Battery Module Thermal Design Challenges
Designing battery modules for usage cycles presents several unique thermal engineering challenges.
Usage cycles, such as drive cycles, involve variable loads, speeds, and environmental conditions, requiring batteries to deliver consistent performance under dynamic stress. Managing thermal behavior is critical, as fluctuating currents generate heat that can degrade cells. Designers must ensure optimal energy density, power output, and safety, while balancing size, weight, and cost constraints. Additionally, cells must be carefully matched to avoid imbalances that affect performance and lifespan. Predicting long-term degradation under real-world cycling further complicates design. Overall, achieving durability, efficiency, and reliability across diverse driving scenarios demands careful engineering and advanced control strategies.
Engineering Solution
To address battery module challenges in drive cycles, engineers employ several solutions. Thermal management systems, such as liquid cooling or phase-change materials, regulate temperature and prevent overheating. Battery management systems (BMS) monitor voltage, current, and temperature to ensure cell balancing and safe operation. Advanced modeling and simulation tools help predict performance and degradation under various drive conditions. Cell selection and matching improve uniformity and longevity. Structural design optimizes packaging for weight, durability, and crash safety. Additionally, adaptive control algorithms adjust power delivery in real time to enhance efficiency and extend battery life across diverse driving scenarios.
Using ANSYS Fluent is an effective tool for evaluating battery thermal system solutions; however, these evaluations can present several challenges. Creating accurate models requires detailed input data, including material properties and cell behavior under various conditions, which can be difficult to obtain. Validating high-fidelity simulations in Fluent is computationally intensive and time-consuming when considering usage cycles. By leveraging Reduced Order Models in Ansys Digital Twin thermal solutions for usage cycles can be evaluated in real time. This blog addresses the Linear Time Invariant (LTI) Reduced Order Model (ROM) for a battery module.
Method
Setting up battery module thermal simulation with Ansys Fluent and Digital Twin in this discussion involves several steps. These steps include thought map, product map, Fluent case setup, and Twin Builder Digital Twin setup.
Thought Map: A thought map of blow molding characteristics is generated to organize and represent ideas, concepts, or information in a structured way. The thought map below shows the objective of the simulation study and questions asked to address the objective. Each question is followed by a theory, action, and prediction to address each question. Results would also be added to the bottom of each branch as they are generated.
Product Maps: A product map of the blow molding parison and molds is generated to list and categorize product features. A product map indicates some factors that correspond to theories/actions in the thought map.
Fluent Training Simulation: Fluent models are executed for training purposes per the studies produced by the thought map. A steady-state cold flow simulation is performed first to generate a solution for the cold plate coolant flow with zero cell heat release and zero tab current. Then the flow equations are deactivated, and the energy equation is activated. The images below show the sequence of steps for training the LTI model with Single Input Multiple Output and with Multiple Input Multiple Output Reduced Order Models in the Fluent Battery Model.
The following image shows the activation of the Battery ROM Tool Kit and the selection of the LTI ROM type from the Battery Model panel.
The following image shows the different selection procedure for Single-Input Multiple Output (SIMO) versus Multiple-Input Multiple Output (MIMO) ROMs when selecting the Volume Heat. Tip: Specify the wattage Value before clicking the "Add as a Group" or "Add Individually" buttons.
The Input Tab Current for Joule Heat is activated in both cases; and both cases use cells added individually for the Cell Zone Average as shown below. After setting the Transient Setup, the settings are Applied, and Run Training is activated.
Digital Twin Simulation: The Digital Twin functionality for Linear Time Invariant ROM in Twin Builder is accessed via Twin Builder > Toolkit > Thermal Model Identification. The images below show the sequence of steps for executing the LTI model with Single Input Multiple Output Reduced Order Model (left) and Multiple Input Multiple Output Reduced Order Model (right) in Twin Builder.
