Battery thermal runaway propagation is a serious concern in battery systems. Ansys Fluent provides tools to simulate battery thermal behavior under such conditions to help in understanding and mitigating the risks associated with thermal runaway.
Battery thermal runaway propagation is a serious concern in battery systems, especially with lithium-ion and other high-energy-density batteries. The phenomenon involves a chain reaction of increasing temperature, leading to potential fires or explosions. Engineering solutions are essential to contain, mitigate, or prevent this kind of thermal runaway. The challenges of battery cell and module design in the context of thermal abuse runaway propagation are significant due to the complex nature of thermal runaway reactions, which are material-specific and can lead to severe consequences such as damage to the battery cell, fire, or explosion.
Safety has become an important issue in battery design. Under abuse conditions, thermal runaway may occur in a battery. Increased temperature could trigger thermal runaway reactions. Excessive heat released as a result of such conditions could damage a battery cell or even cause fire or explosion. A thermal management system is important in preventing the propagation of thermal runaway across battery cells and modules.
There are several typical solutions and strategies for thermal abuse runaway propagation. Thermal management cooling systems use active cooling help maintain battery temperature within safe operating ranges. Battery management systems provide temperature monitors of individual cells and initiate protective actions if temperatures rise beyond a threshold. Physical isolation and containment use thermal barriers or separation to prevent thermal runaway from propagating from one cell to the next. Cell design features such as relief vents permit release of built-up gas, and separator materials delay short-circuiting. Fire suppression and mitigation systems help extinguish fires in the early stages of thermal runaway, and ventilation systems help prevent the buildup of hazardous gases. Alternate cell chemistry batteries with more stable chemistries can reduce the likelihood of thermal runaway. Passive safety features such as thermal fuses disconnect the battery when it overheats, preventing further heat buildup and avoiding propagation. Thermal abuse testing, such as overcharge, short circuit, crush, and puncture testing verify batteries are safe under extreme conditions, and safety standards such as UL 2580, IEC 62133, or UN 38.3 can help ensure that a battery pack design incorporates all the necessary precautions to prevent thermal runaway. These solutions are typically combined to provide multiple layers of safety, aiming to minimize the risk of thermal runaway propagation and protect both the battery system and its environment.
The Fluent simulation method and results below focus on thermal management systems, cell chemistry, and physical isolation. Setting up battery module simulations with Fluent in this discussion involves several steps. These steps include thought map, product map, and Fluent case set up.
Thought Map: A thought map of the battery 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 Map: Product maps of a battery cell and a module are generated to list and categorize product features. The product map indicates design factors that correspond to theories/actions in the thought map. The maps below show an example battery module and one of its cells.
Fluent Simulation: Fluent models are generated per the studies produced by the thought map. In this case a two 3-factor factorial DOEs are employed which results in 20 unique Fluent models. Inputs pertaining to the battery are set up using the Battery Model. The simulations use CHT Coupling with active cells and passive tabs. The image below shows the sequence of steps for populating inputs for the battery model. A transient table is used to add thermal energy to a simulated nail in the front cell.
The simulation calculations are executed to generate the results, focusing on temperature distribution and internal short variable. Design treatments data are analyzed to answer the theory questions and confirm and/or contradict predictions.
Cell Results: Average cell temperature and internal short are the key outputs of the cell simulation. Observations of the data for average cell temperature indicate that the heat of the short-circuit electrochemical reaction is the most significant factor of the study.
Cell Significant Factors: Time to reach zero internal short is the key metric under consideration for the cell study. Analysis of the DOE for internal short time indicates that the heat of the short-circuit electrochemical reaction is the most significant factor of the study, followed by nail energy source. The time drops sharply when the heat value is increased. The time also decreased as the nail heat source was increased. Convective heat transfer at the cell bottom had an almost negligible impact on delay time.
The transient temperature distribution is shown below for the fastest and the slowest propagation over a period of 2 minutes. The whole cell has experienced runaway within 11 seconds for the fastest propagation while the slowest propagation took 37 seconds.
Cell Graphical Analysis: The time to reach zero internal short is graphically displayed below. The negligible relationship between time and convective heat transfer coefficient is shown in blue. The stronger relationship between time and nail heat source is shown in orange. The time decreases as the nail source is increased at the low heat of internal short while the time slightly decreases with the high heat of reaction. The strongest relationship between the time is shown in the separate groups. The time drops sharply as heat of reaction is increased.
Module Raw Results: Average cell temperature and internal short are the key outputs of the module simulation. Observations of the data of the factorial for average cell temperature indicates that gap size between cells is the most significant factor.
Module Temperature: The transient cell volume-average temperature of each cell for each treatment is shown below over a period of 2 minutes. Propagation occurs in all 12 cells when the gap size is 0 mm. Propagation is much slower when the gap size is 2 mm.
The transient temperature distribution is shown below for the fastest and the slowest propagation over a period of 2 minutes. The whole module has experienced runaway within 1 minute for the fastest propagation while only a fraction of the module has experienced runaway within 2 minutes with the slowest propagation.
Module Internal Short: The transient cell volume-average internal short variable of each cell for each treatment is shown below over a period of 2 minutes. The average time between curves at a short value of 0.5 was extracted.
Module Significant Factors: Internal short delay is the key metric under consideration for the module study. Analysis of the factorial DOE for internal short delay indicates that gap size is the most significant factor of the study. The delay rises sharply when the gap size was increased to 2mm. The delay was slightly higher for a coolant inlet temperature of 15 degrees versus 25 degrees. Coolant flow rate had an almost negligible impact on delay time.
Module Graphical Analysis: The internal short delay time is graphically displayed for the key input factors as shown below. The negligible relationship between delay time and coolant flow rate is shown in blue. The stronger relationship between delay time and coolant inlet temperature is shown by the paired results. The delay time decreases slightly as coolant inlet temperature is increased from 15 to 25 [C]. The strongest relationship between the delay time and gap size is displayed in orange pair. The delay time rises sharply as gap size is increased from 0 to 2 mm.
Setup Details: The following video steps through highlights of the set up for the cell and module models.
Mitigating thermal runaway propagation is a key focus in battery safety research. Ansys Fluent provides innovative solutions by enabling the simulation of various mitigation strategies such as cooling systems and thermal barriers. By using Ansys Fluent, engineers can model the heat generation and dissipation within a battery pack, analyze temperature distribution, and predict the onset of thermal runaway. For instance, engineers can use Ansys Fluent to evaluate the effectiveness of different thermal management systems, such as liquid cooling or phase change materials, in preventing runaway propagation.
ANSYS offers advanced capabilities for simulating battery modules which offer numerous benefits, including enhanced design optimization, improved safety, and cost savings. By accurately predicting module performance, manufacturers can design batteries that meet specific requirements more efficiently.
Ansys Fluent enables the evaluation of multiple design/input factors such as coolant flow, coolant inlet temperature, material properties, external heat transfer, and cell separation. A thermal engineer can evaluate multiple design options to understand the thermal behavior. Beyond Fluent, ANSYS provides tools such as LS-Dyna, DesignXplorer, OptiSLang, and Twin Builder for further design parametrization and 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 module thermal behavior for abnormal as well as normal operation.
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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