Fast Estimation of State of Charge for Lithium-Ion Batteries

Wu, Shing-Lih; Chen, Hung-Cheng; Chou, Shuo-Rong

doi:10.3390/en7053438

Cited by 21 publications

(16 citation statements)

References 27 publications

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“…This assumption is also consistent with the low dispersion found in the parameters of the Lithium-Ion cells when dealing with the traditionally used low-frequency models [16], [17]. It is also easily perceived the expected inductive behavior of the cells on the high frequency region.…”

Section: Battery Cells High Frequency Modelsupporting

confidence: 71%

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Switching Frequency Optimization for a Solid State Transformer With Energy Storage Capabilities

Garcı́a

Saeed

Navarro-Rodríguez

et al. 2018

IEEE Trans. on Ind. Applicat.

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Abstract-This paper is focused on establishing a procedure for measuring the efficiency dependency on the switching frequency for a solid state transformer (SST), being one of the ports connected to an energy storage device (Lithium-Ion battery). Multiple contributions for measuring the efficiency/losses for different power converter structures for energy storage applications can be found in the literature. However, there are few references which consider the effects of the high frequency model of the battery in the complete system performance. This research will obtain a parametric high frequency model of the battery cells, based on a vector fitting method in frequency domain. This model will be used for the estimation of the overall system losses. It will be demonstrated that the contribution of the battery losses, as well as its behavior as a function of the switching frequency, can significantly affect the selection of the converter's switching frequency.

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Section: Battery Cells High Frequency Modelsupporting

confidence: 71%

“…13. The aforementioned conditions, switching frequencies between [16,20]kHz and 90deg for the phase shift were used in order to get the maximum power transfer.…”

Section: A Simulation Resultsmentioning

confidence: 99%

Switching Frequency Optimization for a Solid State Transformer With Energy Storage Capabilities

Garcı́a

Saeed

Navarro-Rodríguez

et al. 2018

IEEE Trans. on Ind. Applicat.

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“…[11][12][13][14]), Kalman filter (KF), extended KF (EKF) (e.g. [15][16][17]), sigma point KF <3> Search using title:(batter*) AND ("state of charge" OR "capacity estimation" OR "SOC") on Web of Science core journals resulted in a collection of SOC-related publications with sub-topics from reviews [6,8,9], combination of the estimations of SOF and SOH [52][53][54][55][56], resistance or impedance-based calibration [57][58][59][60], mechanical stress-based calibration [49], pack SOC [19,29,61,62], OCV-based model [63][64][65], electrochemical models [66][67][68][69][70][71][72], to algorithm improvements .…”

Section: Algorithm Evolutionmentioning

confidence: 99%

On state-of-charge determination for lithium-ion batteries

Huang

Liaw

et al. 2017

Journal of Power Sources

246

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Accurate estimation of state-of-charge (SOC) of a battery through its life remains challenging in battery research. Although improved precisions continue to be reported at times, almost all are based on regression methods empirically, while accuracy is often not properly addressed. Here, a comprehensive review is set to address such issues, from fundamental principles that are supposed to define SOC to methodologies to estimate SOC for practical use. It covers topics from calibration, regression (including modeling methods) to validation in terms of precision and accuracy. At the end, we intend to answer the following questions: 1) can SOC estimation be self-adaptive without bias? 2) Why Ah-counting is a necessity in almost all battery-model-assisted regression methods? 3) How to establish a consistent framework of coupling in multi-physics battery models? 4) To assess the accuracy in the SOC estimation, statistical methods should be employed to analyze factors that contribute to the uncertainty. We hope, through this proper discussion of the principles, accurate SOC estimation can be widely achieved.

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“…9, a band-pass filter is designed to determine a resonance frequency of 1 kHz and a bandwidth of 200 Hz, and then to calculate R 15 , R 16 , and R 17 using Eqs. (8)- (11).…”

Section: + -mentioning

confidence: 99%

AC Impedance-based Online State-of-charge Estimation for Li-ion Batteries

Wu¹,

Chen²,

Tsai³

2018

Sensors and Materials

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The accurate estimation of state-of-charge (SOC) is one of the most important core functions of a battery management system (BMS), which provides the essential information needed for battery management, monitoring, and status analysis. This paper presents an online SOC estimation method based on AC impedance. A circuit for sensing the voltage and current of the battery is also proposed. The sensed values can be utilized to calculate the AC impedance and then accurately estimate the SOC of a Li-ion battery. First, a 1 kHz sinusoidal ripple is injected into an 18650 Li-ion battery and the AC impedance values are measured from 0 to 100% charging status in 10% increments. The correlations between SOC and AC impedance are then determined by linear regression. In terms of practical application, we simply use the sensing circuit to measure the battery AC voltage and current and calculate the AC impedance. This AC impedance allows a fairly accurate estimation of the battery SOC. To validate the accuracy of the proposed method, a power analyzer was used to actually measure SOC. The average error in estimating SOC is within 4%. This encouraging result shows that the proposed method can provide a simple and practical solution for online estimation of the SOC of Li-ion batteries.

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Fast Estimation of State of Charge for Lithium-Ion Batteries

Cited by 21 publications

References 27 publications

Switching Frequency Optimization for a Solid State Transformer With Energy Storage Capabilities

Switching Frequency Optimization for a Solid State Transformer With Energy Storage Capabilities

On state-of-charge determination for lithium-ion batteries

AC Impedance-based Online State-of-charge Estimation for Li-ion Batteries

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