A Ni–Cd battery model considering state of charge and hysteresis effects

Garcia-Plaza, M.; Somoza, Blanca Castells; Carrasco, J. Eloy-Garcia; Alonso-Martínez, Jaime

doi:10.1016/j.jpowsour.2014.11.031

Cited by 52 publications

(22 citation statements)

References 26 publications

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“…Letao Zhu et al [13] believe that more and more attention being paid to the OCV hysteresis by researchers recently, some possible explanations of OCV hysteresis origin have been proposed. M. García-Plaza et al [39] considered the hysteresis phenomenon and the model parameters dynamic response for SOC estimation. Due to the different chemical materials and electrochemical reaction mechanism, the charge-discharge process of the battery is not completely reversible, which is reflected in the OCV-SOC relationship of the battery, which is not completely a coincident with the charge and discharge process curves.…”

Section: Discussionmentioning

confidence: 99%

A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature

et al. 2018

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Open circuit voltage (OCV) is an important characteristic parameter of lithium-ion batteries, which is used to analyze the changes of electronic energy in electrode materials, and to estimate battery state of charge (SOC) and manage the battery pack. Therefore, accurate OCV modeling is a great significance for lithium-ion battery management. In this paper, the characteristics of high-capacity lithium-ion batteries at different temperatures were considered, and the OCV-SOC characteristic curves at different temperatures were studied by modeling, exponential, polynomial, sum of sin functions, and Gaussian model fitting method with pulse test data. The parameters of fitting OCV-SOC curves by exponential model (n = 2), polynomial model (n = 3~7), sum of sin functions model (n = 3), and Gaussian model (n = 4) at temperatures of 45 °C, 25 °C, 0 °C, and −20°C are obtained, and the errors are analyzed. The experimental results show that the operating temperature of the battery influences the OCV-SOC characteristic significantly. Therefore, these factors need to be considered in order to increase the accuracy of the model and improve the accuracy of battery state estimation.

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Section: Discussionmentioning

confidence: 99%

A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature

et al. 2018

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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

251

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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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“…Considering the hysteresis phenomenon, the first‐order resistor‐capacitor model with hysteresis is used to describe the dynamic characteristics of the battery in this paper, which is the best choice for LiFePo 4 battery . As shown in Figure , the model includes 2 parts.…”

Section: Experimental Research and Modeling For The Lithium‐ion Batterymentioning

confidence: 99%

“…Data‐driven method can solve the problem of estimation nonlinearity but requires large amount of data to train the model . Model‐based method has been paid more attention recently for its robust performance with close‐loop feedback mechanism . Model‐based method usually uses equivalent circuit model or electrochemical model to describe the dynamic characteristics of battery.…”

Section: Introductionmentioning

confidence: 99%

A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo₄ battery

Chao

Gong

2017

Int J Energy Res

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Summary LiFePo4 battery is widely used in electric vehicles; however, its flatness and hysteresis of the open‐circuit voltage curve pose a big challenge to precise state of charge (SOC) estimation. The issue is discussed and addressed in this paper. First, a cell model with hysteresis is built to describe real‐time dynamic characteristics of the LiFePo4 battery. Second, the model parameters and SOC are estimated independently to avoid the possibility of cross interference between them. For model identification, an adaptive unscented Kalman filter (AUKF) algorithm is used to identify the cell parameters as they change slowly. While SOC could change rapidly, wavelet transform AUKF algorithm is put forward to estimate SOC. In the novel algorithm, the measurement noise can be estimated and updated online. Finally, the performance of the proposed method is verified under dynamic current condition. The experimental results show that estimated value based on the proposed method is more accurate than unscented Kalman filter‐based method and AUKF‐based algorithm. Meanwhile, the proposed estimator also has the merits of fast convergence and good robustness against the initialization uncertainty.

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A Ni–Cd battery model considering state of charge and hysteresis effects

Cited by 52 publications

References 26 publications

A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature

A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature

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

A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo₄ battery

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A Ni–Cd battery model considering state of charge and hysteresis effects

Cited by 52 publications

References 26 publications

A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature

A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature

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

A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo4 battery

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Product

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A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo₄ battery