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17th IEEE International Conference on Industrial Informatics (INDIN)

作者： Pajovic, Milutin Orlik, Philip V. Wada, Toshihiro Takegami, Tomoki MERL Cambridge MA 02139 USA Mitsubishi Electr Corp Amagasaki Hyogo Japan

ISBN: (纸本)9781728129273

Rechargeable batteries supply numerous devices with electric power and are critical part in a variety of applications. An accurate monitoring and prediction of capacity degradation is directly related to making timely decision as to when a battery should be replaced, so that power disruption of the system it supplies power to is avoided. We propose a methodology for predicting capacity of a battery over future time horizon. The proposed method is based on training data consisting of occasional measurements, taken under the same conditions, of capacity and charge/discharge voltage/current of a certain number of batteries sharing the same chemistry and manufacturer, that otherwise undergo different usage patterns. In the operational/online stage, capacity degradation over future time horizon of a test battery cell of unknown state of health and previous usage pattern is predicted based on its capacity and voltage/current measurements over one charge/discharge cycle and the training dataset. The experimental validation reveals that the proposed method predicts capacity of a test battery cell over prediction time horizon of few hundred days of battery's operation with relative prediction error below 1%.

关键词： battery prognosis degradation prediction data-driven method rechargeable battery state of health

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A data-driven method for Life Prediction Based on Performance Degradation data Under Complicated Stress

A Data-Driven Method for Life Prediction Based on Performanc...

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IEEE International Conference on Industrial Engineering and Engineering Management IEEM

作者： Meng, J. R. Feng, J. Liu, T. Y. Sun, Q. Pan, Z. Q. Natl Univ Def Technol Coll Informat Syst & Management Changsha Hunan Peoples R China

ISBN: (纸本)9781467380669

This paper presents a novel life prediction approach for degraded components under complicated stress. The approach mainly consists of two parts: a data discretization method for computing degradation rates and a clustering analysis rule to divide the complicated stress into several categories. An empirical model is built to describe the relationship between the center of stress and the degradation rate. Finally, life prediction under any given stress level can be conducted based on the empirical model. The data-driven method in this paper can avoid the difficulty of degradation modeling under complicated stress profiles. The effectiveness of the proposed method is demonstrated by a case study.

关键词： data-driven method data discretization clustering analysis complicated stress

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Optimal ordering policy for platelets:data-driven method vs model-driven method

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Fundamental Research 2021年第5期1卷 508-516页

作者： Mingfang Yang Xu Chen Zheng Luo School of Management and Economics University of Electronic Science and Technology of ChinaChengdu 611731P.K China

Platelets,one of the most significant materials in treating leukemia,have a limited shelf life of approximately five *** platelets cannot be manufactured and can only be centrifuged from whole or donated blood directly,an accurate ordering policy is necessary for the efficient use of this limited blood *** this motivation,the present study examines an ordering policy for platelets to minimize the expected shortage and *** than using the two-step model-driven method that first fits a demand distribution and then optimizes the order quantity,we solve the issue using an integrated datadriven ***,the data-driven method works directly with demand data and does not rely on the assumption of demand ***,we derive theoretical insights into the optimal *** a comparative analysis,we find that the data-driven method has a mean anchoring effect,and the amounts of shortage and overage reduced by this method are greater than those reduced by the model-driven ***,we present an extended model with the service level requirement and conclude that the order decided by the data-driven method can precisely satisfy the service level requirement;however,the order decided by the model-driven method may be either higher or lower than the service level requirement and can lead to a higher cost.

关键词： Platelets Ordering policy Model-driven method data-driven method Mean anchoring effect Service requirement

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Lithium-Ion Battery Anode-Voltage Estimation Using data-driven method For DC Fast Charging Control To Mitigate Lithium Plating 4

Lithium-Ion Battery Anode-Voltage Estimation Using Data-Driv...

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4th Modeling, Estimation, and Control Conference (MECC)

作者： Hegde, Bharatkumar Haskara, Ibrahim Gen Motors Co Detroit MI 48301 USA

DC fast charging (DCFC) of a lithium-ion battery for EV applications can cause deterioration to the battery cell health due to lithium-plating of the anode. While anode potential is a good indicator for lithium plating reaction occurrence, it cannot be monitored in commercial cells due to lack of a reference electrode. A DCFC controller informed by anode potential has the potential to achieve fast charging while simultaneously mitigating the lithium plating risk. We propose a data -driven anode voltage estimation method based on novel physics inspired features, a time delay neural network, and experimental 3-electrode data. The estimation method is shown to be accurate and fast, and its use in a closed-loop DCFC controller is illustrated through a simulation study. Copyright (c) 2024 The Authors.

