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Predicting the state of health of VRLA batteries in UPS using data-driven method

ENERGY REPORTS 2023年 9卷 184-190页

作者： Shang, Yitong Zheng, Weike Yan, Xiaoyun Nguyen, Dinh Hoa Jian, Linni Tencent Inc Shenzhen 518052 Peoples R China Southern Univ Sci & Technol Jiaxing Res Inst Jiaxing 314050 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Kyushu Univ WPI I2CNER Fukuoka 8190395 Japan Kyushu Univ IMI Fukuoka 8190395 Japan

Uninterruptible power battery (UPS) is an important part to ensure the stable operation of data center. Its security is related to the reliability and stability of power system. Among them, the state of health (SOH) prediction is a key issue of the valve regulated lead-acid (VRLA) battery operation and maintenance in data center. In this work, the battery SOH is predicted by the correlation between the nadir voltage value of Coup De Fouet (CDF) phenomenon and SOH. Then, the CDF phenomenon is combined with popular data-driven methods, such as linear regression, regression tree, support-vector machine, gaussian process, neural network, to predict battery SOH through 215 features. Finally, the above method is verified with the real discharge dataset of UPS battery in data center. The experimental results show that the data-driven method combining big data has higher accuracy than the simple prediction of battery SOH based on the nadir voltage value of CDF phenomenon and its variants. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： data center Battery state of health Coup De Fouet phenomenon data -driven method

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Multi-objective optimization for composition design of civil materials based on data-driven method

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MATERIALS TODAY COMMUNICATIONS 2024年 38卷

作者： Zhao, Hongbo Li, Min Zhang, Lin Zhao, Lihong Zang, Xiaoyu Liu, Xinyi Ren, Jiaolong Shandong Univ Technol Sch Civil Engn & Geomat Zibo 255000 Peoples R China Shandong Univ Technol Sch Fine Art Zibo 255000 Peoples R China

The performance of civil materials depends on their compositions. The material design is an important, challenging, and time-consuming work during the construction due to the complex relationship among different compositions and the conflict between properties and economic cost. This study developed a data-driven multiobjective optimization design framework to determine the reasonable material compositions based on laboratory experiment, reduced order model (ROM), and multi-objective optimization (MOO). The ROM was used to capture the complex relationship between the material compositions and the corresponding material performance. The MOO was utilized to determine the pareto optimal solution of compositions in the global space. Taking examples of cement grouts and modified asphalt binders, the develop framework was demonstrated and verified. The developed framework provides an excellent tool for composition design of civil materials.

关键词： Civil materials Composition design Multiple objective optimization data -driven method Reduced order model

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Strategies of Parameter Optimization and Soil Moisture Sensor Deployment for Accurate Estimation of Evapotranspiration Through a data-driven method

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AGRICULTURAL AND FOREST METEOROLOGY 2023年第1期331卷

作者： Chai, Yuanyuan Liu, Hu Yu, Yang Yang, Qiyue Zhang, Xiaoyou Zhao, Wenzhi Guo, Li Yetemen, Omer Chinese Ecosyst Res Network Linze Inland River Basin Res Stn Lanzhou 730000 Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Peoples R China Univ Chinese Acad Sci Beijing 100029 Peoples R China Beijing Forestry Univ Sch Soil & Water Conservat Beijing 100038 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610000 Peoples R China Istanbul Tech Univ Eurasia Inst Earth Sci TR-34469 Istanbul Turkiye

A soil moisture data-driven method through inversely solving the Richards' equation was tested, and proved to be a reliable alternative estimator of actual evapotranspiration (ETa) for relatively homogeneous soils in an arid region. Few studies have examined the feasibility of the method using soil moisture for soils with more heterogeneities, which may lead to biased parameters and varied root depths, and thus inaccurate estimates of ETa. We used a genetic algorithm to optimize the parameters most sensitive to ETa estimation, then estimated daily and seasonal ETa using hourly soil moisture data, measured from four sites (from surface to 2-m depth at 20-cm intervals) with different soil textures and crop species in desert-oasis farmlands in northwest China. The seasonal ETa values were 487, 574, 436, and 377 mm at the sites cropped with field maize, spring wheat, seed maize, and seed maize, respectively, in 2018. The daily rates from one site were validated against the eddy-covariance measurements, and the daily rates from all the sites were compared against the estimations from a simple water balance method, and both showed good consistency (R2 = 0.72-0.93). The inverse method has great potential for relatively accurate estimates of ETa in the middle and late growing stages but may induce a slight underestimation in the initial growing season. To determine the minimum monitoring depths of sensors for relatively accurate estimates, ETa values were estimated using soil moisture data at various observation depths, and were compared with eddy-covariance measurements and hypothetical ETa-observed depth curves. The results showed that inaccurate stratification of soil types in the profiles may cause great uncertainty in the estimates, and if texture-contrasting interlayers exist in the profiles, the lowest soil moisture sensor should be placed sufficiently deep, with all the interlayers fully included.

