Review of Parameter Determination for Thermal Modeling of Lithium Ion Batteries

Madani, Seyed Saeed; Schaltz, Erik; Kær, Søren Knudsen

doi:10.3390/batteries4020020

Cited by 34 publications

(22 citation statements)

References 42 publications

(104 reference statements)

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“…Several publications have appeared in recent years documenting the amount of entropic heat coefficient to analyze and determine thermal behavior and thermal modeling of batteries. A summary of these investigations was described [12,13]. However, most of the previous studies did not take into account different influential parameters on entropic heat coefficient such as state of charge (SOC), charge or discharge process before the thermal cycle, time duration, and thermal cycle.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Analysis of Entropic Coefficient of a Lithium Titanate Oxide Battery

2019

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In order to understand the thermal behaviour of a lithium-ion battery, the heat generation within the cell should be determined. The entropic heat coefficient is necessary to determine for the heat generation calculation. The entropic heat coefficient is one of the most important factors, which affects the magnitude of the reversible heat. The purpose of this research is to analyze and investigate the effect of different parameters on the entropic coefficient of lithium titanate oxide batteries. In this research, a lithium ion pouch cell was examined in both charging and discharging situations. The state of charge levels range was considered from 10% to 90%, and vice versa, in 10% increments. The temperature levels vary from 5 °C to 55 °C and the voltage levels vary from 1.5 V to 2.8 V. The effect of different parameters such as initial temperature, state of charge, thermal cycle, time duration for thermal cycles, and procedure prior to the thermal cycle on the entropic coefficient of lithium titanate oxide batteries were investigated. It was concluded that there is a strong influence of the battery cell state of charge on the entropic heat coefficient compared with other parameters.

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Section: Introductionmentioning

confidence: 99%

An Experimental Analysis of Entropic Coefficient of a Lithium Titanate Oxide Battery

2019

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“…If an ambient temperature in operation environment of the battery varies and the battery model which does not reflect thermal properties of the battery is used to estimate the SOC, errors of estimated SOC become large [54][55][56][57], leading to large error of R ISCr estimates. Although in this study the proposed method focused on detecting the soft ISCr at constant temperature, depending on real applications the ambient temperature can be changed [58].…”

Section: Other Discussionmentioning

confidence: 99%

Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell

et al. 2018

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Early detection of internal short circuit which is main cause of thermal runaway in a lithium-ion battery is necessary to ensure battery safety for users. As a promising fault index, internal short circuit resistance can directly represent degree of the fault because it describes self-discharge phenomenon caused by the internal short circuit clearly. However, when voltages of individual cells in a lithium-ion battery pack are not provided, the effect of internal short circuit in the battery pack is not readily observed in whole terminal voltage of the pack, leading to difficulty in estimating accurate internal short circuit resistance. In this paper, estimating the resistance with the whole terminal voltages and the load currents of the pack, a detection method for the soft internal short circuit in the pack is proposed. Open circuit voltage of a faulted cell in the pack is extracted to reflect the self-discharge phenomenon obviously; this process yields accurate estimates of the resistance. The proposed method is verified with various soft short conditions in both simulations and experiments. The error of estimated resistance does not exceed 31.2% in the experiment, thereby enabling the battery management system to detect the internal short circuit early.

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“…Therefore, the heat management system is also very important in the battery system of electric vehicles. It is necessary to study the battery heat model and design a proper heating and cooling system for the battery [74]. …”

Section: State Of the Artmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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Review of Parameter Determination for Thermal Modeling of Lithium Ion Batteries

Cited by 34 publications

References 42 publications

An Experimental Analysis of Entropic Coefficient of a Lithium Titanate Oxide Battery

An Experimental Analysis of Entropic Coefficient of a Lithium Titanate Oxide Battery

Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

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