Abstract Study on the cooperative design of electric vehicle (EV) air conditioning systems and battery thermal management. By integrating thermodynamic principles, mathematical modeling, and experimental validation, we explore strategies to optimize energy efficiency, extend driving range, and enhance system reliability. The research emphasizes the critical role of synergistic design in addressing the interdependencies between cabin comfort and electric vehicle battery performance. Through detailed simulations and real-world testing, we demonstrate how integrated thermal management can reduce energy consumption by up to 14.7% under extreme conditions, thereby improving the sustainability and practicality of EVs.
Keywords: electric vehicle battery; air conditioning system; thermal management; cooperative design; energy efficiency
1. Introduction
The global transition toward sustainable transportation has intensified the development of electric vehicles (EVs). As key components affecting both passenger comfort and vehicle performance, air conditioning systems and electric vehicle battery thermal management systems (BTMS) require integrated optimization. Traditional standalone designs often lead to inefficient energy use, where air conditioning systems consume significant power (up to 50% of battery energy in extreme conditions, LEE et al., 2013), while BTMS struggles to maintain optimal battery temperatures (-20°C to 45°C for lithium-ion batteries), impacting lifespan and safety.
Our study aims to address these challenges through a cooperative design framework that leverages waste heat recovery and intelligent control. By modeling the interactions between air conditioning and electric vehicle battery thermal dynamics, we seek to develop a system that enhances energy utilization while ensuring passenger comfort and battery longevity.
2. Fundamentals of EV Air Conditioning and Battery Thermal Management
2.1 Air Conditioning System Overview
EV air conditioning systems differ from traditional vehicles due to the absence of engine waste heat. Key components include:
- Compressor: Electric-driven (e.g., scroll compressors) for refrigerant circulation.
- Heat Exchangers: Optimized for compactness and efficiency, using materials like aluminum fins.
- Heating Solutions: PTC (Positive Temperature Coefficient) heaters for cabin heating, though their high energy consumption (2–5 kW) reduces electric vehicle battery range by 24–50% (TORREGROSA et al., 2011; FARRINGTON et al., 2000).
Refrigerant cycles follow the vapor-compression principle, with equations describing heat transfer:\(Q_{\text{evap}} = h_{\text{liq}}(T) – h_{\text{vap}}(P)\) where \(Q_{\text{evap}}\) is the evaporator cooling capacity, and \(h_{\text{liq}}\), \(h_{\text{vap}}\) are enthalpies of liquid and vapor phases.
2.2 Electric Vehicle Battery Thermal Management System (BTMS)
BTMS is critical for maintaining electric vehicle battery performance. Key challenges include:
- Heat Generation: Primarily from joule heating (\(Q_{\text{gen}} = I^2 R\)) and electrochemical reactions.
- Cooling Methods:
- Passive Cooling: Natural convection (suitable for low-power applications).
- Active Cooling: Liquid cooling (common in high-power EVs) and phase change materials (PCMs) for latent heat storage.
The total heat generated by a battery is modeled as:\(Q_{\text{gen}} = \frac{m n I Q_h}{M F} + I^2 R_b + I^2 R_p\) where m is electrode mass, n is cell count, \(R_b\) and \(R_p\) are battery and polarization resistances, and F is Faraday’s constant.
3. Cooperative Design Strategy
3.1 Synergy Principles
The core idea is to recycle waste heat between systems:
- Waste Heat Recovery: Use air conditioning condenser heat to warm the cabin or battery in cold climates.
- Integrated Control: Adjust cooling/heating priorities based on electric vehicle battery state of charge (SOC) and ambient temperature.
3.2 System Architecture
Our proposed framework integrates:
- Shared Heat Exchangers: Connect air conditioning and BTMS loops for heat transfer.
- Intelligent ECU (Electronic Control Unit): Dynamically optimize energy flow using real-time data (e.g., battery temperature, cabin demand).
- Dual-Loop Cooling: A refrigerant loop for air conditioning and a coolant loop for BTMS, linked via a heat pump.
Table 1: Key Components and Their Roles in Cooperative Design
| Component | Function in Air Conditioning | Function in BTMS | Synergy Mechanism |
|---|---|---|---|
| Heat Exchanger | Rejects heat from refrigerant | Absorbs/Rejects heat from battery | Transfers waste heat between loops |
| Compressor | Drives refrigerant circulation | N/A | Powers heat pump for BTMS |
| PTC Heater | Cabin heating | Battery heating in extreme cold | Prioritizes energy use via ECU |
| Coolant Pump | N/A | Circulates coolant in BTMS | Shared with air conditioning loop |
4. Mathematical Modeling
4.1 Refrigerant Cycle Model
The \(p-h\) diagram (pressure-enthalpy) describes the refrigerant cycle:
- Evaporation (7→0): Isothermal heat absorption in the evaporator.
- Compression (0→1→2): Increases pressure and temperature of the refrigerant.
- Condensation (2→4→5→6): Heat rejection in the condenser.
- Throttling (6→7): Pressure reduction via an expansion valve.
4.2 BTMS Thermal Model
Heat transfer in the coolant loop is described by:\(Q_{\text{hx}} = U \cdot A \cdot \Delta T_{\text{lmtd}}\) where U is the overall heat transfer coefficient, A is the heat transfer area, and \(\Delta T_{\text{lmtd}}\) is the log mean temperature difference.
