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Article
Chemistry, Physical
Bo Sun et al.
Summary: This paper proposes an adaptive evolution enhanced physics-informed neural network-based time-variant health prognosis framework for lithium-ion batteries. It uses a long short-term memory neural network model with a dynamic sliding window, informed by physical information derived from simulation, to predict the health status and remaining useful life of the batteries. The proposed method provides high prognosis accuracy under different conditions and improves accuracy through adaptive model evolution during long-term operations.
JOURNAL OF POWER SOURCES
(2023)
Article
Energy & Fuels
Hao Tu et al.
Summary: This paper proposes two new frameworks that integrate physics-based models with machine learning to achieve high-precision modeling for lithium-ion batteries. The models have been extensively tested and proven to provide accurate voltage predictions.
Review
Electrochemistry
Long Zhou et al.
Summary: This paper comprehensively reviews the research status, technical challenges, and development trends of state estimation of lithium-ion batteries, which is a core function in the battery management system. It summarizes the key issues and technical challenges in battery state estimation and provides a deep analysis of these challenges. The paper also reviews the joint estimation methods for four typical battery states and proposes feasible estimation frameworks. Furthermore, it discusses the prospect of state estimation development and the influence of advanced technologies like artificial intelligence and cloud networking.
Article
Engineering, Mechanical
Mohammad Vahab et al.
Summary: We explore the application of Physics-Informed Neural Networks (PINNs) with Airy stress functions and Fourier series in finding optimal solutions to reference biharmonic problems in elasticity and elastic plate theory. Our work demonstrates a novel application of classical analytical methods in constructing efficient neural networks with minimal parameters, which are accurate and fast in evaluation. We find that enriching the feature space with Airy stress functions can significantly improve the accuracy of PINN solutions for biharmonic PDEs.
JOURNAL OF ENGINEERING MECHANICS
(2022)
Article
Electrochemistry
Ning He et al.
Summary: This paper proposes an adaptive hybrid model and an improved particle filter for accurately predicting the remaining useful life of lithium-ion batteries. Experimental results demonstrate that the proposed model can effectively characterize battery degradation and achieve higher accuracy.
JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE
(2022)
Article
Mathematics, Applied
Salvatore Cuomo et al.
Summary: Physics-Informed Neural Networks (PINN) are a type of neural network that incorporates model equations, such as partial differential equations, as a component. PINNs have been used to solve various types of equations, including fractional equations and stochastic partial differential equations. Current research focuses on optimizing PINN through different aspects, such as activation functions, gradient optimization techniques, neural network structures, and loss function structures. Despite the demonstrated feasibility of PINN in certain cases compared to traditional numerical techniques, there are still unresolved theoretical issues.
JOURNAL OF SCIENTIFIC COMPUTING
(2022)
Article
Optics
Xiaotian Jiang et al.
Summary: This study investigates a physics-informed neural network (PINN) that combines deep learning with physics to solve the nonlinear Schrodinger equation in fiber optics. PINN is systematically investigated and verified for multiple physical effects in optical fibers, and it exhibits better performance than data-driven neural networks while using less data. The results show that PINN is not only an effective partial differential equation solver, but also a prospective technique for scientific computing and automatic modeling in fiber optics.
LASER & PHOTONICS REVIEWS
(2022)
Article
Engineering, Multidisciplinary
Shahed Rezaei et al.
Summary: Physics Informed Neural Networks (PINNs) can find solutions to boundary value problems by minimizing a loss function that incorporates governing equations, initial and boundary conditions. This study proposes an improved method that uses the spatial gradient of the primary variable as an output and applies the strong form of the equation as a physical constraint. By comparing with finite element methods, it is shown that this approach has advantages, and the potential of combining PINN with physical FE simulations for designing composite materials is discussed.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Chemical
Hui Pang et al.
Journal of Energy Chemistry
(2022)
Article
Computer Science, Information Systems
Gyouho Cho et al.
Summary: This study proposes a physics-informed neural network (PINN) model that combines the advantages of physics-based and data-driven models to achieve better accuracy in battery temperature prediction. The results show that the LSTM-PINN model with chamber temperature as one of the inputs delivers more accurate predictions, demonstrating its potential for practical applications.
Review
Energy & Fuels
F. Brosa Planella et al.
Summary: Physics-based electrochemical battery models are powerful tools for understanding and improving lithium-ion batteries. Different applications require different model complexities. Although simplified models are often independently proposed, they can actually be derived from more complex models. In this review, a reductive framework is showcased and the advantages and shortcomings of each model are discussed. Possible model extensions, particularly thermal models, are also presented.
PROGRESS IN ENERGY
(2022)
Article
Computer Science, Information Systems
Gyouho Cho et al.
Summary: This paper presents the implementation of a physics-informed neural network (PINN) with adaptive normalization in the loss function to predict the temperature of a lithium-ion battery. The PINN was trained using actual battery test data and incorporated a lumped capacitance lithium-ion battery thermal relationship in the loss function, along with the addition of a pre-layer and connection layer to the neural network architecture. The results show that the proposed PINN architecture achieves the most accurate battery temperature prediction compared to a fully connected neural network (FCN) and its variants.
Review
Engineering, Mechanical
Shengze Cai et al.
Summary: Significant progress has been made in simulating flow problems over the last 50 years, but challenges remain in incorporating noisy data, complex mesh generation, and solving high-dimensional problems. Physics-informed neural networks (PINNs) have been demonstrated as effective in solving inverse flow problems related to various fluid dynamics scenarios.