关键词： DC fast charging DCFC anode voltage estimation lithium plating battery state estimation data-driven method neural network machine learning

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data-driven method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory

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SENSORS 2021年第1期21卷 182-182页

作者： Gao, Tianhong Li, Yuxiong Huang, Xianzhen Wang, Changli Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Northeastern Univ Minist Educ China Key Lab Vibrat & Control Aeroprop Syst Shenyang 110819 Peoples R China

Bearings are some of the most critical industrial parts and are widely used in various types of mechanical equipment. Bearing health status can have a significant impact on the overall equipment performance, and bearing failures often cause serious economic losses and even casualties. Thus, estimating the remaining useful life (RUL) of bearings in real time is of utmost importance. This paper proposes a data-driven RUL prediction method for bearings based on Bayesian theory. First, time-domain features are extracted from the bearing vibration signal and data are fused to build a health indicator (HI) and a state model of bearing degradation. Then, according to Bayesian theory, a Bayesian model of state parameters and bearing life is established. The parameters of the Bayesian model are updated and bearing RUL is predicted by the Metropolis-Hastings algorithm. The method was validated by the XJTU-SY bearing open datasets and the prediction results are compared with the existing methods. Accuracy of the proposed method was demonstrated.

关键词： data-driven method Bayesian model Metropolis-Hastings algorithm remaining useful life prediction

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Seq-SVF: An unsupervised data-driven method for automatically identifying hidden governing equations

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COMPUTER PHYSICS COMMUNICATIONS 2023年第1期292卷

作者： Wu, Zhetong Ye, Hongfei Zhang, Hongwu Zheng, Yonggang Dalian Univ Technol Fac Vehicle Engn & Mech Dept Engn Mech State Key Lab Struct Anal Optimizat & CAE Software Dalian 116024 Peoples R China

In this work, an unsupervised data-driven method based on sequential singular value filtering (SeqSVF) is proposed to simultaneously identify multiple partial differential equations from observed data considering potential noises. This method is aimed to extend the Sparse Identification of Nonlinear Dynamics (SINDy) to the identification of general nonlinear partial differential equations by transforming the paradigm based on regression to an unsupervised paradigm. To discover the complex coupled equations of vector or tensor forms without prior knowledge, the techniques of singular value decomposition (SVD) and strong rank-revealing QR factorization (sRRQR) are applied to the data matrix, which ensures that the method can automatically identify the number and the corresponding linearly independent terms as the left-hand terms of governing equations. To balance the complexity and the precision of modeling, a strategy for filtering singular values is designed to determine the sparse structure of governing equations from a large number of nonlinear basis functions. We show the success of the method to extract explicit and succinct models from many complex linear, nonlinear, and multiphysics mechanical systems, and the examples show more accuracy compared with using traditional sparse learning methods. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： System identification data-driven method Singular value decomposition Sparse learning

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Lithium-Ion Battery Anode-Voltage Estimation Using data-driven method For DC Fast Charging Control To Mitigate Lithium Plating

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IFAC-PapersOnLine 2024年第28期58卷 150-155页

作者： Bharat kumar Hegde Ibrahim Haskara General Motors Company Detroit MI 48301 USA

DC fast charging (DCFC) of a lithium-ion battery for EV applications can cause deterioration to the battery cell health due to lithium-plating of the anode. While anode potential is a good indicator for lithium plating reaction occurrence, it cannot be monitored in commercial cells due to lack of a reference electrode. A DCFC controller informed by anode potential has the potential to achieve fast charging while simultaneously mitigating the lithium plating risk. We propose a data-driven anode voltage estimation method based on novel physics inspired features, a time delay neural network, and experimental 3-electrode data. The estimation method is shown to be accurate and fast, and its use in a closed-loop DCFC controller is illustrated through a simulation study.

