The performance, lifetime and safety issues of Li-ion batteries are strongly linked to temperature and many studies in the past three decades have been devoted to improving the understanding of thermal behaviour of Li-ion batteries at cell and pack level . We will present ageing and diagnostic data of a large commercial pouch Li-ion cell and present a thermal model for the internal temperature of the pouch cell during ageing. The thermal model will include data for internal resistance and entropy as functions of state-of-charge.
A selection of the Li-ion NMC-graphite cell was aged and cycled for up to 2 years in the full state-of-charge window at 5, 25 and 45 °C. Charge and discharge rates were both 1C and 1.5 C with an additional reduced charge rate of C/8 for 5 °C. The cells were monitored in detail during the ageing. Surface temperatures, internal resistances and the changes in cell entropy were obtained as functions of changes in state-of-health.
A simple, analytical thermal model was used for a discussion about the impact of the heat sources  and the effect of thermal conductivities on internal temperatures  on the changes of internal temperature profiles with ageing.
Within a secondary Li-ion battery, ohmic heat produced between the electrode surfaces, heat production due to overpotential and (reversible) entropic heat due to the electrode reactions are considered as the main heat sources. The entropic heat can both act as a heat source and heat sink. The transfer of heat inside the battery is described using Fourier’s second law. The ohmic resistance and the cells entropy as well as the thermal conductivities of the cell materials are incorporated as functions of state-of-health. This allows to model changes in the internal temperature with the ageing process. The validation of the thermal model was done, comparing modeled and measured surface temperatures.
The thermal model is used to investigate dependencies between ageing rates and internal temperature as well as internal and external temperature gradients. This allows to evaluate temperature dependency of different ageing mechanisms like e.g. Li plating and SEI growth.
The model also allows differentiating between temperature induced and C-rate induced ageing by quantifying ageing rates due to increased temperature at high C-rates. Together with evaluating the impact of temperature gradients over the cell compared to the maximum cell temperature, this allows to estimate the impact of an improved thermal management system.
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