Hard limitations of polynomial approximations for reduced-order models of lithium-ion cells

Ortiz-Ricardez, Fernando A.; Romero-Becerril, Aldo; Álvarez-Icaza, Luis

doi:10.1007/s10800-019-01395-y

Cited by 3 publications

(3 citation statements)

References 52 publications

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“…In contrast, the actual electrode model does not exhibit these characteristics. However, Second-order polynomials provide smaller bandwidth and less precision in transients as compared to higher-order polynomial approximations [26]. Another feature of polyno-mials is time variance.The coefficients of polynomials are calculated at every iteration.…”

Section: Noveltymentioning

confidence: 99%

Simplification of full homogenized macro-scale model for lithium-ion batteries

Qadir

Chen

2022

Journal of Energy Storage

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Section: Noveltymentioning

confidence: 99%

Simplification of full homogenized macro-scale model for lithium-ion batteries

Qadir

Chen

2022

Journal of Energy Storage

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“…As a consequence of the long computational time, there has been a great effort on simplifying the P2D model into single particle models [16,17] or reduced-order P2D models with polynomial approximations [18]. These simplifications increase the calculation efficiency but also limits the conditions where the models are valid [19]. Furthermore, the physics-based models with complex mathematical equations remains challenging to use and interpret for researchers without a solid mathematics or physics background.…”

Section: Introductionmentioning

confidence: 99%

Bridging physics-based and equivalent circuit models for lithium-ion batteries

Geng

Wang

Lacey

et al. 2021

Electrochimica Acta

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“…However, PA method is accurate for describing the solution profiles only if steady or slow-varying currents are applied to the cell [31,42,43], which undermines the validity conditions of the SPM [20]. Besides, structural limitations of higher order PADMs, such as model instability or phase nonminimality, were recently demonstrated [44].…”

Section: Introductionmentioning

confidence: 99%

Residue grouping order reduction method in solid-phase lithium-ion battery models

2021

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Obtaining precise reducer order battery models is a key component in designing lithium-ion battery management systems. In general, with conventional order reduction techniques, low order is followed by low accuracy. To overcome such limitation, this paper presents a frequency-based constructive method to obtain broad-band low-order approximations of the solid-phase lithium-ion diffusion submodel, the core of the reference pseudo-two-dimensional electrochemical model of lithium-ion cells. The proposed method is a combination of an improvement on the residue grouping technique and of standard balancing and truncation/residualization methods borrowed from control systems theory. As main result, dramatic order reductions from 3000th to 5th-order with minimum accuracy losses are obtained. Taking a well-known case-study of a lithium-ion battery model, it is shown that 5th-order models offer approximations with maximum absolute errors of 0.5%, for characteristic times up to 9.54 × 10 4 s . The proposed method will help to obtain accurate real-time estimations of surface concentration and state of charge of lithium-ion batteries.

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Hard limitations of polynomial approximations for reduced-order models of lithium-ion cells

Cited by 3 publications

References 52 publications

Simplification of full homogenized macro-scale model for lithium-ion batteries

Simplification of full homogenized macro-scale model for lithium-ion batteries

Bridging physics-based and equivalent circuit models for lithium-ion batteries

Residue grouping order reduction method in solid-phase lithium-ion battery models

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