Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

Singh, Soumya; Ebongue, Yvonne Eboumbou; Rezaei, Shahed; Birke, Kai Peter

doi:10.3390/batteries9060301

Cited by 19 publications

(6 citation statements)

References 52 publications

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“…Ensuring the robustness of DL models against uncertainties such as sensor noise, operational variations, and environmental changes remains a challenging task in BPHM [129]. Physics-informed neural networks combine physical laws with data-driven insights to provide comprehensive and reliable BPHM [130]. With LIB data scarcity, physics-guided DL can conserve a high performance of DL algorithms under unseen conditions [70].…”

Section: A Research Challengesmentioning

confidence: 99%

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Massaoudi,

Abu-Rub,

Ghrayeb

2024

IEEE Open J. Ind. Applicat.

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Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in addressing the challenges faced by the battery research and innovation community. This review paper analyzes the mainstream developments in BPHM using DL techniques. The fundamental concepts of BPHM are discussed, followed by a detailed examination of the emerging DL techniques. A case study using a data-driven DLinear model for state of health estimation is introduced, achieving accurate forecasts with minimal data and high computational efficiency. Finally, the potential future pathways for research and development in BPHM are explored. This review offers a holistic understanding of emerging DL techniques in BPHM and provides valuable insights and guidance for future research endeavors.INDEX TERMS Deep learning, health and life-cycle analysis, Lithium-ion battery management system, prognostics and health management, remaining useful life prediction, state of charge estimation. Nomenclature

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Section: A Research Challengesmentioning

confidence: 99%

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Massaoudi,

Abu-Rub,

Ghrayeb

2024

IEEE Open J. Ind. Applicat.

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“…Just in the past few months, scholars have extensively attempted to use PINN to solve PDE in their own industry field. Roh et al (2023) have attempted to use PINN to solve PDE in salinity transfer kinetics, and have demonstrated that this method has lower absolute deviation than ordinary artificial neural network (ANN) [61]; Singh et al (2023) have integrated particle models into the training process of NN, and have used PINN to predict the charging state and health state of lithium-ion batteries for PDE [62]; Zhou et al ( 2023) have even combined PINN with traditional FEM and have proposed an integrated smooth finite element method (SFEM) to solve elastic-plastic forward and inverse PDEs, which have achieved lower error [63].…”

Section: Solving Pdes Based On Pinnmentioning

confidence: 99%

Compressible Non-Newtonian Fluid Based Road Traffic Flow Equation Solved by Physical-Informed Rational Neural Network

Yang,

Li,

Nai

et al. 2024

IEEE Access

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The study of road traffic flow theory utilizes physics and applied mathematics to analyze relevant parameters and their relationships quanlitatively and quantitatively, in order to explore their dynamic changes. The fluid dynamics model used for traffic flow analysis is highly favored by scholars due to its solid mathematical foundation and good simulation results. However, existing models have two main shortcomings: firstly, existing research is mostly limited to non-viscoelastic fluid equation or incompressible non-Newtonian fluid equation, making it difficult to accurately describe the viscosity state and micro cluster properties of the actual traffic flow; secondly, the existing non-Newtonian fluid partial differential equations (PDEs) rely heavily on the finite element method (FEM) for solving, requiring higher computational cost, larger storage space, and more constraint conditions. Thus, in this paper, a traffic flow equation based on compressible non-Newtonian fluid has been constructed, and it has been solved by using physical-informed rational neural network (PIRNN) and noise heavy-ball acceleration gradient descent (NHAGD) to ensure learning and training speed and accuracy. Numerical results indicate that the proposed method can truly reflect the gradual change process in the viscosity of traffic flow, and has better solving performance than traditional FEM and physical-informed neural network (PINN) with activation functions; under the same conditions, the prediction error of the proposed method is also smaller than that of traditional traffic flow models.

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“…There are also relevant studies using PINN [25][26][27][28] in the field of batteries. Li et al [25] used 2d-LSTM to build a state observer for the key parameters for the P2D model.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [28] utilized physics-informed neural networks as a solver for the PDE of lithium-ion concentration diffusion in electrode particles. The established battery model was then used to estimate the battery's state-of-charge (SOC) and state-of-health (SOH).…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Single-Particle Model Using a Physics-Informed Neural Network Considering Electrolyte Dynamics for Lithium-Ion Batteries

Xue,

Jiang,

Zhu

et al. 2023

Batteries

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As power sources for electric vehicles, lithium-ion batteries (LIBs) have many advantages, such as high energy density and wide temperature range. In the algorithm design process for LIBs, various battery models with different model structures are needed, among which the electrochemical model is widely used due to its high accuracy. However, the electrochemical model is composed of multiple nonlinear partial differential equations (PDEs) that make the simulating process time-consuming. In this paper, a physics-informed neural network single-particle model (PINN SPM) is proposed to improve the accuracy of the single-particle model (SPM) under high C-rates, while ensuring high solving speed. In PINN SPM, an SPM-Net is designed to solve the distribution of lithium-ion concentration in the electrolyte. In the neural network learning process, a loss function is designed based on the physical constraints brought by the PDEs, which reduces the error of the neural network under dynamic working conditions. Finally, the PINN SPM proposed in this paper can achieve a maximum relative error of up to 1.2% compared with the high-fidelity data generated from the P2D model under various conditions. Additionally, the PINN SPM is 20.8% faster than traditional numerical solution methods with the same computational resources.

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Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

Cited by 19 publications

References 52 publications

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Compressible Non-Newtonian Fluid Based Road Traffic Flow Equation Solved by Physical-Informed Rational Neural Network

An Enhanced Single-Particle Model Using a Physics-Informed Neural Network Considering Electrolyte Dynamics for Lithium-Ion Batteries

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