In the global shift toward new energy vehicles, the market penetration of pure electric vehicles (EVs) is steadily increasing. From my perspective as a researcher in automotive thermal systems, the temperature of the EV battery pack directly influences its charge-discharge performance, which in turn affects the vehicle’s thermal safety, power performance, and overall economy. Simultaneously, temperature control within the passenger cabin is crucial for occupant comfort. Typically, the thermal management system of the EV battery pack is integrated with the cabin air conditioning system, imposing higher demands on the design of the air conditioning system. In this study, we focus on an EV model and design an integrated thermal management system. Through calculations of the thermal loads for the EV battery pack and the air conditioning system, we decompose the system requirements. Subsequently, we employ simulation analysis to predict the cooling performance for both the EV battery pack and the passenger cabin. For operational conditions that do not meet the requirements, we conduct optimizations. The results demonstrate that the optimized solution can satisfy the cooling needs of the EV battery pack and the passenger compartment.
The performance of the EV battery pack is highly sensitive to temperature variations. To ensure the EV battery pack operates within an optimal range, the thermal management system must preheat the battery under low-temperature conditions to enhance charge-discharge performance and extend driving range. In high-temperature environments, effective cooling is essential to prevent overheating, which could compromise reliability and thermal safety. Pure electric vehicles often use coolant for cooling or heating the EV battery pack, with a battery chiller (Chiller) connected in parallel to the evaporator of the air conditioning system. Under high-temperature conditions, refrigerant undergoes phase change to absorb heat transferred from the EV battery pack to the coolant in the cold plate. Therefore, the thermal management system significantly impacts the overall vehicle performance. Given that battery thermal management and the air conditioning system are integrated, a rational system design must balance both EV battery pack cooling and cabin temperature control objectives. This article details the architectural design of a thermal management system for an EV model, derives cooling requirements for the EV battery pack and cabin based on vehicle specifications, and uses one-dimensional simulation tools for computational analysis and optimization, ultimately ensuring the designed air conditioning system meets the cooling demands for both the EV battery pack and the passenger cabin.
Vehicle Target Definition and Architectural Design
To begin, we define the key parameters of the vehicle under study. The vehicle is a compact EV with seating for two, dimensions of 2778 mm × 1603 mm × 1619 mm (length × width × height), and a curb weight of 1105 kg. Based on the application scenarios, we identify high-temperature operating conditions for validating the thermal management system of the EV battery pack and the air conditioning system. These primarily include driving and fast-charging scenarios under high ambient temperatures. Combining temperature requirements for powertrain components and benchmarking data from multiple vehicles, we establish performance targets for the EV battery pack thermal management and cabin air conditioning system, as summarized in Table 1.
| Operating Condition | Environmental Parameters | Cabin Average Temperature Target (°C) | EV Battery Pack Inlet Coolant Temperature Target (°C) |
|---|---|---|---|
| 50 km/h, 45 min | 40°C, 50% humidity, 1000 W/m² solar load, face vent, max cooling, recirculation | ≤ 24 | – |
| 100 km/h, 20 min | Same as above | ≤ 22 | – |
| Idle, 20 min | Same as above | ≤ 27 | – |
| Fast Charging | Same as above | ≤ 27 | ≤ 20 |
| High-speed 120 km/h | Same as above | ≤ 22 | ≤ 20 |
The thermal management system architecture designed for this EV model is illustrated below. The electric drive system is liquid-cooled, with heat dissipated via a low-temperature radiator at the front. Given the high cooling demands of the EV battery pack, a liquid-cooled approach is adopted. Under high-temperature conditions, the EV battery pack is cooled by a Chiller, where refrigerant exchanges heat with the coolant, removing heat from the EV battery pack. The evaporator exchanges heat with cabin air to reduce the passenger compartment temperature. The Chiller and evaporator are connected in parallel, controlled via electromagnetic thermal expansion valves and electronic expansion valves to meet the cooling needs of the cabin and EV battery pack under various scenarios. Heating for the EV battery pack is achieved using a film heater.
The architecture integrates three main loops: the motor cooling loop, the EV battery pack thermal management loop, and the air conditioning refrigerant loop. This integrated design allows for efficient heat exchange and control, ensuring that the EV battery pack remains within safe temperature limits while maintaining cabin comfort.
