Temperature is an important factor for an optimal battery performance. To gain knowledge about the internal temperature distribution in a battery, many thermal simulation studies are performed. Among other factors, they differ in the level of homogenization (LoH) of the geometry, which directly influences the computing time. However, the effects of different LoH, in particular of the cell layers, on the modeling and prediction quality of the temperature field are scarcely investigated. This work discusses the effect of different LoH of the cell stack on a numerical 3D thermal battery model for different thermal management strategies. A new approach of reducing the number of cell layers of the pouch cell geometry while keeping their volumetric proportions constant is proposed. It is clearly shown that the LoH has a large impact on the thermal transport paths, especially through the current collectors and tabs, and therefore on the predicted internal temperature distribution. In addition, the effect of the LoH differs for different thermal management strategies, because they affect the heat transport paths as well.
Thermal battery modeling is important for further battery development and optimization. The temperature strongly influences the performance and aging behavior. In the cell stack, electrochemical processes take place resulting in a large amount of heat release, which, in turn, affects the temperature distribution. Therefore, the main focus is on the cell stack, the most complex structure inside the cell. In particular, the discontinuous and anisotropic material properties represent a major challenge for simulations due to the layering. This work proposes self‐developed methods, based on the Finite Volume Method and the Finite Element Method, taking on these challenges. First, for both methods the functionality is verified and numerical convergence is validated. These, and also classical methods, are compared based on test problems with a known analytical solution in view of numerical errors as well as computing time. It if found that their accuracy and efficiency depends strongly on the specific problem, which makes their numerical investigation necessary and inevitable. Second, the methods are evaluated on a specific battery problem. Their results are plausible and correspond to the physical phenomena.
The thermal conductivity represents a key parameter for the consideration of temperature control and thermal inhomogeneities in batteries. A high‐effective thermal conductivity will entail lower temperature gradients and thus a more homogeneous temperature distribution, which is considered beneficial for a longer lifetime of battery cells. Herein, the impact of the microstructure within the porous electrode coating obtained by different compression rates and its thermal contact to the current collector is investigated as both factors significantly determine the overall conduction through the electrode. The effective thermal conductivity of two graphite anodes and two lithium nickel manganese cobalt oxide cathodes is evaluated at different compression rates. It is found that the thermal conductivity does not have a monotone dependence on the porosity with changing compression rates. The results show a strong correlation with the adhesion strength, thus a significant impact of the thermal contact resistance between the coating and current collector is assumed.
Lithium-ion battery temperature is driven by two key heat source terms – irreversible and reversible heat. The reversible heat generation term is often neglected when characterising and simulating battery temperature. This is primarily due to the long experimental time (~20 hours) associated with the widely followed potentiometric technique used to quantify reversible heat. Reversible heat can however contribute to around 70% of the total heat generation, when a battery is subjected to low charge and discharge currents, and it is quantified via a coefficient termed as the entropy coefficient. Robust techniques are therefore required that can significantly reduce the experimental time and quantify the entropy coefficient to improve battery temperature prediction and subsequent design of battery pack thermal management systems. In this abstract a bespoke experimental rig using a Peltier element is built that allows a user-defined thermal profile to be applied on to the surface of a coin cell (Figure 1). The applied temperature profile is designed in a manner that perturbs the cell open-circuit-voltage (OCV) to identify the underlying dynamics between the measured OCV response (at a given state-of-charge, SoC) and applied cell surface temperature profile. This dynamic relationship is characterised by a kernel function relating temperature and OCV. Using system identification techniques, the temperature profile can be designed to minimise the experimental time required to estimate the underlying open-circuit voltage and temperature kernel function and robustly determine the entropy coefficient at the particular cell SoC. The approach is applied on a coin cell consisting of Kokam 5 Ah electrodes and the entropy coefficient, is estimated as -0.57 ± 0.02 mV/K (when at 30% SoC) within an experimental time interval of 8 hours. The initial results are encouraging and further work is in progress to compare and validate the procedure against the default potentiometric technique. Figure 1: Experimental rig design for the thermal excitation and open-circuit voltage (OCV) measurement of the coin cell under test. Figure 1
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