Presently, the world is grappling with numerous environmental challenges, such as global warming and resource depletion [1]. Studies have indicated that the transportation sector significantly contributes to global energy consumption, with automotive energy consumption accounting for over 30% of total oil energy consumption in recent years [2]. The exhaustion of oil reserves and the harmful environmental consequences of burning fossil fuels have led the United Nations and governments globally to enforce restrictions on fuel vehicles to achieve sustainable development goals [3]. Consequently, many companies are vigorously developing more environmentally friendly and energy-efficient products, including electric vehicles (EVs).

EVs have surfaced as the most viable alternative to internal combustion engine vehicles (ICEVs) due to their zero-emission capabilities on the road and various power source options [4]. Depending on the power source, EVs can be categorized as battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), or hybrid electric vehicles (HEVs) [5]. Despite their potential, the evolution of EVs faces substantial thermal challenges, such as ensuring cabin thermal comfort, battery thermal safety, electric motor heat resistance, and addressing thermal integration. These challenges necessitate the adoption of novel thermal management solutions to improve the safety, reliability, and performance of EVs.

The thermal management of EVs involves the management of heat generated by various components, including the cabin, battery, and motor. The EV thermal management systems (EVTMSs) encompass cabin thermal management (CTM), battery thermal management (BTM), and motor thermal management (MTM) [6]. Air conditioning (AC) systems are a crucial component of EVTMS as they provide the necessary cooling or heating capacity for the cabin or the entire system [7]. However, traditional AC systems employing the PTC heating method consume significant energy, reducing the EV range at low ambient temperatures as well as contributing to lower energy efficiency. Heat pump systems offer an alternative but face challenges such as low-temperature heat production capability [8]. Enhancing the efficiency of compressors, the heat transfer performance of heat exchangers, and other critical AC system components is an area of significant research focus to improve their overall performance. Meanwhile, traditional refrigerants such as R134a have high global warming potential (GWP) values, making them environmentally unfriendly. The roadmap for the phasing out of HFC refrigerants has been proposed and R134a will shortly be replaced by more friendly refrigerants [9]. With the emergence of more eco-friendly refrigerants, it is very essential to develop efficient AC systems to extend EV range and to address environmental concerns.

Another critical aspect of EVTMS is managing heat accumulation within battery packs, particularly at high ambient temperatures, to prevent thermal runaways and ensure driving safety [10]. Advanced cooling systems such as liquid cooling provide efficient heat dissipation and maintain optimal temperature ranges for EV components. Phase change materials (PCM) and heat pipes offer alternative cooling methods, which can provide advantages such as being lightweight and having high cooling efficiency [11]. The transition toward higher power densities, torque densities, and faster speeds in electric motors has resulted in increased heat generation within the windings and mechanical components during operation, ultimately leading to decreased performance and service life [12]. Various thermal management systems have been extensively investigated to address these issues. In summary, efficient EVTMS components are crucial for driving safety and the lifespan of EVs.

The EVTMS systems manage the heat generated by different components either independently or in an integrated manner. Stand-alone thermal management systems operate independently, which allows for the identification and optimization of heat transfer weaknesses within a particular system but does not consider the energy coupling between subsystems [13]. As battery and motor power densities increase, separate thermal management systems struggle to meet thermal management requirements. Several relevant review papers critically reviewed solutions for separate cabin [14,15,16], battery [17,18], and motor [19] thermal management systems, offering new perspectives. By contrast, integrated thermal management systems combine multiple subsystems to distribute heat appropriately, making them a popular trend in EVTMS due to their ability to increase energy density. 

The cabin of a vehicle provides the environmental conditions for the driver during travel. Harsh driving conditions can cause driver fatigue and cognitive impairment, as demonstrated by existing literature [20], which highlights the importance of cabin thermal management in the automotive industry [21]. Achieving optimal thermal comfort in the cabin requires regulating various parameters, including temperature, humidity, and airflow, to maintain levels within the desired comfort range. It is essential to note that thermal management needs for the cabin may vary between summer and winter seasons [22].