System Requirement Analysis
Thermal Load Calculation for the EV Battery Pack
The thermal load of the EV battery pack is derived from its power demand during different operating conditions. Based on vehicle dynamics and parameters, we calculate the current and power requirements for the EV battery pack, as shown in Table 2. The power demand $P_{batt}$ is computed using the vehicle speed $v$, road load coefficients, and auxiliary power consumption. For instance, at a steady speed, the power demand can be approximated as:
$$ P_{batt} = \frac{1}{\eta} \left( F_r v + \frac{1}{2} \rho C_d A v^3 \right) + P_{aux} $$
where $\eta$ is the drivetrain efficiency, $F_r$ is the rolling resistance force, $\rho$ is air density, $C_d$ is the drag coefficient, $A$ is the frontal area, and $P_{aux}$ is auxiliary power consumption. Using this approach, we obtain the power values listed in Table 2.
| Vehicle Speed (km/h) | Battery Power Demand (kW) |
|---|---|
| 50 | 6.68 |
| 100 | 18.45 |
| Idle | 3.20 |
| Fast Charging | 24.43 |
| 120 | 27.81 |
Next, the heat generation $Q_{batt}$ of the EV battery pack is estimated using the battery’s internal resistance $R_i$ and current $I$, based on the formula:
$$ Q_{batt} = I^2 R_i $$
However, for accuracy, we rely on cell-level calorimetry data correlating heat generation with discharge rate (C-rate). The resulting thermal loads for the EV battery pack across conditions are presented in Table 3. Notably, the EV battery pack exhibits higher heat generation during fast charging and high-speed driving due to elevated current flow.
| Operating Condition | EV Battery Pack Heat Generation (kW) |
|---|---|
| 50 km/h | 0.239 |
| 100 km/h | 1.15 |
| Idle | 0.065 |
| Fast Charging | 2.34 |
| 120 km/h | 2.41 |
These values represent the heat that must be dissipated by the thermal management system to maintain the EV battery pack temperature within limits.
Thermal Load Calculation for the Passenger Cabin
The passenger cabin thermal load $Q_{cabin}$ is the sum of heat gains from various sources when the cabin temperature stabilizes. At equilibrium, the cabin thermal load approximately equals the cooling capacity required from the air conditioning system. Thus, we compute $Q_{cabin}$ and then apply a safety factor to determine the refrigeration load $Q_{cool}$.
The heat exchange between the cabin and the external environment occurs via conduction, convection, and radiation. The main components of $Q_{cabin}$ include:
- Heat transfer through body panels: $Q_{body} = U A_{body} (T_{out} – T_{in})$
- Heat transfer through windows: $Q_{window} = U_{window} A_{window} (T_{out} – T_{in}) + \alpha I_{solar} A_{window}$
- Heat dissipation from occupants: $Q_{occupant} = n \cdot q_{person}$, where $n$ is the number of occupants and $q_{person}$ is the heat output per person.
- Heat from fresh air ventilation: $Q_{vent} = \dot{m}_{air} c_p (T_{out} – T_{in})$
- Heat from electrical devices: $Q_{elec}$
Here, $U$ denotes overall heat transfer coefficients, $A$ areas, $T$ temperatures, $\alpha$ solar absorptivity, $I_{solar}$ solar irradiance, $\dot{m}_{air}$ air mass flow rate, and $c_p$ specific heat capacity.
Based on the target cabin temperatures from Table 1 and standard thermal parameters, we calculate the cabin thermal loads. Applying a design margin factor of 1.05, we obtain the required cooling capacities, as summarized in Table 4.
| Vehicle Speed (km/h) | Target Cabin Temperature (°C) | Required Cooling Capacity (kW) |
|---|---|---|
| 50 | 24 | 3.02 |
| 100 | 22 | 3.45 |
| Idle | 27 | 2.67 |
| Fast Charging | 27 | 2.67 |
| 120 | 22 | 3.42 |
These calculations guide the sizing of the air conditioning system components to ensure adequate cooling for the cabin while also addressing the EV battery pack cooling needs.
Performance Simulation Analysis
System Modeling Methodology
We develop a one-dimensional (1D) simulation model to analyze the integrated thermal management system. The EV battery pack model is simplified by inputting the heat generation values from Table 3 as a thermal source. Each heat exchanger in the air conditioning system—compressor, condenser, evaporator, Chiller, and expansion valves—is modeled and calibrated based on component performance data. The refrigerant circuit is constructed according to the system architecture, with the Chiller and evaporator in parallel.
For the air-side circuits, we model the front-end module air path, including grille, motor radiator, condenser, fan, and evaporator airflow. The air-side pressure coefficient ($C_p$) values are obtained from computational fluid dynamics (CFD) simulations of the vehicle, while the underhood air resistance (BIR) is calibrated by setting target condenser airflow rates. The governing equations for heat exchangers use the ε-NTU method. For example, the heat transfer rate $Q$ in a heat exchanger is given by:
$$ Q = \epsilon C_{min} (T_{h,in} – T_{c,in}) $$
where $\epsilon$ is effectiveness, $C_{min}$ is the minimum heat capacity rate, and $T_{h,in}$ and $T_{c,in}$ are inlet temperatures of hot and cold fluids, respectively.