关键词： DC fast charging DCFC anode voltage estimation lithium plating battery state estimation data-driven method neural network machine learning

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A Hybrid data-driven method to Predict Battery Capacity of Medical Devices and Analyze Component Effects

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Fang, Run Liao, Chengsheng Quan, Hong Zeng, Libo Peng, Qiao Wuhan Univ Sch Phys & Technol Wuhan Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China Queens Univ Belfast Belfast North Ireland

Lithium-ion batteries are currently the most utilized power source in medical devices due to their long service life, high energy performance, and being portable. The performance of battery-powered medical devices is heavily dependent on battery capacity, which would be directly affected by related battery component parameters. To widen the application of battery-powered medical devices, it is vital to effectively monitor battery capacity and analyze the effects of battery component parameters. This article derives a hybrid data-driven method to achieve accurate early predictions of battery capacity and reliable analysis of battery component effects. To be specific, a Gaussian process regression-based data-driven model is first developed to efficiently capture the underlying fitting among four component parameters and battery capacity. Then two effect analysis tools including the automatic relevance determination kernel-based weights and tree-based local interpretable model-agnostic explanation are equipped to quantify and analyze both global and local effects of these four component parameters, respectively. Illustrative results show that the designed hybrid data-driven method is able to provide accurate battery capacity predictions with 0.97 R 2 , while both global effects and local effects of four component parameters are successfully quantified. Due to the merits of data-driven characteristics, the designed hybrid data-driven method is capable of efficiently helping users to monitor/predict battery capacity and analyze/understand the effects of interested component parameters. This could further benefit battery-powered medical devices for higher-performance and longer-lifetime applications.

关键词： data-driven method artificial intelligence battery-powered medical devices battery capacity prediction component parameter analysis medical applications

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Simulation-assisted data-driven method for glare control with automated shading systems in office buildings

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BUILDING AND ENVIRONMENT 2021年 196卷 107801-107801页

作者： Xie, Jiarong Sawyer, Azadeh Omidfar Carnegie Mellon Univ Sch Architecture Pittsburgh PA 15213 USA

An automated shading system is expected to effectively prevent visual discomfort associated with glare while providing adequate daylight penetration. However, the concept of visual comfort has not been well integrated into existing commercial shading systems, leaving potential for visual discomfort and resulting in occupants? dissatisfaction. Meanwhile, advanced methods in the academic literature are not always suitable for industrial applications due to problems associated with intensive real-time computation or privacy concerns. This research aims to address this gap by developing a simulation-assisted data-driven method for glare control with automated shades. The proposed strategy utilizes data from pre-simulated daylight analyses to develop glare predictive models using machine learning algorithms. With real-time solar irradiance measurements and the sun position as the input variables to feed the predictive models, the control algorithm can predict the glare condition and set the shades to an appropriate position that maximizes daylight ingress without causing glare. The presented method was verified using climate-based simulation to adjust the slat angle of automated venetian blinds in an office building. Its performance was compared to that of the conventional cut-off angle control for glare elimination, lighting use savings, and view access. The results showed that the proposed strategy was able to prevent 86.5%?96.9% of the glare and potentially reduce lighting energy use by 80.8% while the cut-off control only resulted in 28.9% glare elimination and 67.6% lighting energy savings. The presented method also allowed for unobstructed view more frequently, outperforming the cut-off angle control in all the examined categories.

关键词： Visual comfort data-driven method Glare-based shading control Model-based control

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A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第8期34卷 085112-085112页

作者： Lyu, Zhichao Wu, Guangqiang Wang, Qiming Tongji Univ Sch Automot Studies Shanghai 201048 Peoples R China Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200090 Peoples R China

An accurate fuel consumption prediction is significant to fuel-economy-oriented optimization, which can improve vehicle fuel economy. Based on the data-driven prediction framework, this paper proposes a real-time fuel consumption prediction model (RFCPM) using kernel extreme learning machine (KELM) which is optimized by fast converged grey wolf algorithm (FCGWA). A new steering function of FCGWA and a new activation function for KELM are presented to ensure the fast converge speed and higher accuracy performance. First, the characteristic variables of RFCPM are selected by reference to the model-based fuel consumption prediction method. Second, a KELM is adopted to predict fuel consumption. Third, FCGWA is adopted to select the best parameters of KELM using k-fold cross-validation method. Finally, the best model is chosen through real-vehicle test. Test results are compared with the original ELM and the wildly used WNN. The experiment shows that the proposed method significantly outperforms the original ELM and WNN in terms of both prediction accuracy and training time.

关键词： fuel efficiency economy fuel consumption prediction kernel extreme learning machine fast converged grey wolf algorithm data-driven method

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