Impedance-based non-linear dynamic battery modeling for automotive applications

Buller, S.; Thele, Marc; Karden, Eckhard; Doncker, Rik W. De

doi:10.1016/s0378-7753(02)00558-x

Cited by 158 publications

(86 citation statements)

References 4 publications

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“…Another limitation of this method is the difficulty for measurement of impedance when a DC current is superposed on the AC measurement signal in order to characterize the impedance of the battery during charge or discharge phases. With high values of current, the SOC of the battery could be largely modified during the measurements; in this case the impedance is not constant all along the test and compensation drift procedures must be applied [1,19]; -chronopotentiometry is based on the voltage response of the battery when a current profile is imposed. The impedance is obtained with the u(t)/i(t) ratio.…”

Section: Existing Methods For Identification Of the Model Of Batterymentioning

confidence: 99%

“…The reasons are [1]: -batteries are not stationary due to the SOC variation and diffusion phenomenon; -they are systems highly non-linear regarding to the current, temperature, etc. Two families of identification of the model parameters, temporal and frequency, are currently used but not necessarily for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

“…The measurement of impedance can be done with or without current polarization. When the spectrometer can supply sufficient continuous current, it is possible to highlight non linearity of the battery impedance with the current level [1,9,10,12,13,14]. Another frequency method to identify battery impedance consists of using Laplace transform of temporal solicitations [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Frequency and Temporal Identification of a Li-ion Polymer Battery Model Using Fractional Impedance

Montaru

Pélissier

2009

Oil & Gas Science and Technology

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Résumé -Identification fréquentielle et temporelle d'un modèle de batterie Li-polymère utilisant une impédance fractionnaire -La modélisation des batteries lithium de fortes puissances est un moyen performant de dimensionner le pack de batterie des véhicules hybrides. De plus, cet outil peut être utilisé pour évaluer précisément les caractéristiques d'une batterie à chaque étape de sa vie. Jusqu'à maintenant, les structures de modèles diffèrent selon l'utilisation du modèle et les limitations de la simulation. Il apparaît au travers des mesures de spectroscopie d'impédance que le modèle doit comprendre une impédance d'ordre non entier. Or, ce type d'impédance ne peut pas être simulé par les logiciels dédiés aux véhicules hybrides et nécessite donc une approximation. Par ailleurs, les mesures d'impédance sont réalisées à faibles courants par rapport aux intensités observées en usage normal, ce qui peut induire des erreurs de modélisation dans le cas où l'impédance varie avec le courant. Ce papier présente un modèle qui utilise une impédance d'ordre non entier de gamme de fréquence bornée et qui prend en compte le phénomène de non linéarité. Les paramètres sont identifiés partiellement par spectroscopie d'impédance pour la partie haute fréquence et par chronopotentiométrie pour la partie basse fréquence dans une large gamme d'intensité. Le modèle est validé avec des échelons de courant de fortes intensités et un profil de courant simulé sur un logiciel de simulation de véhicule hybride. L'hypothèse de non linéarité est vérifiée. Abstract -Frequency and Temporal Identification of a Li-ion Polymer Battery Model Using Fractional Impedance -The modelling of high power Lithium batteries may work as a powerful tool in

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Section: Existing Methods For Identification Of the Model Of Batterymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Frequency and Temporal Identification of a Li-ion Polymer Battery Model Using Fractional Impedance

Montaru

Pélissier

2009

Oil & Gas Science and Technology

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“…where ā 1 is normalised to unity and the basis functions of the static output non-linear functions g i (x), in the last term of (17), are considered to be known a priori. Therefore, they can be described by a function of time ḡ i (t) = g i (x(t)) if x(t) is known.…”

Section: Problem Reformulation Based On Wfc Modelmentioning

confidence: 99%

Parameter estimation of the fractional‐order Hammerstein–Wiener model using simplified refined instrumental variable fractional‐order continuous time

Allafi

Zajic

Uddin

et al. 2017

IET Control Theory & Applications

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This study proposes a direct parameter estimation approach from observed input-output data of a stochastic singleinput-single-output fractional-order continuous-time Hammerstein-Wiener model by extending a well known iterative simplified refined instrumental variable method. The method is an extension of the simplified refined instrumental variable method developed for the linear fractional-order continuous-time system, denoted. The advantage of this novel extension, compared with published methods, is that the static output non-linearity of the Wiener model part does not need to be invertible. The input and output static non-linear functions are represented by a sum of the known basis functions. The proposed approach estimates the parameters of the linear fractional-order continuous-time subsystem and the input and output static non-linear functions from the sampled input-output data by considering the system to be a multi-input-single-output linear fractional-order continuoustime model. These extra inputs represent the basis functions of the static input and output non-linearity, where the output basis functions are simulated according to the previous estimates of the fractional-order linear subsystem and the static input nonlinear function at every iteration. It is also possible to estimate the classical integer-order model counterparts as a special case. Subsequently, the proposed extension to the simplified refined instrumental variable method is considered in the classical integer-order continuous-time Hammerstein-Wiener case. In this paper, a Monte Carlo simulation analysis is applied for demonstrating the performance of the proposed approach to estimate the parameters of a fractional-order Hammerstein-Wiener output model.

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“…The application of impedance spectroscopy to large industrial batteries at low frequencies is discussed in [18], however, the resulting low frequency data is not directly applicable to HEV use due to the dynamic nature of driving patterns and the extremely long time constant involved in the measurements. Furthermore, impedance techniques to model parameter quantities for a 36V mild hybrid battery, is discussed in [19], and the impedance measurement of small to medium sized lead acid cells in [20].…”

Section: B Cell Parameter Characterizationmentioning

confidence: 99%

Observer techniques for estimating the State-of-Charge and State-of-Health of VRLABs for Hybrid Electric Vehicles

Bhangu

Bentley

Stone

et al.

2005 IEEE Vehicle Power and Propulsion Conference

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Abstract-The paper describes the application of observerbased state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, an approach based on the well-known Kalman Filter, is employed, to estimate SoC, and the subsequent use of the EKF to accommodate model non-linearities to predict battery SoH. The underlying dynamic behaviour of each cell is based on a generic Randles' equivalent circuit comprising of twocapacitors (bulk and surface) and three resistors, (terminal, transfer and self-discharging). The presented techniques are shown to correct for offset, drift and long-term state divergence-an unfortunate feature of employing stand-alone models and more traditional coulomb-counting techniques. Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC, with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior with SoC being estimated to be within 1% of measured. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.

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Impedance-based non-linear dynamic battery modeling for automotive applications

Cited by 158 publications

References 4 publications

Frequency and Temporal Identification of a Li-ion Polymer Battery Model Using Fractional Impedance

Frequency and Temporal Identification of a Li-ion Polymer Battery Model Using Fractional Impedance

Parameter estimation of the fractional‐order Hammerstein–Wiener model using simplified refined instrumental variable fractional‐order continuous time

Observer techniques for estimating the State-of-Charge and State-of-Health of VRLABs for Hybrid Electric Vehicles

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