Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries

Pei, Lei; Wang, Tiansi; Lu, Rengui; Zhu, Chunbo

doi:10.1016/j.jpowsour.2013.12.083

Cited by 93 publications

(50 citation statements)

References 15 publications

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“…The irreversible overpotential heat, which represents the dominant part in the total heat generation, can be calculated by the product of current and overpotential. The cell voltage could be measured directly, while the Open Circuit Voltage-State of Charge (OCV-SOC) curve was extracted experimentally [15]. Due to the hysteresis of the LiFePO4 chemistry, the average of the charge and discharge voltage curve is taken as the open circuit voltage curve in this study.…”

Section: Internal Temperature Estimation Modelmentioning

confidence: 99%

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

et al. 2015

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Abstract:The battery internal temperature estimation is important for the thermal safety in applications, because the internal temperature is hard to measure directly. In this work, an online internal temperature estimation method based on a simplified thermal model using a Kalman filter is proposed. As an improvement, the influences of entropy change and overpotential on heat generation are analyzed quantitatively. The model parameters are identified through a current pulse test. The charge/discharge experiments under different current rates are carried out on the same battery to verify the estimation results. The internal and surface temperatures are measured with thermocouples for result validation and model construction. The accuracy of the estimated result is validated with a maximum estimation error of around 1 K.

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Section: Internal Temperature Estimation Modelmentioning

confidence: 99%

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

et al. 2015

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“…The ECM comprises the voltage source (U oc ), the hysteresis potential (U h ), the internal resistor (R Ω ), the activation polarization RC (resistor-capacitor) pair (R pc //C pc ), and the concentration polarization RC pair (R pd //C pd ) [19]. Then, the electrical relationships can be written as Equation (7) [11,13]:…”

Section: Ecmmentioning

confidence: 99%

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Xie

2018

Energies

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To achieve accurate state-of-charge (SoC) estimation for LiFePO 4 (lithium iron phosphate) batteries under harsh conditions, this paper resorts to the Peukert's law to accommodate different temperatures and load excitations. By analyzing battery heat generation and dissipation, a thermal evolution model (TEM) is elaborated and exploited for on-line parameter identification of the equivalent circuit model (ECM). Then, a SoC estimation framework is proposed based on the Adaptive Extended Kalman Filter (AEKF) algorithm. Experimental results on a LiFePO 4 pack subject to the Federal Urban Driving Schedule (FUDS) profile under different temperatures and initial states suggest that the proposed SoC estimator provides good robustness and accuracy against changing temperature and highly dynamic loads.

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“…Due to the limitations of manufacturing, there is always a slight discrepancy in capacity among the newly formatted cells, even among the same model of batteries from the same production. In practical applications, the inconsistency of batteries' capacities can be alleviated by a sorting process [5,6]. A compromise has to be made between the consistency and the production cost.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Estimation with State-of-Health Calibration for Lithium-Ion Batteries

Moo

2017

Energies

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This research is focused on state-of-charge (SOC) estimation with state-of-health (SOH) calibration for lithium-ion batteries on the basis of the coulomb counting method. The proposed approach intends to present an easy-to-use solution with high accuracy for estimating battery statuses without the need for demanding calculations or hard-earned databases. To estimate the SOC of an aged battery more accurately, the degradation of its full capacity has to be taken into account. By scheduling the battery's charging/discharging current and monitoring the battery's status, the existing full capacity can be updated regularly by regular calibration or occasionally by partial calibration, in which the charging/discharging rates are normalized with the latest updated full capacity to agree with the battery's statuses. To exclude the misestimation caused by current measuring error, the SOC is reset to 0% when the battery is exhausted and 100% for a fully charged battery. With an updated SOH, the battery C-rate is re-scaled accordingly. Experimental tests are carried out to demonstrate that the proposed approach can provide an accurate online indication of batteries' SOCs. With an implanted error of 0.3% in current measuring, the SOC estimation error can always be less than 1.905% after a number of SOH calibrations.

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Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries

Cited by 93 publications

References 15 publications

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

State-of-Charge Estimation with State-of-Health Calibration for Lithium-Ion Batteries

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