The generated model is dragged from the component library into the schematic window. Constant inputs for heat load and current are added and connected to the model. A square function is added between the current constant block and the joule heat input because heat load is a function of current squared. For the SIMO ROM a constant heat load corresponds to the head load of the module. For the MIMO ROM a constant heat load is connected to all the inputs and has a value equal to the heat load per cell.
Twin Builder analysis is performed to generate the transient temperature results. The simulation calculations are executed to generate the results, focusing on temperature and simulation time. Fluent runs were performed in parallel with 10 processors and used a time step size equal to the maximum time step size specified for the Digital Twin runs. Treatments data are analyzed to answer the theory questions and confirm or contradict predictions.
Fluent and Digital Twin Simulation Results
Graphical Analysis of Training Time: The charts below display the time spent on training the ROMs in Fluent. The Multiple Input Multiple Outlet training (MIMO) took over six times longer than the Single Input Multiple Output (SIMO) training because there were 13 inputs compared to 2.
Graphical Analysis of Simulation Time: The charts below display the time spent on simulating the usage in Fluent and in Digital Twin. The first scenario had constant heat load, while the second and third scenarios had transient heat loads. The Digital Twin run times were less than 4 seconds. The corresponding Fluent runs took hours to run.
Graphical Analysis of Simulation Temperature: The charts below display the temperature comparison between the Fluent runs and the corresponding SIMO and MIMO ROMs with constant heat load. It is very difficult to see a difference in the temperature; however, the simulation time difference is large.
Graphical Analysis of Simulation Temperature: The charts below display the temperature comparison between the Fluent runs and the corresponding Digital Twin runs with forward and reverse cycle loads. It is very difficult to see a difference in the temperature; however, the simulation time difference is large.
Graphical Analysis of Battery Current Influence: The charts below display the temperature comparison between the two current levels with forward and reverse cycle loads. A half-degree difference in temperature can be seen at the end of the cycles. Each run took less than 4 seconds to execute.
Video
Setup Details: The following video steps through highlights of the setup for both SIMO and MIMO using Fluent and Twin Builder.
Ansys Solution Benefits
ANSYS offers advanced capabilities for simulating battery module thermal systems which offer numerous benefits, including enhanced design optimization, improved reliability, and cost savings. By accurately predicting battery module performance per usage cycles, manufacturers can design products that meet specific requirements more efficiently.
Ultimately, ANSYS Fluent and Digital Twin provide a comprehensive, virtual environment to evaluate usage cycles and to fine-tune cooling systems.
Ansys Fluent and Digital Twin enable the evaluation of multiple design/input factors such as current and constant or variable heat load. A battery thermal engineer can evaluate multiple design options in Digital Twin to understand the thermal behavior in real time. Beyond Digital Twin and Fluent, ANSYS provides tools such as LS-Dyna, DesignXplorer, OptiSLang, and Mechanical for further design parametrization and evaluation.
Ozen Engineering Expertise
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 module cooling.
We offer support, mentoring, and consulting services to enhance the performance and reliability of your battery cooling 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.
Suggested Blogs
- https://www.ozeninc.com/industry-solutions/battery-solutions/
- https://blog.ozeninc.com/resources/fluent-equivalent-circuit-model-simulation-in-twin-builder
- https://blog.ozeninc.com/resources/workflow-for-battery-module-thermal-simulation-with-ansys
- https://blog.ozeninc.com/industry-applications/meshing-complex-battery-models-in-ansys
- https://blog.ozeninc.com/resources/battery-thermal-management-solutions-optimizing-ev-battery-design
- https://blog.ozeninc.com/resources/battery-cell-potential-field-simulation-with-fluent-battery-model
- https://blog.ozeninc.com/resources/fluent-conjugate-heat-transfer-coupling-applied-to-a-battery-module
- https://blog.ozeninc.com/resources/battery-thermal-abuse-runaway-propagation-simulation-with-ansys-fluent
- https://blog.ozeninc.com/resources/fluent-equivalent-circuit-model-applied-to-steady-state-battery
