Abstract In recent years, the new energy vehicle industry has witnessed remarkable growth, with sales of electric vehicles (EVs) reaching unprecedented heights. However, the driving range remains a critical challenge for pure electric vehicles, prompting extensive research into energy consumption reduction. This study focuses on energy flow analysis for a light-duty pure electric vehicle, aiming to understand energy distribution across components and identify optimization directions. Through real-world testing under various temperature conditions, we quantified energy consumption patterns and proposed strategies to enhance energy efficiency and extend driving range.
1. Introduction
The global adoption of electric vehicles has surged, driven by environmental concerns and technological advancements. In 2023, China produced 9.587 million new energy vehicles and sold 9.495 million, representing year-on-year growth of 35.8% and 37.9%, respectively, with sales accounting for 31.6% of total vehicle sales. By the first five months of 2024, production and sales reached 3.926 million and 3.895 million, with sales share climbing to 33.9%. Despite this growth, range anxiety persists due to limited driving range, making energy consumption reduction a priority for automakers.
Energy flow analysis plays a pivotal role in understanding how energy is distributed and consumed in EVs. By mapping energy paths and measuring component-specific，we can identify inefficiencies and devise targeted improvements. This study utilizes a light-duty pure electric vehicle as a case study, conducting comprehensive energy flow tests under the China Light-duty Vehicle Test Cycle (CLTC-C) to analyze energy distribution and temperature impacts.
2. Concept and Significance of Energy Flow Analysis
2.1 Energy Flow Paths in EVs
The energy flow in pure electric vehicles encompasses three primary paths:
- Charging Path: Energy transfer from the grid to the power battery.
- Discharging Path: Energy distribution from the battery to drive systems and auxiliary components.
- Regenerative Braking Path: Energy recovery during braking, converted back to electrical energy stored in the battery.
This research focuses on the discharging path, which directly influences driving range and energy efficiency.
2.2 Theoretical Framework
Energy flow analysis is grounded in the law of energy conservation, expressed as:\(E_{\text{battery,discharge}} = E_{\text{motor}} + E_{\text{AC}} + E_{\text{12V}} + E_{\text{losses}}\) where \(E_{\text{battery,discharge}}\) is the total energy discharged from the battery, \(E_{\text{motor}}\) is energy consumed by the drive motor, \(E_{\text{AC}}\) is air conditioning system energy, \(E_{\text{12V}}\) is low-voltage component energy, and \(E_{\text{losses}}\) represents energy losses in transmission and conversion.
2.3 Significance of Analysis
- Component Efficiency Evaluation: Quantifies energy consumption of key components (e.g., motor, air conditioner, PTC heater) to identify high-energy-consuming parts.
- Design Optimization: Provides data-driven insights for component selection and system design, such as improving motor efficiency or optimizing thermal management.
- Range Extension: By reducing energy waste, driving range can be enhanced, addressing the core challenge of EVs.
3. Experimental Setup and Methodology
3.1 Test Vehicle Specifications
The test vehicle is a 4×2 rear-wheel-drive light-duty electric commercial vehicle. Key components and their specifications are listed in Table 1:
Table 1. Major Energy-Consuming Components and Working Voltages
| Component | Working Voltage |
|---|---|
| Drive Motor | AC 230V |
| Air Conditioner Compressor | DC 200–450V |
| PTC Heater | DC 250–450V |
| 12V Electrical Components | DC 12V |
The power battery is a lithium iron phosphate (LiFePO₄) battery with liquid cooling. The drive system features a 130kW permanent magnet synchronous motor (liquid-cooled), and the high-voltage controller is an integrated four-in-one unit. The air conditioning system uses a high-voltage electric compressor for cooling and a high-voltage PTC heater for heating, both capable of regulating cabin and battery temperature.
3.2 Test Equipment and Environment
Tests were conducted in an environmental chamber with a chassis dynamometer, enabling precise temperature control and road load simulation. Key equipment included:
- Environmental chamber for temperature regulation (–20°C to 40°C)
- Chassis dynamometer for road load simulation
- Power analyzer for real-time energy consumption monitoring
- Temperature acquisition system for thermal management analysis
- Data logging computer for comprehensive data recording
Table 2. Major Test Equipment
| Equipment | Function |
|---|---|
| Environmental Chamber | Controls ambient temperature |
| Chassis Dynamometer | Simulates driving resistance |
| Power Analyzer | Measures electrical power consumption |
| Temperature Acquisition 仪 | Monitors component temperatures |
| Computer | Records and processes test data |
3.3 Test Cycle and Conditions
The China Light-duty Vehicle Test Cycle (CLTC-C) was employed, designed for light commercial vehicles. The cycle consists of three segments:
- Low Speed: 120 seconds, average speed 11.2 km/h
- Medium Speed: 400 seconds, average speed 36.6 km/h
- High Speed: 1280 seconds, average speed 69.4 km/h
Total cycle duration: 1800 seconds (30 minutes), total distance: 16.43 km, maximum speed: 92 km/h, average speed: 41.23 km/h.
