OCV hysteresis effect-based SOC estimation in extended Kalman filter algorithm for a LiFePO&lt;inf&gt;4&lt;/inf&gt;/C cell

Kim, Jong-Hoon; Seo, Gab-Su; Chun, Chang-Yoon; Cho, Bo-Hyung; Lee, Seongjun

doi:10.1109/ievc.2012.6183174

Cited by 31 publications

(27 citation statements)

References 8 publications

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“…However, the detected OCV points in the charge and discharge phases, as illustrated in Figure 4, are not exactly superimposed. Indeed, the OCV curve presents a hysteresis that is well reported in the literature [30,[46][47][48]. The OCV hysteresis is caused by the lack of relaxation between charging and discharging and the diffusion phenomenon of the transition of the active material during charging and discharging.…”

Section: Battery Model Characterizationsupporting

confidence: 62%

Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

et al. 2017

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Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

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Section: Battery Model Characterizationsupporting

confidence: 62%

Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

et al. 2017

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“…To shorten the measurement time, [30] gives the test cell a short relaxation time and finds the average EMF-SOC curve from both of the charge-relaxation and discharge-relaxation test results. This method speeds up the test time, but the effect of EMF hysteresis [31,32] will lead to some errors.…”

Section: Linear Extrapolation For Emf Extractionmentioning

confidence: 99%

A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

et al. 2016

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Abstract:A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF) and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.

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“…More complex approaches have appeared to solve some of the problems of the http://dx.doi.org/10.1016/j.apenergy.2015.06.063 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved. basic methods, by means of a dynamic recalibration [13,14], neural networks [15,16], fuzzy logic [17] or closed loop estimators like an observer or an adaptive filter [6,7,9,[18][19][20][21][22][23]. This last type of algorithms has been commonly employed because they present certain advantages as their capacity for correcting estimation errors.…”

Section: Introductionmentioning

confidence: 99%

Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter

et al. 2015

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OCV hysteresis effect-based SOC estimation in extended Kalman filter algorithm for a LiFePO<inf>4</inf>/C cell

Cited by 31 publications

References 8 publications

Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter

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