关键词： Soil moisture data -driven method Richards ? equation Evapotranspiration Installation depth

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A data-driven performance analysis and prediction method for electric vehicle cabin thermal management system

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APPLIED THERMAL ENGINEERING 2024年 240卷

作者： Zhao, Yihang Wei, Mingshan Dan, Dan Xie, Yi Zheng, Siyu Zhang, Yuxuan Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China CATARC New Energy Vehicle Test Ctr TianJin Co Ltd Tianjin 300300 Peoples R China

Urban conditions and passenger behaviors primarily affect the performance of the electric vehicle (EV) cabin thermal management system (CTMS). The systematic analysis and accurate prediction of performance variations are crucial for the holistic optimization of the system. This study evaluated the effect of different ambient temperatures, solar radiations, driving patterns, and preset temperatures in Beijing, China. Energy analysis, exergy analysis, and the adaptive predictive mean vote method were employed to comprehensively analyze the performance of air conditioning (AC) system and cabin thermal comfort. A support vector machine model based on the multi-improved snake optimizer (MISO-SVM) was proposed to predict the CTMS performance. Chaotic mapping, subtraction-average-based optimizer, and lens imaging reverse learning strategy were applied to enhance the optimization capabilities of the traditional snake optimizer. A CTMS model was developed and validated to facilitate analysis and dataset acquisition. The results indicate that the energy consumption increases dramatically with elevated ambient temperatures (9.27 times increase from 26 degrees C to 40 degrees C) and solar radiation (11.42 times increase from 0 to 1200 W/m2). Conversely, high-speed driving patterns and higher preset tem-peratures (16 degrees C to 26 degrees C) are associated with improved AC system performance, leading to a reduction in energy consumption by up to 79.59 %. Moreover, the MISO-SVM model outperforms the traditional prediction models regarding CTMS performance prediction efficiency. The optimized MISO algorithm reduces the average convergence iterations by 20.59 % and achieves an average reduction of 18.92 % in fitness values. Across diverse prediction tasks, the MISO-SVM consistently maintains R2 above 0.99 and the lowest mean squared error. This study may provide new insights into the performance analysis and prediction of CTMS in EVs.

关键词： Cabin thermal management system Air conditioning system Urban condition Performance analysis Thermal comfort data -driven method

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Stability and reliability analysis of nonlinear stochastic system using data-driven dimensional analysis method

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 212卷

作者： Chen, Xi Jin, Xiaoling Huang, Zhilong Zhejiang Univ Dept Engn Mech Key Lab Soft Machines & Smart Devices Zhejiang Pro Hangzhou 310027 Peoples R China

Stability and reliability are two critical aspects in the qualitative analysis of dynamic systems. In this paper, a data -driven dimensional analysis methodology is proposed to analyze the stability and reliability of nonlinear stochastic dynamical systems. The analytical solutions for the largest Lyapunov exponent and the conditional reliability function with minimal arguments are identified. Beginning with random state data, it first discovers the unique and relevant set of dimensionless groups through the Buckingham Pi theorem and sensitivity analysis. The expressions for the quantities of interest (the largest Lyapunov exponent for stability analysis, and the conditional reliability function for reliability assessment) are determined using Gaussian process regression. Subsequently, the dimensionality of these expressions is reduced based on the relative importance of the dimensionless parameter cluster within the unique dimensionless set. The effectiveness and accuracy of this data -driven method for analyzing stochastic stability and reliability are illustrated by two representative numerical examples, i.e., a 2-DOF nonlinear system subjected to purely parametrical Gaussian white noises, and a 2-DOF system subjected to both parametrical and external noises, respectively. Furthermore, the extensionality of the analytical expressions is also validated. The data -driven dimensional analysis methodology specified here overcomes the limitations of classical dimensional analysis and offers more accurate expressions with fewer arguments compared to other data -driven methods found in the existing literature.