Recent studies have classified cabin thermal load calculation methods into three categories: steady-state heat transfer method, quasi-steady-state heat transfer method, and unsteady-state heat transfer method. Due to the complex nature of cabin thermal load, which involves various factors, as shown in Figure 1, the steady-state heat transfer method is commonly used for thermal load estimation in current investigations [23]. To simplify the modeling process of the electric vehicle cabin and improve the speed and accuracy of thermal simulation, models custom-built or pre-designed using multiphysics field simulation software such as MATLAB SimDriveline [24], MATLAB/Simulink [25], AMESim [26], and Dymola/Modelica [27] are employed.

Li-ion batteries are considered the most suitable power source for electric vehicles due to their numerous advantages over other rechargeable technologies, such as high energy density, low self-discharge rates, extended cycle life, and lightness [18,28,29,30]. Studies have indicated that the theoretical operational temperature range of Li-ion batteries is between –10 °C and 50 °C [31]. However, investigations have demonstrated that the battery undergoes a capacity decay phenomenon at low temperatures, and aging is accelerated when the temperature exceeds 50 °C [32]. Operating at high temperatures can impact charge/discharge efficiencies, battery life, internal electrochemical reactions, and safety [33,34].

Various heat generation models have been proposed to accurately describe the thermal characteristics of batteries, including the electrochemical-thermal model [38], the electro-thermal model [39], and data-driven models based on intelligent algorithms [40]. The electrochemical-thermal model focuses on the internal structure of the cell, the thermal characteristics of the components, and the electrochemical reaction process. This model, based on electrochemical reaction kinetics and thermodynamics, describes the cell’s current, voltage, heat production, and heat transfer processes during electrochemical reactions, including the diffusion migration of lithium ions and changes in electrolyte concentration. The most commonly used electrochemical-thermal models are the single particle (SP) model [41] and the pseudo-two-dimensional (P2D) model [42]. However, from an engineering perspective, simplification of the models is necessary due to the inclusion of multiple coupled partial differential equations (PDEs) [43]. The electro-thermal model examines the electric and temperature fields inside a single cell from a macroscopic viewpoint. Based on the equivalent circuit approach, the model studies the battery’s heat production and temperature distribution based on the current density distribution and potential over the electrodes [44]. Hence, the physics-based equivalent circuit model has been considered the core of advanced safety and health management algorithms for next-generation battery thermal management systems [45]. Moreover, data-driven models based on intelligent algorithms make it feasible to estimate the electrical and thermal states of Li-ion batteries [46,47,48].

To meet the increasing demand for electric vehicles, motors must exhibit higher power density, torque density, and speed [49,50]. This leads to a significant increase in loss density and temperatures that can cause short circuits, demagnetization of the magnet, and other issues [51]. Insufficient cooling of the motor will result in a rapid decrease in performance parameters of the electric motor, including power density, durability, and driving range per single charge [52]. For permanent magnet motors, high temperatures can also lead to demagnetization of the magnets embedded within the rotor [53,54]. The motor plays a crucial role in the energy output of electric vehicles and generates a considerable amount of heat during operation [19]. The most common electric vehicle motors include AC induction motors, switched reluctance motors, and permanent magnet synchronous motors [51]. Depending on the size of the electric vehicle motor, the heat generation power can range from 2.5–6 kW, 6–10 kW, and 10–15 kW, resulting in different thermal management requirements [55]. Two commonly used motor thermal management methods are air and liquid cooling [56,57,58]. Air cooling typically involves natural and forced air cooling and air impingement cooling [59]. On the other hand, liquid cooling mediums typically include coolant fluids and transformer oil, among others [60]. Numerous studies have been conducted to design various structural forms for both cooling methods to address the thermal management requirements of motors.

Several theoretical modeling methods have been proposed to provide academic support for meeting the thermal management requirements of electric motors. The primary theoretical modeling methods include the finite element method (FEM), lumped-parameter thermal network (LPTM) method, and computational fluid dynamics (CFD) method [61]. The FEM and CFD methods provide detailed information, such as heat flux, temperature, and pressure distribution in the motor domain under given boundary conditions. By contrast, the LPTM method can provide fast temperature prediction of the thermal nodes with lower accuracy [62].