A robust numerical treatment of solid-phase diffusion in pseudo two-dimensional lithium-ion battery models

Kim, Jinyong; Mallarapu, Anudeep; Santhanagopalan, Shriram; Newman, John

doi:10.1016/j.jpowsour.2022.232413

Cited by 7 publications

(5 citation statements)

References 53 publications

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“…From a technical point of view, ECMs perform with reduced computational demands compared to P2D models [36]. To strike a balance between modeling accuracy and computational efforts, a simplified LIB electrochemicalbased model, namely the single particle model (SPM) is proposed [37]. The SPM originates from the comprehensive full-order electrochemical model, thereby inheriting several crucial properties.…”

Section: Volume mentioning

confidence: 99%

“…Within the SPM, each of the two electrodes is conceptualized as a singular spherical solid particle. Unlike the P2-D models, the SPM does not take electrolyte dynamics into consideration and the LIB concentration in the electrolyte reaction is assumed to be uniform [37]. The SPM exhibits a more limited dynamic behavior compared to the full-order model, particularly excluding mechanical responses.…”

Section: Volume mentioning

confidence: 99%

“…The SPM exhibits a more limited dynamic behavior compared to the full-order model, particularly excluding mechanical responses. The impact of these responses on diffusion becomes notably significant when the electrode material possesses a high modulus and elevated partial molar volume [37]. Nonetheless, the accuracy of the SPM commonly diminishes at elevated charge/discharge rates due to its inability to account for electrolyte dynamics.…”

Section: Volume mentioning

confidence: 99%

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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: Volume mentioning

confidence: 99%

Section: Volume mentioning

confidence: 99%

Section: Volume mentioning

confidence: 99%

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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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“…A significant order reduction with a maximum error of 0.5% was obtained when the reduction method was applied to a well-established physics-based battery model. A simple yet effective numerical scheme to solve solid-phase diffusion equations in active materials of lithium-ion batteries was proposed by Kim et al 8 Robustness and computational stability of the numerical treatment when implemented into a pseudo two-dimensional electrochemicalbased battery model were validated for different operating scenarios, including a dynamic current profile based on a benchmark driving profile and a multi-stage charging profile. Cui and Lu 9 applied the spectral approach to reduce differential equations of the solid-phase diffusion process.…”

mentioning

confidence: 99%

A Robust Model Order Reduction Scheme for Lithium-Ion Batteries in Control-Oriented Vehicle Models

Masoudi,

Taghavipour,

Azad

et al. 2024

J. Electrochem. Soc.

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The role of batteries in electrification of vehicles is eminent; thus, a dynamic model that represents the physics-based phenomena of the battery system at a minimum computational cost is essential in the model-based design of electrified vehicle control systems.Furthermore, robustness of the reduced-order battery model when maintaining the dominant physics-based phenomena governing the dynamic behavior of the battery system is crucial. Characterization of the power signal applied to the lithium-ion battery in the energy management controller of a plug-in hybrid electric vehicle shows that there is a dominant frequency range in the input signal to the battery. This key feature can be considered as a basis to construct a reduced-order model in which the training input is different from the original power signal. The original idea in this paper is to generate the training input by applying a low-pass filter to the white-noise random signal to maintain the same dominant frequency range observed in the original power signal. Response of the reduced-order model, constructed using the proper orthogonal decomposition, compared to the high-fidelity battery model shows promising results; a maximum relative error of 1% was obtained for the battery state of charge while simulation time was reduced by 42.9%.

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“…13,14 Extensive research has been performed on modified forms of these "Newman" models, applying them to various application 15 along with improvements to its computational efficiency. 16,17 These models have been mostly used in time-domain form for understanding battery performance, reliability, and safety. [18][19][20] However, use of these models requires tracking a large number of parameters related to phenomena such as electrochemical reaction rates, species conservation and charge conservation, in solid and electrolyte phase.…”

mentioning

confidence: 99%

Simulation of Impedance Changes with Aging in Lithium Titanate-based Cells Using Physics-Based Dimensionless Modeling

Mallarapu,

Santhanagopalan,

Uno

et al. 2023

J. Electrochem. Soc.

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Quantifying aging effects in lithium-ion cells with chemistries that have a flat open circuit potential is challenging. We implement a physics-based electrochemical model to track changes in the electrochemical impedance response of lithium titanate-based cells. Frequency domain equations of a pseudo two-dimensional model are made dimensionless, and the corresponding non-dimensional parameters are estimated using a Levenberg-Marquardt routine. The model weighs the relative contributions of changes in diffusion, ionic conduction within the electrolyte phase against solid phase electronic conduction towards cell aging. Solid-phase diffusion, charge transfer resistance and double layer capacitance at the solid-liquid interface are accounted for in the particle impedance. The estimation routine tracks dimensionless parameters using accelerated cycling data from full cells over 1000 cycles. The model can be deployed within a short time for state estimation using physics-based models without requiring prior knowledge of the battery chemistry, format, or capacity.

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A robust numerical treatment of solid-phase diffusion in pseudo two-dimensional lithium-ion battery models

Cited by 7 publications

References 53 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

A Robust Model Order Reduction Scheme for Lithium-Ion Batteries in Control-Oriented Vehicle Models

Simulation of Impedance Changes with Aging in Lithium Titanate-based Cells Using Physics-Based Dimensionless Modeling

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