Lithium-ion batteries are gaining significant interest as energy storage devices in high power demand applications like power grids and EVs as the world seeks to reduce dependence on fossil fuels. High energy and power densities, high coulombic efficiency and low self-discharge of lithium-ion batteries make them a preferred choice in such applications. When a cell is cycled, various degradation processes occur, leading to a reduction in cell capacity over time. Among the various ageing mechanisms, electrolyte decomposition at the graphite electrode is the most significant contributor to capacity loss due to the consumption of cyclable lithium ions. Another mechanism for capacity loss is lithium plating on the surface of the graphite electrode particles and is observed under harsh cycling conditions or after extended cycling. These reactions lead to the formation of a passive layer, called the solid-electrolyte interface (SEI) layer, on the surface of the anode graphite particles. The ionic resistance offered by the SEI layer to the flow of lithium ions in the electrolyte leads to increased heat generation within the cell, thereby leading to higher cell temperature for the same operating conditions with cell ageing. Moreover, the ageing of the cell due to capacity loss, power reduction and impedance rise, is strongly dependent on the cell’s operating temperature and current. The internal cell temperature can be significantly different from its surface temperature for large-format cells when subjected to high current and different extent of forced cooling leading to a spatially varying degradation effects.Even though highly relevant, the coupled effect of current flow, heat generation and capacity fade have not been adequately examined in the literature. While several works have simulated the temperature distribution in large format cells [1,2] or capacity fade for cells [3] under isothermal conditions, very few have examined their coupled effect. Studies considering the coupled effect of capacity fade with heat generation have incorporated a pseudo-2D electrochemical degradation model [4,5]. The thermal model ranges from one to three-dimensional, with higher dimensional thermal models considering uniform heat generation within the cell. However, in a practical scenario, a large-format cell suffers from a non-uniform temperature distribution, leading to non-uniform electrochemical reactions and degradation.Hence, a detailed coupled thermo-electrochemical, capacity-fade model is required to understand cell degradation and temperature rise during its operational life. In a step towards this goal, a two-dimensional physics-based, coupled thermo-electrochemical model with capacity degradation will be demonstrated for cylindrical lithium-ion cells. The electrochemical model with capacity fade is based on the porous-electrode and concentrated solution theories [6]. The thermal model considers the effect of ohmic heat in various cell components and the reversible and irreversible heat of reactions in the electrodes. The contribution of side reactions in heat generation is incorporated. The effect of changing porosity and thermal and electronic impedance with ageing on the cell is considered in the model. The results will give a better understanding of (a) the safe operation of the cell as the internal temperature of the cell changes with ageing, and of (b) the dependence of cell ageing on temperature. Fig.1 shows the spatial distribution of temperature at the end of discharge for an LMO cell under different convective heat transfer coefficients. The cells were subjected to a 5C current at an ambient temperature of 25°C. While the temperature reached for a convective heat transfer coefficient h=5 W/m2.K is much higher, the temperature gradient developed is more significant for h=50 W/m2.K. This can be attributed to the increased Biot number for the cell under forced cooling. A difference of 8°C for h=50 W/m2.K between the internal and external cell temperature show that the capacity-fade model needs to consider the local temperature distribution within the cell for better prediction of cell degradation with cycling.
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Open Access
Aug 19, 2022
Simon B B Solberg + 5
