Degradation Testing and Modeling of 200 Ah LiFePO<sub>4</sub> Battery

Panchal, Satyam; Rashid, Mahir; Long, Frank; Mathew, Manoj; Fraser, Roydon; Fowler, Michael

doi:10.4271/2018-01-0441

Cited by 33 publications

(17 citation statements)

References 27 publications

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“…Using representative real-world drive cycles, a close fit between model and experimental results was obtained [16], while noting the effects of regenerative braking and ambient temperature on model error. In [17], consistent cycling capacity fade test of a 200 Ah lithium-ion battery yielded a model accuracy greater than 96.5%, for all 400 cycles.…”

Section: Background/literature Reviewmentioning

confidence: 91%

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A Thermodynamic Model for Lithium-Ion Battery Degradation: Application of the Degradation-Entropy Generation Theorem

Osara¹,

Bryant²

2019

Inventions

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Presented is a lithium-ion battery degradation model, based on irreversible thermodynamics, which was experimentally verified, using commonly measured operational parameters. The methodology, applicable to all lithium-ion batteries of all chemistries and composition, combined fundamental thermodynamic principles, with the Degradation-Entropy Generation theorem, to relate instantaneous capacity fade (loss of useful charge-holding capacity) in the lithium-ion battery, to the irreversible entropy generated via the underlying dissipative physical processes responsible for battery degradation. Equations relating capacity fade-aging-to battery cycling were also formulated and verified. To show the robustness of the approach, nonlinear data from abusive and inconsistent battery cycling was measured and used to verify formulations. A near 100% agreement between the thermodynamic battery model and measurements was achieved. The model also gave rise to new material and design parameters to characterize all lithium-ion batteries.

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Section: Background/literature Reviewmentioning

confidence: 91%

“…In [16,17], lithium-ion battery degradation in electric vehicle application is presented. In both studies, the authors used empirically fitted Thevenin (for battery characterization) and DoD-dependent (for battery aging) models.…”

Section: Background/literature Reviewmentioning

confidence: 99%

A Thermodynamic Model for Lithium-Ion Battery Degradation: Application of the Degradation-Entropy Generation Theorem

Osara¹,

Bryant²

2019

Inventions

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“…The experimental data of Cell #2 under two cycle conditions are not listed here for brevity. Capacity and hybrid pulse power characterization (HPPC) experiments [25] were first performed to determine the capacity and OCV of LIBs. The capacity test process is as follows: Place the test LIB in the temperature chamber at 25 • C for 3 h. Then, discharge the LIB at a constant discharge current 1/3 C to 2.5 V. After waiting for 1 h, fully charge the LIB using the constant current-constant voltage (CC-CV) method.…”

Section: Methodsmentioning

confidence: 99%

A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries

et al. 2018

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In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC). In the proposed model, the solid-phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks. To improve the global accuracy of the model, a subarea parameter-identification method based on particle swarm optimization is proposed, in order to determine the optimal model parameters in the entire SOC area. Then, an SOC estimator is developed based on extended kalman filter. The comparative study shows that a model considering solid-phase diffusion with two RC networks is the best choice. Finally, experimental results show that the accuracy of the proposed model is one times higher than that of the traditional ECM in the low SOC area, and is able to estimate SOC with errors less than 1% in the entire SOC area. Furthermore, estimation results of two types of batteries under two working conditions indicate that the developed model and SOC estimator have satisfactory global accuracy and guaranteed robustness with low computational complexity, which can be applied in real-time situations.

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“…Batteries correspond to containers of electrochemical reactions which generate heat during charge and discharge due to electrochemical polarization, resistive heating, and enthalpy changes [10]. There are studies regarding the electrochemical thermal model of lithium-ion batteries [11][12][13]. However, Cylindrical lithium-ion battery dimension In addition to the Joule heat generation and reversible heat generation, heat dissipation is an important factor for the thermal analysis of lithium-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

Thermal Analysis of a Parallel-Configured Battery Pack (1S18P) Using 21700 Cells for a Battery-Powered Train

et al. 2020

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In this study, the thermal behavior of a 1S18P battery pack is examined based on the power demand during train propulsion between two stations. The proposed thermal prediction model is classified into Joules heating with equivalent resistance, reversible heat, and heat dissipation. The equivalent resistances are determined by 5% of the state of charge intervals using the hybrid pulse power characterization test. The power demand profile during train propulsion between two stations is provided by the Korea Railroad Research Institute. An experiment is conducted to examine the 1S18P battery pack thermal behavior during the propulsion between two stations. A comparison of the simulation and experiment results validated the proposed thermal model. Electronics 2020, 9, 447 2 of 13 electrochemical thermal model require chemical parameters which are hard to examine. Therefore, a simplified electro-thermal model was introduced and classified the heat generation term into reversible heat and Joule heat. An electrochemical reaction (including polarization and entropy change) generates reversible heat. Joule heating occurs due to the resistance during the transfer of ions and electrons. A simplified thermal model of a lithium-ion battery was examined by Onda et al. [9]. Equivalent resistances are obtained by state of charge (SOC) intervals and employed for Joule heating. Additionally, entropy change is measured at different SOCs and applied to reversible heat [14]. Tables 1 and 2 are definitions and descriptions of abbreviation and symbols used in this paper.

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Degradation Testing and Modeling of 200 Ah LiFePO₄ Battery

Cited by 33 publications

References 27 publications

A Thermodynamic Model for Lithium-Ion Battery Degradation: Application of the Degradation-Entropy Generation Theorem

A Thermodynamic Model for Lithium-Ion Battery Degradation: Application of the Degradation-Entropy Generation Theorem

A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries

Thermal Analysis of a Parallel-Configured Battery Pack (1S18P) Using 21700 Cells for a Battery-Powered Train

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Degradation Testing and Modeling of 200 Ah LiFePO4 Battery

Cited by 33 publications

References 27 publications

A Thermodynamic Model for Lithium-Ion Battery Degradation: Application of the Degradation-Entropy Generation Theorem

A Thermodynamic Model for Lithium-Ion Battery Degradation: Application of the Degradation-Entropy Generation Theorem

A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries

Thermal Analysis of a Parallel-Configured Battery Pack (1S18P) Using 21700 Cells for a Battery-Powered Train

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Degradation Testing and Modeling of 200 Ah LiFePO₄ Battery