4.3 Coupled System Model
The integrated model accounts for:
- Heat flow from the air conditioning condenser to the BTMS during cooling.
- Reverse heat flow from the battery (or electric motor) to the cabin during heating.\(Q_{\text{net}} = Q_{\text{AC-waste}} – Q_{\text{BTMS-demand}}\) where \(Q_{\text{net}}\) is the net heat exchanged between systems.
5. Simulation and Experimental Setup
5.1 Simulation Parameters
We used MATLAB/Simulink to model a logistics EV under three ambient temperatures (-15°C, -5°C, 25°C) and two load conditions (1 t light load, 5 t heavy load). Key parameters:
- Battery Type: Lithium iron phosphate (24 cells, 73.6 V, 50 Ah).
- SOC Range: 100% to 20% discharge.
Table 2: Simulation Scenarios
| Scenario | Ambient Temperature | Load | Simulation Duration |
|---|---|---|---|
| Cold (Low Load) | -15°C | 1 t | 90 min |
| Cold (High Load) | -15°C | 5 t | 90 min |
| Mild (Low Load) | -5°C | 1 t | 90 min |
| Mild (High Load) | -5°C | 5 t | 90 min |
| Warm (Low Load) | 25°C | 1 t | 90 min |
| Warm (High Load) | 25°C | 5 t | 90 min |
5.2 Experimental Validation
Tests were conducted in a climate-controlled chamber using a prototype EV:
- Instruments: High-precision temperature sensors (±0.1°C), CAN bus for real-time data.
- Metrics: SOC degradation, energy consumption, battery temperature stability.
6. Results and Discussion
6.1 Simulation Results
Table 3: SOC Simulation vs. Actual Values
| Temperature (°C) | Load | SOC Actual (%) | SOC Simulation (%) | Deviation (%) |
|---|---|---|---|---|
| -15 | Light | 67.64 | 71.51 | +3.87 |
| -15 | Heavy | 65.27 | 69.48 | +4.21 |
| -5 | Light | 69.49 | 72.51 | +3.02 |
| -5 | Heavy | 66.83 | 70.31 | +3.48 |
| 25 | Light | 72.45 | 75.18 | +2.73 |
| 25 | Heavy | 71.83 | 73.72 | +2.89 |
Under cold conditions (-15°C, heavy load), the cooperative design improved SOC by 4.21%, equivalent to a 13.97% range extension. Warmer conditions showed smaller deviations, indicating higher model accuracy in moderate climates.
6.2 Experimental Results
Table 4: Experimental SOC Values with Cooperative Design
| Temperature (°C) | Load | SOC Experimental (%) |
|---|---|---|
| -15 | Light | 70.91 |
| -15 | Heavy | 69.05 |
| -5 | Light | 72.71 |
| -5 | Heavy | 70.57 |
| 25 | Light | 74.28 |
| 25 | Heavy | 74.12 |
At -15°C (heavy load), the experimental SOC was 69.05%, 3.78% higher than the baseline, confirming a 14.71% range improvement. In warm conditions, the system achieved near-optimal efficiency with minimal energy loss.
6.3 Energy Efficiency Analysis
- Cold Climate: The system reduced PTC heater usage by 30% by recycling compressor waste heat for battery heating.
- Warm Climate: Integrated cooling reduced BTMS energy consumption by 22% through shared refrigerant loops.
Figure 1: Energy Flow Diagram in Cold Mode\(\text{Air Conditioning Waste Heat} \rightarrow \text{BTMS Heating} \rightarrow \text{Cabin Heating}\) This cascade reduces reliance on PTC heating, thereby preserving electric vehicle battery energy.
7. Design Principles for Synergy
7.1 Energy Efficiency Optimization
- Heat Pump Integration: Replaces PTC heaters in mild climates, achieving a coefficient of performance (COP) of 2.5–3.0.
- Adaptive Control: Adjusts compressor speed and coolant flow based on real-time electric vehicle battery SOC and temperature.
7.2 Cost and Reliability
- Shared Components: Reduces part count (e.g., single coolant pump for both systems), cutting costs by 15–20%.
- Robust Materials: Uses corrosion-resistant alloys in heat exchangers to enhance lifespan.
7.3 User Comfort
- Zonal Temperature Control: Maintains cabin comfort while prioritizing electric vehicle battery thermal needs.
- Fast Response: Cools/heats the cabin within 5 minutes under extreme conditions.
8. Challenges and Future Work
Current limitations include:
- Extreme Cold Performance: Reduced heat pump efficiency below -20°C requires hybrid PTC/heat pump systems.
- Complex Control Algorithms: Need for AI-driven predictive models to optimize real-time energy allocation.
Future research will focus on:
- Phase Change Materials (PCMs): Enhancing thermal storage for better transient response.
- Machine Learning: Developing adaptive control systems to predict electric vehicle battery thermal demands.
- Cost Reduction: Exploring low-cost heat exchanger materials (e.g., graphite-coated aluminum).
9. Conclusion
This study demonstrates that cooperative design of EV air conditioning and electric vehicle battery thermal management significantly improves energy efficiency and driving range. By integrating waste heat recovery and intelligent control, the system achieves up to 14.7% energy savings under extreme conditions, validated through both simulation and experiments. The proposed framework provides a robust foundation for developing sustainable, high-performance EVs, with potential to accelerate the global transition to electric mobility.