Universal Adaptive Stabilizer Based Optimization for Li-Ion Battery Model Parameters Estimation: An Experimental Study

Usman, Hafiz Muhammad; Mukhopadhyay, Shayok; Rehman, Habibur

doi:10.1109/access.2018.2867560

Cited by 21 publications

(34 citation statements)

References 24 publications

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“…And helps capture the effects of aging. Further, as shown in [37] the UAS based methods produce accurate parameter estimates with very low computation cost. Also upon using optimization methods along with UAS results in increasing estimation accuracy and reducing computation time requirements substantially, compared to other wellknown optimization methods.…”

Section: Advantages Of the Proposed Uas+o Approachmentioning

confidence: 89%

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Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Mukhopadhyay

Dhaouadi

Takrouri

et al. 2020

IEEE Open J. Ind. Electron. Soc.

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This paper presents a simplified supercapacitor model and a universal adaptive stabilization, optimization (UAS+O) based parameter identification technique. Analytic solutions for the description of supercapacitors current, voltage, subject to cyclic voltage and current sources of varying amplitudes and frequency, consistent with electric vehicle driving cycles, are developed. Supercapacitor I-V relationships show hysteresis, indicating simultaneous energy storage and dissipation mechanisms. A reduced equivalent circuit model is proposed to accurately represent hysteresis I-V characteristics. The proposed UAS+O based technique for estimating model parameters, is supported by mathematical proofs, simulation, and experimental results.

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Section: Advantages Of the Proposed Uas+o Approachmentioning

confidence: 89%

“…(37) By multiplying both sides of (37) by -1, and then substituting ̂ from (17) into the R.H.S. of equation (37) we get,…”

Section: B Proof Of Theorem 4d2mentioning

confidence: 99%

Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Mukhopadhyay

Dhaouadi

Takrouri

et al. 2020

IEEE Open J. Ind. Electron. Soc.

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“…The present study aimed to estimate the parameters of this model accurately. In [19] and [20], the Chen-Mora model parameters for a lithium-ion battery pack were estimated using deterministic parameter estimation (DPE) in the form of a universal adaptive stabilizer (UAS) and a constrained optimization technique. The estimated parameters of a single battery were assumed to be identical for all batteries of the same type; however, this is not entirely the case.…”

Section: Volume X Xxxxmentioning

confidence: 99%

“…1, is discussed. The continuous form found in [19] and [20] is discretized using the Euler method [30] because the UKF algorithm requires a discrete model. The resulting discrete model is expressed by Eqs.…”

Section: Discrete Battery Modelmentioning

confidence: 99%

Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model

Tameemi

2020

IEEE Access

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In this study, a vehicle localization technique was employed to determine the required quantities in the identification of battery models by considering the behavior of multiple batteries instead of data from a single battery. In previous studies, a plant (e.g., a battery, motor, super-capacitor, or fuel cell) was identified based on a single piece of data. However, such an approach is disadvantageous in that it neglects the effect of process and measurement noise and assumes that the parameters obtained using data from a single plant are identical for all plants of the same type. First, deterministic parameter estimation (DPE), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) were initially applied to estimate the battery model parameters using data from a single battery. Second, a fusion-based approach was used to address the process and measurement noise problems through an adaptive unscented Kalman filter algorithm. With this approach, maximum likelihood estimation was employed to fuse multiple-battery data streams to enable the DPE, PSO, and TLBO to recalculate the model parameters based on filtered and fused quantities. A comparison between the experimental results and model outputs obtained using the aforementioned methods for parameter estimation indicated that the proposed multiple-battery approach enhances the accuracy of several identification methods. In contrast, it requires a high computational effort. INDEX TERMS universal adaptive stabilizer (UAS), particle swarm optimization (PSO), teachinglearning-based optimization (TLBO), unscented Kalman filter (UKF), and maximum likelihood estimation (MLE).

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“…Recent studies have focused on the small-scale battery modeling with greater emphasis on single cell operations [2]- [4]. Other researchers have worked towards developing standardized procedures for the estimation of the parameters of a single cell.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Estimation of the Equivalent Circuit Parameters for Multi-MW Battery Systems

Akeyo

Rallabandi

Jewell³

et al. 2019

2019 IEEE Energy Conversion Congress and Exposition (ECCE)

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This paper proposes and validates through simulations and measurements, a procedure for the determining the equivalent circuit parameters of large utility-scale batteries. It is considered that a large battery includes multiple cells connected in series and parallel, and therefore, its equivalent circuit can be represented as a series-parallel network of state of charge (SOC) dependent resistors and capacitors. Tests for determining these equivalent circuit parameters are proposed. These tests involve subjecting the battery energy storage system (BESS) to multiple charge and discharge cycles, while monitoring the terminal voltage and current response. A method for postprocessing and analyzing the measurements in order to obtain an equivalent circuit model that accounts for the dynamic properties of the battery system and differences between the parameters of each cell is developed. The measurements and simulations are conducted for a 1MW/2MWh BESS demonstrator located at the Louisville Gas and Electric and Kentucky Utilities (LG&E and KU) E.W. Brown generating plant.

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Universal Adaptive Stabilizer Based Optimization for Li-Ion Battery Model Parameters Estimation: An Experimental Study

Cited by 21 publications

References 24 publications

Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Supercapacitor Characterization Using Universal Adaptive Stabilization and Optimization

Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model

Measurement and Estimation of the Equivalent Circuit Parameters for Multi-MW Battery Systems

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