Tests were conducted under three temperature conditions:
- Low temperature: –10°C
- Normal temperature: 25°C
- High temperature: 40°C
Energy consumption was measured at each component, including the battery, motor, air conditioner, and 12V system. Test procedures followed GB/T 18386.1—2021, “Test Methods for Energy Consumption and Driving Range of Electric Vehicles—Part 1: Light-duty Vehicles”.
4. Data Acquisition Points
Key energy consumption measurement points are listed in Table 3:
Table 3. Energy Consumption Data Acquisition Points
| No. | Acquisition Point |
|---|---|
| 1 | Power battery output terminal (+) |
| 2 | High-voltage input terminal (+) of air conditioner compressor |
| 3 | High-voltage input terminal (+) of PTC heater |
| 4 | DCDC 12V low-voltage output terminal (+) |
| 5 | Motor U/V/W high-voltage input terminals |
Data was recorded at 1-second intervals to capture transient energy changes during the test cycle.
5. Test Results and Analysis
5.1 Energy Consumption under Different Temperatures
Table 4 summarizes the energy consumption data for various components under low, normal, and high temperatures:
Table 4. Energy Consumption Statistics (Wh) under CLTC-C Cycle
| Component | Normal Temperature (25°C) | High Temperature (40°C) | Low Temperature (–10°C) |
|---|---|---|---|
| Battery Discharge | 3566 | 3685 | 4906 |
| Motor Consumption | 3046 | 3053 | 3349 |
| 12V System Consumption | 166 | 209 | 127 |
| Air Conditioning Consumption | 155 | 223 | 1265 |
5.2 Analysis of Battery Discharge Energy
- Normal Temperature: Lowest total energy consumption (3566 Wh), indicating optimal efficiency at 25°C.
- High Temperature: Slight increase (3.3% higher than normal temperature) due to increased cooling demand for the battery and cabin.
- Low Temperature: Significant increase (37.6% higher than normal temperature), primarily driven by PTC heater activation for heating.
The relationship between temperature and battery discharge energy can be modeled as:\(E_{\text{battery}}(T) = E_0 \left[1 + \alpha (T – T_0) + \beta (T – T_0)^2\right]\) where \(E_0\) is energy consumption at reference temperature \(T_0\) (25°C), and \(\alpha\) and \(\beta\) are temperature coefficients. For low temperatures, the linear term dominates due to PTC heating, while for high temperatures, the quadratic term reflects increased cooling loads.
5.3 Motor Energy Consumption Analysis
The drive motor accounts for the largest proportion of energy consumption, exceeding 90% under normal temperature conditions (Figure 1). At low temperatures, motor energy consumption increases by 10% (3349 Wh vs. 3046 Wh) due to:
- Increased internal resistance of motor windings at low temperatures
- Higher lubricant viscosity, leading to greater mechanical losses
- Reduced efficiency of the cooling system in low-temperature environments
Under high temperatures, motor consumption remains nearly unchanged (3053 Wh), as the liquid cooling system effectively maintains optimal operating temperature.
Figure 1. Energy Consumption Proportion under Normal Temperature (25°C) (Note: Drive motor: 90.5%, 12V system: 4.6%, air conditioning: 4.9%)
5.4 12V System Energy Consumption
The 12V system shows distinct temperature dependency:
- High Temperature: 209 Wh (25.9% higher than normal temperature), driven by increased fan and water pump operation for cooling.
- Low Temperature: 127 Wh (23.5% lower than normal temperature), as cooling demands decrease, though heating-related loads (e.g., seat heaters) may slightly increase energy use.
Despite these fluctuations, the 12V system’s total energy consumption remains a small fraction (4–6%) of the total, making its impact on range relatively minor.
5.5 Air Conditioning System Energy Consumption
The air conditioning system demonstrates the most significant temperature sensitivity:
- High Temperature: 223 Wh (43.9% higher than normal temperature), due to continuous operation of the electric compressor for cabin and battery cooling.
- Low Temperature: 1265 Wh (716% higher than normal temperature), primarily from PTC heater operation for cabin and battery heating.
The energy consumption ratio of the air conditioning system at low temperature versus normal temperature is:\(\text{Growth Rate} = \frac{1265 – 155}{155} \times 100\% = 816\%\) This dramatic increase highlights the critical need for efficient thermal management in cold climates.
