Experimental characterization and development of a SoC/SoH estimator for a LiFePO&lt;inf&gt;4&lt;/inf&gt; battery cell

Pavković, Danijel; Komljenović, Ante; Hrgetić, Mario; Krznar, Matija

doi:10.1109/eurocon.2015.7313708

Cited by 5 publications

(9 citation statements)

References 20 publications

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“…4(b) indicate that the battery model can track both the quasi-steady-state and transient profiles of the experimentally recorded battery terminal voltage u b profile. However, a relatively small dynamic error may be present due to measurement quantization error over the slightly narrow cell voltage range (less than 0.1 V) [40].…”

Section: Experimental Characterization Results Of the Batterymentioning

confidence: 99%

“…3. [39], [40]. The quasi-steady-state portions of the experiments (characterized by negligible dynamic effects of polarization voltage u p ) have been used to fit the internal resistance map data based on a simple battery voltage perturbation model (k, sampling step):…”

Section: Experimental Characterization Results Of the Batterymentioning

confidence: 99%

“…The experimental characterization procedure, outlined in [39] and [40], has been used to estimate the battery model parameter maps for the considered 3.3 V/100 Ah LiFePO 4 battery cell (type SE100AHA [35]), distinguished by a slightly narrow range of terminal voltage values and extremely low internal resistance. The final battery identification results, obtained over a relatively narrow range of battery temperature  b (between 24 °C and 32 °C), are shown in Fig.…”

Section: Experimental Characterization Results Of the Batterymentioning

confidence: 99%

“…A weighting scheme is employed herein based on the error covariance of the parameter estimator used as the measure of external excitation persistence to facilitate a seamless (smooth) transition between the internal model and parameter estimator-supplied battery parameters [43]. The weighting factor used for such "soft" transition between the internal battery model and online parameter estimation is calculated as follows [40]:…”

Section: A Structure Of the Adaptive Soc Estimatormentioning

confidence: 99%

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Dual EKF-Based State and Parameter Estimator for a LiFePO₄ Battery Cell

Pavković¹,

Krznar²,

Komljenović³

et al. 2017

Journal of Power Electronics

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This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate (LiFePO 4 ) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

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Section: Experimental Characterization Results Of the Batterymentioning

confidence: 99%

Section: Experimental Characterization Results Of the Batterymentioning

confidence: 99%

Section: Experimental Characterization Results Of the Batterymentioning

confidence: 99%

Section: A Structure Of the Adaptive Soc Estimatormentioning

confidence: 99%

See 2 more Smart Citations

Dual EKF-Based State and Parameter Estimator for a LiFePO₄ Battery Cell

Pavković¹,

Krznar²,

Komljenović³

et al. 2017

Journal of Power Electronics

Self Cite

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“…A Battery management system (BMS) is required to keep the battery within a safe operating window and to ensure a long cycle life [4]. Thus proper modeling is a major function of the Battery Management System.…”

Section: Introductionmentioning

confidence: 99%

Battery Performance Monitoring and Optimal Observation

Magarde¹,

Surender²

2017

IJCA

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Battery Modeling is required to improve the efficiency and reliability of the battery. The Lithium-ion batteries are widely used as a power source for several applications. An accurate battery model and model parameters help in the estimation of the state of charge and state of health. However, battery parameters are variable and depend on several factors such as Temperature, cycle lifetime, the state of charge and depth of discharge and age. By taking account of the characteristics of battery the paper includes circuit oriented model approach of lithium-ion battery. The model characteristics are dependent and linear with respect to the battery's state of charge. This paper presents a state of charge estimation of lithium ion battery using Kalman filter. Rather than other methods, Kalman filter provides weighted average between the measured value and predicted value. Thus the battery modeling helps to improve the performance of Photovoltaic module and other applications KeywordsBattery Modeling, Lithium-ion battery, Kalman filter, State Of charge(SOC), Sum Square Error(SSE).

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