In recent years, the electric vehicle market has experienced rapid growth, with range extended electric vehicles gaining significant attention due to their unique technological advantages. These vehicles operate primarily on electric power while utilizing an onboard auxiliary power unit to extend driving range, making them a popular choice in the evolving landscape of China EV development. However, the performance of electric vehicles in extreme environments, particularly high-cold conditions, poses challenges that affect energy consumption and overall user experience. This study focuses on developing a robust testing methodology to evaluate the energy consumption of range extended electric vehicles under high-cold conditions, addressing gaps in current research and providing insights for industry applications.
The importance of this research stems from the increasing adoption of electric vehicles in regions with harsh climates, where low temperatures can significantly impact battery efficiency and vehicle range. In China, the push for electrification has led to a surge in EV production, yet standardized testing methods for cold weather performance remain underdeveloped. We aim to bridge this gap by adapting existing standards, such as the Test Methods for Energy Consumption of Light-Duty Hybrid Electric Vehicles (GB/T 19753-2021), to real-world road conditions in high-cold areas. Our approach emphasizes practical applicability, ensuring that the results can guide manufacturers in optimizing their designs for better performance in challenging environments.
To begin, we defined the test conditions based on a comprehensive analysis of high-cold environments. The test vehicle was selected to represent typical range extended electric vehicles available in the market, with specifications detailed in Table 1. The vehicle’s state was maintained to ensure reliability, including adjustments for cold-weather fluids and snow tires. The test mass was calculated as the sum of the base mass, optional equipment mass, and a representative load, which is crucial for simulating real-world usage scenarios. This setup allows for a fair assessment of how an electric vehicle performs under adverse conditions, contributing to the broader understanding of China EV capabilities.
| Parameter | Value |
|---|---|
| Vehicle Category | Plug-in Range Extended Electric Multi-Purpose Vehicle |
| Fuel Type | Gasoline/Electric Hybrid |
| Curb Weight (kg) | 2460 |
| Battery Type | Ternary Lithium-Ion Battery |
| WLTC All-Electric Range (km) | 175 |
| Charge-Sustaining Mode Fuel Consumption (L/100 km) | 7.4 |
| Drive Type | All-Wheel Drive |
| Tire Specification | 255/50R21 |
The test equipment included a driver assistance system for maintaining specified driving cycles, a fuel consumption meter to accurately measure energy usage, and data loggers with temperature sensors to monitor internal conditions. The temperature sensors were strategically placed inside the vehicle, as illustrated in the methodology, to ensure consistent environmental control. This equipment is essential for capturing precise data on how an electric vehicle behaves in high-cold settings, which is vital for advancing China EV technologies. The test was conducted on a closed snow-covered high-speed circular track, with road conditions meeting the requirements outlined in relevant standards, such as slopes not exceeding 1% and a compacted snow index between 75 and 85.
For the driving cycle, we converted the China Light-duty vehicle Test Cycle-Passenger car (CLTC-P) into a road-based test cycle to simulate real-world conditions. The original CLTC-P cycle has a duration of 1800 seconds and a distance of 14.48 km, but we adapted it to a 900-second cycle covering 7.5 km, as shown in Table 2. This conversion ensured that key parameters like average speed and idle ratio remained within acceptable deviations, allowing for consistent and repeatable tests. The velocity profile of the road test cycle was designed to mimic urban and highway driving, which is representative of typical electric vehicle usage in China.
| Characteristic | CLTC-P Cycle | Road Test Cycle |
|---|---|---|
| Total Duration (s) | 1800 | 900 |
| Running Average Speed (km/h) | 37.75 | 37.28 |
| Average Speed (km/h) | 28.96 | 29.07 |
| Average Acceleration (m/s²) | 0.45 | 0.45 |
| Average Deceleration (m/s²) | -0.49 | -0.49 |
| Idle Ratio (%) | 22.11 | 22.03 |
The test procedure involved several critical steps to ensure accuracy and reliability. First, the vehicle’s battery was discharged on public roads until it reached a state of charge balance, simulating real-world depletion. Next, the battery was charged indoors using AC charging with a power limit of 42 kW, without interruptions or special programs. After charging, the vehicle was conditioned in the test environment for 12 hours at an average temperature of -23.5°C, as depicted in the temperature curve during conditioning. This step is crucial for assessing how an electric vehicle’s energy systems respond to cold soak, which is a common issue in high-latitude regions of China.
During the test, the vehicle’s settings were optimized for high-cold conditions. The air conditioning was set to maximum temperature initially, with adjustments made during idle periods to maintain an internal average temperature between 20°C and 24°C. This approach mimics user behavior in cold climates, where heating is essential for comfort. Other functions, such as seat and steering wheel heaters, were disabled to isolate the impact of cabin heating on energy consumption. The vehicle was operated in forced electric mode or electric priority mode to evaluate all-electric range, and the driving mode was selected to follow the test cycle accurately. These settings help in understanding the trade-offs between comfort and efficiency in an electric vehicle under extreme conditions.
