As the global shift toward sustainable transportation accelerates, electric vehicles (EVs) have emerged as a cornerstone of this transformation, particularly in markets like China where government policies and technological advancements drive rapid adoption. The thermal management system (TMS) is critical for ensuring the performance, safety, and longevity of EVs, as it regulates temperatures for key components such as batteries, motors, power electronics, and the cabin. In China’s EV industry, the demand for efficient TMS solutions has grown exponentially due to extreme weather conditions and the need for extended driving range. Traditional TMS architectures often rely on dispersed components like multiple valves and heat exchangers, leading to complexity, high energy consumption, and reliability issues. To address these challenges, we developed an integrated thermal management module centered around a multi-port valve, which consolidates functions and reduces energy losses. This article details the design, computational fluid dynamics (CFD) simulation, structural optimization, and experimental validation of this module, highlighting its impact on pressure loss reduction and overall system efficiency in China’s EV applications.
The proliferation of electric vehicles in China has underscored the importance of thermal management in mitigating range anxiety and enhancing safety. EVs operate multiple subsystems—battery packs, electric motors, power converters, and passenger compartments—each with distinct thermal requirements. For instance, lithium-ion batteries perform optimally within a narrow temperature range of 15–35°C, while motors and inverters can tolerate higher temperatures but require cooling to prevent efficiency drops. In China’s diverse climates, from frigid winters in the north to scorching summers in the south, the TMS must adapt dynamically to maintain component integrity. Conventional systems use separate circuits with three-way or four-way valves, resulting in numerous connections, increased pressure drops, and higher pumping energy. Our approach integrates these elements into a single module, leveraging a ten-port valve to streamline fluid pathways and eliminate redundant heat exchangers. This not only reduces complexity but also minimizes thermal losses, a crucial factor for China’s EV market where energy efficiency directly influences consumer adoption and regulatory compliance.
In designing the integrated module, we focused on replacing traditional valve assemblies with a compact ten-port valve that directs coolant flow across six operational modes: active battery cooling, passive battery cooling, waste heat recovery, defrosting, passive battery heating, and active battery heating. The module incorporates a flow distribution plate, pumps, sensors, a chiller, an electromagnetic expansion valve, and an expansion tank, all housed within a unified structure. The ten-port valve features a rotating spool that connects internal passages to manage fluid routing, with specifications adhering to industry standards such as QHZ 2098-2024, which mandates control accuracy within ±1.5°, mode switching times under 3 seconds, and internal leakage below 0.5% of circuit flow. For the coolant, we used a 50% ethylene glycol-water solution, common in China’s EV systems due to its antifreeze properties and thermal stability. The Reynolds number (Re) for flow within the module is calculated as:
$$ Re = \frac{\rho v L}{\mu} $$
where \( \rho \) is the density (1048.83 kg/m³ at 65°C), \( v \) is the velocity, \( L \) is the characteristic length, and \( \mu \) is the dynamic viscosity (1.29 mPa·s). Given that Re exceeds 4000 in all operational scenarios, the flow is turbulent, necessitating advanced CFD modeling to analyze pressure losses and optimize performance.
We employed STAR-CCM+ software with the K-ε turbulence model to simulate the internal flow field, as this model effectively captures complex turbulent behaviors in confined spaces. The geometry was preprocessed using ANSYS SCDM to extract the fluid domain, including inlet/outlet channels, valve passages, and the expansion tank region. To ensure accuracy, we extended inlet and outlet sections to 10 times the pipe diameter, minimizing boundary effects. The mesh consisted of polyhedral cells with prism layers, refined to a minimum size of 0.1 mm after grid independence tests confirmed stability. Residual monitoring ensured convergence, with the normalized residual for pressure (\( R_{pres} \)) defined as:
$$ R_{pres} = \frac{R_{rms}}{R_{norm}} $$
where \( R_{rms} \) is the root-mean-square residual and \( R_{norm} \) is the maximum residual over iterations. Simulations revealed significant pressure gradients in areas with abrupt directional changes, leading to localized high-pressure zones and vortices. For example, at a coolant flow rate of 12 L/min, the initial pressure drop in the motor circuit reached 8.95 kPa, while the battery and cabin heating circuits showed drops of 5.01 kPa and 8.05 kPa, respectively. The velocity vector plots indicated flow separation and secondary flows, particularly at valve junctions, which contributed to energy losses. The relationship between flow rate (\( Q \)) and pressure loss (\( \Delta P \)) can be expressed empirically as:
$$ \Delta P = k Q^n $$
where \( k \) is a loss coefficient and \( n \) is an exponent typically between 1.5 and 2 for turbulent flow, reflecting the nonlinear increase in pressure drop with flow rate.
