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.

To protect the environment and reduce dependence on fossil fuels, the world is shifting towards electric vehicles (EVs) as a sustainable solution. The development of fast charging technologies for EVs to reduce charging time and increase operating range is essential to replace traditional internal combustion engine (ICE) vehicles. Lithium-ion batteries (LIBs) are efficient energy storage systems in EVs. However, the efficiency of LIBs depends significantly on their working temperature range. However, the huge amount of heat generated during fast charging increases battery temperature uncontrollably and may lead to thermal runaway, which poses serious hazards during the operation of EVs. In addition, fast charging with high current accelerates battery aging and seriously reduces battery capacity. Therefore, an effective and advanced battery thermal management system (BTMS) is essential to ensure the performance, lifetime, and safety of LIBs, particularly under extreme charging conditions. In this perspective, the current review presents the state-of-the-art thermal management strategies for LIBs during fast charging. The serious thermal problems owing to heat generated during fast charging and its impacts on LIBs are discussed. The core part of this review presents advanced cooling strategies such as indirect liquid cooling, immersion cooling, and hybrid cooling for the thermal management of batteries during fast charging based on recently published research studies in the period of 2019–2024 (5 years). Finally, the key findings and potential directions for next-generation BTMSs toward fast charging are proposed. This review offers an in-depth analysis by providing recommendations and potential solutions to develop reliable and efficient BTMSs for LIBs during fast charging.

The rapidly growing commercialization of electric vehicles demands higher capacity lithium-ion batteries with higher heat generation which degrades the lifespan and performance of batteries. The currently widely used indirect liquid cooling imposes disadvantages of the higher thermal resistance and coolant leakage which has diverted the attention to the direct liquid cooling for the thermal management of batteries. The present study conducts the experimental investigation on discharge and heat transfer characteristics of lithium-ion battery with direct liquid cooling for the thermal management. The 18,650 lithium-ion cylindrical battery pack is immersed symmetrically in dielectric fluid. The discharge voltage and capacity, maximum temperature, temperature difference, average temperature, heat absorbed, and heat transfer coefficient are investigated under various conditions of discharge rates, inlet temperatures, and volume flow rates of coolant. The operating voltage and discharge capacity are decreasing with increase in the volume flow rate and decrease in the inlet temperature for all discharge rates. At the higher discharge rate of 4C, the lowest battery maximum temperatures of 60.2 °C and 44.6 °C and the highest heat transfer coefficients of 2884.25 W/m²-K and 2290.19 W/m²-K are reported for the highest volume flow rate of 1000 mLPM and the lowest inlet temperature of 15 °C, respectively.

Electric vehicles (EVs) are incorporated with higher energy density batteries to improve the driving range and performance. The lithium-ion batteries with higher energy density generate a larger amount of heat which deteriorates their efficiency and operating life. The currently commercially employed cooling techniques are not able to achieve the effective thermal management of batteries with increasing energy density. Direct liquid cooling offers enhanced thermal management of battery packs at high discharging rates compared to all other cooling techniques. However, the flow distribution of coolant around the battery module needs to be maintained to achieve the superior performance of direct liquid cooling. The objective of the present work is to investigate the heat transfer characteristics of the lithium-ion battery pack with dielectric fluid immersion cooling for different fin structures. The base structure without fins, circular, rectangular and triangular fin structures are compared for heat transfer characteristics of maximum temperature, temperature difference, average temperature, Nusselt number, pressure drop and performance evaluation criteria (PEC). Furthermore, the heat transfer characteristics are evaluated for various fin dimensions of the best fin structure. The heat transfer characteristics of the battery pack with dielectric fluid immersion cooling according to considered fin structures and dimensions are simulated using ANSYS Fluent commercial code. The results reveal that the symmetrical temperature distribution and temperature uniformity of the battery pack are achieved in the case of all fin structures. The maximum temperature of the battery pack is lower by 2.41%, 2.57% and 4.45% for circular, rectangular, and triangular fin structures, respectively, compared to the base structure. The triangular fin structure shows higher values of Nusselt number and pressure drop with a maximum value of PEC compared to other fin structures. The triangular fin structure is the best fin structure with optimum heat transfer characteristics of the battery pack with dielectric fluid immersion cooling. The heat transfer characteristics of a battery pack with dielectric fluid immersion cooling are further improved for triangular fin structures with a base length -to -height ratio (A/B) of 4.304. The research outputs from the present work could be referred to as a database to commercialize the dielectric fluid immersion cooling for the efficient battery thermal management system at fast and higher charging/discharging rates.

The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.