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modeling the Dynamics of Cylindrical Lithium-ion Battery Aging Due to Evolving Solid Electrolyte Interphase Layer

JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2025年第3期172卷 030508-030508页

作者： Zhao, Jin Abu Qahouq, Jaber A. Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

The solid electrolyte interphase (SEI) layer plays a critical role in the aging and degradation of lithium-ion batteries (LIBs), directly influencing their performance and longevity. This paper presents a physics-based model that quantitatively characterizes SEI layer growth in cylindrical LIBs by incorporating ionic current density as a governing parameter. The presented approach captures localized SEI dynamics by coupled state-space Eqs. (SSEs) within an convex optimization framework. The model accounts for both uniform and nonlinear SEI growth phases, predicting capacity fade and impedance evolution over cycling aging. Validation against experimental charge-discharge profiles, electrochemical impedance spectroscopy (EIS) characterization, and equivalent circuit modeling demonstrates the model's precision in tracking SEI-related degradation. The proposed framework offers a robust, interpretable, and computationally efficient tool for battery diagnostics and lifetime prediction.

关键词： lithium-ion battery battery aging mechanisms SEI EIS physics-based modeling

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A physics-based FORMAL VOCABULARY OF ENERGY VERBS FOR FUNCTION modeling

A PHYSICS-BASED FORMAL VOCABULARY OF ENERGY VERBS FOR FUNCTI...

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American-Society-of-Mechanical-Engineers(ASME) International Design Engineering Technical Conferences / 39th American-Society-of-Mechanical-Engineers(ASME) Computers and Information in Engineering Conference (IDETC-CIE)

作者： Sen, Chiradeep Summers, Joshua D. Mao, Xiaoyang Florida Inst Technol Melbourne FL 32901 USA Clemson Univ Clemson SC USA

ISBN: (纸本)9780791859179

Function modeling ofcomplex systems relies on predefined vocabularies offunctions and flows. These vocabularies are usually developed in a top-down approach, i.e., by starting with a survey of existing systems and ident0iing their functions empirically. These vocabularies, while highly useful in manual modeling due to their expressive power and coverage, can be unsuitable for computerized modeling and reasoning, esp. for physics-based reasoning. To this end, this paper presents a physics-based vocabulary offunction verbs developed using the bottom-up approach, where the needfor the verbs is identified through a survey ofphysics phenomena involving operations on various energy forms allowed in physics. This survey results in a minimal set of only six verbs and two logical nodes that are proposed here. Each term is formally defined as object-oriented classes derived from more foundational classes proposed in prior research. The paper shows many applications of these terms, for modeling both simpler devices and more complex engineered systems. Collectively, this new vocabulary provides sufficient coverage over modeling needs and ensures models that are logically consistent and physics-wise valid.

关键词： function modeling vocabularies physics-based modeling formal language reasoning

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Dynamic Motion Transition: A Hybrid Data-Driven and Model-Driven Method for Human Pose Transitions

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2025年第3期31卷 1848-1861页

作者： Chai, Zhi Qin, Hong SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

The rapid, accurate, and robust computation of virtual human figures' "in-between" pose transitions from available and sometimes sparse inputs is of fundamental significance to 3D interactive graphics and computer animation. Various methods have been proposed to produce natural lifelike transitions of human pose automatically in recent decades. Nevertheless, conventional pure model-driven methods require heuristic knowledge (e.g., least motion guided by physics laws) and ad-hoc clues (e.g., splines with non-uniform time warp) that are difficult to obtain, learn, and infer. With the fast emergence of large-scale datasets readily available to animators in the most recent years, deep models afford a powerful alternative to tackle the aforementioned challenges. However, pure data-driven methods still suffer from the remaining challenges such as unseen data in practice and less generative power in model/domain/data transfer, and the measurement of the generative power has always been omitted in these works. In essence, data-driven methods solely rely on the qualities and quantities of training datasets. In this paper, we propose a hybrid approach built upon the seamless integration of data-driven and model-driven methods, called Dynamic Motion Transition (DMT), with the following salient modeling advantages: (1) The data augmentation capability based on the limited human locomotion data capture and the concept of force-derived directly from physical laws;(2) Force learning by which skeleton joints are driven to move, and the Conditional Temporal Transformer (CTT) being trained to learn the force change in the local range, both at the fine level;and (3) At the coarse level, the effective and flexible creation of the subsequent step motion using Dynamic Movement Primitives (DMP) until the target is reached. Our extensive experiments have confirmed that our model can outperform the state-of-the-art methods under the newly devised metric by virtue of the least action loss fu

关键词： Force Dynamics Animation Transformers Data models Computational modeling Three-dimensional displays Computer animation dynamics modeling hybrid methods motion transitions neural networks physics-based modeling

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Day-Ahead Solar Power Forecasting Using LightGBM and Self-Attention based Encoder-Decoder Networks

