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Online identification of knee point in conventional and accelerated aging lithium-ion batteries using linear regression and bayesian inference methods

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APPLIED ENERGY 2025年 388卷

作者： Ni, Yulong Li, Xiaoyu Zhang, He Wang, Tiansi Song, Kai Zhu, Chunbo Xu, Jianing Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China Shenzhen Univ Coll Phys & Optoelect Engn Shenzhen 518060 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China

Accurate online knee point identification is crucial for predictive maintenance and secondary utilization of batteries. The "knee point" refers to the point in the battery capacity degradation curve where the degradation rate changes from linear to non-linear, marking a critical transition indicating the onset of accelerated capacity loss. However, challenges such as incomplete monitoring data, prevalent noise, difficulty in extracting characteristic parameters, and capacity regeneration phenomena hinder precise, real-time knee point detection. This study integrates physical mechanism modeling, signal processing techniques, and statistical inference to propose a robust, efficient solution for knee point identification. The proposed method employs feature extraction based on capacity loss mechanism models, denoising using variational mode decomposition (VMD), and a hybrid framework that combines linear regression with bayesian inference. This dynamic model updates boundary limits in real-time, enabling highly accurate knee point identification across two positive materials, lithium cobalt oxide (LCO) and lithium iron phosphate (LFP), under various operating conditions. Comprehensive evaluations show that the proposed method achieves accuracies exceeding 94 % for conventional aging batteries and 92 % for accelerated aging batteries, surpassing existing methods. Additionally, the method demonstrates resilience to noise interference and capacity regeneration phenomena, maintaining high accuracy even under complex conditions. These results suggest that the proposed method has broad adaptability, making it a valuable tool for real-time battery health monitoring and providing a solid foundation for future research on battery aging diagnostics.

关键词： Lithium-ion batteries Conventional aging Accelerated aging Knee point Linear regression bayesian inference method

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A bayesian Framework for Spectral Reprojection

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JOURNAL OF SCIENTIFIC COMPUTING 2025年第3期102卷 1-28页

作者： Li, Tongtong Gelb, Anne Univ Maryland Baltimore Cty Dept Math & Stat Baltimore MD 21250 USA Dartmouth Coll Dept Math Hanover NH 03755 USA

Fourier partial sum approximations yield exponential accuracy for smooth and periodic functions, but produce the infamous Gibbs phenomenon for non-periodic ones. Spectral reprojection resolves the Gibbs phenomenon by projecting the Fourier partial sum onto a Gibbs complementary basis, often prescribed as the Gegenbauer polynomials. Noise in the Fourier data and the Runge phenomenon both degrade the quality of the Gegenbauer reconstruction solution, however. Motivated by its theoretical convergence properties, this paper proposes a new bayesian framework for spectral reprojection, which allows a greater understanding of the impact of noise on the reprojection method from a statistical point of view. We are also able to improve the robustness with respect to the Gegenbauer polynomials parameters. Finally, the framework provides a mechanism to quantify the uncertainty of the solution estimate.

关键词： bayesian inference method Spectral reprojection Noisy Fourier data Gibbs phenomenon Gegenbauer polynomials Uncertainty quantification

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Multiple parameter determination in textile material design: A bayesian inference approach based on simulation

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MATHEMATICS AND COMPUTERS IN SIMULATION 2018年 151卷 1-14页

作者： Xu, Dinghua He, Yangao Yu, Yue Zhang, Qifeng Zhejiang Sci Tech Univ Coll Sci Dept Math Hangzhou 310018 Zhejiang Peoples R China Shanghai Univ Finance & Econ Sch Math 777 Guoding Rd Shanghai 200433 Peoples R China

A mathematical model of heat-moisture transfer within textiles and a corresponding inverse problem of textile material design (IPTMD) are reformulated. A stability theorem for the forward problem is given to show wellposedness of the heat-moisture transfer model. A bayesian inference approach is presented to solve the IPTMD based on thermal comfort of clothing. The triple parameters (thickness, thermal conductivity, porosity of textiles) are simultaneously determined in the sense of the statistical point estimation by the likelihood function. The bayesian techniques based on Markov chain Monte Carlo (MCMC) methods are employed to simultaneously determine three parameters in IPTMD, where the Metropolis-Hastings algorithm is applied in the inversion process. The interpolated likelihood function reduces significantly the computational cost associated with the implementation of MCMC method without loss of accuracy in the parameters estimation. Numerical experiments confirm that bayesian inference method can provide more accurate solutions to the IPTMD. (C) 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Inverse problems bayesian inference method Textile material design Coupled heat-moisture transfer model Parameters determination

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Approximate bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density

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BMC BIOINFORMATICS 2014年第12-Sup期15卷 S3-S3页

作者： Wu, Qianqian Smith-Miles, Kate Tian, Tianhai Monash Univ Sch Math Sci Melbourne Vic 3004 Australia

Background: Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on the experimental data sets, in particular, when the data are sparse and the regulatory network is stochastic. Results: To address this issue, this work proposed a new algorithm to estimate parameters in stochastic models using simulated likelihood density in the framework of approximate bayesian computation. Two stochastic models were used to demonstrate the efficiency and effectiveness of the proposed method. In addition, we designed another algorithm based on a novel objective function to measure the accuracy of stochastic simulations. Conclusions: Simulation results suggest that the usage of simulated likelihood density improves the accuracy of estimates substantially. When the error is measured at each observation time point individually, the estimated parameters have better accuracy than those obtained by a published method in which the error is measured using simulations over the entire observation time period.