The refrigerant properties are modeled using equations of state, and the compressor map defines mass flow rate $\dot{m}_{ref}$ as a function of speed and pressure ratio:
$$ \dot{m}_{ref} = f(N_{comp}, \frac{P_{discharge}}{P_{suction}}) $$
Similarly, expansion valve behavior is modeled based on orifice equations. This comprehensive model allows us to simulate system performance under various operating conditions.
Simulation Parameters and Boundary Conditions
The simulation inputs include component specifications and front-end module airflow conditions. Key parameters are derived from CAD data and supplier specifications. The front-end airflow temperature and mass flow rate are based on CFD results, as summarized in Table 5. These boundaries are critical for accurate simulation of condenser and radiator performance.
| Vehicle Speed (km/h) | Condenser Inlet Air Temperature (°C) | Condenser Inlet Air Mass Flow Rate (kg/s) | Evaporator Airflow Rate (m³/h) | Compressor Speed (rpm) | Condenser Inlet Air Humidity (%) |
|---|---|---|---|---|---|
| 50 | 41.3 | 0.423 | 370 | 5000 | 50 |
| 100 | 41.4 | 0.65 | 370 | 5000 | 50 |
| Idle | 44.8 | 0.3097 | 370 | 5000 | 50 |
| Fast Charging | 44.8 | 0.3097 | 370 | 5000 | 50 |
| 120 | 41.8 | 0.756 | 370 | 5000 | 50 |
The evaporator airflow rate is kept constant at 370 m³/h for consistency across simulations, representing the blower setting for maximum cooling. The compressor speed is initially set to 5000 rpm for baseline analysis.
Baseline Analysis Results
Using the simulation model with baseline components and 5000 rpm compressor speed, we obtain the performance results shown in Table 6. The key metrics include condenser heat rejection, evaporator cooling capacity, evaporator outlet air temperature, and EV battery pack inlet coolant temperature.
| Operating Parameter | 50 km/h | 100 km/h | Idle | Fast Charging | 120 km/h |
|---|---|---|---|---|---|
| Condenser Inlet Air Temperature (°C) | 41.3 | 41.4 | 44.8 | 44.8 | 41.8 |
| Condenser Outlet Air Temperature (°C) | 53.9 | 49.9 | 62.6 | 66.2 | 50.2 |
| Condenser Heat Rejection (kW) | 5.00 | 5.29 | 4.99 | 5.95 | 6.04 |
| Evaporator Cooling Capacity (kW) | 3.04 | 2.46 | 2.99 | 1.55 | 1.75 |
| Evaporator Outlet Air Temperature (°C) | 4.64 | 5.32 | 8.89 | 17.2 | 12.4 |
| EV Battery Pack Inlet Coolant Temperature (°C) | 3.8 | 14.0 | 7.0 | 30.4 | 29.3 |
From these results, we observe that at 50 km/h, 100 km/h, and idle conditions, the thermal loads of the EV battery pack are relatively low, and both cabin cooling and EV battery pack cooling meet the targets. However, during fast charging and high-speed (120 km/h) conditions, the heat generation of the EV battery pack is significant, leading to combined thermal loads that exceed the system’s capacity. Specifically, the evaporator outlet air temperature and EV battery pack inlet coolant temperature far exceed the target values, indicating inadequate cooling performance for both the cabin and the EV battery pack.
Optimization Strategy and Results
To address the shortcomings in fast charging and high-speed conditions, we explore several optimization measures. The goal is to enhance the cooling capacity for both the EV battery pack and the cabin simultaneously.