The all-electric range test was conducted by repeatedly driving the vehicle on the snow-covered track until the range extender started. The distance covered at this point was recorded as the high-cold all-electric range. We observed that the vehicle’s state of charge dropped to 21% when the range extender engaged, resulting in an all-electric range of 53.0 km. Compared to the advertised WLTC range of 175 km, this represents a retention rate of 30.3%. The environmental conditions during this test averaged -19.8°C, with internal temperatures managed as per the protocol. This significant drop in range highlights the challenges faced by electric vehicles in cold weather, which is a critical consideration for China EV adoption in northern regions.
Following the all-electric range test, we proceeded to the charge-sustaining mode fuel consumption test. The vehicle continued operating until the battery reached a state of charge balance, indicated by the SOC value stabilizing or increasing after a cycle. Then, two additional cycles were driven, and fuel consumption was measured using a dedicated meter. The results showed a fuel consumption of 9.0 L/100 km, which is a 21.6% increase over the advertised value of 7.4 L/100 km. The environmental temperature during this phase averaged -18.4°C, and internal temperatures were controlled within the specified range. This increase in fuel consumption underscores the inefficiencies introduced by cold weather, such as reduced battery efficiency and higher auxiliary power demands.
To analyze the energy consumption mathematically, we can express the all-electric range retention rate (R_retention) and fuel consumption growth rate (G_fuel) using the following formulas:
$$ R_{\text{retention}} = \frac{D_{\text{high-cold}}}{D_{\text{advertised}}} \times 100\% $$
where \( D_{\text{high-cold}} \) is the measured all-electric range in high-cold conditions and \( D_{\text{advertised}} \) is the advertised range under standard conditions. For our test, this gives:
$$ R_{\text{retention}} = \frac{53.0}{175} \times 100\% = 30.3\% $$
Similarly, the fuel consumption growth rate is calculated as:
$$ G_{\text{fuel}} = \left( \frac{C_{\text{high-cold}} – C_{\text{advertised}}}{C_{\text{advertised}}} \right) \times 100\% $$
where \( C_{\text{high-cold}} \) is the measured fuel consumption in high-cold conditions and \( C_{\text{advertised}} \) is the advertised consumption. Substituting the values:
$$ G_{\text{fuel}} = \left( \frac{9.0 – 7.4}{7.4} \right) \times 100\% = 21.6\% $$
These formulas provide a quantitative basis for comparing performance across different electric vehicle models and conditions, which is essential for standardizing evaluations in the China EV market.
Further analysis involved examining the impact of temperature on energy efficiency. We recorded temperature data throughout the tests, as summarized in Table 3. The conditioning phase had an average temperature of -23.5°C, while the all-electric range test averaged -19.8°C, and the charge-sustaining test averaged -18.4°C. These variations illustrate how even slight changes in ambient temperature can affect vehicle performance, emphasizing the need for robust thermal management systems in electric vehicles.
| Test Phase | Average Environmental Temperature (°C) | Internal Temperature Range (°C) |
|---|---|---|
| Conditioning | -23.5 | N/A |
| All-Electric Range Test | -19.8 | 20-24 |
| Charge-Sustaining Test | -18.4 | 20-24 |
In addition to temperature, other factors such as driving behavior and road surface conditions play a role in energy consumption. For instance, the snow-covered track used in our tests may have increased rolling resistance compared to dry pavement, contributing to higher energy usage. This aspect is particularly relevant for electric vehicles in China, where winter road maintenance varies by region. To account for this, we ensured that the test track met standardized compactness criteria, but future studies could explore public road testing to capture a wider range of scenarios.
The consistency and operability of our methodology were validated through repeated trials, showing minimal deviations in results. This reliability is crucial for industry applications, as it allows manufacturers to benchmark their electric vehicles against common standards. Moreover, our approach aligns with global trends in EV testing, while addressing specific challenges in high-cold environments. For example, the use of a driver assistance system ensured that the vehicle adhered to the test cycle within acceptable tolerances, reducing human error and enhancing data accuracy.
Looking ahead, we recommend expanding this research to include public road testing based on China-specific driving cycles. This would create a comprehensive testing framework combining closed-track and open-road evaluations, better reflecting real-world usage of electric vehicles. Such developments are vital for the continued growth of the China EV sector, as they provide consumers with reliable information on vehicle performance in diverse conditions. Additionally, incorporating advanced modeling techniques, such as machine learning for predicting energy consumption based on environmental factors, could further enhance the understanding of electric vehicle behavior in extreme climates.
In conclusion, our study demonstrates that range extended electric vehicles experience significant reductions in all-electric range and increases in fuel consumption under high-cold conditions. The developed testing method offers a practical and consistent approach for evaluating these effects, with potential applications in product development and industry assessments. As the electric vehicle market evolves, particularly in China, addressing cold-weather performance will be key to widespread adoption. We encourage ongoing research to refine these methods and explore innovative solutions for improving the resilience of electric vehicles in challenging environments.