Abstract

Large-format prismatic lithium-ion cells can generate substantial heat during operation, making thermal management significant. Some cells have high surface-to-volume ratios, and the positioning of tabs can make surface temperature highly non-uniform [1], posing further thermal management challenges. Non-uniformity of temperature is a major problem because a battery operated at a safe average temperature can still have ‘hot spots’ that are prone to thermal runaway [2]. Depending on the battery’s ability to dissipate heat to its surroundings, thermal runaway may nucleate in very small volumes. The thermal responses of prismatic lithium-ion batteries arise from a number of distinct phenomena that occur as the cell is subjected to an electrical load. Current flow generates joule heat, which is always dissipated, regardless of the sign of the current; this may arise either from ohmic resistance or from the resistances associated with charge-transfer reactions. There is also reversible heat, which may be dissipated or consumed according to the current’s sign; such heat owes to the entropy associated with the electrochemical reaction that occurs as the state of charge (SOC) is changed [3]. We have developed a 3D finite-element model that accounts for these various modes of heating within the battery cell, which runs with sufficient computational efficiency to allow parameterization by fitting model output to dynamic experimental voltage and temperature-distribution data. Our approach provides an ‘outside-in’ method that allows external measurements to be correlated with internal material properties. A simple physics-based model based on the Newman-Tobias porous-electrode theory [4] is augmented with energy equations to predict electrical and thermal behavior. The model can then also be used to extract material properties. We aim to characterize the macroscopic effects of charge/discharge induced thermal response across a cell’s available charge states to provide insight into battery state of health (SOH). Square wave signals with amplitudes of C-rates ranging from 1C to 4C and periods ranging from 20s to 100s were applied to A123 20Ah LiFePO4 prismatic cells beginning at fixed SOC. This input signal was chosen so that the average SOC remains constant, eliminating concerns regarding how material properties change depending on charge state. Temperature distributions were measured using infrared thermography; this data was gathered dynamically and synchronized with the cell-voltage data gathered during the square-wave excitation tests. Figure 1 shows typical temperature distributions across three C-rates (1C, 2C, 4C) at a fixed period of 100s at an average SOC of 50%. As C-rate increases, not only does the overall temperature of the cell increase, but the difference in temperature across its surface also varies. Material properties of batteries – including the propensity for thermal runaway – depend strongly on SOC [5]. By fitting the thermographic data gathered around different mean SOCs, it is possible to extract these SOC-dependent parameter values. Some discussion will also touch on how the model can be used to track material properties as they vary with respect to cycling history, providing possible insight into SOH estimation. 1. Robinson, J.B., et al., Detection of Internal Defects in Lithium-Ion Batteries Using Lock-in Thermography. ECS Electrochemistry Letters, 2015. 4(9): p. A106-A109. 2. Bandhauer, T.M., S. Garimella, and T.F. Fuller, A Critical Review of Thermal Issues in Lithium-Ion Batteries. Journal of The Electrochemical Society, 2011. 158(3): p. R1-R25. 3. Thomas, K.E. and J. Newman, Heats of mixing and of entropy in porous insertion electrodes. Journal of Power Sources, 2003. 119–121: p. 844-849. 4. Newman, J.S. and C.W. Tobias, Theoretical Analysis of Current Distribution in Porous Electrodes. Journal of The Electrochemical Society, 1962. 109(12): p. 1183-1191. 5. Maleki, H., et al., Thermal Stability Studies of Li‐Ion Cells and Components. Journal of The Electrochemical Society, 1999. 146(9): p. 3224-3229. Figure 1

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