As global demand for reducing greenhouse gas emissions and promoting energy transformation intensifies, electric vehicles (EVs) have emerged as pivotal clean energy transportation solutions. In particular, the advancement of China EV technologies underscores the importance of optimizing key systems like air conditioning and battery thermal management, which directly influence vehicle performance, range, and user comfort. This paper explores a cooperative design strategy that integrates the electric vehicle air conditioning system with battery thermal management to enhance energy efficiency and overall reliability. We employ mathematical modeling, simulation analysis, and experimental validation to develop a comprehensive framework, addressing gaps in existing research by focusing on synergistic interactions between these systems. Our approach not only improves energy utilization but also contributes to the sustainable development of the electric vehicle industry, emphasizing the critical role of environmental considerations in design processes.
The electric vehicle air conditioning system is essential for maintaining cabin comfort, yet it significantly impacts energy consumption, especially in extreme climates. Traditional systems often rely on refrigerants like R134a, but evolving environmental standards have spurred interest in alternatives such as carbon dioxide (CO2) due to their lower global warming potential. Compressor technologies have also advanced, with scroll compressors gaining prominence for their efficiency and compactness. Similarly, battery thermal management in electric vehicles is crucial for ensuring safety, longevity, and performance. Techniques range from passive cooling, which uses natural convection, to active methods involving liquid cooling or phase change materials (PCMs). However, standalone optimizations of these systems overlook potential synergies; for instance, waste heat from the air conditioning system can be repurposed for battery heating, reducing overall energy drain. In China EV markets, where range anxiety and efficiency are top concerns, integrating these systems through cooperative design offers a pathway to address these challenges effectively.
Our research begins by reviewing the current state of electric vehicle air conditioning and battery thermal management systems. The air conditioning system typically comprises compression, condensation, expansion, and evaporation stages, as illustrated in the following schematic. Key components include the compressor, which elevates refrigerant pressure and temperature; the evaporator and condenser, which facilitate heat exchange; and the expansion valve, which regulates refrigerant flow. For heating, electric vehicles often use Positive Temperature Coefficient (PTC) heaters, which draw power from the battery and can reduce range by up to 50% in cold conditions. Battery thermal management, on the other hand, commonly employs liquid cooling loops that interface with the air conditioning system to dissipate heat. By designing these systems cooperatively, we aim to harness waste energy and optimize thermal flows, thereby enhancing the overall efficiency of electric vehicles.
In developing the cooperative design architecture, we focus on key components such as heat exchangers, electronic control units (ECUs), sensor networks, and coolant pumps. Heat exchangers are designed for high thermal conductivity and compactness, enabling efficient waste heat recovery. The ECU acts as an intelligent hub, processing real-time data from sensors to dynamically adjust system parameters. This ensures optimal performance under varying conditions, such as changes in ambient temperature or driving load. For example, in a typical China EV setup, the ECU might prioritize battery cooling during high-speed driving while allocating residual capacity to cabin cooling. The sensor network monitors critical parameters like temperature, pressure, and flow rates, providing the data necessary for precise control. Coolant pumps drive the circulation of cooling fluids, facilitating heat transfer between the air conditioning system and battery modules. This integrated approach not only reduces energy consumption but also minimizes component count and cost, as seen in designs that combine reservoirs for multiple systems.
The design principles guiding our cooperative strategy emphasize energy efficiency, cost control, reliability, and user experience. Energy efficiency is paramount, as PTC heaters, while safe and reliable, can drastically cut electric vehicle range. To mitigate this, we incorporate waste heat recovery from powertrain components or the air conditioning system. For instance, the heat generated during battery operation can be modeled using the following equation for battery heat generation: $$ Q_{\text{gen}} = I \times V \times \eta_{\text{bat}} $$ where \( Q_{\text{gen}} \) is the heat generated, \( I \) is the current, \( V \) is the voltage, and \( \eta_{\text{bat}} \) is the battery efficiency, typically ranging from 0.5 to 0.9. A more detailed model accounts for joule heating, electrochemical reactions, and polarization effects: $$ Q_{\text{gen}} = I^2 R_b + I^2 R_p + \frac{m n I Q_h}{M F} $$ where \( R_b \) is the battery resistance, \( R_p \) is the polarization resistance, \( m \) is the electrode mass, \( n \) is the number of cells, \( Q_h \) is the total chemical heat, \( M \) is the molar mass, and \( F \) is Faraday’s constant. By optimizing these factors, we enhance the overall energy utilization in electric vehicles.
Cost control is achieved through material selection and standardization, while reliability is ensured by using robust components tested under extreme conditions. User experience is enhanced via intelligent controls that allow personalized temperature settings and adaptive learning. For example, the ECU can automatically adjust airflow and heating based on historical data, improving comfort without manual intervention. In one implementation for a China EV model, we integrated a four-way valve to connect cooling circuits, enabling waste heat from the motor to preheat the battery, thus reducing the need for PTC heating and extending range.
