The rapid development of the electric car market has brought the challenge of high energy consumption during high-speed driving into sharp focus. Range anxiety on highways remains a significant barrier to the wider adoption of electric vehicles. While daily commutes often involve lower speeds, statistics show that journeys with speeds exceeding 90 km/h can constitute up to 30% of total travel during holidays. A nominally 600 km range electric car can see its effective range drop to around 350 km when cruising at 120 km/h. This reduction is exacerbated by thermal management needs for the cabin and powertrain systems in extreme temperatures, where range retention rates can plummet to 50% in heat and 35% in cold conditions.
The energy consumption of an electric car at high speed is complex. Aerodynamic drag increases with the square of velocity, becoming the dominant resistance force. The Active Air Intake Grille System (AGS) is a key technology that can modulate front-end airflow to manage cooling demands. However, its operation presents a fundamental trade-off: opening the AGS reduces intake airflow resistance for cooling components but increases the vehicle’s aerodynamic drag coefficient (Cd). Conversely, keeping the AGS closed minimizes drag but may necessitate the use of electric cooling fans to pull air through restricted apertures, introducing significant low-voltage electrical load. This conflict between thermal management needs and drivetrain efficiency for range optimization is precisely the challenge this research addresses. Many current implementations prioritize thermal safety with less emphasis on systematic energy optimization. This study proposes a co-optimization strategy for the AGS and cooling fans to minimize the total system energy impact on the electric car’s range during high-speed operation.
1. Methodology and Experimental Design
The core objective was to quantify the individual and combined effects of AGS opening angle and cooling fan duty cycle on the energy consumption of an electric car. The methodology was bifurcated into two main experimental campaigns: one to measure the aerodynamic impact of the AGS and another to measure the electrical power consumption of the cooling fans under various operating conditions.
1.1 Aerodynamic Impact of AGS Opening
A full-scale wind tunnel test was conducted to establish the precise relationship between AGS opening angle and the vehicle’s drag coefficient. The test vehicle had a frontal projection area (A) of 2.8 m². Using the vehicle fixed method, tests were performed at a constant wind speed of 120 km/h. The AGS was adjusted to specific angles: 0°, 6°, 12°, 18°, 24°, 30°, 42°, 54°, 66°, and 90°. An ABA testing logic was employed, where the final test point repeated the initial condition to verify data repeatability and accuracy. The primary output was the drag coefficient (Cd) for each AGS configuration.
1.2 Cooling Fan Electrical Power Characterization
A separate test was performed in a vehicle environmental simulation chamber to map the power consumption of the front-end cooling fans. The test matrix spanned a comprehensive set of 185 operating points, combining different vehicle speeds, AGS angles, and fan duty cycles.
- Vehicle Speed: From 90 km/h to 120 km/h.
- AGS Angle: Varied in 6° increments from a minimum to fully open.
- Fan Duty Cycle: Varied in 20% increments from 0% to 100%.
At each steady-state operating point, the electrical power draw of the cooling fan assembly was measured accurately. The test procedure for mapping fan power is summarized in the following flowchart, with each condition stabilized for 20 seconds before measurement.
Process: Set vehicle speed & AGS angle -> Stabilize for 20s -> Set fan duty cycle -> Measure fan electrical power -> Repeat for all duty cycles -> Repeat for all AGS angles -> Repeat for all speeds.
2. Experimental Results and Data Analysis
2.1 Aerodynamic Test Results
The wind tunnel tests revealed a clear relationship between AGS opening and aerodynamic drag. The results are plotted below, showing the change in drag coefficient relative to the fully closed position.
The data indicates that for the specific electric car tested, the AGS assembly exhibited some deflection at high speed, resulting in negligible Cd difference between the 0° and 6° positions. From 6° to 66°, the relationship between AGS opening angle (α) and the increase in drag coefficient (ΔCd) was essentially linear. The derived relationship is:
$$ \Delta C_d = k_1 \cdot \alpha $$
where, for this vehicle, $k_1 \approx 3.33 \times 10^{-4}$ per degree ($0.002$ per 6°). Beyond 66°, further opening had minimal additional impact on the drag coefficient, suggesting the flow separation point was largely unchanged.
