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The limitations of existing commercial indirect liquid cooling have drawn attention to direct liquid cooling for battery thermal management in next-generation electric vehicles. To commercialize direct liquid cooling for battery thermal management, an extensive database reflecting performance and operating parameters needs to be established. The development of prediction models could generate this reference database to design an effective cooling system with the least experimental effort. In the present work, artificial neural network (ANN) modeling is demonstrated to predict the thermal and electrical performances of batteries with direct oil cooling based on various operating conditions. The experiments are conducted on an 18650 battery module with direct oil cooling to generate the learning data for the development of neural network models. The neural network models are developed considering oil temperature, oil flow rate, and discharge rate as the input operating conditions and maximum temperature, temperature difference, heat transfer coefficient, and voltage as the output thermal and electrical performances. The proposed neural network models comprise two algorithms, the Levenberg–Marquardt (LM) training variant with the Tangential-Sigmoidal (Tan-Sig) transfer function and that with the Logarithmic-Sigmoidal (Log-Sig) transfer function. The ANN_LM-Tan algorithm with a structure of 3-10-10-4 shows accurate prediction of thermal and electrical performances under all operating conditions compared to the ANN_LM-Log algorithm with the same structure. The maximum prediction errors for the ANN_LM-Tan and ANN_LM-Log algorithms are restricted within ±0.97% and ±4.81%, respectively, considering all input and output parameters. The ANN_LM-Tan algorithm is suggested to accurately predict the thermal and electrical performances of batteries with direct oil cooling based on a maximum determination coefficient (R2) and variance coefficient (COV) of 0.99 and 1.65, respectively. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) offer a potential solution to face the global energy crisis and climate change issues in the transportation sector. Currently, lithium-ion (Li-ion) batteries have gained popularity as a source of energy in EVs, owing to several benefits including higher power density. To compete with internal combustion (IC) engine vehicles, the capacity of Li-ion batteries is continuously increasing to improve the efficiency and reliability of EVs. The performance characteristics and safe operations of Li-ion batteries depend on their operating temperature which demands the effective thermal management of Li-ion batteries. The commercially employed cooling strategies have several obstructions to enable the desired thermal management of high-power density batteries with allowable maximum temperature and symmetrical temperature distribution. The efforts are striving in the direction of searching for advanced cooling strategies which could eliminate the limitations of current cooling strategies and be employed in next-generation battery thermal management systems. The present review summarizes numerous research studies that explore advanced cooling strategies for battery thermal management in EVs. Research studies on phase change material cooling and direct liquid cooling for battery thermal management are comprehensively reviewed over the time period of 2018–2023. This review discusses the various experimental and numerical works executed to date on battery thermal management based on the aforementioned cooling strategies. Considering the practical feasibility and drawbacks of phase change material cooling, the focus of the present review is tilted toward the explanation of current research works on direct liquid cooling as an emerging battery thermal management technique. Direct liquid cooling has the potential to achieve the desired battery performance under normal as well as extreme operating conditions. However, extensive research still needs to be executed to commercialize direct liquid cooling as an advanced battery thermal management technique in EVs. The present review would be referred to as one that gives concrete direction in the search for a suitable advanced cooling strategy for battery thermal management in the next generation of EVs. [ABSTRACT FROM AUTHOR]

The transportation sector is sharply shifting towards electric vehicles (EVs) to reduce environmental issues and the energy crisis. To enhance the driving range and performance of EVs, the integral parts of EVs are being developed with higher energy densities and compact structures. Traction inverters are one of the important parts of EVs which are continuously updating to higher power densities with smaller sizes. This has led to issues of high heat generation, which causes the performance degradation and failure of traction inverters. Therefore, an efficient cooling strategy needs to be proposed for the effective thermal management of traction inverters in EVs. In the present work, the magnetohydrodynamics (MHD) pump-based cooling system is developed for the thermal management of traction inverter for EVs. The cooling performance of traction inverters is investigated using a MHD pump-based cooling system with water and ferrofluid as coolants. The outlet velocity, inverter maximum temperature, and Nusselt number are numerically simulated as the cooling performance characteristics for various operating conditions of inlet velocity, magnetic field intensity, voltage, and volume fraction of ferrofluid. The coupled numerical model is developed using COMSOL Multiphysics commercial software to simulate the cooling performance of a traction inverter with an MHD pump-based cooling system under various conditions. The MHD pump improves the cooling performance of a traction inverter for ferrofluid cooling over water cooling. The cooling performance of the traction inverter improves with an increase in inlet velocity for both water and ferrofluid cooling. However, with an increase in voltage, magnetic field intensity, and volume fraction, the cooling performance of the traction inverter improves only for ferrofluid cooling. The outlet velocity, inverter maximum temperature and Nusselt number in the case of water cooling are 4.03 mm/s and 7.02 mm/s, 49.65 °C, respectively, whereas that in the case of ferrofluid cooling are 40.96 °C, 15.35, and 18.49, respectively. Further, the cooling performance improves for ferrofluid cooling at a magnetic field intensity of 0.4 T and volume fraction of 10% with outlet velocity, inverter maximum temperature, and Nusselt number approach to 12.08 mm/s, 32 °C and 21.43, respectively. [ABSTRACT FROM AUTHOR]

The ferrofluid is a kind of nanofluid that has magnetization properties in addition to excellent thermophysical properties, which has resulted in an effective performance trend in cooling applications. In the present study, experiments are conducted to investigate the heat flow characteristics of ferrofluid based on thermomagnetic convection under the influence of different magnetic field patterns. The temperature and heat dissipation characteristics are compared for ferrofluid under the influence of no-magnet, I, L, and T magnetic field patterns. The results reveal that the heat gets accumulated within ferrofluid near the heating part in the case of no magnet, whereas the heat flows through ferrofluid under the influence of different magnetic field patterns without any external force. Owing to the thermomagnetic convection characteristic of ferrofluid, the heat dissipates from the heating block and reaches the cooling block by following the path of the I magnetic field pattern. However, in the case of the L and T magnetic field patterns, the thermomagnetic convection characteristic of ferrofluid drives the heat from the heating block to the endpoint location of the pattern instead of the cooling block. The asymmetrical heat dissipation in the case of the L magnetic field pattern and the symmetrical heat dissipation in the case of the T magnetic field pattern are observed following the magnetization path of ferrofluid in the respective cases. The results confirm that the direction of heat flow could be controlled based on the type of magnetic field pattern and its path by utilizing the thermomagnetic behavior of ferrofluid. The proposed lab-scale experimental set-up and results database could be utilized to design an automatic energy transport system for the cooling of power conversion devices in electric vehicles. [ABSTRACT FROM AUTHOR]