关键词： data -driven method Dimensional analysis Stochastic stability Stochastic reliability Dimensional reduction

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An adaptive data-driven subspace polynomial dimensional decomposition for high-dimensional uncertainty quantification based on maximum entropy method and sparse Bayesian learning

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STRUCTURAL SAFETY 2024年 108卷

作者： He, Wanxin Li, Gang Zeng, Yan Wang, Yixuan Zhong, Changting Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Dalian Univ Technol Ningbo Inst 26 Yucai Rd Ningbo 315016 Peoples R China Hainan Univ Sch Civil Engn & Architecture Haikou 570228 Peoples R China

Polynomial dimensional decomposition (PDD) is a surrogate method originated from the ANOVA (analysis of variance) decomposition, and has shown powerful performance in uncertainty quantification (UQ) accuracy and convergence recently. However, complex high-dimensional problems result in a large number of polynomial basis functions, leading to heavy computational burden, and the probability distributions of the input random variables are indispensable for PDD modeling and UQ, which may be unavailable in practical engineering. This study establishes an adaptive data-driven subspace PDD (ADDSPDD) for high-dimensional UQ, which employs two types of data for modeling the PDD basis function and the low-dimensional subspace directly, namely, the data of input random variables and the input-response samples. Firstly, we propose a data-driven zero-entropy criterion-based maximum entropy method for reconstructing the probability density functions (PDF) of input variables. Then, with the aid of the established PDFs, a data-driven subspace PDD (DDSPDD) is proposed based on the whitening transformation. To recover the subspace of the function of interest accurately and efficiently, we put forward an approximate active subspace method (AAS) based on the Taylor expansion under some mild premises. Finally, we integrate an adaptive learning algorithm into the DDSPDD framework based on the sparse Bayesian learning theory, obtaining our ADDSPDD;thus, the real subspace and the significant PDD basis functions can be identified with limited computational budget. We validate the proposed method by using four examples, and systematically compare four existing dimension-reduction methods with the AAS. Results show that the proposed framework is effective and the AAS is a good choice when the corresponding assumptions are satisfied.

关键词： Uncertainty quantification data -driven method Polynomial dimensional decomposition Maximum entropy method Sparse Bayesian learning

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A data-driven B-spline-enhanced Kriging method for uncertainty quantification based on Bayesian compressive sensing

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 208卷

作者： He, Wanxin Li, Gang Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Dalian Univ Technol Ningbo Inst 26 Yucai Rd Ningbo 315016 Peoples R China

Kriging is a powerful surrogate method for fitting smooth functions, and has been widely used in uncertainty quantification. However, for non-smooth functions, the performance of Kriging may be unsatisfactory. To overcome this drawback, we combine the advantages of spline functions and the Kriging model, and propose a data-driven B-spline-enhanced Kriging method (DDBSEK). Firstly, we establish the data-consistent orthogonal B-spline basis functions with the aid of the sample data of the input variables and replace the trend terms of the traditional Kriging model with these orthogonal B-spline bases. Then, by leveraging the fast marginal likelihood maximization algorithm and the Bayesian compressive sensing technique, an improved analytical Bayesian LASSO (least absolute shrinkage and selection operator) algorithm is developed to retain the active orthogonal B-spline bases. The proposed DDBSEK is validated using two types of numerical examples and one implicit engineering example. Results show that the proposed method is superior to the B-spline function model or the Kriging model alone.

关键词： Uncertainty quantification data -driven method B -spline function Kriging Non -smooth function Bayesian compressive sensing

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data-driven hierarchical multiscale FDEM for simulating rock meso-macro mechanical behavior

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COMPUTER methodS IN APPLIED MECHANICS AND ENGINEERING 2024年 427卷

作者： Zhao, Ruifeng Wu, Zhijun Xu, Xiangyu Wang, Zhiyang Wuhan Univ Sch Civil Engn Wuhan 430072 Peoples R China Wuhan Univ State Key Lab Water Resources Hydropower Engn Sci Wuhan 430072 Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China

