Design and Optimization of an Intelligent Fuzzy Logic Controller for a Nonlinear Dynamic System

Awelewa, A. A.; Omiloli, Koto; Olajube, Ayobami; Samuel, I. A.

doi:10.1109/dasa53625.2021.9682318

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The Jacobean matrices A k , B k , and C k were derived from the firstorder Taylor series approximation (Awelewa et al, 2021) of ( 13); V 0 is the OCV; Z k is the SOC, and the state vector is…”

Section: The Extended Kalman Filter Algorithmmentioning

confidence: 99%

Robust hybrid estimator for the state of charge of a lithium-ion battery

Awelewa

Omiloli²,

Samuel³

et al. 2023

Front. Energy Res.

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The use of batteries for diverse energy storage applications is increasing, primarily because of their high energy density, and lithium-ion batteries (LiBs) are of particular significance in this regard. However, designing estimators that are robust to compute the state of charge (SOC) of these batteries in the presence of disturbance signals arising from different battery types remains a challenge. Hence, this paper presents a hybrid estimator that combines the extended Kalman filter (EKF) and sliding mode observer (SMO) via a switching function and tracking closed loop to achieve the qualities of noise cancellation and disturbance rejection. Hybridization was carried out in such a way that the inactive observer tracks the output of the used observer, simultaneously feeding back a zero-sum signal to the input gain of the used observer. The results obtained show that noise filtering is preserved at a convergence time of .01 s. Also, the state of charge estimation interval improves greatly from a range of [1, .93] and [.94, .84] obtained from the extended Kalman filter and sliding mode observer, respectively, to a range of [1, 0], in spite of the added disturbance signals from a lithium–nickel (INR 18650) battery type.

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Section: The Extended Kalman Filter Algorithmmentioning

confidence: 99%

Robust hybrid estimator for the state of charge of a lithium-ion battery

Awelewa

Omiloli²,

Samuel³

et al. 2023

Front. Energy Res.

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show abstract

“…Discretizing ( 1), ( 2), ( 3), (4) using t as sample time, k as present value and k-1 as previous value, the battery model is derived in the state space form below [25].…”

Section: Equivalent Circuit Modelmentioning

confidence: 99%

State of charge estimation based on a modified extended Kalman filter

Omiloli

Awelewa

Samuel

et al. 2023

IJECE

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The global transition from fossil-based automobile systems to their electric-driven counterparts has made the use of a storage device inevitable. Owing to its high energy density, lower self-discharge, and higher cycle lifetime the lithium-ion battery is of significant consideration and usage in electric vehicles. Nevertheless, the state of charge (SOC) of the battery, which cannot be measured directly, must be calculated using an estimator. This paper proposes, by means of a modiﬁed priori estimate and a compensating proportional gain, an improved extended Kalman ﬁlter (IEKF) for the estimation task due to its nonlinear application and adaptiveness to noise. The improvement was achieved by incorporating the residuals of the previous state matrices to the current state predictor and introducing an attenuating factor in the Kalman gain, which was chosen to counteract the effect of the measurement and process noise resulting in better accuracy performance than the conventional SOC curve fitting-based estimation and ampere hour methods. Simulation results show that the standard EKF estimator results in performance with an error bound of 12.9% due to an unstable start, while the modified EKF reduces the maximum error to within 2.05% demonstrating the quality of the estimator.

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Design and Optimization of an Intelligent Fuzzy Logic Controller for a Nonlinear Dynamic System

Cited by 2 publications

References 8 publications

Robust hybrid estimator for the state of charge of a lithium-ion battery

Robust hybrid estimator for the state of charge of a lithium-ion battery

State of charge estimation based on a modified extended Kalman filter

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