In recent years, the rapid growth of the electric car market, particularly in regions like China EV, has highlighted the critical role of thermal management systems in enhancing vehicle performance and range. Integrated thermal management systems (ITMS) have emerged as a promising solution due to their ability to reduce system volume and energy consumption. However, the complexity of these systems often leads to challenges in control strategies, such as significant temperature fluctuations and inadequate cooling under varying loads. This paper investigates a variable superheat control strategy for ITMS in electric cars, focusing on improving precision and stability. We analyze the feasibility of this approach, develop a simulation model, and compare its performance with conventional methods. The results demonstrate that variable superheat control can effectively minimize temperature swings, reduce energy consumption, and prevent issues like compressor liquid ingestion, thereby contributing to the advancement of China EV technologies.
The importance of thermal management in electric cars cannot be overstated, as it directly impacts battery life, cabin comfort, and overall energy efficiency. In China EV applications, where extreme temperatures and high-density urban driving are common, effective thermal control is essential. Traditional systems often rely on separate components for cabin air conditioning, battery cooling, and motor thermal management, leading to inefficiencies. Integrated systems combine these functions, but their coupled nature requires sophisticated control strategies. Conventional approaches, such as fixed superheat control, may cause large temperature variations when loads change abruptly. For instance, sudden increases in battery heat generation can disrupt cabin temperature stability. This study explores how variable superheat control, which adjusts evaporation pressure via the compressor and branch cooling capacity via electronic expansion valves (EEVs), can address these limitations. By leveraging simulations, we evaluate this strategy’s potential to enhance performance in electric cars, with a focus on applications in the growing China EV sector.
To begin, we describe the integrated thermal management system architecture for electric cars. The system comprises refrigerant and coolant loops, utilizing R134a as the refrigerant. Key components include a variable-speed compressor with a displacement of 45 cm³/r and a speed range of 0–8,000 r/min, plate heat exchangers for water cooling and chiller functions, and microchannel evaporators. EEVs with maximum diameters of 2.6 mm and 1.8 mm regulate refrigerant flow in different branches. The system operates in multiple modes, such as cooling and heating, by switching coolant loops via solenoid valves, without altering the refrigerant circuit direction. This design simplifies operation and improves reliability, which is crucial for electric cars in diverse environments, including those typical in China EV deployments.
The simulation model was developed using AMESim, incorporating mathematical representations of core components. The compressor model uses efficiency parameters to compute mass flow rate, enthalpy change, and energy consumption. The mass flow rate is given by:
$$ \dot{m} = \eta_v \cdot N \cdot V_d \cdot \rho_s $$
where $\dot{m}$ is the mass flow rate, $\eta_v$ is the volumetric efficiency, $N$ is the compressor speed, $V_d$ is the displacement, and $\rho_s$ is the suction density. The enthalpy difference across the compressor is calculated as:
$$ h_d – h_s = \eta_i \cdot (h_{t} – h_s) $$
with $h_d$ and $h_s$ being the discharge and suction enthalpies, respectively, $h_t$ the isentropic discharge enthalpy, and $\eta_i$ the isentropic efficiency. The compressor power consumption $E$ is:
$$ E = \frac{\dot{m} \cdot (h_d – h_s)}{\eta_m} $$
where $\eta_m$ is the mechanical efficiency. For heat exchangers, such as the evaporator and chiller, the heat transfer rate $Q$ is modeled as:
$$ Q = \alpha \cdot S \cdot (T_r – T_w) $$
and
$$ Q = \dot{m} \cdot (h_n – h_o) $$
where $\alpha$ is the convective heat transfer coefficient, $S$ is the surface area, $T_r$ and $T_w$ are refrigerant and wall temperatures, and $h_n$ and $h_o$ are inlet and outlet enthalpies. The coefficient of performance (COP) is defined as:
$$ \text{COP} = \frac{Q}{E} $$
where $Q$ is the cooling or heating capacity. The model was validated against experimental data under various conditions, such as different ambient temperatures and compressor speeds, showing a maximum error of 10.5%, confirming its accuracy for control strategy analysis in electric cars.
The feasibility of variable superheat control in ITMS for electric cars was analyzed by examining the effects of compressor speed and EEV openings on system performance. As compressor speed increases, evaporation pressure decreases, and superheat rises, enabling indirect superheat control through pressure regulation. Similarly, increasing EEV opening in one branch raises evaporation pressure and reduces superheat, while significantly boosting that branch’s cooling capacity with minimal impact on others. For example, in a China EV scenario, if the battery EEV opens wider, battery cooling increases, and cabin cooling decreases slightly due to reduced heat transfer temperature difference. This decoupled behavior allows for precise branch control. The relationship between superheat and EEV opening is approximately linear above 10°C superheat, but nonlinear below that, necessitating careful tuning to avoid compressor liquid ingestion. Thus, variable superheat control is viable for electric cars, as it leverages compressor speed for pressure setpoints and EEVs for branch-level adjustments.
