Lithium ion batteries are widely adopted due to their high energy density, low self-discharge rate and fast charging capabilities¹. However, during their operation, capacity fade occurs as a result of various parasitic reactions causing loss of cyclable lithium ions. A dominant and widely studied fade mechanism is the formation of the Solid Electrolyte Interphase (SEI) layer caused by the reduction of electrolyte at the anode-electrolyte boundary. Temperature plays a crucial role on the electrochemical performance of individual cells, affecting the diffusivities of ions, kinetics of reactions and rate of degradation of the battery. Thermal battery models allow us to study the thermal behavior of cells and are often used for cell configuration optimization and thermal management system design².

In literature, thermal models are incorporated with empirical and physics-based models to capture battery dynamics influenced by temperature. Empirical thermal models are simple and fast to solve but are applicable only for specific operating conditions and cannot capture electrochemical subtleties. Conversely, physics-based models use equations that incorporate transport phenomena and electrochemical reactions occurring in the cell. Common physics-based models are the Single Particle Model (SPM) and the Psuedo-2D (P2D) model. The P2D model is robust but computationally expensive³ and the SPM model does not capture electrolyte transport which has a non-linear temperature dependence.

The Thermal Tank-In-Series^4,5 model is a systematically volume-averaged form of the P2D model with energy balance equations for the current collectors, cathode, separator and anode. This reduces the number of equations in the model and improves computational speed while maintaining accuracy. This model can be ideal to predict capacity fade during fast charging.

Extending on our previous work, we take the Thermal Tanks-in-Series model coupled with SEI layer formation for a single-cell sandwich and apply it to analyze capacity fade under different temperature cycling conditions. Experimental validation of the model will facilitate understanding of diffusive, kinetic and temperature effects on the growth of the SEI layer and cell capacity fade.

References

1. M. Pathak, S. Kolluri, and V. R. Subramanian, J. Electrochem. Soc., 164, A973–A986 (2017).
2. H. Liu, Z. Wei, W. He, and J. Zhao, Energy Convers. Manag., 150, 304–330 (2017).
3. V. Ramadesigan et al., J. Electrochem. Soc., 159, R31–R45 (2012).
4. A. Subramaniam, S. Kolluri, S. Santhanagopalan, and V. R. Subramanian, J. Electrochem. Soc., 167, 113506 (2020).
5. A. Subramaniam et al., J. Electrochem. Soc., 167, 013534 (2020).