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Estimation of reversible entropic heat of lithium battery based on inverse heat transfer analysis and least-squares optimization.

Number: 2202

Author(s) : HAN U., LEE H.

Summary

Accurate battery thermal analysis under diverse operating conditions is a key requirement to ensure the safe use of the battery system. The battery heat generation is mainly composed of irreversible joule and reversible entropic heats, and to measure the latter, a separate experiment should be preferentially conducted to determine the entropic heat coefficients for different ranges of battery state of charge. However, the present experimental methods, such as potentiometric or calorimetric methods, are very time-consuming and cannot practically estimate the entropic coefficients for entire ranges of battery state charge, which could cause a severe error in predicting the battery thermal behaviors. In this context, the current study proposes a numerical method to estimate the entropic heat coefficients based on inverse heat transfer analysis. The battery discharge experiments under 0.25 to 1.0 current rates are basically conducted to obtain the data of entropic heat coefficients, and it is identified that the entropic heat coefficients have a very similar trend according to the state of charge of the battery, even discharging under different current-rate. The obtained data are then optimized through the least-squares estimation to determine the representative values for the entire range of state of charge of the battery cell. Using these representative entropic coefficients, the validation of the proposed method is conducted under diverse operating conditions of different current rates, ambient temperatures, and dynamic load conditions. As a result, the proposed method is well validated within a maximum of 5% error deviations compared to the experimental data and has been used to characterize the reversible entropic heat under diverse operating conditions.

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  • Original title: Estimation of reversible entropic heat of lithium battery based on inverse heat transfer analysis and least-squares optimization.
  • Record ID : 30030606
  • Languages: English
  • Subject: Technology
  • Source: 2022 Purdue Conferences. 19th International Refrigeration and Air-Conditioning Conference at Purdue.
  • Publication date: 2022

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