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A Review on Lithium-Ion Battery modeling from Mechanism-Based and data-driven Perspectives

PROCESSES 2024年第9期12卷 1871页

作者： Ji, Cheng Dai, Jindong Zhai, Chi Wang, Jingde Tian, Yuhe Sun, Wei Beijing Univ Chem Technol Coll Chem Engn 15 North Third Ring Rd Beijing 100029 Peoples R China Kunming Univ Sci & Technol Fac Chem Engn Kunming 650500 Peoples R China West Virginia Univ Chem & Biomed Dept Morgantown WV 26506 USA

As the low-carbon economy continues to advance, New Energy Vehicles (NEVs) have risen to prominence in the automotive industry. The design and utilization of lithium-ion batteries (LIBs), which are core component of NEVs, are directly related to the safety and range performance of electric vehicles. The requirements for a refined design of lithium-ion battery electrode structures and the intelligent adjustment of charging modes have attracted extensive research from both academia and industry. LIB models can be divided into mechanism-based models and data-driven models;however, the distinctions and connections between these two kinds of models have not been systematically reviewed as yet. Therefore, this work provides an overview and perspectives on LIB modeling from both mechanism-based and data-driven perspectives. Meanwhile, the potential fusion modeling frameworks including mechanism information and a data-driven method are also summarized. An introduction to LIB modeling technologies is presented, along with the current challenges and opportunities. From the mechanism-based perspective of LIB structure design, we further explore how electrode morphology and aging-related side reactions impact battery performance. Furthermore, within the realm of battery operation, the utilization of data-driven models that leverage machine learning techniques to estimate battery health status is investigated. The bottlenecks for the design, state estimation, and operational optimization of LIBs and potential prospects for mechanism-data hybrid modeling are highlighted at the end. This work is expected to assist researchers and engineers in uncovering the potential value of mechanism information and operation data, thereby facilitating the intelligent transformation of the lithium-ion battery industry towards energy conservation and efficiency enhancement.

关键词： lithium-ion batteries mechanism modeling data-driven modeling battery aging mechanism structure-activity relationship state of health estimation

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Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems

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PHYSICA D-NONLINEAR PHENOMENA 2024年 462卷

作者： Sharma, Harsh Kramer, Boris Univ Calif San Diego Mech & Aerosp Engn Dept San Diego CA 92161 USA

This work presents a nonintrusive physics -preserving method to learn reduced -order models (ROMs) of Lagrangian systems, which includes nonlinear wave equations. Existing intrusive projection -based model reduction approaches construct structure -preserving Lagrangian ROMs by projecting the Euler-Lagrange equations of the full -order model (FOM) onto a linear subspace. This Galerkin projection step requires complete knowledge about the Lagrangian operators in the FOM and full access to manipulate the computer code. In contrast, the proposed Lagrangian operator inference approach embeds the mechanics into the operator inference framework to develop a data -driven model reduction method that preserves the underlying Lagrangian structure. The proposed approach exploits knowledge of the governing equations (but not their discretization) to define the form and parametrization of a Lagrangian ROM which can then be learned from projected snapshot data. The method does not require access to FOM operators or computer code. The numerical results demonstrate Lagrangian operator inference on an Euler-Bernoulli beam model, the sine -Gordon (nonlinear) wave equation, and a large-scale discretization of a soft robot fishtail with 779,232 degrees of freedom. The learned Lagrangian ROMs generalize well, as they can accurately predict the physical solutions both far outside the training time interval, as well as for unseen initial conditions.

关键词： Structure-preserving model reduction data-driven modeling Lagrangian dynamics Scientific machine learning Operator inference

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modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 951卷 175491页

作者： Secci, Daniele Saysel, Ali Kerem Uygur, Izel Yologlu, Onur Cem Zanini, Andrea Copty, Nadim K. Univ Parma Dept Engn & Architecture Parma Italy Bogazici Univ Inst Environm Sci TR-34342 Istanbul Turkiye Univ Bergen Dept Geog Syst Dynam Grp N-5020 Bergen Norway

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: processbased, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

关键词： Process-based modeling System dynamics modeling data-driven modeling Water resources management Irrigated agriculture

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Evolutionary computational approaches for data-driven modeling of multi-dimensional memory-dependent systems

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SMART STRUCTURES AND SYSTEMS 2015年第3期15卷 897-911页

作者： Bolourchi, Ali Masri, Sami F. Univ So Calif Viterbi Sch Engn Los Angeles CA 90089 USA

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

关键词： computational intelligence genetic algorithms differential equations hysteretic behavior data-driven modeling identification multi-dimensional systems genetic programming

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Advancing Blast Furnace Thermal State Prediction: A data-driven Approach Using Thermocouple Integration and Multimodal modeling

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STEEL RESEARCH INTERNATIONAL 2025年第0期