This study presents a data -driven based hierarchical multiscale combined finite -discrete element method (DHM-FDEM) for accurately reproducing rock macro -scale mechanical behavior while ensuring acceptable computational costs. To construct the DHM-FDEM scheme, firstly, upscale finite elements assembly (UFEA) and upscale crack elements assembly (UCEA) are constructed, incorporating meso-scale finite elements and crack elements with mechanical parameters directly derived from meso mechanics test results. Then, a conversion method is proposed to transform the mechanical response of UFEA and UCEA into a strain -stress pair of the macro -scale element, avoiding directly solving the mechanical state of macro -scale elements with mismatched mechanical parameters, thus ensuring the accurate reproduction of rock mechanical properties. Furthermore, UFEA and UCEA subjected to various loading paths are conducted by meso-scale FDEM to generate data sets comprising sequential strain -stress pairs with history dependence and one -to -many mapping relationships. The generated data sets are then applied to train a datadriven method, long short-term memory with mixture density network (LSTM-MDN), which directly maps strain input to stress output, thus replacing tedious and repetitive meso-scale FDEM computation on UFEA and UCEA to significantly save computational costs. Subsequently, UFEA and UCEA driven by LSTM-MDN are equivalent to macro -scale FDEM elements, replacing their phenomenological constitutive relationships and thereby achieving the multiscale numerical computation scheme. After that, the uniaxial compression test and Brazilian disk test are conducted to validate the computational accuracy and efficiency of DHM-FDEM. Finally, the capability of DHM-FDEM to accurately reproduce rock accumulated damage from loading -unloading cycles is verified through uniaxial and biaxial cyclic loading -unloading tests.

关键词： data -driven method Multiscale numerical simulation Rock mechanics FDEM Failure process

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Enhancing digital twin efficiency in indoor environments: Virtual sensor-driven optimization of physical sensor combinations

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AUTOMATION IN CONSTRUCTION 2024年 161卷

作者： Shin, Hakjong Kwak, Younghoon Elect & Telecommun Res Inst ETRI Agr Anim & Aquaculture Intelligence Res Ctr Daejeon South Korea Univ Seoul Dept Architecture Seoul South Korea

Multiple sensor nodes are preferred for the development of digital twins in indoor environments;however, it poses significant financial burdens. Therefore, considering both cost-effectiveness and information richness, optimal physical sensor placement and efficient virtual sensor operation must be balanced. data-driven techniques for sensor placement prioritize locations via statistical metrics, information fidelity, and signal strength. When extending spatial sensing through virtual sensors, the indirectly determined physical sensor sites encounter challenges like multicollinearity and overfitting, hampering the reliability of predictive models (virtual sensors). To address this, this study introduces an algorithm that selects optimal physical sensor combinations based exclusively on virtual sensor performance. The results highlight the superiority of our proposed algorithm over conventional methods for virtual sensor development. Notably, our approach reduces computational workload compared to brute-force methods. This algorithm provides an economical means to manage extensive data and holds substantial promise for advancing digital twin capabilities in indoor environments.

关键词： Digital twin Virtual sensor Physical sensor placement data -driven method

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SOH estimation for lithium-ion batteries: An improved GPR optimization method based on the developed feature extraction

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JOURNAL OF ENERGY STORAGE 2024年 83卷

作者： He, Ye Bai, Wenyuan Wang, Lulu Wu, Hongbin Ding, Ming Hefei Univ Technol Anhui Prov Key Lab Renewable Energy Utilizat & Ene Hefei 230009 Peoples R China

Accurate estimation of the State of Health (SOH) for lithium-ion batteries is necessary for the stable operation of the battery system. To accurately estimate the SOH for lithium-ion batteries, we propose an SOH estimation method based on the features of the variation coefficient of partial charging curves, feature processing, and Gaussian Process Regression (GPR). Firstly, the features of the variation coefficient are extracted from the partial charging voltage and current curves as health indicators. The extracted features are efficient and practical, and can effectively reflect the aging phenomenon of batteries. Subsequently, to suppress existing noises, Box-Cox transform (BCT) and discrete wavelet packet transform (DWPT) are employed for the extracted feature signals, thus improving the correlation between the features and the SOH, and ensuring the reliability of the overall framework. Moreover, aiming at the parameters selection problem of the GPR model, an improved particle swarm optimization algorithm with mutation factor and self-adaptive weight adjustment according to population diversity is introduced. Finally, the proposed SOH estimation framework is verified on the NASA battery data set. The experimental results show that the estimation error of the proposed model can be kept within 1.5 % based on different training sample sizes. The results show that the proposed model has high estimation accuracy, generalization, and adaptability.

关键词： Lithium -ion battery SOH estimation Health indicator data -driven method Coefficient of variation

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