关键词： Approximate bayesian Computation Discrepancy Tolerance Sequential Monte Carlo Stochastic Simulation Algorithm bayesian inference method

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Stochastic modeling of breakdown at freeway merge bottleneck and traffic control method using connected automated vehicle

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TRANSPORTATION RESEARCH PART B-methodOLOGICAL 2018年 107卷 146-166页

作者： Han, Youngjun Ahn, Soyoung Univ Wisconsin Dept Civil & Environm Engn 1415 Engn Dr Madison WI 53706 USA

This paper proposes a novel breakdown probability model based on microscopic driver behavior for a freeway merge bottleneck. Extending Newell's car following model to describe the transition from free-flow to congested regimes, two elements of breakdown, trigger and propagation, are derived in terms of vehicle headway. Combining these elements, a general breakdown probability is derived in terms of various parameters related to driver behavior and traffic conditions - other than flow - that can be treated as constants or stochastic with probability distributions. The proposed model is validated with real data. It was found that the theoretical breakdown probability distribution accords well with the empirical counterpart within reasonable ranges of parameter values. Our model suggests that the breakdown probability (i) increases with flow (both mainline and merging) as expected, and the merging spacing, (ii) decreases with the merging speed and aggressive driver characteristics, and interestingly, (iii) increases with the deviation in headway. A proactive traffic control method to achieve uniform headway is developed considering low penetration rates of connected automated vehicle technologies. Published by Elsevier Ltd.

关键词： Traffic breakdown Bottleneck Stochastic capacity Traffic control strategy Connected automated vehicle bayesian inference method

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Distributed process monitoring based on joint mutual information and projective dictionary pair learning

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JOURNAL OF PROCESS CONTROL 2021年 106卷 130-141页

作者： Deng, Ziqing Chen, Xiaofang Xie, Shiwen Xie, Yongfang Sun, Yubo Cent South Univ Sch Automat Changsha 410083 Peoples R China

In modern industrial processes, each subsystem interacts frequently and involves a large number of process variables with complex relations, which challenge process monitoring. In this paper, a distributed process monitoring method based on joint mutual information (JMI) and projective dictionary pair learning (DPL) is proposed for effective process monitoring in industrial systems with multimode, complex, and high-dimensional data. Firstly, considering the interactive information, redundancy and irrelevance among process variables, an automatic block division method based on JMI is proposed to divide process variables into several low dimensional blocks. Secondly, DPLbased monitoring model is established in each block of each mode. According to the multimode characteristic of industrial processes, a joint probability based on reconstruction error is proposed for mode recognition. Then, bayesian inference method that fuses block statistics into global statistics is introduced for anomaly detection. The anomaly source is further determined by defining the block contribution coefficient and variable contribution coefficient. Finally, the effectiveness of the proposed method is demonstrated by a numerical simulation, Tennessee Eastman benchmark test, and experiments in an aluminum electrolysis industrial process. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Distributed process monitoring Projective dictionary pair learning Joint mutual information bayesian inference method Anomaly source

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A calibrated weakest-link model for probabilistic assessment of LCF life considering notch size effects

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INTERNATIONAL JOURNAL OF FATIGUE 2020年 137卷 105631-105631页

作者： Liu, Xi Wang, Rongqiao Hu, Dianyin Mao, Jianxing Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China Beijing Key Lab Aeroengine Struct & Strength Beijing 100191 Peoples R China Beihang Univ Res Inst Aeroengine Beijing 100191 Peoples R China

This study proposes a calibrated weakest-link model for probabilistic life in low cycle fatigue (LCF) regime considering the geometric size effect and the statistical size effect. Weakest-link model is first extended to LCF regime by using damage parameter, and then calibrated based on crack propagation process. Meanwhile, experiments on standard smooth specimens and central hole plate (CHP) specimens are conducted. To improve model accuracy, parameters in the proposed model are calibrated by using bayesian inference method. Finally, probabilistic LCF life is predicted for CHP specimens with different scales, which shows good agreement with test data.

