A Battery with MSMD model & NTGK E-chemistry CFD simulation is a critical tool for engineers designing modern energy systems. For applications like electric vehicles, managing the heat from lithium-ion battery CFD models is essential for safety and performance. A Battery fluent simulation allows engineers to see exactly how temperature, voltage, and current behave inside a battery cell without expensive and slow physical testing.

This report details a Battery with MSMD model simulation using ANSYS Fluent. The analysis uses the powerful MSMD model fluent provides, which stands for Multi-Scale Multi-Domain. This model calculates the flow, heat, and electricity all at once. It is combined with the NTGK Fluent electrochemistry model, which accurately simulates the chemical reactions that make the battery work. This combined Battery CFD approach is essential for predicting hot spots, understanding performance limits, and designing effective cooling systems for large battery packs, leading to safer and longer-lasting products.

• Reference [1]: Kim, Ui Seong, et al. “Modeling the dependence of the discharge behavior of a lithium-ion battery on the environmental temperature.” Journal of The Electrochemical Society5 (2011): A611.

Figure 1: The reference geometry of the LG Chem 14.6Ah lithium-ion battery, showing the electrode and tab dimensions [1].

Simulation Process: A Coupled MSMD-NTGK Model for Discharge Analysis

The simulation process for this lithium-ion battery CFD study was based on a reference model. The computational geometry was created to represent the three essential parts of the battery: the cathode (positive tab), the anode (negative tab), and the separator region in between. Using a structured blocking technique, a very efficient mesh of only 1264 hexahedral cells was generated. This seemingly coarse mesh is highly effective because the MSMD battery model in ANSYS Fluent works with area-averaged properties, allowing it to accurately predict overall thermal and electrical behavior without the high computational cost of a very fine grid.

Inside Fluent, the physics was set up to simulate a standard discharge cycle. The Multi-Scale Multi-Domain (MSMD) model was activated and coupled with the NTGK electrochemistry model. This powerful combination allows the simulation to solve for the complex interactions between the electrical field and the chemical reactions. The discharge rate was set to 1C, which means the battery would fully discharge in one hour, a typical rate for many applications. The operating voltage was limited to a safe window between 3V and 4.3V. To model heat removal, a convective heat transfer boundary condition was applied to all outer walls of the battery, simulating how it would be cooled by the surrounding air or a dedicated cooling system.

Figure 2: The geometry model of the battery cell used in the Fluent CFD simulation, including the positive tab (cathode), negative tab (anode), and separator.

Post-processing: Engineering Analysis of Thermal and Electrochemical Performance

The simulation results indicating a clear cause-and-effect relationship between the electrical current flow, the chemical reactions, and the resulting heat generation inside the battery cell. The analysis starts with the overall thermal behavior. The temperature plot in Figure 3 shows that the maximum temperature in the cell rises steadily and linearly from a starting point of 300K to a final temperature of 300.62K after 300 seconds of discharge. The perfectly straight line is significant; it proves that at a constant 1C discharge rate, the electrochemical reactions produce heat at a constant and predictable rate. While a 0.62K rise may seem small for a single cell, this number is critical for battery pack designers. In a pack with thousands of cells, this small amount of heat multiplies, and if it is not removed effectively, it can lead to dangerous overheating.

Figure 3: The maximum temperature evolution plot from the battery CFD simulation, showing a linear rise from 300K to 300.62K over a 300-second discharge.

The voltage plot in Figure 4 reveals the detailed physics of the discharge process. The voltage starts at 4.12V and shows a rapid initial drop, followed by a slower, more linear decrease to 4.015V. The NTGK model accurately captures this real-world behavior. The initial fast drop is caused by activation polarization—the energy needed to get the chemical reactions started on the electrode surfaces. The slower drop that follows is due to ohmic resistance and changes in lithium concentration. This plot confirms the electrochemical model is working correctly.

Figure 4: The battery voltage drop plot from the MSMD model in Fluent, illustrating the typical discharge curve from 4.12V to 4.015V at a 1C rate.

The most important engineering insight comes from connecting the heat source and current contours. The heat source contour in Figure 5 clearly shows that heat is not generated evenly. A significant hot spot with a peak heat generation of 4655 W/m³ appears near the tabs. The current magnitude contour in Figure 6 provides the reason why. The current is forced to “funnel” from the large electrode plates into the small tabs, causing the current density to become extremely high in this area, reaching a peak of 102,785 A/m². In the main body of the electrode, the current density is much lower. Heat generation from electrical resistance (Joule heating) is proportional to the square of the current density. Therefore, this extreme concentration of current is what creates the dangerous hot spot.

Figure 5: The total heat source contour, identifying the critical hot spots near the tabs with a maximum heat generation of 4655 W/m³.

Figure 6: The current magnitude contour from the NTGK Fluent model, showing current density is highly concentrated near the tabs, reaching a peak of 102,785 A/m².

The MSMD solution method in Fluent calculates these distributions by solving both the electrochemical equations and the electrical potential field together.

The most important achievement of this simulation is the clear identification and explanation of this critical hot spot at the base of the tabs. For a battery designer or manufacturer, this data is invaluable:

1. Optimized Cooling Design: Knowing exactly where the hottest part of the cell is allows designers to place cooling channels or heat sinks precisely where they are needed most. This leads to a more efficient, lighter, and cheaper thermal management system.
2. Improved Safety and Lifespan: By using this model, designers can test new tab designs (e.g., making them wider or using a more conductive material) to reduce the current concentration. Lowering this peak temperature directly leads to a safer battery that degrades slower, extending its useful life.
3. Accurate System Sizing: The simulation provides a precise value for the total heat generated over time (a 0.62K rise in 300s). This allows engineers to accurately size the complete thermal management system for a large battery pack, ensuring the pump, radiator, and fans are powerful enough to keep the entire pack in its optimal temperature range.