| Circuit Type | Flow Rate (L/min) | Pressure Loss (kPa) |
|---|---|---|
| Motor Circuit | 6 | 3.21 |
| Motor Circuit | 12 | 8.95 |
| Battery Circuit | 6 | 2.58 |
| Battery Circuit | 12 | 5.01 |
| Cabin Heating Circuit | 6 | 3.87 |
| Cabin Heating Circuit | 12 | 8.05 |
Based on the CFD results, we optimized the module’s internal geometry using gradient-based methods in STAR-CCM+, focusing on smoothing transitions and reducing abrupt area changes. The objective was to minimize the pressure loss while maintaining functional integrity. Post-optimization, the flow paths showed marked improvements: vortices and secondary flows were suppressed, and velocity distributions became more uniform. For instance, at 12 L/min, the motor circuit pressure loss decreased by 37% to 5.64 kPa, the battery circuit by 35% to 3.26 kPa, and the cabin heating circuit by 39% to 4.91 kPa. The optimization not only enhanced energy efficiency but also reduced the risk of erosion and noise, which are critical for China’s EV standards on durability and NVH (noise, vibration, and harshness). The reduction in pressure loss can be attributed to the decreased form drag and friction, as described by the Darcy-Weisbach equation:
$$ \Delta P = f \frac{L}{D} \frac{\rho v^2}{2} $$
where \( f \) is the friction factor, \( L \) is the pipe length, and \( D \) is the diameter. By optimizing the geometry, we effectively reduced \( f \) and minimized local resistance coefficients.
| Circuit Type | Pre-Optimization Loss (kPa) | Post-Optimization Loss (kPa) | Reduction (%) |
|---|---|---|---|
| Motor Circuit | 8.95 | 5.64 | 37 |
| Battery Circuit | 5.01 | 3.26 | 35 |
| Cabin Heating Circuit | 8.05 | 4.91 | 39 |
To validate the module’s feasibility, we analyzed the overall system backpressure against the pump’s capabilities. The centrifugal pump used has a maximum operating pressure of 250 kPa, with a flow-dependent characteristic curve. The total backpressure (\( P_{total} \)) for each circuit includes losses from the module and external components, calculated as:
$$ P_{total} = \Delta P_{module} + \Delta P_{external} $$
Simulations showed that the maximum backpressure occurred in the motor circuit at 12 L/min, reaching 222.08 kPa, which is 88.83% of the pump’s rated pressure. This ratio remains within safe limits, as operational guidelines recommend keeping it below 90% for extreme conditions and under 80% for typical use. The pump’s performance curve follows a parabolic trend, with pressure output decreasing as flow increases, ensuring that the integrated module does not overload the system. This is crucial for China’s EV applications, where reliability under varying loads is paramount.
We fabricated a prototype and conducted bench tests using an HT-221218 fluid testing rig, which controls flow rates from 1 to 300 L/min and measures pressure differentials up to 200 kPa. Tests were performed at 65°C with the ethylene glycol-water solution, in accordance with QHZ 2098-2024. Results confirmed that the module met all specifications: mode switching time was under 3 seconds, internal leakage was 23 mL/min (below the 40 mL/min threshold), and no external leaks were detected. Pressure loss measurements aligned closely with simulations, with a maximum error of 5.79% in the combined motor-battery circuit at high flow rates. This minor discrepancy is attributed to model simplifications, such as neglecting bubble formation in the coolant. Field tests in a -15°C environment demonstrated the module’s superiority over traditional systems: battery preheating to 20°C was 23% faster, and energy consumption for temperature maintenance dropped by 19%. Additionally, the integrated design reduced the number of coolant lines from 23 to 10, cutting component costs by 15.6% and installation time by 60%, offering significant advantages for mass production in China’s competitive EV market.
In conclusion, the integrated thermal management module with a ten-port valve represents a substantial advancement for electric vehicles, particularly in China where efficiency and cost-effectiveness are driving forces. Through CFD-driven design and optimization, we achieved significant reductions in pressure loss and flow instability, validated by rigorous testing. This approach not only enhances thermal performance but also supports the broader adoption of electric vehicles by addressing key challenges like range and reliability. Future work will focus on scaling this technology for different vehicle classes and integrating smart controls for adaptive energy management, further solidifying the role of advanced thermal systems in the evolution of China’s EV industry.