State and parameter estimation of lithium-ion battery by Kreisselmeier-type adaptive observer for fractional calculus system

Takamatsu, Takahiro; Ohmori, Hitoshi

doi:10.1109/sice.2015.7285394

Cited by 6 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is well known that if the pair (A, b) is completely controllable, there exists a vector k such that arbitrary eigenvalues can be assigned to the matrix H := A − bk T . In particular, if x is available the controller u = −k T x applied to system (24) drives x to zero aymtoticall with speed determined by the eigenvalues of matrix H. Since we are assuming that x is unknown, we will use an estimationx for (24) obtained from the following FOO of order β whose design is based on (12) and (13) (25) whereũ,ỹ are filtered versions of u, y given, in Laplace domain, by…”

Section: Applicationmentioning

confidence: 99%

“…The plant is unstable, controllable, observable and in observable canonical form. Thus, an observer with structure given by (13) and 25is implemented, using…”

Section: Simulation Examplementioning

confidence: 99%

“…The conditions obtained meet those of the integer order observers (IOO) [4] when particularized to them, and constitutes a contribution in the non-mixed fractional order observers (FOO) literature for the adaptive case (cf. [8,9,13,14]), the latter being especially challenging, since currently there is not a fractional Lasalle theorem [15] available. The importance of this contribution is supported by the capability of fractional order systems (FOS) to model complex phenomena [10,16,17], whereby observers are needed since the internal variables are usually not accessible.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Mixed Order Fractional Observers for Minimal Realizations of Linear Time-Invariant Systems

et al. 2018

View full text Add to dashboard Cite

Adaptive and non-adaptive minimal realization (MR) fractional order observers (FOO) for linear time-invariant systems (LTIS) of a possibly different derivation order (mixed order observers, MOO) are studied in this paper. Conditions on the convergence and robustness are provided using a general framework which allows observing systems defined with any type of fractional order derivative (FOD). A qualitative discussion is presented to show that the derivation orders of the observer structure and for the parameter adjustment are relevant degrees of freedom for performance optimization. A control problem is developed to illustrate the application of the proposed observers.

show abstract

Section: Applicationmentioning

confidence: 99%

“…The plant is unstable, controllable, observable and in observable canonical form. Thus, an observer with structure given by (13) and 25is implemented, using…”

Section: Simulation Examplementioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Mixed Order Fractional Observers for Minimal Realizations of Linear Time-Invariant Systems

et al. 2018

View full text Add to dashboard Cite

show abstract

“…11 Unfortunately, recently developed parameter estimation methods in FOECM may be computationally intensive because of the existence of a large number of convolution and integration 12,13 proposed least squares based state variable filter to the FOECM identification of LIBs which could accurately capture the battery system dynamics. 14 Designed the Kreisselmeier type adaptive observer to identify parameters of the LIBs described as Randles circuit including fractional calculus. 15 Proposed a novel fractional order kinetic battery model to describe the LIBs with nonlinear capacity and the parameters were achieved by the experimental data from the capacity test.…”

Section: Introductionmentioning

confidence: 99%

Improved coyote optimization algorithm for parameter estimation of lithium-ion batteries

Hao

Ding

Huang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part A:

View full text Add to dashboard Cite

This paper studies the parameter estimation of fractional order equivalent circuit model of lithium-ion batteries. Since intelligent optimization algorithms can achieve parameters with high accuracy by transforming the parameter estimation into optimization problem, coyote optimization algorithm is taken in this paper by modifying two key steps so as to improve the accuracy and convergence speed. Firstly, tent chaotic map is introduced to avoid falling into local optimum and enhance population diversity. Secondly, dual strategy learning is employed to improve the searching ability, accuracy and convergence speed. Non-parametric statistical significance is tested by 6 benchmark functions with the comparison of other 5 optimization algorithms. Furthermore, the proposed algorithm is applied to identify the fractional order model of the Samsung ICR18650 (2600 mAh) and compared with conventional coyote optimization algorithm and particle swarm algorithm, which declared the excellence in identification accuracy.

show abstract

Fractional Order Error Models With Parameter Constraints

Aguila‐Camacho

Duarte‐Mermoud

Mayol-Suárez

2018

Mathematical Techniques of Fractional Order Systems

View full text Add to dashboard Cite

State and parameter estimation of lithium-ion battery by Kreisselmeier-type adaptive observer for fractional calculus system

Cited by 6 publications

References 3 publications

Mixed Order Fractional Observers for Minimal Realizations of Linear Time-Invariant Systems

Mixed Order Fractional Observers for Minimal Realizations of Linear Time-Invariant Systems

Improved coyote optimization algorithm for parameter estimation of lithium-ion batteries

Fractional Order Error Models With Parameter Constraints

Contact Info

Product

Resources

About