Optimization Scheme 1: Increased Compressor Speed
First, we increase the compressor speed from 5000 rpm to 7000 rpm to boost refrigerant mass flow rate $\dot{m}_{ref}$. The higher flow rate can improve heat exchange in both the evaporator and Chiller. The simulation results for this change are compared with the baseline in Table 7.
| Operating Parameter | Fast Charging | 120 km/h | ||
|---|---|---|---|---|
| Baseline (5000 rpm) | Optimized (7000 rpm) | Baseline (5000 rpm) | Optimized (7000 rpm) | |
| Compressor Speed (rpm) | 5000 | 7000 | 5000 | 7000 |
| Condenser Inlet Air Temperature (°C) | 44.8 | 44.8 | 41.8 | 41.8 |
| Condenser Outlet Air Temperature (°C) | 66.2 | 70.6 | 50.2 | 52.2 |
| Condenser Heat Rejection (kW) | 5.95 | 7.10 | 6.04 | 7.50 |
| Evaporator Cooling Capacity (kW) | 1.55 | 2.10 | 1.75 | 2.43 |
| Evaporator Outlet Air Temperature (°C) | 17.2 | 14.1 | 12.4 | 8.25 |
| EV Battery Pack Inlet Coolant Temperature (°C) | 30.4 | 27.4 | 29.3 | 25.2 |
While the evaporator cooling capacity increases and outlet temperatures decrease, the EV battery pack inlet coolant temperature remains above the 20°C target. This indicates that although cabin cooling improves, the Chiller’s capacity is still insufficient for the EV battery pack under these high-load conditions. The elevated condenser outlet temperature also suggests potential limitations in condenser performance.
Optimization Scheme 2: Larger Condenser with Increased Compressor Speed
Next, we combine the higher compressor speed (7000 rpm) with a larger condenser to enhance heat rejection. The new condenser dimensions are 640 mm × 368 mm × 16 mm (length × width × thickness), providing increased surface area for airflow and heat transfer. The simulation results are shown in Table 8.
| Operating Parameter | Fast Charging | 120 km/h | ||
|---|---|---|---|---|
| Baseline (5000 rpm) | Optimized (7000 rpm + Larger Condenser) | Baseline (5000 rpm) | Optimized (7000 rpm + Larger Condenser) | |
| Compressor Speed (rpm) | 5000 | 7000 | 5000 | 7000 |
| Condenser Inlet Air Temperature (°C) | 44.8 | 44.8 | 41.8 | 41.4 |
| Condenser Outlet Air Temperature (°C) | 66.2 | 62.8 | 50.2 | 49.0 |
| Condenser Heat Rejection (kW) | 5.95 | 7.60 | 6.04 | 7.75 |
| Evaporator Cooling Capacity (kW) | 1.55 | 2.70 | 1.75 | 2.90 |
| Evaporator Outlet Air Temperature (°C) | 17.2 | 10.6 | 12.4 | 5.23 |
| EV Battery Pack Inlet Coolant Temperature (°C) | 30.4 | 24.0 | 29.3 | 22.7 |
The larger condenser improves heat rejection, leading to higher subcooling and increased evaporator cooling capacity. Cabin cooling targets are now met, with evaporator outlet temperatures well within desired ranges. However, the EV battery pack inlet coolant temperature, though reduced, still slightly exceeds the 20°C target in fast charging (24°C) and is close in high-speed (22.7°C). This suggests that the Chiller’s performance remains a bottleneck for the EV battery pack cooling.
Optimization Scheme 3: Enhanced Chiller with Larger Condenser and Higher Compressor Speed
Finally, we upgrade to a higher-performance Chiller while maintaining the larger condenser and 7000 rpm compressor speed. The improved Chiller features enhanced heat transfer coefficients and flow design, increasing its cooling capacity for the EV battery pack loop. The results are presented in Table 9.
| Operating Parameter | Fast Charging | 120 km/h | ||
|---|---|---|---|---|
| Scheme 2 (7000 rpm + Larger Condenser) | Optimized (7000 rpm + Larger Condenser + Enhanced Chiller) | Scheme 2 (7000 rpm + Larger Condenser) | Optimized (7000 rpm + Larger Condenser + Enhanced Chiller) | |
| Water Pump Speed (rpm) | 4900 | 4900 | 4900 | 4900 |
| Compressor Speed (rpm) | 7000 | 7000 | 7000 | 7000 |
| Condenser Inlet Air Temperature (°C) | 44.8 | 44.8 | 41.4 | 41.4 |
| Condenser Outlet Air Temperature (°C) | 62.8 | 62.8 | 49.0 | 49.0 |
| Condenser Heat Rejection (kW) | 7.60 | 7.63 | 7.75 | 7.76 |
| Evaporator Cooling Capacity (kW) | 2.70 | 2.70 | 2.90 | 2.90 |
| Evaporator Outlet Air Temperature (°C) | 10.6 | 10.6 | 5.23 | 5.36 |
| EV Battery Pack Inlet Coolant Temperature (°C) | 24.0 | 19.7 | 22.7 | 18.9 |
With the enhanced Chiller, the EV battery pack inlet coolant temperature drops to 19.7°C in fast charging and 18.9°C at 120 km/h, both below the 20°C target. Cabin cooling remains satisfactory. Thus, this optimized configuration—comprising a larger condenser, higher compressor speed (7000 rpm), and an enhanced Chiller—successfully meets the cooling requirements for both the passenger cabin and the EV battery pack under all specified conditions.