作者： Zhou, Guanwei Liu, Weiqiang Yu, Yaowei Saxen, Henrik Abo Akad Univ Fac Sci & Engn Proc & Syst Engn Lab FI-20500 Turku Finland Chongqing Univ Sci & Technol Sch Met & Mat Engn Chongqing 401331 Peoples R China Shanghai Univ Sch Mat Sci & Engn State Key Lab Adv Special Steel Shanghai Key Lab Adv Ferrometallurgy Shanghai 200444 Peoples R China

This study develops a data-driven framework to predict the thermal state of blast furnaces using feature fusion from thermocouple data and spatial temperature distribution. The article proposes a hybrid framework based on multimodal integration and clustering algorithms, utilizing data extracted from thermocouples and the temperature distribution features around the furnace hearth. Through these fused features, multiple ensemble models are constructed to predict the thermal state of the blast furnace, with a focus on the thermocouple readings at the hearth. This method enhances understanding of the thermal state of the blast furnace, aiming to improve prediction accuracy and operational reliability. By validating the model with actual industrial data, its effectiveness in thermal state monitoring is demonstrated. The integration of multimodal data sources allows for the extraction of rich information from the thermocouple data, significantly enhancing the model's predictive performance.

关键词： blast furnace optimizations data-driven modeling ensemble machine learning thermal state predictions thermocouple data integrations

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A paradigm for data-driven predictive modeling using field inversion and machine learning

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JOURNAL OF COMPUTATIONAL PHYSICS 2016年 305卷 758-774页

作者： Parish, Eric J. Duraisamy, Karthik Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

We propose a modeling paradigm, termed field inversion and machine learning (FIML), that seeks to comprehensively harness data from sources such as high-fidelity simulations and experiments to aid the creation of improved closure models for computational physics applications. In contrast to inferring model parameters, this work uses inverse modeling to obtain corrective, spatially distributed functional terms, offering a route to directly address model-form errors. Once the inference has been performed over a number of problems that are representative of the deficient physics in the closure model, machine learning techniques are used to reconstruct the model corrections in terms of variables that appear in the closure model. These reconstructed functional forms are then used to augment the closure model in a predictive computational setting. As a first demonstrative example, a scalar ordinary differential equation is considered, wherein the model equation has missing and deficient terms. Following this, the methodology is extended to the prediction of turbulent channel flow. In both of these applications, the approach is demonstrated to be able to successfully reconstruct functional corrections and yield accurate predictive solutions while providing a measure of model form uncertainties. (C) 2015 Elsevier Inc. All rights reserved.

关键词： data-driven modeling Machine learning Closure modeling

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An Efficient data-driven Model for Millimeter-Wave 5G Channel modeling Using Machine Learning and High-Performance Computing

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES INDIA SECTION A-PHYSICAL SCIENCES 2025年第1期95卷 41-54页

作者： Tripathi, Animesh Prakash, Shiv Tiwari, Pradeep Kumar Shukla, Narendra Kumar Univ Allahabad Dept Elect & Commun Engn Prayagraj Uttar Pradesh India

The fifth generation (5G) technology is efficiently designed to perform many things for the betterment of lives, such as Artificial Intelligence, Cyber-Physical Systems, the Internet of Things, etc. To facilitate this huge amount of data very high bandwidth is needed, hence 5G extensively uses the millimeter wave (mm-Wave) to enhance bandwidth. The technology of mm-Wave communication operates at very high frequencies, typically between 30 and 300 GHz. Some of the challenges will be key to realizing the full potential of mm-Wave communication technology for high-speed wireless communication in the future. The difficulties caused by mm-Wave are directivity, propagation loss, and sensitivity to blockage. To overcome these difficulties, we surveyed existing solutions and standards and identified research gaps. As a high data rate, mm-Wave may be considered in future generation communication and propagation channel requirements for mm-Wave investigated precisely for the prior knowledge of the quality of service (QoS) parameters. Therefore, channel modeling is the key need for the estimation of QoS parameters namely delay, angle of arrival, path loss, angle of departure, etc. In this paper, an efficient data-driven model for mm-Wave 5G Channel modeling using machine learning and high-performance computing is proposed which outperformed the other state-of-the-art in terms of various performance matrices.

