Department of Mathematics University of ConnecticutStorrsCT 06269USA US Army CCDC Ground Vehicle Systems Center in Warren WarrenMI 48397USA School of Electrical Engineering Beijing Jiaotong UniversityBeijing 100044China

This paper analyzes the system-level state of health(SOH)and its dependence on the SOHs of its component battery *** to stochastic natures of battery aging processes and their dependence on charge/discharge rate and depth,operating temperature,and environment conditions,capacities of battery modules decay unevenly and *** on estimated SOHs of battery modules during battery operation,we analyze how the SOH of the entire system deteriorates when battery modules age and become increasingly diverse in their capacities.A rigorous mathematical analysis of system-level capacity utilization is *** is shown that for large battery strings with uniformly distributed capacities,the average string capacity approaches the minimum,implying an asymptotically near worst-case capacity utility without *** is demonstrated that the overall battery usable capacities can be more efficiently utilized to achieve extended operational ranges by using battery *** optimal regrouping algorithm is *** methods,simulation examples,and a case study using real-world battery data are presented.

关键词： Battery system state of health Battery aging Capacity utilization Energy efficiency Battery regrouping

A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

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Journal of Energy Chemistry 2022年第11期31卷 159-173,I0006页

作者： Kai Luo Xiang Chen Huiru Zheng Zhicong Shi School of Materials and Energy Guangdong University of TechnologyGuangzhou 510006GuangdongChina Department of Chemical Engineering Tsinghua UniversityBeijing 100084China School of Computing Ulster UniversityBelfast BT151EDUnited Kingdom

In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion *** this paper,we review the current widely used equivalent circuit and electrochemical models for battery state *** review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery *** details,advantages,and limitations of these approaches are presented,compared,and ***,future key challenges and opportunities are discussed.

关键词： Lithium-ion battery state of health state of charge Remaining useful life Data-driven

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

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

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Journal of Energy Chemistry 2023年第7期82卷 1-17,I0001页

作者： Bin Ma Lisheng Zhang Hanqing Yu Bosong Zou Wentao Wang Cheng Zhang Shichun Yang Xinhua Liu School of Transportation Science and Engineering Beihang UniversityBeijing 102206China China Software Testing Center Beijing 100038China Institute for Clean Growth and Future Mobility Coventry UniversityCV15FBUnited Kingdom Dyson School of Design Engineering Imperial College LondonLondon SW72AZUK

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal *** estimation and prediction of battery health conditions are crucial for battery safety *** this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven *** on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate *** double exponential decay model is utilized as an empirical model to output highly real-time prediction *** Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time ***,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μ*** proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

关键词： state of health Remaining useful life End-cloud collaboration Ensemble learningDifferential thermal voltammetry

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

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Journal of Energy Chemistry 2024年第10期97卷 630-649,I0013页

作者： Yanxin Xie Shunli Wang Gexiang Zhang Paul Takyi-Aninakwa Carlos Fernandez Frede Blaabjerg School of Information Engineering Southwest University of Science and TechnologyMianyang 621010SichuanChina College of Electric Power Inner Mongolia University of TechnologyHohhot 010080Inner MongoliaChina School of Pharmacy and Life Sciences Robert Gordon UniversityAberdeen AB10-7GJUK Department of Energy Technology Aalborg UniversityPontoppidanstraede 1119220 Aalborg EastDenmark

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the *** not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage *** their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal *** degradation is reflected in the state of health(SOH)***,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven *** delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation *** approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation *** enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the *** will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health *** on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment *** will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation *** article reviews the current research landscape from four perspectives and discusses the challenges that lie *** and practitioners can gain

关键词： Lithium-ion batteries Whole life cycle Aging mechanism Data-driven approach state of health Battery management system

A review of lithium-ion battery state of health and remaining useful life estimation methods based on bibliometric analysis

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Journal of Traffic and Transportation Engineering(English Edition) 2024年第6期11卷 1420-1446页

作者： Xu Lei Fangjian Xie Jialong Wang Chunling Zhang School of Electronics and Control Engineering Chang'an University

In recent years, research on the state of health(SOH) and remaining useful life(RUL)estimation methods for lithium-ion batteries has garnered significant attention in the new energy sector. Despite the substantial volume of annual publications, a systematic approach to quantifying and analyzing these contributions is lacking. This study focuses on selecting pertinent literature related to lithium-ion battery SOH and RUL estimation from CNKI and WOS databases, spanning January 2010 to December 2023. Employing bibliometric tools such as VOSviewer and Cite Space, we conduct visual analyses to elucidate the current state, development trends, and research frontiers in this domain. Our examination encompasses scholarly activity, year-wise literature distribution, international collaboration networks, structural dissemination, and journal contributions. The findings indicate an upward trend in annual publication output, with China, the United states, the United Kingdom, and Canada at the forefront of collaborative research *** is increasingly recognized as a pivotal hub for global scholarly partnerships. Notably,Harbin Institute of Technology, Beijing Institute of Technology, Chongqing University,Chinese Academy of Sciences, and Beijing Jiaotong University are the top institutions in China and the world in terms of publications. The Journal of Energy Storage emerges as a prominent periodical, acclaimed both domestically and internationally for its rigorous standards and high-quality articles. Based on the research content from CNKI and WOS,VOSviewer clusters the main research directions into three themes: aging mechanisms,SOH estimation methods, and RUL prediction methods. Keywords such as 'online estimation', 'hybrid models', and 'artificial neural networks' feature prominently, signaling a strong emphasis on artificial intelligence strategies. The study concludes with a prospective outlook on imminent research trajectories regarding the health and longevity est

关键词： Lithium-ion battery state of health Remaining useful life Estimation Bibliometric analysis