ACTA MECHANICA SINICA
(2021)
Article
Engineering, Multidisciplinary
Ehsan Haghighat et al.
Summary: SciANN is a Python package for scientific computing and physics-informed deep learning. It utilizes TensorFlow and Keras to build deep neural networks and optimization models, allowing for solving partial differential equations with flexibility in setting complex functional forms.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Chemistry, Physical
Weihan Li et al.
Summary: There is a growing demand for modern diagnostic systems for batteries in real-world operation, especially for estimating their health status such as remaining capacity. A data-driven capacity estimation model using recurrent neural networks with long short-term memory capability has been developed for cells under real-world working conditions. This model is robust, can handle input noise, adapt to variations in input time series length, and generate viable estimation even with incomplete input data.
JOURNAL OF POWER SOURCES
(2021)
Article
Chemistry, Physical
Renato G. Nascimento et al.
Summary: Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs) for which modeling and forecasting the remaining useful life is crucial. A hybrid modeling approach combining reduced-order models capturing overall battery discharge behavior and deep neural networks with data-driven kernels is presented. This approach, validated using publicly available NASA data, shows promising results in calibrating a battery prognosis model with limited observations and optimizing battery operation with long-term capacity forecasts.
JOURNAL OF POWER SOURCES
(2021)
Article
Electrochemistry
Muratahan Aykol et al.
Summary: This article discusses several architectures for integrating physics-based and machine learning models to improve the ability to forecast battery lifetime. The ease of implementation, advantages, limitations, and viability of each architecture are analyzed based on the latest technology in the battery and machine learning fields.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2021)
Article
Multidisciplinary Sciences
Xing Shu et al.
Summary: This paper explores the significance of accurate state of health (SOH) prediction for lithium-ion batteries, looking at the development of machine learning techniques in this area. It defines the concept of SOH, classifies prediction methods, surveys health feature extraction methods, conducts comparison, and discusses challenges and research directions for more reliable SOH prediction.
Article
Chemistry, Physical
Weihan Li et al.
Summary: The study introduces a hybrid state estimation method that combines physics-based and machine learning models to accurately estimate the internal states of lithium-ion batteries, demonstrating high reliability and generalization ability.
JOURNAL OF POWER SOURCES
(2021)
Article
Chemistry, Physical
Changlong Li et al.
Summary: This paper develops a simplified yet high-precision electrochemical model for lithium-ion batteries and identifies its parameters over a wide temperature range. The model is thoroughly verified under galvanostatic discharge tests over the temperature range of -20 degrees C to 45 degrees C, showing superior performance over traditional models, especially at subzero temperatures. Furthermore, a health-conscious characteristic map for battery power capability prediction is generated based on the validated model.
JOURNAL OF POWER SOURCES
(2021)
Article
Electrochemistry
Soumya Singh et al.
Summary: This paper investigates the functionalities of battery Digital Twin (DT) and quantifies its attributes across different life cycle stages, discussing the practicality and feasibility of using battery DT to address challenges in the battery industry.
Review
Physics, Applied
George Em Karniadakis et al.
Summary: Physics-informed learning seamlessly integrates data and mathematical models through neural networks or kernel-based regression networks for accurate inference of realistic and high-dimensional multiphysics problems. Challenges remain in incorporating noisy data seamlessly, complex mesh generation, and addressing high-dimensional problems.
NATURE REVIEWS PHYSICS
(2021)
Article
Chemistry, Physical
Jacqueline S. Edge et al.
Summary: The article summarizes the degradation of lithium-ion batteries into three levels: mechanisms, observable consequences, and operational effects. It identifies five principal and thirteen secondary mechanisms leading to five observable modes, and presents a flowchart and table illustrating the feedback loops and experimental conditions for investigating battery degradation.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2021)
Article
Engineering, Multidisciplinary
Luning Sun et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2020)
Article
Engineering, Multidisciplinary
Zhiping Mao et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2020)
Article
Energy & Fuels
Xu-Dong Chen et al.
ENERGY EXPLORATION & EXPLOITATION
(2020)
Article
Materials Science, Multidisciplinary
M. Torabi Rad et al.
COMPUTATIONAL MATERIALS SCIENCE
(2020)
Article
Engineering, Electrical & Electronic
Nassim Noura et al.
WORLD ELECTRIC VEHICLE JOURNAL
(2020)
Article
Electrochemistry
Mostafa Al-Gabalawy et al.
Article
Computer Science, Interdisciplinary Applications
M. Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Article
Engineering, Electrical & Electronic
Datong Liu et al.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2019)
Proceedings Paper
Automation & Control Systems
Zheng Li et al.
PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019)
(2019)
Article
Computer Science, Information Systems
Xiangbao Song et al.
Review
Electrochemistry
Ulrike Krewer et al.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2018)
Article
Automation & Control Systems
Satadru Dey et al.
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2017)
Article
Automation & Control Systems
Shu-Xia Tang et al.
Article
Automation & Control Systems
Ji Liu et al.
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2016)
Review
Physics, Multidisciplinary
Musheng Wu et al.
Review
Chemistry, Physical
Ali Jokar et al.
JOURNAL OF POWER SOURCES
(2016)
Review
Chemistry, Physical
Victor Agubra et al.
Article
Electrochemistry
VR Subramanian et al.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2001)
