A Battery Pack model CFD simulation is an essential tool in modern engineering, especially for designing electric vehicles and large-scale energy storage systems. Understanding the performance of a complete Battery Pack simulation is much more complex than looking at a single cell. This report details a 1P3S battery CFD simulation, which models a pack of three cells connected in series. The analysis uses ANSYS Fluent and its advanced MSMD Fluent module. The Multi-Scale Multi-Domain (MSMD) model is perfect for this task because it can simulate the entire pack as one system while also calculating the detailed physics inside each individual cell.

This MSMD CFD analysis is coupled with the NTGK electrochemistry model to accurately predict the link between electrical current, voltage drop, and heat generation—the critical factors in battery design. The Battery model fluent provides allows engineers to see potential problems, like electrical bottlenecks or uneven cell usage, before a physical prototype is ever built. By performing a virtual test under a constant 200W power load, this Battery Pack fluent study provides the detailed data needed to design safer, more reliable, and longer-lasting battery systems.

Figure 1: A schematic showing the 1P3S battery pack configuration, with three cells connected in series by busbars.

Simulation process: A Coupled MSMD-NTGK Model for a Series Battery Pack

The simulation process for this 1P3S battery pack CFD study was built around a complete system model in ANSYS Fluent. The geometry included all critical components: the three individual battery cells, the electrical tabs on each cell, and the copper busbars that connect the cells in a series circuit. The core of the simulation was the activation of the MSMD battery model. This advanced module is specifically designed for pack-level analysis, allowing Fluent to treat each cell as an individual component with its own internal physics, all while being electrically connected to its neighbors.

The internal physics of each lithium-ion cell was governed by the NTGK electrochemistry model. This model calculates the chemical reactions that produce electricity and heat. A constant system power demand of 200W was set as the main operational load. A key feature of the MSMD Fluent setup is that the software automatically distributes this power load across the three cells based on their individual voltage and resistance, just like in a real battery pack. The material properties for the busbars (copper) and the internal cell components, including the electrolyte (e-material), were defined to ensure the simulation was physically accurate. The entire setup was run as a transient simulation to capture how the pack’s voltage and State of Charge changed over the 1500-second discharge period.

Post-processing: CFD Analysis of the Energy Journey in a 1P3S Pack

The simulation results allow us to follow the journey of energy from inside the cells to the outside world. This story reveals not just what happens during discharge, but why it happens, and what it means for the health and performance of the battery pack. The story begins with the 200W power demand. To deliver this power, the pack must create a flow of electrical current. The current magnitude contour in Figure 2 shows us the path this current takes. It is like an electrical highway. Inside the large body of each cell, the current is spread out, with a low density. However, to get from one cell to the next, all of that current must be funneled through the small tabs and busbars. This creates a massive electrical bottleneck. The simulation shows the current density in these small connection points increases to 698,500 A/m², which is thousands of times higher than in the main cell body. This is the first critical finding: the tabs and busbars are the most electrically stressed components in the entire pack. This intense current flow is the primary source of resistive heat generation, meaning these connection points will become the hottest parts of the pack.

Figure 2: The current magnitude contour from the MSMD CFD simulation, highlighting the high-current-density “hot spots” in the tabs and busbars.

Drawing this high current comes at a price: a drop in voltage. The plot in Figure 3 shows the pack’s terminal voltage over the 1500-second discharge. The voltage starts at a healthy 12.4V but immediately drops to 12.15V in the first 200 seconds. This initial, steep drop is the immediate electrical “cost” of creating that high current flow through the internal resistance of the cells and busbars. After this initial loss, the voltage declines more slowly and steadily to a final value of 11.4V. This predictable, linear drop is caused by the chemical changes inside the battery as it loses charge. For a designer, this plot is a performance guarantee. It confirms that the pack can supply the required 200W of power for the full 1500 seconds without the voltage falling below a critical level. At 11.4V, each cell is at 3.8V, which is well above the safe minimum of 3.0V.

Figure 3: The battery pack’s terminal voltage (Passive-Zone Potential) drop over 1500 seconds of discharge at a constant 200W power, as predicted by the Fluent simulation.

The high current density shown in the first contour has an unavoidable consequence: heat. The temperature plot tells the thermal story of the pack. The maximum temperature rises from 300K to 305.8K over the 1500-second discharge. The shape of this curve is very important. For the first ~800 seconds, the temperature rises quickly. After this point, the curve begins to flatten. This indicates that the battery pack is starting to approach a state of thermal equilibrium, where the heat being generated by the electrical current is starting to be balanced by the heat being lost to the environment through convection. This 5.8K temperature rise is a direct result of the intense resistive heating happening in the tabs and busbars.

Figure 4: The final temperature evolution plot, showing the maximum temperature in the battery pack rising from 300K to 305.8K during the 1500-second discharge.

Finally, we zoom into the inner world of a single cell to see how this process affects it on a micro-level. The cell voltage contour in Figure 5 shows that the voltage is not perfectly uniform. There is a small but important difference of 4 millivolts (mV) from the bottom of the cell (3.808V) to the top (3.804V). This tiny gradient represents the “effort” required to pull the current through the cell’s own internal resistance to the tab at the top.

This small voltage difference has a direct impact on the battery’s energy. The State of Charge (SOC) contour in Figure 6 is a perfect mirror of the voltage contour. The bottom of the cell, with its slightly higher voltage, still has 52.77% of its charge left. The top of the cell, near the high-current tab, has discharged slightly faster and is down to 52.08% SOC. This is the second critical finding: even in a perfectly made pack, the physics of current flow will cause a small but real imbalance inside every single cell. Over thousands of charge and discharge cycles, this tiny imbalance can grow, causing one part of the cell to age faster than the other.

Figure 5: The cell voltage contour from the battery pack fluent analysis, visualizing the small voltage gradient within each of the three cells.

Figure 6: The State of Charge (SOC) contour, showing the distribution of remaining energy inside the cells and identifying the areas that are discharging the fastest.

The most important achievement of this simulation is its ability to connect the macro (pack-level) performance to the micro (cell-level) behavior. For a battery designer or manufacturer, this data is invaluable:

1. Designing Better Connections: The simulation clearly identifies the tabs and busbars as electrical hot spots. A designer can now use this model to test a new design with wider tabs or thicker busbars to reduce the peak current density, leading to less heat, higher efficiency, and a safer battery pack.
2. Developing Smarter Battery Management Systems (BMS): The simulation shows how voltage and SOC are linked. This data can be used to write more accurate algorithms for the BMS, allowing it to estimate the pack’s remaining energy more precisely and to prevent the cells from being over-discharged.
3. Improving Longevity and Reliability: By revealing the small SOC imbalance inside each cell, the simulation gives designers a target for improvement. They can work on new electrode materials or cell structures that reduce internal resistance, minimizing this imbalance. This would lead to cells that age more evenly, dramatically increasing the lifespan and reliability of the entire battery pack.