Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm

Chang, Chun; Wang, Qiyue; Jiang, Jiuchun; Wu, Tiezhou

doi:10.1016/j.est.2021.102570

Cited by 110 publications

(31 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation results demonstrate the effectiveness of the proposed algorithms and show that the F-MI-RGLS algorithm can obtain higher parameter estimation accuracy and provide reliable model predictions. The proposed approaches in the article can combine other identification methods [105][106][107][108][109][110][111][112] to study the parameter estimation issues of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises [113][114][115][116][117][118][119][120] and can be applied to literatures [121][122][123][124][125][126][127][128] such as paper-making systems, information processing, engineering systems, and so on.…”

Section: Discussionmentioning

confidence: 99%

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Fan

Liu

2022

Optim Control Appl Methods

View full text Add to dashboard Cite

Piecewise-linear nonlinearity is an effective representation for hard nonlinearities such as saturation, dead zone, and backlash, which can be also used as a general tool for approximating nonlinear characteristics. For the input piecewise-linear output-error autoregressive systems, we develop a parametric expression of the piecewise-linear nonlinearity through a switching function and position functions, and derive the identification model of the system by using the key item separation. Based on the optimization criterion, an auxiliary model-based multi-innovation recursive generalized least-squares algorithm is deduced for estimating the unknown parameters according to the obtained model. Since the system is disturbed by colored noise, we introduce the data filtering technique from a view point of improving the parameter estimation accuracy. The filtering identification model of the system is derived and a filtering-based multi-innovation recursive generalized least-squares algorithm is proposed. The simulation example demonstrates the effectiveness of the proposed algorithms and shows that the filtering-based multi-innovation recursive generalized least-squares algorithm has better identification performance.

show abstract

Section: Discussionmentioning

confidence: 99%

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Fan

Liu

2022

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…The simulation examples show the merits and effectiveness of the proposed algorithms. In addition, The proposed approaches in the article can combine other mathematical tools and statistical strategies and identification methods [89][90][91][92][93][94] to study the parameter estimation issues of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises and can be applied to literatures [95][96][97][98][99][100][101][102] such as paper-making systems, information processing, transportation communication systems [103][104][105][106][107][108][109][110][111] and so on.…”

Section: Discussionmentioning

confidence: 99%

Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector

Jiang

Wan

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article deals with the parameter identification of the generalized time-varying systems. The time-varying parameter vector can be expressed as a coefficient matrix multiplied by a measurable disturbance vector, the common identification methods cannot be used to estimate the parameters of the generalized time-varying systems directly. This motivates us to develop new iterative identification algorithms. The gradient-based iterative (GI) algorithm is proposed by means of the iterative technique. Moreover, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. The MDW GI algorithm is proposed to improve the parameter estimation accuracy. The numerical simulation is provided and the simulation results show that the proposed identification methods are effective for the generalized time-varying systems.

show abstract

“…The proposed parameter estimation algorithms in this article are based on this identification model in (10). Many identification methods are derived based on the identification models of the systems [38][39][40][41] and can be used to estimate the parameters of other linear systems and nonlinear systems [42][43][44][45][46] and can be applied to fields [47][48][49][50][51][52] such as chemical process control systems. The objective of this article is to present a KT-FFSG algorithm and a KT-HFFSG algorithm for estimating the parameters by using the available input-output data {u(kT + jh), y(kT)} (j = 0, 1).…”

Section: Problem Descriptionmentioning

confidence: 99%

Gradient‐based recursive parameter estimation for a periodically nonuniformly sampled‐data Hammerstein–Wiener system based on the key‐term separation

Liu

Ding

2021

Adaptive Control & Signal

View full text Add to dashboard Cite

The identification of the Hammerstein-Wiener (H-W) systems based on the nonuniform input-output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.

show abstract

Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm

Cited by 110 publications

References 26 publications

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector

Gradient‐based recursive parameter estimation for a periodically nonuniformly sampled‐data Hammerstein–Wiener system based on the key‐term separation

Contact Info

Product

Resources

About