An official website of the United States government
Immersion cooling, where cooling fluid flows in direct contact with the Li-ion cell, provides superior temperature control compared to other battery thermal management systems (BTMSs). Although the temperature rise during charging/discharging is low for immersion cooling, the inherent complexity of the cooling approach makes it difficult to predict and control performance. In general, numerical approaches are required to design and analyze immersion cooled BTMS configurations. For accurate results, models must fully couple the electrochemical and thermal-fluid physics solvers. However, such a numerical approach is computationally expensive and may not be feasible, in particular, for large BTMS systems. In the present study, we develop a computationally-efficient approach with coupled electrochemical and thermo-fluid physics to study immersion cooling based BTMSs. The core strategy is to use either analytical expressions or combination of analytical and numerical solutions for all the governing physics. Depending on the discharge rate and on the final design objective, different levels of simplification are leveraged to analyze the thermal and electrochemical response of the system. For the present analysis, we consider discharging of a cylindrical nickel manganese cobalt oxide (NCM) 18650 cell that is immersed in a cooling fluid stream. Different combinations of mass flow rates and fluids (deionized water, mineral oil, and air) are considered to evaluate performance. For every configuration, we first analyze the system with a fully-coupled fully numerical model and this data serves as the reference to judge the accuracy of the newly developed models. Note that the parameters used in the electrochemical models (such as electrolyte and electrode properties, as well as the reaction rates) are the same for the fully-numerical and the quasi-analytical models allowing direct comparison of the results. To isolate the impact of using the simplified models for the electrochemical aspects, a second set of comparison data is generated using the analytical electrochemical model in conjunction with the full-scale numerical thermo-fluid model. The analytical models rely on calculating a heat transfer coefficient to include the impact of the fluid flow and the heat transfer coefficient can be estimated from correlations or from the fully-coupled fully-numerical models. In general, for air-cooled configuration, the thermal and electrochemical performance (i.e., trend and magnitude of temperature rise and cell potential) of the quasi-analytical models matches the fully-coupled full-scale numerical model. But for other fluids, the results deviate from the baseline fully-coupled fully-numerical models unless the numerical fluid models are used to estimate the evolution of heat transfer coefficient throughout the discharging process. Specifically, a small number of fully-coupled fully-numerical simulations are leveraged to train the quasi-analytical model (based on a particular geometry) for rapid analysis and optimization of the BTMS. In summary, the newly developed models including the numerical data-driven learning provide an efficient trade-off between computation cost and accuracy.
more »
« less
This content will become publicly available on May 1, 2026
A computationally efficient approach for immersion cooling of a Li-Ion battery cell
Immersion-cooled battery thermal management systems (BTMSs) are generally designed and analyzed using numerical simulations. These models must couple the electrochemical and thermal–fluid physics for accurate results. However, such a numerical approach is computationally expensive and may not be feasible, particularly for large systems. Here, we develop a computationally efficient approach to study immersion cooling-based BTMSs with the coupled physics. After validating the simplified immersion-cooled battery model for fixed convection coefficient, we then define two simplified immersion cooling models: one using existing heat transfer correlations and the other employing customized correlations trained from fully-coupled numerical models. The trained models are highly accurate (error <3%). Moreover, they are very flexible as they can be formulated to study different combinations of mass flow rates, fluids, and discharge rates using a single heat transfer correlation. Additionally, the trained models are data-frugal, requiring only data from two mass flow rates (for a given fluid and discharge rate) to predict the response for other mass flow rates. The significant reduction in computation cost [from hours or days for the fully-coupled numerical models to seconds for proposed models] makes the proposed approach more suitable for rapid analysis, optimization, and real-time implementation of the immersion-cooled BTMSs.