| AGS Opening Angle (α) | Δ Drag Coefficient (ΔCd) | Notes |
|---|---|---|
| 0° | 0.000 | Baseline (Closed) |
| 6° | ~0.000 | Similar to closed due to part deflection |
| 12° | 0.002 | |
| 18° | 0.004 | |
| 24° | 0.006 | Linear region |
| 30° | 0.008 | |
| 42° | 0.012 | |
| 54° | 0.016 | |
| 66° | 0.020 | End of linear region |
| 90° | ~0.020 | Minimal further increase |
2.2 Cooling Fan Power Consumption Results
The fan power tests yielded several key insights, visualized in the composite plot of fan power versus AGS angle for different fan duty cycles and speeds. The general trends are:
- Effect of AGS Opening: For a fixed fan duty cycle and vehicle speed, fan power consumption decreases as the AGS opens. A larger opening reduces airflow restriction (backpressure), allowing the fan to move air more easily.
- Effect of Vehicle Speed: For a fixed AGS angle and fan duty cycle, fan power consumption decreases as vehicle speed increases. The higher ram air pressure at speed assists the fan, reducing its workload.
- Non-linear Gradient: The power increase per fan duty cycle step is not constant. The gradient becomes significantly steeper for duty cycles above approximately 60%, especially when the AGS is only slightly open. For example, at 100% fan duty, the power difference between minimum and maximum AGS opening could exceed 120W.
| Vehicle Speed | AGS Angle | Cooling Fan Duty Cycle | ||||
|---|---|---|---|---|---|---|
| 20% | 40% | 60% | 80% | 100% | ||
| 90 km/h | 6° | 45 | 92 | 155 | 285 | 420 |
| 30° | 38 | 78 | 128 | 210 | 320 | |
| 90° | 32 | 65 | 105 | 165 | 250 | |
| 120 km/h | 6° | 30 | 62 | 105 | 195 | 310 |
| 30° | 26 | 53 | 88 | 145 | 230 | |
| 90° | 22 | 45 | 73 | 120 | 185 | |
2.3 Quantifying Impact on Electric Car Range
To enable a direct comparison between the two competing energy losses—increased drag from AGS opening vs. electrical load from fan operation—their impact on vehicle range was calculated. The following fundamental formulas were used.
Driving Energy Consumption (EC) and Range (R):
$$ EC = \frac{E}{D} \times 100 \quad \text{(kWh/100km)} $$
$$ R = \frac{UBE}{EC} \times 100 \quad \text{(km)} $$
where $E$ is energy consumed, $D$ is distance, and $UBE$ is usable battery energy.
Impact of Auxiliary Load (W) on range:
$$ EC_W = EC + \frac{W \cdot s}{D} \times 100 $$
$$ R_W = \frac{UBE}{EC_W} \times 100 $$
where $s$ is travel time in hours. For a constant speed $v$, $s = D/v$.
Aerodynamic Drag Power (P_aero):
$$ P_{aero} = \frac{1}{2} \cdot \rho \cdot C_d \cdot A \cdot v^3 $$
where $\rho$ is air density (~1.187 kg/m³), and $v$ is velocity.
The range sensitivity was calculated for a baseline electric car configuration. This quantifies how many kilometers of range are lost per unit increase in fan power or drag coefficient.
| Vehicle Speed | Sensitivity to Fan Power ($k_{2,fan}$) (km lost per 50W increase) |
Sensitivity to Drag Coefficient ($k_{2,Cd}$) (km lost per ΔCd=0.002 increase) |
|---|---|---|
| 90 km/h | 1.87 km | 2.25 km |
| 100 km/h | 1.27 km | 2.08 km |
| 110 km/h | 0.89 km | 1.91 km |
| 120 km/h | 0.64 km | 1.75 km |
These sensitivities reveal a crucial insight: at lower high-speeds (e.g., 90 km/h), both fan power and drag have a significant impact on the range of an electric car. As speed increases, the relative impact of fan power diminishes while drag remains very influential. The gradient analysis shows a distinct “knee point” for fan operation around 60% duty cycle, beyond which each incremental step consumes disproportionately more power.
