Challenges
Thermal management is critical in battery modules to ensure optimal performance, longevity, and safety. As these modules operate, they generate heat, which, if not properly managed, can lead to overheating and potential failure.
Effective thermal management helps maintain the battery cells within a safe temperature range, preventing thermal runaway, enhancing efficiency, and extending the battery's operational life.
Computational Fluid Dynamics (CFD) modeling for battery modules presents several significant challenges due to the complex nature of battery systems. These challenges arise from the need to accurately simulate various physical phenomena that occur during battery operation. Some of the primary challenges include accurate modeling of heat transfer and the identification of thermal hotspots crucial for designing safe systems.
Challenges are further compounded when battery modules are stacked together in racks/cabinets. The simulation of a module can contain many mesh elements. When modules are stacked together in a rack, the rack simulation would contain multiples of module mesh count plus the mesh count for the rack domain surrounding the modules. The high mesh counts can cause challenges for hardware memory as well as for simulation solution time.
CFD (Computational Fluid Dynamics) simulation offers significant benefits for the thermal management of battery modules. It allows engineers to visualize and analyze the heat distribution and fluid flow within the battery module and rack/cabinet without the need for physical prototypes.
The engineering solution shown here is to use a two-stage approach for CFD simulation of a rack/cabinet. First, a Fluent CFD simulation is performed on one module/enclosure. Heat flux from the external surfaces is stored in a profile file. Second, a Fluent CFD simulation is performed on the rack with empty enclosures along with heat flux from stage 1 that is read and applied to enclosure surfaces.
The models shown here are for steady-state thermal/airflow conditions. They include an example module in an enclosure as well as a rack with 7 enclosures.
Setting up a CFD simulation for a battery module/enclosure involves several key steps:
Set up the Fluent case.
Solver = Steady-State; Gravity = off; SST k-omega turbulence
Battery model = on; Solution Method = CHT-Coupling; Set Active and Passive conductive zones
Set Battery Model Parameters, including Energy Source per Battery.
Set up Boundary Conditions for coolant inlet and outlet. Set up thermal boundary conditions for external walls using convection. It is recommended to rename the prefix of this zones to something like wx-*
Generate a custom field function for the negative of total surface heat flux.
Activate “Write Merge Profiles” under Merge Profile Options. Select "neg_heat_flux" as the value.
Include rack/cabinet components like panels, covers, support, etc.
Cap openings to obtain a watertight geometry.
A subassembly structure tree as shown below is recommended for component management and for profile assignment.
Solver = Steady-State; Gravity = on; SST k-omega turbulence; Energy = On; Battery Model = Off.
Set up materials for air and solid rack components.
One common challenge in CFD simulation of battery modules and rack closets is accurately modeling the complex geometry while simulating within the constraints of compute and time resources.
In the above example the mesh count for one module/enclosure was 3 million elements. The mesh count for the rack model was 1.2 million. If 7 module/enclosures were modeled in detail, the rack model would have had 7*3 + 1.2 = 22.2 million elements.
| Model | Mesh Element Count |
| Module/Enclosure | 3.0 million |
| Rack with 7 module/enclosures | 21.0 million |
| Rack with 7 black boxes | 1.2 million |
The above procedure could be extended to transient analysis, including thermal abuse runaway, by writing profiles from the module/enclosure at regular intervals, processing these profiles in spreadsheet, and reading these profiles during rack/cabinet simulation at the same regular intervals. In both cases the writing and reading should be done via a journal file and should specify .csv file type.
Fluid flow simulation with Ansys Fluent can be further leveraged for passive and/or active venting design. Structural simulation with Ansys Mechanical can be leveraged for further analysis of the rack/cabinet structural integrity. Sensitivity simulation with Ansys OptiSLang can be leveraged for further analysis of design, operation, and noise parameters. Reduced Order Model simulations with Ansys TwinBuilder can be leveraged for rapid parameter evaluation.
Ozen Engineering Inc. leverages its extensive consulting expertise in CFD, FEA, optics, photonics, and electromagnetic simulations to achieve exceptional results across various engineering projects, addressing complex challenges like battery thermal behavior under normal and abnormal operating conditions.
We offer support, mentoring, and consulting services to enhance the performance and reliability of your battery system. Trust our proven track record to accelerate projects, optimize performance, and deliver high-quality, cost-effective results for both new and existing systems. For more information, please visit https://ozeninc.com.
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