To validate our cooperative design, we developed a mathematical model that simulates the interactions between the air conditioning and battery thermal management systems. The refrigerant cycle is described using pressure-enthalpy (p-h) diagrams and thermodynamic equations. For instance, the cooling capacity of the evaporator is given by: $$ q_{\text{evap}} = h_{\text{liq}}(T) – h_{\text{vap}}(P) $$ where \( q_{\text{evap}} \) is the evaporator cooling capacity, \( h_{\text{liq}} \) is the enthalpy of the liquid refrigerant, and \( h_{\text{vap}} \) is the enthalpy of the vapor refrigerant. The p-h diagram outlines key processes: from point 7 to 0 (evaporation), 0 to 1 (superheating), 1 to 2 (compression), 2 to 4 (condensation), 4 to 5 (subcooling), and 5 to 7 (expansion). Similarly, the heat exchanger performance is modeled as: $$ Q_{\text{hx}} = U \times A \times \Delta T_{\text{lmtd}} $$ where \( U \) is the overall heat transfer coefficient, \( A \) is the surface area, and \( \Delta T_{\text{lmtd}} \) is the log mean temperature difference. The coolant circulation follows the energy conservation equation: $$ Q_{\text{in}} – Q_{\text{out}} = m \times c \times \Delta t \times \Delta T $$ where \( m \) is the coolant mass, \( c \) is the specific heat capacity, \( \Delta t \) is the time interval, and \( \Delta T \) is the temperature change.
We simulated these models using MATLAB/Simulink, focusing on a representative electric vehicle under various conditions. The test scenarios included ambient temperatures of -15°C, -5°C, and 25°C, with light (1 t) and heavy (5 t) loads, at a constant speed of 60 km/h over 90 minutes. The battery state of charge (SOC) was monitored to assess energy efficiency. Table 1 summarizes the simulated SOC changes and comparisons with baseline values without cooperative measures.
| Temperature (°C) | Load State | Actual SOC (%) | Simulated SOC Change (%) | Deviation (%) |
|---|---|---|---|---|
| -15 | Light Load | 67.64 | 71.51 | 3.87 |
| -15 | Heavy Load | 65.27 | 69.48 | 4.21 |
| -5 | Light Load | 69.49 | 72.51 | 3.02 |
| -5 | Heavy Load | 66.83 | 70.31 | 3.48 |
| 25 | Light Load | 72.45 | 75.18 | 2.73 |
| 25 | Heavy Load | 71.83 | 73.72 | 2.89 |
The results indicate that the cooperative design consistently improves SOC retention, with the smallest deviations at 25°C (e.g., 2.73% for light load) and the largest at -15°C (4.21% for heavy load). This translates to a potential range extension of up to 13.97% under extreme conditions, highlighting the benefits of synergistic energy management in electric vehicles. For instance, in a China EV context, this could mean an additional 50–100 km per charge, addressing common range limitations.
Experimental validation was conducted using a controlled environment chamber that replicated various climatic conditions, from cold to hot. We equipped the test setup with high-precision sensors to monitor parameters like coolant temperature, pressure, and battery temperature. The battery pack used was a lithium iron phosphate type with a total capacity of 73.6 V and 50 Ah, managed by a BMS with passive balancing. Data collection focused on SOC under cooperative conditions, as shown in Table 2.
| Temperature (°C) | Load State | Actual SOC (%) |
|---|---|---|
| -15 | Light Load | 70.91 |
| -15 | Heavy Load | 69.05 |
| -5 | Light Load | 72.71 |
| -5 | Heavy Load | 70.57 |
| 25 | Light Load | 74.28 |
| 25 | Heavy Load | 74.12 |
Analysis of the experimental data revealed close alignment with simulations, with deviations ranging from 1.83% to 3.78%. For example, at 25°C and light load, the SOC improvement could save up to 7.12% of energy, extending range by approximately 50 km. In colder conditions (-15°C, heavy load), savings reached 14.71%, potentially adding over 100 km to the electric vehicle’s range. These findings underscore the efficacy of cooperative design in real-world applications, particularly for China EV models operating in diverse environments. However, we noted that under high-load scenarios, battery temperature rise exceeded expectations, suggesting a need for enhanced cooling strategies, such as incorporating advanced PCMs or optimized flow channels.
Discussion of the results highlights several areas for improvement. For instance, the control system’s response time could be refined using machine learning algorithms to better handle transient conditions. Additionally, extending the model to lower temperatures (e.g., below -20°C) would provide insights into extreme climate performance. The integration of artificial intelligence could enable adaptive thermal management, further boosting efficiency and user comfort in electric vehicles. Moreover, cost-benefit analyses should explore ways to reduce expenses without compromising performance, such as through modular designs or shared components across systems.
In conclusion, our cooperative design strategy for electric vehicle air conditioning and battery thermal management demonstrates significant advantages in energy efficiency, reliability, and user experience. By leveraging mathematical models and experimental validation, we have shown that synergistic integration can reduce energy consumption and extend range, which is crucial for the growing China EV market. Future work will focus on optimizing heat exchanger materials, advancing control algorithms, and incorporating AI-driven adaptations. This research lays a solid foundation for the continued evolution of electric vehicles, emphasizing the importance of holistic design in achieving sustainable transportation solutions.