关键词： Size effect Weakest-link method Low cycle fatigue Probabilistic life prediction bayesian inference method

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A hybrid framework for developing empirical model for seismic deformations of anchored sheetpile bulkheads

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2019年 116卷 192-204页

作者： Gong, Wenping Martin, James R., II Juang, C. Hsein Dickenson, Stephen E. McCullough, Nason J. China Univ Geosci Fac Engn Wuhan 430074 Hubei Peoples R China Clemson Univ Glenn Dept Civil Engn Clemson SC 29634 USA New Albion Geotech Inc 3400 San Juan Dr Reno NV 89509 USA CH2M 1100 NE Circle BlvdSuite 300 Corvallis OR 97330 USA Natl Cent Univ Dept Civil Engn Taoyuan 32011 Taiwan

Damages to port facilities supported by anchored sheetpile walls are commonly observed at moderate to high seismic load levels. Predicting the extent of seismic deformations of anchored sheetpile walls is a necessary step in the performance-based design;however, the lack of straightforward and practical models poses great challenges for this prediction. In this study, a hybrid framework is proposed for developing practical empirical models for predicting seismic deformations of anchored sheetpile walls. First, a 2-D numerical model is calibrated with field case history data. Next, a series of numerical simulations are undertaken to characterize the relationship between the responses (i.e., seismic deformations of anchored sheetpile walls) and the inputs (i.e., variables that affect the responses). The model bias factor and coefficients (i.e., model parameters) in the derived coarse solution model are then characterized probabilistically, and further updated and refined with the collected database of field cases and/or experimental data. Finally, this hybrid framework is demonstrated through the development of data-driven empirical models for predicting seismic deformations of anchored sheetpile walls. The results show that the proposed framework is effective in developing a data-driven empirical model, and the obtained model can be easily updated with additional case history data, as needed, to improve the accuracy.

关键词： Anchored sheetpile wall bayesian inference method Data-driven empirical model Seismic deformation

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Advancing data reuse in phyloinformatics using an ontology-driven Semantic Web approach

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BMC MEDICAL GENOMICS 2013年第3期6卷 1-10页

作者： Panahiazar, Maryam Sheth, Amit P. Ranabahu, Ajith Vos, Rutger A. Leebens-Mack, Jim Wright State Univ Coll Comp Sci & Engn Ohio Ctr Excellence Knowledge Enabled Comp Kno E Dayton OH 45435 USA Univ Georgia Dept Plant Biol Athens GA 30602 USA Nat Biodivers Ctr Leiden Netherlands Univ Georgia Bioinformat Inst Athens GA 30602 USA

Phylogenetic analyses can resolve historical relationships among genes, organisms or higher taxa. Understanding such relationships can elucidate a wide range of biological phenomena, including, for example, the importance of gene and genome duplications in the evolution of gene function, the role of adaptation as a driver of diversification, or the evolutionary consequences of biogeographic shifts. Phyloinformaticists are developing data standards, databases and communication protocols (e.g. Application Programming Interfaces, APIs) to extend the accessibility of gene trees, species trees, and the metadata necessary to interpret these trees, thus enabling researchers across the life sciences to reuse phylogenetic knowledge. Specifically, Semantic Web technologies are being developed to make phylogenetic knowledge interpretable by web agents, thereby enabling intelligently automated, high-throughput reuse of results generated by phylogenetic research. This manuscript describes an ontology-driven, semantic problem-solving environment for phylogenetic analyses and introduces artefacts that can promote phyloinformatic efforts to promote accessibility of trees and underlying metadata. PhylOnt is an extensible ontology with concepts describing tree types and tree building methodologies including estimation methods, models and programs. In addition we present the PhylAnt platform for annotating scientific articles and NeXML files with PhylOnt concepts. The novelty of this work is the annotation of NeXML files and phylogenetic related documents with PhylOnt Ontology. This approach advances data reuse in phyloinformatics.

关键词： Markov Chain Monte Carlo Semantic Annotation Provenance Information Amino Acid Substitution Model bayesian inference method

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Combining Sparse Appearance and bayesian inference Models for Object Tracking 5

Combining Sparse Appearance and Bayesian Inference Models fo...

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5th International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Jiang, Zhengqiang Xu, Benlian Gong, Shengrong Changshu Inst Technol Sch Comp Sci & Engn Changshu 215500 Peoples R China Changshu Inst Technol Sch Elect & Automat Engn Changshu 215500 Peoples R China

ISBN: (纸本)9781509006502

In this paper, we present a method that combines a sparse appearance model into the bayesian inference framework for tracking pedestrians in video sequences captured by a fixed camera. We formulate sparse appearance model as a linear combination of a set of 4D smoothed colour histograms for each pedestrian. These colour histograms are computed for all detection windows with different confidence values from human detector proposed by Dalal and Triggs. Object tracking is carried out using the bayesian inference method. For occlusion handling, we integrate the Kalman filter to get the potential region containing target's observation and then use maximum a posteriori estimation to get the most likely observation. We test our tracking method on the benchmark video datasets. Our experimental results show that our tracking method outperforms one without using occlusion handling technique and can handle partial occlusion and false negative errors from human detector.

关键词： Object tracking bayesian inference method Occlusion handling appearance model

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