3. Synthesis and Integrated Control Strategy Development
The experimental data provides the foundation for a co-optimized control strategy. The goal is to meet the cooling airflow demand ($Q_{req}$) for the electric car’s thermal management systems while minimizing the total range penalty. The total penalty ($\Delta R_{total}$) for a given state is the sum of penalties from AGS opening and fan operation:
$$ \Delta R_{total} = \Delta R_{Cd}(\alpha) + \Delta R_{fan}(v, \alpha, \delta) $$
where $\alpha$ is AGS angle and $\delta$ is fan duty cycle.
The strategy is based on comparing the range impact gradient of adjusting either actuator. The AGS angle was discretized into steps of 6°, and the fan duty cycle into steps of 20%. For a given required airflow, the controller evaluates whether meeting that demand by opening the AGS one more step or by increasing the fan duty cycle one more step results in a lower $\Delta R_{total}$.
A critical input is the actual airflow capability. For the tested electric car, the front-end airflow at 120 km/h for different AGS angles was characterized:
| AGS Opening Angle | Approximate Airflow (m³/h) |
|---|---|
| 6° | 650 |
| 12° | 985 |
| 18° | 1,320 |
| 24° | 1,650 |
| 30° | 1,990 |
| 60° | 3,280 |
| 90° | 3,620 |
Analysis showed that for common high-speed cooling demands (e.g., ~2000 m³/h for cabin cooling in 40°C ambient), an AGS opening of 30° could often be sufficient without any fan assistance, providing a favorable balance.
3.1 Proposed High-Speed Control Logic
Based on the gradient analysis and airflow data, a sequential, energy-optimal control logic for high-speed operation (v ≥ 90 km/h) is formulated:
- Initial State: Start with AGS at minimum open angle (e.g., 6°).
- Step 1 – Use Low-Power Fan Range: If cooling demand $Q_{req}$ is not met, increase the fan duty cycle progressively up to 60%. This leverages the lower energy gradient in this operating region.
- Step 2 – Moderate AGS Opening: If demand is still unmet after fan is at 60%, increase the AGS opening angle stepwise up to 30°. This provides significant extra airflow with a moderate, linear drag penalty.
- Step 3 – High-Power Fan Range: If demand remains unmet, increase the fan duty cycle from 60% to 100%. This is less efficient but necessary.
- Step 4 – Full AGS Opening: As a last resort, open the AGS beyond 30° to its maximum to meet extreme cooling demands.
This logic prioritizes operating the fan in its more efficient lower-duty region before incurring the permanent drag penalty of a larger AGS opening. It then uses a moderate AGS opening to avoid operating the fan in its very inefficient high-duty region for extended periods. The logic flow is illustrated below:
[Logical Flow: Cooling Demand -> AGS at Min (6°) -> Increase Fan to 60% -> If demand met, hold. If not -> Open AGS to 30% -> If demand met, hold. If not -> Increase Fan to 100% -> If demand met, hold. If not -> Open AGS to Max.]
4. Conclusion and Implications for Electric Car Development
This research demonstrates a systematic, data-driven approach to optimizing the energy consumption of ancillary systems in an electric car. By rigorously quantifying the range impact of both aerodynamic drag from the AGS and electrical load from the cooling fans, a co-optimized control strategy was developed. The key findings are:
- The relationship between AGS opening and drag coefficient is often linear within a middle range (e.g., 6° to 66° for the test vehicle), providing a predictable penalty per degree of opening.
- Cooling fan power consumption is highly non-linear, with a marked increase in the energy gradient beyond approximately 60% duty cycle, especially when intake restriction is high.
- Range sensitivity analysis provides the common currency ($\Delta R$) to compare these two disparate energy drains, enabling intelligent trade-off decisions.
- The proposed sequential control logic seeks the lowest-total-impact path to meet cooling demands, directly contributing to extended highway range for the electric car.
The specific numerical results and the derived “knee points” (e.g., 60% fan duty, 30° AGS angle) are vehicle-dependent. The primary contribution of this work is the methodology. For any electric car platform, applying this methodology—comprising wind tunnel testing for Cd(α), fan power mapping, and range sensitivity calculation—enables the derivation of an optimal, vehicle-specific control strategy. This moves beyond a safety-focused thermal management approach to a true energy-optimizing system. Implementing such cross-domain optimization strategies is essential for advancing the energy efficiency and alleviating the range anxiety associated with modern electric cars, particularly in demanding high-speed and extreme climate driving scenarios.