Investigation of Model Order Reduction Techniques: A Supercapacitor Case Study

Aizad, Toheed; Sumisławska, Malgorzata; Maganga, Othman; Agbaje, Oluwaleke; Phillip, Navneesh; Burnham, Keith J.

doi:10.1007/978-3-319-01857-7_76

Cited by 4 publications

(1 citation statement)

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“…Our central hypothesis is that models obtained by RG can undergo a further order reduction by singular value decomposition techniques using, among many others [53], two schemes: residualized balanced reduction (RBR) and truncated balanced reduction (TBR) [54]. These balancing-reduction methods, found on the theory of linear time-invariant (LTI) systems, are constructed from a decomposition based on Hankel singular values, which are closely related to the controllability and observability gramians of the initial model [54][55][56][57]. Balancing-reduction of systems still thrives [58][59][60], particularly for marginally stable and unstable systems [61][62][63][64].…”

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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