As a researcher focused on the advancement of battery electric cars, I have observed that the rapid growth of the electric vehicle market has brought to light critical challenges, particularly the significant reduction in driving range under low-temperature conditions. This issue severely impacts user experience and adoption, especially in colder regions. In this article, I will share my insights and findings from a detailed study aimed at analyzing the low-temperature driving range capabilities of battery electric cars, proposing an evaluation model based on the “shortening method,” and exploring the key influences of air conditioning systems on energy consumption. The goal is to provide a robust framework for predicting and optimizing the performance of battery electric cars in cold environments, thereby supporting industry standards and technological improvements.
The proliferation of battery electric cars is a cornerstone of global efforts to achieve carbon neutrality. However, the substantial decline in driving range during winter months has emerged as a major bottleneck. Factors such as increased battery internal resistance, reduced electrochemical activity, and heightened energy demands from heating systems contribute to this problem. Traditional full-cycle vehicle testing methods are often costly, time-consuming, and resource-intensive, hindering rapid iteration and large-scale efficiency evaluations for new models. Therefore, there is an urgent need to develop simplified, efficient, and repeatable assessment techniques. The “shortening method,” which estimates whole-vehicle low-temperature range performance using partial test data, offers promising potential. This approach aligns with industry trends, such as China’s “Technology Roadmap 2.0 for Energy-Saving and New Energy Vehicles,” which targets a 70% retention rate for low-temperature driving range by 2025, and upcoming regulations like GB 22757 (draft) that may mandate the disclosure of high- and low-temperature range data. My research seeks to address these gaps by integrating data-driven models with multi-factor optimization to enhance prediction accuracy and support the development of energy-efficient thermal management systems for battery electric cars.
To systematically evaluate the low-temperature driving range of battery electric cars, I designed and conducted experiments using five representative models available in the market. These battery electric cars were selected to cover variations in key parameters such as air conditioning type, battery capacity, and curb weight, ensuring a comprehensive analysis. The details of the sample vehicles are summarized in Table 1.
| Vehicle ID | Air Conditioning Type | Battery Capacity (kWh) | Curb Mass (kg) | Normal-Temperature CLTC-P Range (km) |
|---|---|---|---|---|
| A | PTC Heater | 65 | 1850 | 420 |
| B | Heat Pump | 72 | 1980 | 480 |
| C | PTC Heater | 58 | 1720 | 380 |
| D | Heat Pump | 70 | 1900 | 460 |
| E | PTC Heater | 60 | 1800 | 400 |
The testing was performed in a controlled environmental chamber, with conditions set to normal temperature (25°C) and low temperature (-10°C). A standard CLTC-P cycle was employed, with each test run lasting 30 minutes, resulting in an approximate state-of-charge (SOC) drop of 10%. Three repetitions were conducted per condition to ensure reliability, and data parameters—including SOC, drive power, air conditioning energy consumption, and battery temperature—were recorded at a frequency of 10 Hz to capture high-frequency variations. This setup allowed for a precise comparison of energy consumption under different thermal environments.
The experimental data revealed a marked increase in energy consumption per unit time under low-temperature conditions. For instance, in battery electric car A equipped with a PTC heater, the average energy consumption rose by 62.7%, highlighting the adverse impact of cold weather on the efficiency of battery electric cars. In contrast, battery electric cars B and D, which utilized heat pump systems, showed smaller increases of 53.3% and 56.3%, respectively. This underscores the energy-saving advantages of heat pump technology in battery electric cars. The data further indicated that battery electric car C, with a smaller battery capacity (58 kWh) and less advanced thermal management, experienced the most significant energy consumption fluctuations, with a standard deviation of ±0.04, suggesting lower stability. Conversely, battery electric car B demonstrated the best error control, with a standard deviation of only ±0.02, showcasing superior system adaptability. These findings emphasize that air conditioning type, battery capacity, and thermal management capabilities are critical determinants of energy performance in battery electric cars under low temperatures.