The variable superheat control strategy involves setting target evaporation pressures based on cabin and battery temperature requirements, along with environmental factors. EEVs regulate branch cooling capacities using PID control, while the compressor adjusts speed to maintain the desired pressure. This contrasts with conventional methods, where compressor speed primarily controls cabin temperature, and EEVs manage superheat, leading to coupling issues. For instance, in a China EV under dynamic driving, battery heat fluctuations can cause cabin temperature swings. Variable superheat decouples this by allowing independent branch control. The control flowchart includes steps like determining pressure setpoints from target temperatures, adjusting EEV openings via PID, and modulating compressor speed. This approach enhances stability and precision, which is critical for electric cars facing varying loads.
Simulations were conducted to compare variable superheat control with conventional strategies under a 40°C ambient temperature, typical for China EV summer conditions. The cabin air outlet temperature was set to 28°C, and battery coolant inlet target was also 28°C. Battery heat generation varied from a steady 1.5 kW to a peak of 9 kW, mimicking real-world scenarios like WLTC cycles. For variable superheat, target evaporation pressures of 0.4 MPa, 0.5 MPa, and 0.6 MPa were tested, while conventional control used fixed superheat of 5°C and threshold-based EEV adjustments.
Results showed that variable superheat control significantly reduced temperature fluctuations. For example, with a target pressure of 0.4 MPa, cabin air outlet temperature varied by less than 1.1°C (from 27.5°C to 28.6°C), whereas conventional control caused swings up to 3.6°C. Under extreme battery heat (9 kW), variable superheat at 0.4 MPa limited battery coolant temperature rise to 32.3°C, compared to 42.2°C with conventional method—a 9.9°C reduction. This highlights its effectiveness in maintaining stability in electric cars. However, lower target pressures increased energy consumption due to higher compressor speeds and pressure ratios. The table below summarizes key performance metrics:
| Control Strategy | Max Cabin Temp Fluctuation (°C) | Max Battery Coolant Temp (°C) | Energy Consumption (kW) | Superheat Range (°C) |
|---|---|---|---|---|
| Conventional | 3.6 | 42.2 | Moderate | 0 to 15 |
| Variable (0.6 MPa) | 1.8 | 35.1 | Similar to Conventional | 0 to 10 |
| Variable (0.5 MPa) | 1.3 | 33.5 | Higher | 5 to 12 |
| Variable (0.4 MPa) | 1.1 | 32.3 | Highest | 8 to 15 |
Evaporation pressure and superheat dynamics further illustrated the benefits. Conventional control exhibited large pressure swings from 0.4 MPa to 0.75 MPa, resulting in superheat drops to 0°C, risking compressor damage. In contrast, variable superheat maintained stable superheat levels, e.g., above 8°C for 0.4 MPa target, ensuring safe operation. Compressor speed and EEV openings adapted accordingly: lower target pressures required higher speeds and finer EEV adjustments, reducing branch coupling. For instance, with 0.4 MPa, EEV pulses varied smoothly, minimizing cabin disturbances during battery load changes. This is crucial for electric cars in China EV markets, where passenger comfort and battery longevity are priorities.
The energy implications were analyzed using the COP formula. Variable superheat at 0.6 MPa achieved COP values similar to conventional control, but lower pressures decreased COP due to increased power draw. The relationship between evaporation pressure and COP can be expressed as:
$$ \text{COP} = \frac{Q_{\text{total}}}{\dot{m} \cdot (h_d – h_s) / \eta_m} $$
where $Q_{\text{total}}$ is the total cooling capacity. As pressure decreases, $\dot{m}$ may drop, necessitating speed increases that reduce efficiency. Thus, for electric cars, target pressure selection should balance energy use and performance: higher pressures for low-load conditions and lower pressures for high-load or transient scenarios. This adaptability is key for China EV applications, where driving patterns vary widely.
In conclusion, variable superheat control offers significant advantages for integrated thermal management systems in electric cars. By decoupling branch controls and enabling precise regulation, it reduces temperature fluctuations, enhances battery cooling under extreme conditions, and prevents compressor issues. For China EV development, this strategy supports finer temperature control, improving range and comfort. Future work should explore its application in heating modes and optimize pressure setpoints across diverse operating conditions. Overall, this research underscores the potential of advanced control strategies to elevate electric car thermal management, contributing to the sustainable growth of the China EV industry.
The findings emphasize that variable superheat control not only addresses the limitations of conventional methods but also aligns with the evolving needs of electric cars. As the China EV market expands, implementing such strategies can lead to more efficient and reliable vehicles, ultimately driving adoption and reducing environmental impact. Further studies could integrate real-time data and machine learning for adaptive control, enhancing responsiveness in dynamic environments characteristic of electric car usage.