A Reduced-Order Model of a Lithium-Ion Cell Using the Absolute Nodal Coordinate Formulation Approach

Limoge, Damas W.; Bi, Pierre Y.; Annaswamy, Anuradha M.; Krupadanam, Ashish

doi:10.1109/tcst.2017.2692743

Cited by 14 publications

(4 citation statements)

References 27 publications

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“…Several publications propose EChMs that could be used for SoC and other battery states estimation but do not present the SoC estimation process [67,[83][84][85][86][87]. For example, [84] presents the full equation descriptions and model algorithm of an EChM based on the absolute nodal coordinate formulation approach, and also a brief model order reduction techniques review for EChMs.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

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SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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Section: Model-based Estimation Methodsmentioning

confidence: 99%

“…For example, [84] presents the full equation descriptions and model algorithm of an EChM based on the absolute nodal coordinate formulation approach, and also a brief model order reduction techniques review for EChMs. Most methods that use EChMs develop reduced-order models and use an estimator.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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“…In the Galerkin methods [44], the test functions equal the trial functions f k (z). Cosine functions [44], Chebyshev polynomials [45], and Legendre polynomials [46], [47] are usually chosen as the trial or test functions in the spectral methods due to their good characteristics for non-periodic signal reconstruction. In the orthogonal collocation methods or pseudospectral methods [45], [48], [49], the test functions are the Dirac delta functions defined at specific locations (namely, the collocation points).…”

Section: B Function Approximationmentioning

confidence: 99%

Model Order Reduction Techniques for Physics-Based Lithium-Ion Battery Management: A Survey

Karunathilake

Vilathgamuwa

et al. 2022

EEE Ind. Electron. Mag.

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To unlock the promise of electrified transportation and smart grid, emerging advanced battery management systems (BMSs) shall play an important role in health-aware monitoring, diagnosis, and control of widely used lithium-ion (Li-ion) batteries. Sophisticated physics-based battery models incorporated in the advanced BMS can offer valuable battery internal information to achieve improved operational safety, reliability and efficiency, and to extend the lifetime of the batteries. However, developed from the fundamental electrochemical and thermodynamic principles, the rigorous physics-based models are saddled with exceedingly high cognitive and computational complexity for practical applications. This article reviews prevailing order reduction techniques of physics-based Li-ion battery models to facilitate the development of next-generation BMSs. We analyze and comparatively characterize these techniques, mainly from perspectives of model fidelity, computational efficiency, and the scope of applications. By representing many effective and flexible reduced-order models as equivalent circuits, designers and practitioners, who do not have electrochemical expertise but with knowledge of circuit theory, can readily gain insights into multi-physical dynamics as well as their coupling effects inside the batteries. In addition, recommendations are made on how to select appropriate physics-based models for various model-based applications in battery management. Finally, the prospect of physical model-enabled BMSs is discussed, including the potential challenges and future research directions.

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“…They usually supplement the SPM with characterizations of thermal behavior [5,6], electrolyte dynamics [7][8][9][10], and stress buildup [10]. Another important line of research lies in applying model order reduction methods to the DFN, SPM or other electrochemical models, with the aim of accelerating numerical computation [11][12][13][14][15][16].…”

Section: Literature Reviewmentioning

confidence: 99%

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

Tu¹,

Moura²,

Wang³

et al. 2021

Preprint

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Mathematical modeling of lithium-ion batteries (LiBs) is a primary challenge in advanced battery management. This paper proposes two new frameworks to integrate a physics-based model with machine learning to achieve high-precision modeling for LiBs. The frameworks are characterized by informing the machine learning model of the state information of the physical model, enabling a deep integration between physics and machine learning. Based on the frameworks, a series of hybrid models are constructed, through combining an electrochemical model and an equivalent circuit model, respectively, with a feedforward neural network. The hybrid models are relatively parsimonious in structure and can provide considerable predictive accuracy under a broad range of C-rates, as shown by extensive simulations and experiments. The study further expands to conduct aging-aware hybrid modeling, leading to the design of a hybrid model conscious of the state-of-health to make prediction. Experiments show that the model has high predictive accuracy throughout a LiB's cycle life.

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A Reduced-Order Model of a Lithium-Ion Cell Using the Absolute Nodal Coordinate Formulation Approach

Cited by 14 publications

References 27 publications

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Model Order Reduction Techniques for Physics-Based Lithium-Ion Battery Management: A Survey

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

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