关键词： 5G cellular networks Mm-wave Path loss data-driven modeling Machine learning (ML)

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modeling of Nonlinear SOEC Parameter System Based on data-driven Method

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ATMOSPHERE 2023年第9期14卷 1432-1432页

作者： Hou, Dehao Ma, Wenjun Hu, Lingyan Huang, Yushui Yu, Yunjun Wan, Xiaofeng Wu, Xiaolong Li, Xi Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Based on the basic nonlinear parameter system of the solid oxide electrolysis cell, the data-driven method was used for system identification. The basic model of the solid oxide electrolysis cell was accomplished in Simulink and experiments were performed under a diversified input/output operating environment. The experimental results of the solid oxide electrolysis cell basic parameter system generated 15 datasets. The system identification process involved the utilization of these datasets with the application of nonlinear autoregressive-exogenous models. Initially, data identification came from the Matlab mechanism model. Then, the nonlinear autoregressive-exogenous structures were estimated and selected exploratively through an individual operating condition. In terms of fitness, we conclude that the solid oxide electrolysis cell parameter system cannot be satisfied by a solitary autoregressive-exogenous model for all datasets. Nevertheless, the nonlinear autoregressive-exogenous model utilized S-type nonlinearities to fit a total of 2 validation datasets and 15 estimated datasets. The obtained results were compared with the basic parameter system of a solid oxide electrolysis cell, and the nonlinear autoregressive-exogenous projected output demonstrated an accuracy of over 93% across diverse operational circumstances-regardless of whether there was noise interference. This result has positive significance for the future use of the solid oxide electrolysis cell to achieve the dual carbon goal in China.

关键词： parameter estimation data-driven modeling system identification solid oxide electrolysis cell autoregressive-exogenous nonlinear autoregressive-exogenous

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Efficient data-driven Off-Design Constraint modeling for Practical Aerodynamic Shape Optimization

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AIAA JOURNAL 2023年第7期61卷 2854-2866页

作者： Li, Jichao He, Sicheng Martins, Joaquim R. R. A. Zhang, Mengqi Cheong Khoo, Boo ASTAR Fluid Dynam Dept Inst High Performance Comp Singapore 138632 Singapore Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore

Off-design constraints are essential in practical aerodynamic shape optimization. Physics-based data-driven modeling has shown to be a feasible way to formulate generalizable off-design constraints. However, two issues hinder the adoption of this approach: inadequate physical mechanism studies and the requirement of massive training data. To address these issues, we propose a feature-oriented data-driven model to learn fundamental physical mechanisms from high-dimensional data. This is achieved by finding low-dimensional latent space relevant to the investigated off-design performance. Then, we customize the constraint model based on the learned physical mechanisms, improving generalizability without relying on prior knowledge. We also propose a Bayesian-optimization-based sampling method to adjust the training data distribution, prioritizing samples with good aerodynamic performance. This uneven sampling strategy improves data efficiency by ensuring accuracy when approaching optimal aerodynamic shapes. The effectiveness of the proposed methods is shown in a low-Reynolds-number airfoil design optimization case and a transonic airfoil design optimization case. We obtain generalizable data-driven off-design aerodynamic models with no prior physical studies, and we reduce the training data volume by 95% compared with a conventional data-driven approach. This work lays the technical foundation for sample-efficient and generalizable data-driven modeling of off-design aerodynamic constraints.

关键词： Gaussian Mixture Models Aerodynamic Shape Optimization data-driven modeling Off-design Constraint Adaptive Sampling Physical Mechanism

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Control-Oriented data-driven and Physics-Based modeling of Maximum Pressure Rise Rate in Reactivity Controlled Compression Ignition Engines

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SAE INTERNATIONAL JOURNAL OF ENGINES 2023年第6期16卷 711-722页

作者： Irdmousa, Behrouz Khoshbakht Basina, L. N. Aditya Naber, Jeffrey Velni, Javad Mohammadpour Borhan, Hoseinali Shahbakhti, Mahdi Michigan Technol Univ Mech Engn Engn Mech Dept Houghton MI 49931 USA Clemson Univ Dept Mech Engn Clemson SC USA Cummins Inc Columbus MS USA Univ Alberta Dept Mech Engn Edmonton AB Canada

Reactivity controlled compression ignition (RCCI) is a viable low-temperature combustion (LTC) regime that can provide high indicated thermal efficiency and very low nitrogen oxides (NOx) and particulate matter (PM) emissions compared to the traditional diesel compression ignition (CI) mode [1]. The burn duration in RCCI engines is generally shorter compared to the burn duration for CI and spark-ignition (SI) combustion modes [2, 3]. This leads to a high pressure rise rate (PRR) and limits their operational range. It is important to predict the maximum pressure rise rate (MPRR) in RCCI engines and avoid excessive MPRRs to enable safe RCCI operation over a wide range of engine conditions. In this article, two control-oriented models are presented to predict the MPRR in an RCCI engine. The first approach includes a combined physical and empirical model that uses the first principle of thermodynamics to estimate the PRR inside the cylinder, and the second approach estimates MPRR through a machine learning method based on kernelized canonical correlation analysis (KCCA) and linear parameter-varying (LPV) methods. The KCCA-LPV approach proved to have higher prediction accuracy compared to physics-based modeling while requiring less amount of calibration. The KCCA-LPV approach could estimate MPRR with an average error of 47 kPa/CAD while the physics-based approach's average estimation error was 87 kPa/CAD. [GRAPHICS] .

关键词： RCCI engine Maximum pressure rise rate Control-oriented model data-driven modeling Physics-based modeling

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