Broadband Identification of Battery Electrical Impedance for HEVs

Nazer, Rouba Al; Cattin, Viviane; Granjon, Pierre; Montaru, Maxime; Ranieri, Marco

doi:10.1109/tvt.2013.2254140

Cited by 67 publications

(33 citation statements)

References 43 publications

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“…Though its robustness and high accuracy results, it is still more common in laboratory tests than in online field equipment. [6], [7] and [8] investigate identification methods based on broadband signals. [6] aims to identify biological impedance while [7] and [8] work on battery impedance measurements.…”

Section: Introductionmentioning

confidence: 99%

Classical EIS and square pattern signals comparison based on a well-known reference impedance

Nazer

Cattin

Granjon

et al. 2013

2013 World Electric Vehicle Symposium and Exhibition (EVS27)

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Section: Introductionmentioning

confidence: 99%

Classical EIS and square pattern signals comparison based on a well-known reference impedance

Nazer

Cattin

Granjon

et al. 2013

2013 World Electric Vehicle Symposium and Exhibition (EVS27)

Self Cite

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“…Before proceeding to model the battery dynamics, it is very important to characterize the battery electrical response under varying operational conditions in terms of the level as well as kind of non-linearities. It is possible to use a broadband [19] excitation signal so that maximum information about the behaviour in the band of excitation can be efficiently extracted. In this paper, to characterize the battery's short term electrical response at the different levels of SoC, while keeping the temperature constant at 25°Celcius, we make use of a nonparametric characterization technique proposed by [20].…”

Section: Multisine As the Perturbation Signalmentioning

confidence: 99%

“…The minimization of the non-convex cost function (19) is tackled via the Levenberg-Marquardt scheme [41]. To ensure good initial values, the G ss is used for initialize the nonlinear model.…”

Section: Remarkmentioning

confidence: 99%

Data-Driven Nonlinear Identification of Li-Ion Battery Based on a Frequency Domain Nonparametric Analysis

Relan

Firouz

Timmermans

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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Lithium ion (Li-ion) batteries are attracting significant and growing interest because their high energy and high power density render them an excellent option for energy storage, particularly in hybrid and electric vehicles. In this paper, a data-driven polynomial nonlinear state-space model (PNLSS) is proposed for the operating points at the cusp of linear and nonlinear regime of the battery's electrical operation, based on the thorough nonparametric frequency domain characterization and quantification of the battery's behaviour in terms of its linear and nonlinear behaviour at different levels of the state-of-charge (SoC).Index Terms-Li-ion battery, input-output response, Nonparametric characterization, polynomial nonlinear state-space (PNLSS), nonlinear system identification. I. BATTERY MODELLINGThe pursuit for battery models with high accuracy and computational efficiency still remains a challenge. Generating a mathematical model of a Li-ion battey, e.g. needed by battery management system (BMS), that can describe the input current-to-output voltage dynamics of a battery is a challenging problem. A primary reason for this is that battery dynamics vary significantly with operating conditions. Depending on the final purpose of the developed model, one can divide battery models into the models that describe the short term behaviour e.g. state of charge, voltage response etc. and models that describe the long term behaviour of the cells, e.g. lifetime models, state of health etc. In the field of battery modelling many different battery models exist for both the short and long term behaviour of battery cells [1]. These models can be broadly classified in the following categories: 1) Equivalent circuit models (ECM), 2) Electro-chemical models, 3) Analytical and impedance based models, 4) Empirical and semi-empirical models. ECMs are structurally simple and computationally efficient due to the use of lumped-parameter circuit elements, e.g. inductors, resistors and capacitors, to represent the battery impedance, and these models frequently The authors * are with the ELEC Department and the authors ** are with MOBI research group of the Vrije Universiteit Brussel (VUB), Belgium respectively. Corresponding author can be contacted at Rishi.Relan@vub.ac.be. incorporate empirical functions to describe the relationship between SoC and open circuit voltage (OCV). ECMs thus are widely used for impedance analysis [2], SoC estimation [3] and charging control [4]. Performances of several commonly used ECMs is compared in [5]. On the other hand electrochemical models [6]-[8]usually use coupled nonlinear partial differential equations to describe ion transport phenomena and electrochemical reactions to achieve high accuracy, but incurring heavy computation load. In general electrochemical models such as pseudo two dimensional models [9], single particle models [10], and extended single particle models [11] are more accurate than ECMs. In comparison ECMs are easier to implement, but have worse accuracy than electrochemical models...

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“…The time taken is longer than the wideband impedance measuring methods. In the wideband method, a square wave [23,24], a pseudo-random binary sequence [25,26], a chirp signal [27], or a signal during a driving cycle [28] may be applied to calculate the battery impedance with Fast Fourier transform, time-sharing Fourier transform processing the voltage and the current profiles. Considering a non-stationary excitation signal [29], wavelet transform can also be applied to calculate the battery impedance in the wideband methods.…”

Section: Introductionmentioning

confidence: 99%

Practical On-Board Measurement of Lithium Ion Battery Impedance Based on Distributed Voltage and Current Sampling

2018

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Abstract:Battery impedance based state estimation methods receive extensive attention due to its close relation to internal dynamic processes and the mechanism of a battery. In order to provide impedance for a battery management system (BMS), a practical on-board impedance measuring method based on distributed signal sampling is proposed and implemented. Battery cell perturbing current and its response voltage for impedance calculation are sampled separately to be compatible with BMS. A digital dual-channel orthogonal lock-in amplifier is used to calculate the impedance. With the signal synchronization, the battery impedance is obtained and compensated. And the relative impedance can also be obtained without knowing the current. For verification, an impedance measuring system made up of electronic units sampling and processing signals and a DC-AC converter generating AC perturbing current is designed. A type of 8 Ah LiFePO 4 battery is chosen and the valuable frequency range for state estimations is determined with a series of experiments. The battery cells are connected in series and the impedance is measured with the prototype. It is shown that the measurement error of the impedance modulus at 0.1 Hz-500 Hz at 5 • C-35 • C is less than 4.5% and the impedance phase error is less than 3% at <10 Hz at room temperature. In addition, the relative impedance can also be tracked well with the designed system.

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Broadband Identification of Battery Electrical Impedance for HEVs

Cited by 67 publications

References 43 publications

Classical EIS and square pattern signals comparison based on a well-known reference impedance

Classical EIS and square pattern signals comparison based on a well-known reference impedance

Data-Driven Nonlinear Identification of Li-Ion Battery Based on a Frequency Domain Nonparametric Analysis

Practical On-Board Measurement of Lithium Ion Battery Impedance Based on Distributed Voltage and Current Sampling

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