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2025年第2期16卷 866-879页

作者： Hokmabad, Hossein Nourollahi Husev, Oleksandr Belikov, Juri Tallinn Univ Technol Tal Tech Elect Power Engn & Mechatron EE-19086 Tallinn Harjuma Estonia Tallinn Univ Technol TalTech Software Sci EE-19086 Tallinn Harjuma Estonia

The burgeoning trend of integrating renewable energy harvesters into the grid introduces critical issues for its reliability and stability. These issues arise from the stochastic and intermittent nature of renewable energy sources. Data-driven forecasting tools are indispensable in mitigating these challenges with their rugged performance. However, tools relying solely on data-driven methods often underperform when an adequate amount of recorded data is unattainable. To bridge this gap, this paper presents a novel day-ahead hybrid forecasting framework for photovoltaic applications. This framework integrates a physics-based model with Machine Learning (ML) techniques, enhancing prediction reliability in environments with scarce data. Additionally, an innovative ML pipeline is introduced for data-abundant environments. The proposed ML tool comprises two branches: a set of regressors, each tailored for specific weather conditions, and a self-attention-based encoder-decoder network. By fusing the outputs from these branches through a meta-learner, the tool achieves predictions of higher quality, as evidenced by its superior performance over benchmark models in an investigated test dataset.

关键词： Data models Predictive models Forecasting Mathematical models Weather forecasting Photovoltaic cells Renewable energy sources Integrated circuit modeling Accuracy Solar irradiance Solar power forecasting photo voltaic systems physics-based modeling hybrid approach data scarce environments machine learning LightGBM encoder-decoder attention

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Optimal Control for Thermal Management of a Proton Exchange Membrane Fuel Cell Stack With Koopman-based modeling

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2025年第2期147卷 021003页

作者： Huo, Da Hall, Carrie M. IIT Dept Mech Mat & Aerosp Engn 10 West 35th St Chicago IL 60616 USA

This study presents a novel approach to optimal control utilizing a Koopman operator integrated with a linear quadratic regulator (LQR) to enhance the thermal management and power output efficiency of an open-cathode proton exchange membrane fuel cell (PEMFC) stack. First, a linear time-invariant dynamic model was derived through Koopman operator to forecast the behavior of the PEMFC stack. Second, this Koopman-based model was directly integrated with LQR for optimizing temperature, temperature variations, and output power efficiency of the PEMFC stack by regulating fan speed, with a physics-based model serving as the plant model. Finally, the performance of the Koopman-based LQRs (KLQR) was compared to a baseline proportional-integral (PI) controller across various ambient temperatures and operating conditions, focusing on temperature, temperature variations, and net power output. The results demonstrate the proposed Koopman-based approach can be seamless integration with linear optimal control algorithms, effectively minimizing temperature, temperature variations across the PEMFC stack, and the net power outputs under different ambient temperature and operating conditions.

关键词： proton exchange membrane fuel cell (PEMFC) data-driven modeling Koopman operator linear optimal control linear quadratic regulator physics-based modeling

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A KKT-Constrained Deep Neural Network for DCOPF Under Large-Scale Renewable Energy Penetration

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-9页

作者： Al-Ismail, Fahad S. King Fahd Univ Petr & Minerals Coll Engn & Phys Elect Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals IRC Sustainable Energy Syst Dhahran 31261 Saudi Arabia

Addressing DC optimal power flow (DCOPF) challenges in power systems with a high penetration of renewable energy sources (RES) necessitates innovative solutions due to the limitations of data accessibility, dynamic network adaptability, and worst-case scenario planning, including load shedding and RES curtailment. Traditional datasets for neural network training often fall short in quality and relevance, impeding the accurate modeling of machine learning solutions for energy distribution. This study introduces a robust deep neural network model that integrates physics-based constraints (via Karush-Kuhn-Tucker parameters) to effectively manage high levels of RES integration by solving the DCOPF problem. The proposed model simulates DCOPF without running the DCOPF equations, thereby minimizing the maximum deviation between its predictions and actual DCOPF results. By leveraging a dataset derived from DCOPF configurations in a multibus power grid, the model demonstrates superior performance compared to conventional neural network approaches, as confirmed through evaluations with real-world data. The proposed framework has been validated on multiple bus systems, including Garver's 6, IEEE 9-, 14-, and 30-bus systems, confirming its scalability in increasingly complex networks. Although the simulation focuses on the IEEE 30-bus system, the promising results indicate potential applicability to larger networks. Nevertheless, this method relies on deterministic assumptions, which offer a practical first step for verifying its robustness and scalability in high-RES scenarios.

关键词： DCOPF High-RES penetration Deep neural networks KKT constraints RES curtailment physics-based modeling

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physics-based Lumped-Parameter modeling of Automotive Canister Fuel Purge

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2016年第7期138卷 071008-071008页

作者： Franchek, Matthew Ebrahimi, Behrouz Grigoriadis, Karolos Makki, Imad Univ Houston Dept Mech Engn Houston TX 77204 USA Ford Motor Co R&D Dept Detroit MI 48125 USA

A physics-based model is presented to estimate the flow rate out of the fuel canister purged into the intake manifold. The lumped parameters of the model, including canister capacitance and flow resistance, are employed to obtain a first-order multi-input and single-output dynamic model. The vacuum pressure in the intake manifold and the fuel tank pressure serve as inputs, and the purged fuel flow rate is considered as the model output. The model does not require cumbersome computation, thereby allowing direct implementation in the fueling control to compensate for the extra fuel in regulation of the stoichiometric air-fuel ratio.

