Improved nonlinear model for electrode voltage-current relationship for more consistent online battery system identification

Juang, Larry W.; Kollmeyer, Phillip J.; Jahns, T.M.; Lorenz, Robert D.

doi:10.1109/ecce.2011.6064120

Cited by 12 publications

(14 citation statements)

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“…The situation is similar with the lead-acid battery, where the nonlinearity is present even in room temperature. In [12] the nonlinear Butler-Volmer behavior is modeled without taking into account the effect of temperature. In [14], recursive estimation is installed so that the value of i0 in (5) can be estimated using a nonlinear estimation technique, along with the other key battery parameters.…”

Section: Significance Of Battery Temperature Dependence and Butlementioning

confidence: 99%

“…For a lithium nickel-manganese-cobalt oxide (NMC) cell, Wagg et al used pulse current data to show that the charge transfer resistance R ct in the equivalent circuit becomes increasingly dependent on the value of the dc bias current at lower temperatures due to the Butler-Volmer relationship [10]. For a lead-acid battery, previous work [12] has established the need to consider the Butler-Volmer relationship even at room temperature.…”

Section: Introductionmentioning

confidence: 98%

“…The nonlinearity in the electrode voltage/current relationship described by the Butler-Volmer equation [10][11][12] requires that the impact of changing dc bias currents must be accurately reflected in the battery model in order to insure the model's usefulness. Andre et al demonstrated this point by calibrating their EIS-based equivalent circuit model using pulsed current test data in the time domain [4].…”

Section: Introductionmentioning

confidence: 99%

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The impact of DC bias current on the modeling of lithium iron phosphate and lead-acid batteries observed using electrochemical impedance spectroscopy

Juang

Kollmeyer

Zhao

et al. 2014

2014 IEEE Energy Conversion Congress and Exposition (ECCE)

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This paper deploys electrochemical impedance spectroscopy (EIS) to investigate the impact of temperature and dc bias current on battery impedance characteristics. Measured test results are used to demonstrate that, under conditions where the nonlinear Butler-Volmer equation is necessary to model the electrode charge transfer characteristics, the semicircular trajectory that typically appears in the EIS results shrinks in diameter as the battery's dc bias current increases. For a lithium-based battery, the nonlinearity introduced by the Butler-Volmer relationship is more pronounced at low temperature, while lead-acid batteries typically exhibit this nonlinearity even at room temperature. The impact of dc bias current on the battery model and EIS characteristics are thoroughly investigated using a combination of experimental tests combined with theoretical justification based on the Arrhenius equation. The value of using this observed relationship to improve the accuracy of battery models and condition monitors (e.g., state-of charge, etc.) is discussed in the paper.Index Terms -Electrochemical impedance spectroscopy (EIS), electric vehicle (EV), lithium iron phosphate battery, lead-acid battery, State-of-Charge (SOC), State-of-Function (SOF), Arrhenius equation, Butler-Volmer equation

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Section: Significance Of Battery Temperature Dependence and Butlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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The impact of DC bias current on the modeling of lithium iron phosphate and lead-acid batteries observed using electrochemical impedance spectroscopy

Juang

Kollmeyer

Zhao

et al. 2014

2014 IEEE Energy Conversion Congress and Exposition (ECCE)

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“…I NTRODUCTION Battery monitors for electric vehicles (EVs) are attracting the attention of many researchers [1][2][3][4]. The goals are to provide accurate and robust state-of-charge (SOC), power capability prediction, and state-of-health (SOH) monitoring for the battery pack.…”

mentioning

confidence: 99%

“…The goals are to provide accurate and robust state-of-charge (SOC), power capability prediction, and state-of-health (SOH) monitoring for the battery pack. To monitor the battery pack online, recursive estimation techniques based on a battery equivalent circuit are typically implemented [2][3][4]. Unlike the offline modeling approach in [1], the recursive approach can adjust the battery model parameters to adapt to changes in the battery characteristics caused by various factors such as aging or temperature changes.…”

mentioning

confidence: 99%

Improved modeling of lithium-based batteries using temperature-dependent resistance and overpotential

Juang

Kollmeyer

Jahns

et al. 2014

2014 IEEE Transportation Electrification Conference and Expo (ITEC)

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The temperature-dependent behavior of the resistance and overpotential of a lithium-iron-phosphate (LiFeP04) battery cell is explored in this paper. Offline experimental results from hybrid pulse power characterization (HPPC) tests and electrochemical impedance spectroscopy (EIS) methods for resistance and overpotential are explained using Arrhenius equations. Using a nonlinear regression technique, simulated drive cycle data are used to confirm the experimental findings and construct a generic cell model that explicitly takes temperature effects on the resistance and overpotential into account. The significance of the work lies in its confirmation of the inadequacy of the baseline linear-circuit model for lithium batteries at low temperatures and its presentation of a modeling approach that provides much better agreement with measured battery characteristics. Index Terms -State-of-Charge (SOC), battery management system (BMS), electric vehicle (EV), lithium iron phosphate battery, Kalman filter, Hybrid Power Pulse Characterization I. I NTRODUCTION Battery monitors for electric vehicles (EVs) are attracting the attention of many researchers [1 -4]. The goals are to provide accurate and robust state-of-charge (SOC), power capability prediction, and state-of-health (SOH) monitoring for the battery pack. To monitor the battery pack online, recursive estimation techniques based on a battery equivalent circuit are typically implemented [2][3][4]. Unlike the offline modeling approach in [1], the recursive approach can adjust the battery model parameters to adapt to changes in the battery characteristics caused by various factors such as aging or temperature changes. That is, the configuration of the battery equivalent circuit model remains fixed, while the recursive estimation scheme adjusts the model parameters when the battery temperature changes without specifically including temperature as a model input.This paper investigates the validity of the baseline linear model that is often adopted for lithium-iron-phosphate (LiFeP04) battery cells. After exposing its limitations, the paper proposes a modeling approach that includes temperature as a model input so that the battery resistance and overpotential values are defined functions of the temperature.In [2], a Butler-Volmer (BV) equation-based nonlinear model was adopted for the recursive estimation of lead-acid batteries in an EV. Statistical analysis confirmed the benefits of the BV model over the baseline linear-circuit model. For 978-1-4799-2262-8/14/$31.00 ©2014 IEEE lithium batteries, the literature has typically adopted a linear circuit model [3,4], and, for the most part, this approach has not been questioned. In this paper, a LiFeP04 cell ' s overpotential is demonstrated to exhibit significant nonlinear BV behavior at low temperatures, even though such behavior is not observed at room temperature. Additionally, the cell ' s ohmic resistance is shown to increase rapidly as the temperature drops.Increased cell resistance and overpotential at low temperatures h...

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An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

et al. 2016

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Improved nonlinear model for electrode voltage-current relationship for more consistent online battery system identification

Cited by 12 publications

References 10 publications

The impact of DC bias current on the modeling of lithium iron phosphate and lead-acid batteries observed using electrochemical impedance spectroscopy

The impact of DC bias current on the modeling of lithium iron phosphate and lead-acid batteries observed using electrochemical impedance spectroscopy

Improved modeling of lithium-based batteries using temperature-dependent resistance and overpotential

An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

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