Building on this data, I developed a prediction model based on the “shortening method” to estimate the low-temperature driving range of battery electric cars. The core idea is to use short-duration test data to extrapolate full-cycle performance, reducing the need for extensive testing. The model incorporates multiple weighting factors to account for key influences. The general form of the prediction equation can be expressed as:
$$ R_{\text{low}} = R_{\text{norm}} \times \left(1 – \frac{E_{\text{low}} – E_{\text{norm}}}{E_{\text{norm}}} \times \sum_{i=1}^{n} w_i \cdot f_i \right) $$
where \( R_{\text{low}} \) is the predicted low-temperature driving range, \( R_{\text{norm}} \) is the normal-temperature range, \( E_{\text{low}} \) and \( E_{\text{norm}} \) are the energy consumption values under low and normal temperatures, \( w_i \) are weight coefficients, and \( f_i \) are factors such as air conditioning type, battery capacity, curb mass, and thermal control strategy. To optimize accuracy, I compared three weighting models: a fixed-weight model, a time-varying energy consumption model, and a multi-factor regression model. The performance of these models in terms of prediction error rates is summarized in Table 2.
| Vehicle ID | Air Conditioning Type | Fixed-Weight Model Error Rate (%) | Time-Varying Model Error Rate (%) | Multi-Factor Regression Model Error Rate (%) |
|---|---|---|---|---|
| A | PTC Heater | 8.2 | 6.5 | 3.8 |
| B | Heat Pump | 4.1 | 3.7 | 2.9 |
| C | PTC Heater | 9.1 | 7.8 | 3.9 |
| D | Heat Pump | 4.5 | 4.0 | 3.2 |
| E | PTC Heater | 8.7 | 7.2 | 3.6 |
The multi-factor regression model, which integrates four independent weights (air conditioning type, battery capacity, curb mass, and battery thermal control strategy) and two average weights (drive system efficiency and environmental interaction factor), achieved the lowest average prediction error of 3.8% with a small dispersion (±1.2%). This model effectively captures nonlinear couplings between variables and demonstrates strong generalization capabilities for battery electric cars. In contrast, the fixed-weight model showed higher errors, particularly for PTC-equipped battery electric cars like C and E, with errors up to 9.1%. The time-varying model improved overall accuracy but remained sensitive to operational fluctuations. These results validate the feasibility of the “shortening method” for predicting low-temperature driving range in battery electric cars, especially for those with heat pump systems, where prediction accuracy is significantly better than for PTC heating systems.
From an engineering perspective, the “shortening method” offers a lightweight and efficient assessment tool for battery electric cars. Based on my analysis, I recommend that manufacturers prioritize the adoption of heat pump air conditioning systems in battery electric cars, particularly for models intended for cold climates, as they substantially reduce energy consumption and prediction errors. Additionally, enhancing the responsiveness of battery thermal management systems in battery electric cars can maintain optimal operating temperatures, mitigating the impact of low-temperature fluctuations on energy use and prediction precision. The “4+2” weighted model (four independent and two average factors) should be incorporated into corporate prediction standards, enabling rapid range estimation during early development stages when testing resources are limited. This approach not only accelerates the design cycle for battery electric cars but also supports the establishment of industry-wide standardized evaluation methods.
Beyond range prediction, evaluating the energy efficiency of air conditioning systems in battery electric cars is crucial for holistic thermal management. Current standards, such as GB/T 40711.3-2021, include qualitative descriptions for low-temperature air conditioning energy saving but lack quantifiable metrics. To address this, I propose a comprehensive evaluation framework with three core indicators tailored for battery electric cars.
First, the Energy Consumption per Unit Heat (ECUH), measured in Wh/kcal, reflects the electricity consumed by the air conditioning system to provide each kilocalorie of heat. A lower value indicates higher heating efficiency. For battery electric cars, this metric directly relates to range preservation. The ECUH can be calculated as:
$$ \text{ECUH} = \frac{E_{\text{AC}}}{Q_{\text{heat}}} $$
where \( E_{\text{AC}} \) is the energy consumption of the air conditioning system (in Wh) and \( Q_{\text{heat}} \) is the heat output (in kcal).