关键词： canister purge model physics-based modeling multi-input single-output system parameter estimation

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physics-based Machine-Learning Approach for modeling the Temperature-Dependent Yield Strength of Superalloys

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ADVANCED ENGINEERING MATERIALS 2023年第14期25卷 2201903-2201903页

作者： Steingrimsson, Baldur Fan, Xuesong Adam, Benjamin Liaw, Peter K. Imagars LLC POB 261 Wilsonville OR 97070 USA Oregon Inst Technol Dept Mfg Mech & Engn Technol Wilsonville OR 97070 USA Oregon State Univ Sch Mech Ind & Mfg Engn Corvallis OR 97331 USA Univ Tennessee Dept Mat Sci & Engn Knoxville TN 37996 USA

In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering has played a crucial role. Herein, a machine learning approach is presented, capable of predicting the temperature-dependent yield strengths of superalloys utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature, T-break, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black-box approaches, our model is based on the underlying fundamental physics built directly into the model. A technique of global optimization, one allowing the concurrent optimization of model parameters over the low- and high-temperature regimes, is presented. The results presented extend previous work on high-entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature-dependent strength behavior of superalloys as well as HEAs.

关键词： high temperature machine learning physics-based modeling structural materials superalloys yield strength

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modeling a High-Temperature Electrochemically Driven Water-Gas-Shift Process Using a Mixed-Conducting Membrane without External Electrical Power

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2025年第4期172卷

作者： Zhu, Huayang Blackburn, Bryan M. Wang, Ruofan Kelly, Madison A. Hack, Ashley Reinartz, Stefan Jackson, Gregory S. Kee, Robert J. Colorado Sch Mines Dept Mech Engn Golden CO 80401 USA Utility Global Englewood CO 80112 USA

This paper develops a model to predict and interpret the performance of an elevated-temperature, electrochemical, membrane-assisted, water-gas-shift process. The process uses separated feed streams of H2O and CO to produce separated streams of H-2 and CO2, without an external electrical power source. The dense ceramic membrane is mixed ionic-electronic-conducting (MIEC) gadolinium-doped ceria (GDC) and the porous composite electrodes are Ni-YSZ. At elevated temperature, GDC conducts both oxygen ions and small polarons. The present process uses chemical potential to drive the process. Electrochemical oxidation of CO proceeds within the composite anode and H2O reduction proceeds within the composite cathode. At high temperature (e.g., T > 700 degrees C), GDC has significant electronic leakage in the form of a reduced-cerium small polaron, which supports the charge-transfer reactions. In a typical electrolyzer or fuel cell, this leakage is significantly problematic. However, the present process depends on the leakage current to complete the electrochemical circuit. Model development and validation is based on measured material properties and reactor performance. Potential applications include using CO-rich blast-furnace off gases in steel processing, producing separated streams of H-2 and CO2.

关键词： chemically driven electrolysis electrochemical membrane reactor water-gas shift gadolinium-doped ceria physics-based modeling

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SHAPE AND NONRIGID MOTION ESTIMATION THROUGH physics-based SYNTHESIS

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1993年第6期15卷 580-591页

作者： METAXAS, D TERZOPOULOS, D UNIV TORONTO DEPT COMP SCI TORONTO M5S 1A4 ONTARIO CANADA

This paper presents a physics-based framework for 3-D shape and nonrigid motion estimation aimed at real-time computer vision. The framework features dynamic models that incorporate the mechanical principles of rigid and nonrigid bodies into conventional geometric primitives. Through the efficient numerical simulation of Lagrange equations of motion, the models can synthesize physically correct behaviors in response to applied forces and imposed constraints. We exploit the shape and motion synthesis capabilities of our models for the purposes of visual estimation. Applying continuous nonlinear Kalman filtering theory, we construct a recursive shape and motion estimator that employs the Lagrange equations as a system model. We interpret the continuous Kalman filter physically: The system model continually synthesizes nonrigid motion in response to generalized forces that arise from the inconsistency between the incoming observations and the estimated model state. The observation forces also account formally for instantaneous uncertainties and incomplete information. A Riccati procedure updates a covariance matrix that transforms the forces in accordance with the system dynamics and prior observation history. The transformed forces modify the translational, rotational, and deformational state variables of the system model to reduce inconsistency, thus producing nonstationary shape and motion estimates from the time-varying visual data. We demonstrate the dynamic estimator in experiments involving model fitting and tracking of articulated and flexible objects from noisy 3-D data.

关键词： ANALYSIS BY SYNTHESIS COMPUTER VISION CONSTRAINTS DEFORMABLE MODELS KALMAN FILTERING NONRIGID MOTION ESTIMATION physics-based modeling

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