Discussion and Further Insights
The optimization process highlights the interdependencies within the integrated thermal management system. The performance of the EV battery pack cooling is not only dependent on the Chiller but also on the overall refrigerant cycle efficiency, which is influenced by compressor speed and condenser capability. We can express the overall cooling capacity for the EV battery pack $Q_{batt,cool}$ as a function of several variables:
$$ Q_{batt,cool} = f(\dot{m}_{ref}, \Delta T_{ref}, A_{Chiller}, U_{Chiller}, \dot{m}_{coolant}) $$
where $\dot{m}_{ref}$ is refrigerant mass flow rate (controlled by compressor speed), $\Delta T_{ref}$ is refrigerant temperature difference across the Chiller, $A_{Chiller}$ is heat transfer area, $U_{Chiller}$ is overall heat transfer coefficient of the Chiller, and $\dot{m}_{coolant}$ is coolant mass flow rate. Similarly, cabin cooling capacity $Q_{cabin,cool}$ depends on evaporator performance and airflow.
Moreover, the integration of the EV battery pack thermal management with the cabin air conditioning system necessitates careful control strategy development. Using expansion valves, we can modulate refrigerant distribution between the evaporator and Chiller based on real-time demands. For instance, during fast charging, priority might be given to the EV battery pack cooling to prevent overheating, while maintaining cabin comfort within acceptable bounds. The control logic can be optimized using algorithms that minimize total energy consumption while meeting temperature constraints for both the EV battery pack and cabin.
We also consider the impact of ambient conditions on system performance. Higher ambient temperatures reduce the temperature differential for heat exchangers, necessitating greater capacity. The front-end airflow, influenced by vehicle speed and fan operation, plays a critical role. The air-side heat transfer for the condenser can be modeled as:
$$ Q_{cond} = \dot{m}_{air} c_{p,air} (T_{air,out} – T_{air,in}) $$
where $T_{air,in}$ and $T_{air,out}$ are condenser inlet and outlet air temperatures, respectively. Improving airflow through better fan design or grille optimization can further enhance performance.
Additionally, the thermal mass of the EV battery pack affects transient behavior. The temperature rise $\Delta T_{batt}$ of the EV battery pack over time $t$ can be estimated using:
$$ \Delta T_{batt} = \frac{Q_{batt} – Q_{batt,cool}}{m_{batt} c_{p,batt}} t $$
where $m_{batt}$ is the mass of the EV battery pack and $c_{p,batt}$ is its specific heat capacity. This equation underscores the need for sufficient cooling capacity $Q_{batt,cool}$ to limit temperature rise during high-power operations.
Conclusion
In this study, we designed an integrated thermal management system for an electric vehicle to address the cooling requirements of both the EV battery pack and the passenger cabin. Through systematic analysis and optimization using 1D simulation, we derived the following conclusions:
- We designed an integrated thermal management architecture for an EV model, incorporating motor cooling, EV battery pack thermal management, and air conditioning loops. The Chiller and evaporator are arranged in parallel, with control via expansion valves to dynamically manage cooling for the EV battery pack and cabin.
- Simulation analysis revealed that with a baseline compressor speed of 5000 rpm, the refrigerant flow was insufficient to meet the cooling demands of the EV battery pack and cabin during high-load conditions such as fast charging and high-speed driving. This highlighted the critical need for component sizing and system optimization.
- Optimization steps included increasing compressor speed to 7000 rpm, upgrading to a larger condenser, and selecting a higher-performance Chiller. The combined solution—featuring a larger condenser, compressor speed of 7000 rpm, and an enhanced Chiller—successfully achieved the target cabin temperatures and maintained the EV battery pack inlet coolant temperature below 20°C in all specified conditions.
- The methodology presented here, encompassing thermal load calculation, system modeling, simulation, and iterative optimization, provides a robust framework for the early-stage matching and design of thermal management systems in electric vehicles. It ensures that the EV battery pack operates within safe temperature limits while delivering occupant comfort, thereby enhancing overall vehicle performance and reliability.
Future work could explore advanced control strategies, transient analysis under real-world driving cycles, and integration with other vehicle systems to further optimize energy efficiency. The importance of the EV battery pack thermal management cannot be overstated, as it directly impacts the longevity, safety, and performance of electric vehicles. Continued research in this area will be vital as the adoption of EVs accelerates globally.