more »
« less
- Award ID(s):
- 2143043
- PAR ID:
- 10587032
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- International Communications in Heat and Mass Transfer
- Volume:
- 164
- Issue:
- PB
- ISSN:
- 0735-1933
- Page Range / eLocation ID:
- 108856
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Forced immersion cooling, where a dielectric fluid flows in contact with the cells, is an effective cooling approach for lithium-ion batteries. While previous models demonstrated effectiveness, they generally focused on thermal-fluid aspects and often neglected the coupling between temperature, cell potential, and heat generation (in other words, the electrochemistry remained unaffected by cooling conditions). Here, we use a fully coupled modeling approach that solves the detailed electrochemical model (with temperature-dependent properties) in conjunction with the thermal-fluid transport models at each time step. For an 18650 cell, we compare forced immersion cooling (water and mineral oil) to forced air cooling. Improved temperature control with immersion cooling leads to higher heat generation with increased capacity loss: a 3 K temperature rise corresponds to 10% loss, whereas 42 K temperature rise results in 0.4% loss at 5C discharge. Neglecting two-way coupling prohibits accurate analysis of the effectiveness of immersion cooling. Furthermore, the thermal conductivity and heat capacity of the fluid most significantly impact the electrochemical and thermal response. Finally, we define a new metric to compare performance with different flow parameters without computationally-expensive numerical simulations. Overall, this study provides insights that will be useful in understanding and design of immersion-cooled battery systems.more » « less
-
Abstract Data centers are critical to the functioning of modern society as they host digital infrastructure. However, data centers can consume significant amounts of energy, and a substantial amount of this energy goes to cooling systems. Efficient thermal management of information technology equipment is therefore essential and allows the user to obtain peak performance from a system and enables higher equipment reliability. Thermal management of data center electronics is becoming more challenging due to rising power densities at the chip level. Cooling technologies like single-phase immersion cooling allow overcoming many such challenges owing to their higher thermal mass, lower fluid pumping powers, and potential component reliability enhancements. It is known that immersion cooling deployments require extremely low coolant flow rates, and, in many cases, natural convection can also be used to sufficiently dissipate the heat from the hot server components. It, therefore, becomes difficult to ascertain whether the rate of heat transfer is being dominated by forced or natural convection. This may lead to ambiguity in choosing an optimal heat sink solution and a suitable system mechanical design due to unknown flow regimes, further leading to sub-optimal system performance. Mixed convection can be used to enhance heat transfer in immersion cooling systems. The present investigation quantifies the contribution of mixed convection using numerical methods in an immersion-cooled server. An open compute server with dual CPU sockets is modeled on Ansys Icepak with varying power loads of 115W, 160W and 200W. The chosen dielectric fluid for this single-phase immersion-cooled setup is EC-100. Steady-state Computational Fluid Dynamics (CFD) simulations are conducted for forced, natural, and mixed convection heat transfer in a thermally shadowed server configuration at varying inlet flow rates. A baseline heat sink and an optimized heat sink with an increased fin thickness and reduced fin count are utilized for performance comparison. The effect of varying Reynolds number and Richardson number on the heat transfer rate from the heat sink is discussed to assess the flow regime, stability of the flow around the submerged components which depends on the geometry, orientation, fluid properties, flow rate and direction of the flow. The dimensionless numbers’ influence on heat transfer rate from a conventional air-cooled heat sink in immersion versus an immersion-optimized heat sink is also compared. The impact of server orientation on heat transfer behavior for the immersion optimized heat sink is also studied on heat transfer behavior for the immersion optimized heat sink.more » « less
-
The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption.more » « less
-
Abstract In recent years there has been a phenomenal development in cloud computing, networking, virtualization, and storage, which has increased the demand for high performance data centers. The demand for higher CPU (Central Processing Unit) performance and increasing Thermal Design Power (TDP) trends in the industry needs advanced methods of cooling systems that offer high heat transfer capabilities. Maintaining the CPU temperature within the specified limitation with air-cooled servers becomes a challenge after a certain TDP threshold. Among the equipments used in data centers, energy consumption of a cooling system is significantly large and is typically estimated to be over 40% of the total energy consumed. Advancements in Dual In-line Memory Modules (DIMMs) and the CPU compatibility led to overall higher server power consumption. Recent trends show DIMMs consume up to or above 20W each and each CPU can support up to 12 DIMM channels. Therefore, in a data center where high-power dense compute systems are packed together, it demands efficient cooling for the overall server components. In single-phase immersion cooling technology, electronic components or servers are typically submerged in a thermally conductive dielectric fluid allowing it to dissipate heat from all the electronics. The broader focus of this research is to investigate the heat transfer and flow behavior in a 1U air cooled spread core configuration server with heat sinks compared to cold plates attached in series in an immersion environment. Cold plates have extremely low thermal resistance compared to standard air cooled heatsinks. Generally, immersion fluids are dielectric, and fluids used in cold plates are electrically conductive which exposes several problems. In this study, we focus only on understanding the thermal and flow behavior, but it is important to address the challenges associated with it. The coolant used for cold plate is 25% Propylene Glycol water mixture and the fluid used in the tank is a commercially available synthetic dielectric fluid EC-100. A Computational Fluid Dynamics (CFD) model is built in such a way that only the CPUs are cooled using cold plates and the auxiliary electronic components are cooled by the immersion fluid. A baseline CFD model using an air-cooled server with heat sinks is compared to the immersion cold server with cold plates attached to the CPU. The server model has a compact model for cold plate representing thermal resistance and pressure drop. Results of the study discuss the impact on CPU temperatures for various fluid inlet conditions and predict the cooling capability of the integrated cold plate in immersion environment.more » « less
