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.

In response to the global imperative to reduce greenhouse gas emissions and fossil fuel dependency, electric vehicles (EVs) have emerged as a sustainable transportation alternative, primarily utilizing lithium-ion batteries (LIBs) due to their high energy density and efficiency. However, LIBs are highly sensitive to temperature fluctuations, significantly affecting their performance, lifespan, and safety. One of the most critical threats to the safe operation of LIBs is thermal runaway (TR), an uncontrollable exothermic process that can lead to catastrophic failure under abusive conditions. Moreover, thermal runaway propagation (TRP) can rapidly spread failures across battery cells, intensifying safety threats. To address these challenges, developing advanced battery thermal management systems (BTMS) is essential to ensure optimal temperature control and suppress TR and TRP within LIB modules. This review systematically evaluates advanced cooling strategies, including indirect liquid cooling, water mist cooling, immersion cooling, phase change material (PCM) cooling, and hybrid cooling based on the latest studies published between 2020 and 2025. The review highlights their mechanisms, effectiveness, and practical considerations for preventing TR initiation and suppressing TRP in battery modules. Finally, key findings and future directions for designing next-generation BTMS are proposed, contributing valuable insights for enhancing the safety and reliability of LIB applications.

In this study, the efficiency of an immersion cooling system for controlling the temperature of 5S7P battery modules at high charge and discharge C-rates was experimentally evaluated. The study was conducted in three main stages including the evaluation of different coolant oils followed by the proposition of an optimal volume flow rate (VFR) and cooling performance evaluation under high charging/discharging C-rates. In the first stage, three coolant oils, including Therminol D-12, Pitherm 150B, and BOT 2100, were compared. The Therminol D-12 achieved superior cooling performance, with the highest heat transfer coefficient (HTC) of 2171.93 W/m²⋅K and the ability to maintain the maximum temperature (T_max) and temperature difference (∆T) of the battery module within the safe range. In the next stage, VFR was varied between 0.4 LPM and 1.0 LPM for the selected best coolant oil of Therminol D-12. The 0.8 LPM VFR was determined to be optimal with the highest HTC of 2445.73 W/m²⋅K and an acceptable pressure drop of 12,650 Pa, ensuring a balance between cooling performance and energy consumption. Finally, the cooling performance was evaluated at high charging/discharging C-rates from 1.5C to 3.0C for the proposed best coolant oil and VFR. The immersion cooling system with Therminol D-12 and a VFR of 0.8 LPM is an effective combination to achieve the desired performance of the battery module under extreme C-rate working conditions. The immersion cooling system with the proposed effective combination maintains the T_max and ∆T at 38.6 °C and 4.3 °C under a charging rate of 3.0C and to 43.0 °C and 5.5 °C under a discharging rate of 3.0C.

Accurate state-of-health (SOH) estimation is a cornerstone of safe, reliable, and cost-effective operation of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. In recent years, rapid advances in artificial intelligence technology have led to the widespread adoption of neural-network-based SOH estimation methods, offering strong nonlinear modeling capability and improved adaptability compared with traditional model-based approaches. However, the growing diversity of neural architectures and learning strategies has led to fragmented development and inconsistent evaluation, hindering their practical deployment. This paper presents a critical and systematic review of the most recent representative studies on neural-network-based SOH estimation for LIBs between 2024 and 2025. A unified taxonomy is introduced to distinguish neural architectures from learning strategies. The neural architectures include artificial neural networks, convolutional and recurrent networks, attention-based models, Transformers, and physics-informed neural networks. The learning strategies encompass transfer learning, physics-constrained/physics-informed learning, robustness-oriented training and efficiency-aware design. The reviewed methods are analyzed in terms of modeling capability, generalization across operating conditions and chemistries, data efficiency, interpretability and deployability within battery management systems. Key challenges including nonlinear degradation, degradation diversity, data scarcity, and limited observability are critically examined. The roles of architecture-strategy co-design in addressing these issues are highlighted. Finally, open research directions and practical recommendations are discussed to guide the development of reliable, scalable and physically consistent SOH estimation frameworks. This review provides a structured reference for researchers and practitioners seeking to advance data-driven battery health monitoring toward real-world applications.

Direct liquid cooling technology has the potential to enhance the thermal management performance of electric motors with continuously increasing energy density. However, direct liquid cooling technology has practical limitations for full-scale commercialization. In addition, the conventionally used indirect liquid cooling imposes higher thermal resistance to cope with the increased thermal management performance of high power density electric motors. Therefore, this study proposes a hybrid direct–indirect oil cooling system as a next-generation cooling strategy for the enhanced thermal management of high power density electric motors. The heat transfer characteristics, including maximum winding, stator and motor housing temperatures, heat transfer coefficient, friction factor, pressure drop, and performance evaluation criteria (PEC), are investigated for different spray hole diameters, coolant oil volume flow rates, and motor heat loss levels. The computational model was validated with experimental results within a 5% error developed to evaluate heat transfer characteristics. The results show that spray hole diameter significantly influences cooling performance, with a larger diameter (1.7 mm) reducing hydraulic resistance while causing a slight increase in motor temperatures. The coolant oil volume flow rate has a major impact on heat dissipation, with an increase from 10 to 20 L/minute (LPM) reducing winding, stator, and housing temperatures by 22.05%, 22.70% and 24.02%, respectively. However, higher flow rates also resulted in an increased pressure drop, emphasizing the importance of the selection of a suitable volume flow rate based on the trade-off between cooling performance and energy consumption. Despite the increase in motor heat loss level from 2.6 kW to 8 kW, the hybrid direct–indirect oil cooling system effectively maintained all motor component temperatures below the critical threshold of 180 °C, confirming its suitability for high-performance electric motors. These findings contribute to the development and commercialization of the proposed next-generation cooling strategy for high power density electric motors for ensuring thermal stability and operational efficiency.