Second, the Coefficient of Performance (COP) evaluates the efficiency of the air conditioning system by comparing heat output to energy input. For battery electric cars, a higher COP signifies better performance. To facilitate comparison across different heating technologies (e.g., heat pump vs. PTC), an electrical-equivalent COP can be used:
$$ \text{COP}_{\text{equiv}} = \frac{Q_{\text{heat}}}{E_{\text{AC}} \times \eta_{\text{conv}}} $$
where \( \eta_{\text{conv}} \) is a conversion factor accounting for electrical-to-thermal efficiency. In battery electric cars with heat pumps, COP values typically exceed 2.0 in mild cold conditions, whereas PTC systems often have COP values near 1.0, highlighting the superiority of heat pumps for battery electric cars.
Third, the Range Consumption Impact (RCI) index combines the power consumption share of the air conditioning system and its effect on driving range loss. It provides a holistic measure of how much the air conditioning system degrades the range of battery electric cars. The RCI index is defined as:
$$ \text{RCI} = w_1 \cdot \frac{P_{\text{AC}}}{P_{\text{total}}} + w_2 \cdot \frac{R_{\text{norm}} – R_{\text{low}}}{R_{\text{norm}}} $$
where \( P_{\text{AC}} \) is the air conditioning power, \( P_{\text{total}} \) is the total vehicle power, \( R_{\text{norm}} \) and \( R_{\text{low}} \) are the normal- and low-temperature ranges, and \( w_1 \) and \( w_2 \) are weighting factors (e.g., set to 0.6 and 0.4 based on empirical data). A lower RCI value indicates better energy-saving performance for battery electric cars.
Based on these indicators, I suggest a preliminary energy efficiency grading system for air conditioning systems in battery electric cars, as outlined in Table 3. This system aims to standardize evaluations and guide technological improvements.
| Efficiency Grade | ECUH Range (Wh/kcal) | Typical COP Range | RCI Index Range | Suitability for Battery Electric Cars |
|---|---|---|---|---|
| Grade I (High Efficiency) | ≤ 0.25 | > 2.5 | ≤ 0.15 | Premium models with advanced heat pumps |
| Grade II (Medium Efficiency) | 0.26 – 0.35 | 1.5 – 2.5 | 0.16 – 0.30 | Mainstream battery electric cars |
| Grade III (Low Efficiency) | > 0.35 | < 1.5 | > 0.30 | Basic models or those with inefficient PTC heaters |
This evaluation framework is not only applicable to air conditioning systems but also extensible to other thermal management components in battery electric cars, such as battery temperature control and motor cooling. By integrating these assessments, a full-vehicle thermal management efficiency profile can be developed, supporting the advancement of “smart thermal management systems” that optimize energy use and comfort in battery electric cars. Furthermore, as on-road consumption testing (OCT) protocols evolve, this method can serve as a foundation for broader energy efficiency certifications, promoting continuous innovation in battery electric cars.
In conclusion, my research demonstrates that the low-temperature driving range of battery electric cars is significantly affected by battery performance degradation and high energy consumption from air conditioning systems, with PTC heating contributing up to 45% of total vehicle energy use in cold conditions. The “shortening method,” enhanced by a multi-factor regression model, provides a reliable and efficient means of predicting range loss in battery electric cars, achieving an average error below 4% for heat pump-equipped models. The proposed energy efficiency evaluation metrics—ECUH, COP, and RCI—offer quantifiable tools for grading and improving air conditioning systems in battery electric cars. These findings underscore the importance of adopting heat pump technology and robust thermal management strategies in battery electric cars to alleviate range anxiety in winter. I recommend that automakers focus on optimizing thermal system designs for battery electric cars, standards bodies accelerate the development of quantifiable efficiency指标体系, and industry stakeholders support the standardization of rapid assessment methods like the “shortening method.” By doing so, we can enhance the real-world performance and sustainability of battery electric cars, driving forward the transition to low-carbon transportation. Future work should explore the integration of machine learning algorithms with real-time data from battery electric cars to further refine prediction models and enable adaptive thermal control, ultimately maximizing energy efficiency and user satisfaction in diverse climatic conditions.