6. Impact of Temperature on Energy Flow
6.1 Quantitative Analysis of Temperature Effects
Figure 2 compares component energy consumption across temperature conditions:
Figure 2. Component Energy Consumption under Different Temperatures
- Drive motor: 3046 Wh (25°C), 3053 Wh (40°C), 3349 Wh (–10°C)
- 12V system: 166 Wh (25°C), 209 Wh (40°C), 127 Wh (–10°C)
- Air conditioning: 155 Wh (25°C), 223 Wh (40°C), 1265 Wh (–10°C)
6.2 Mechanisms of Temperature Influence
- Electrical Components: Resistance changes with temperature affect power consumption:\(R(T) = R_0 \left[1 + \alpha (T – T_0)\right]\) where \(R_0\) is resistance at \(T_0\), and \(\alpha\) is the temperature coefficient. For copper windings, \(\alpha \approx 0.004/°C\), so a 30°C drop (25°C to –5°C) increases resistance by 12%, raising energy consumption.
- Thermal Management Systems:
- High temperatures require continuous cooling, driving compressor energy use.
- Low temperatures necessitate heating, with PTC heaters consuming substantial energy (1265 Wh at –10°C vs. 155 Wh at 25°C).
- Lubrication and Mechanical Losses: Lower temperatures increase lubricant viscosity, leading to higher friction losses in the motor and drivetrain, as described by:\(P_{\text{friction}} = \mu(T) \cdot F \cdot v\) where \(\mu(T)\) is temperature-dependent viscosity, F is normal force, and v is velocity.
7. Energy Efficiency Optimization Strategies
7.1 Drive System Optimization
- Motor Efficiency Enhancement:
- Implement advanced magnetic materials (e.g., neodymium iron boron) to reduce core losses.
- Optimize cooling systems to maintain motor temperature within the optimal range (25–40°C), minimizing resistance and friction losses.
- Drivetrain Loss Reduction:
- Use low-viscosity lubricants designed for cold climates to decrease mechanical losses at low temperatures.
- Optimize gear ratios to match typical driving conditions, improving energy conversion efficiency.
7.2 Thermal Management System Improvement
- PTC Heater Replacement:
- Replace traditional PTC heaters with heat pump systems, which achieve a coefficient of performance (COP) of 2–3, compared to PTC’s COP ≈ 1. The energy saving is:\(E_{\text{saved}} = E_{\text{PTC}} \left(1 – \frac{1}{\text{COP}_{\text{heat pump}}}\right)\) For example, at –10°C, replacing a 1265 Wh PTC heater with a COP=2.5 heat pump would save:\(1265 \left(1 – \frac{1}{2.5}\right) = 759 \text{ Wh}\)
- Integrated Thermal Management:
- Recycle waste heat from the motor and power electronics to warm the cabin and battery in cold weather, reducing reliance on external heating sources.
- Optimize battery cooling/heating strategies to balance energy consumption and battery life.
7.3 System-Level Energy Management
- Energy Flow Simulation:
- Develop comprehensive energy flow models to predict consumption under various conditions, aiding in real-time energy distribution optimization.
- Implement model predictive control (MPC) to adjust power allocation based on driving patterns and environmental factors.
- Regenerative Braking Enhancement:
- Improve regenerative braking efficiency, especially at low temperatures, to recover more kinetic energy. The theoretical maximum regenerative energy is:\(E_{\text{regen,max}} = \int_{v_1}^{v_2} \frac{1}{2} m (v_1^2 – v_2^2) \cdot \eta_{\text{regen}} \, dt\) where m is vehicle mass, \(v_1\) and \(v_2\) are initial and final velocities, and \(\eta_{\text{regen}}\) is regenerative efficiency.
8. Conclusion
Through energy flow analysis of a light-duty pure electric vehicle, we conclude:
- The drive motor is the primary energy consumer, accounting for over 90% of total energy use under normal conditions.
- Environmental temperature significantly impacts energy consumption:
- Low temperatures (–10°C) increase total energy use by 37.6% due to PTC heating.
- High temperatures (40°C) raise energy use by 3.3% due to cooling demands.
- The air conditioning system shows the highest temperature sensitivity, with energy consumption increasing 816% at low temperatures compared to normal conditions.
To enhance energy efficiency and extend driving range, key strategies include:
- Improving drive system efficiency to reduce motor energy consumption.
- Replacing PTC heaters with heat pump systems to minimize heating losses in cold weather.
- Optimizing integrated thermal management to recycle waste heat and balance energy demands.
This study provides a theoretical foundation for future EV energy optimization, guiding the development of more energy-efficient electric vehicles and addressing range anxiety through data-driven solutions.