With the rapid advancement of the electric car industry, reducing energy consumption has become a critical goal for manufacturers worldwide. In China EV markets, the focus on enhancing vehicle economy is paramount, as it directly impacts consumer adoption and environmental sustainability. This study explores internal resistance testing methods for electric cars, aiming to improve the efficiency of整车 economy evaluations. Internal resistance, a component of total vehicle resistance, significantly affects energy consumption during operation. By developing a reliable testing approach, we can identify and mitigate excessive internal resistance, thereby optimizing the performance of China EV models.
Total vehicle resistance consists of aerodynamic drag, rolling resistance, and internal resistance. Aerodynamic drag, or wind resistance, is influenced by vehicle design elements such as the shape of the body, intake grille, and underbody. For electric cars, minimizing wind resistance is crucial for high-speed efficiency, as it can dominate energy losses at velocities above 80 km/h. The formula for aerodynamic drag is given by: $$ D = \frac{1}{2} \times \rho \times V^2 \times A \times C_d $$ where D is the air resistance force, ρ is the air density, V is the vehicle speed, A is the frontal area, and C_d is the drag coefficient. In typical China EV designs, optimizing these parameters can lead to substantial energy savings. For instance, a lightweight van with a C_d of 0.35 and A of 4.2 m² experiences air resistance that escalates rapidly with speed, as shown in the analysis below.
| Speed (km/h) | Air Density (kg/m³) | Frontal Area (m²) | Drag Coefficient | Air Resistance (N) |
|---|---|---|---|---|
| 50 | 1.204 | 4.2 | 0.35 | ~150 |
| 80 | 1.204 | 4.2 | 0.35 | ~400 |
| 100 | 1.204 | 4.2 | 0.35 | ~600 |
Rolling resistance, another key factor, arises from the interaction between tires and the road surface. It is particularly significant at lower speeds, below 60 km/h, and depends on tire construction, material, and inflation pressure. The formula for rolling resistance is: $$ F = k \times N $$ where F is the rolling friction force, k is the rolling resistance coefficient, and N is the normal force. In China EV applications, selecting low-rolling-resistance tires can reduce energy consumption, but it often involves trade-offs with grip and wet-road performance. The following table compares rolling resistance coefficients across different tire brands commonly used in electric cars.
| Tire Brand | Rolling Resistance Coefficient (N/kg) | Typical Application |
|---|---|---|
| Brand A | 6.5 | High-efficiency China EV |
| Brand B | 7.0 | Standard electric car |
| Brand C | 7.5 | Performance models |
Internal resistance, the focus of this study, comprises three main components: motor drag torque, brake caliper drag torque, and wheel bearing friction torque. For electric cars, internal resistance can account for a notable portion of total energy losses, especially during coasting or low-speed operations. Motor drag torque, T_d, is influenced by electromagnetic effects and can be expressed as: $$ T_d = K \times \phi \times I $$ where K is a constant, φ is the magnetic flux, and I is the phase current. This torque varies with motor output and operational conditions. Brake caliper drag torque results from incomplete retraction of brake pads after application, leading to persistent friction. Wheel bearing friction torque, M, is given by: $$ M = M_0 + M_1 $$ where M_0 is the viscosity-related moment and M_1 is the load-dependent moment. In China EV designs, these components are often optimized to minimize internal resistance, as even small reductions can enhance overall economy.
To accurately measure internal resistance in electric cars, we developed a testing method using specialized equipment, such as torque wrenches, under controlled environmental conditions. The test setup involves lifting the vehicle to ensure wheels are free-rotating, with the transmission in neutral and parking brake disengaged. Pre-lubrication is performed by rotating wheels at least 10 times before measurements. For each wheel, torque is recorded during three consecutive rotations in the forward direction, and the process is repeated for driven wheels with the opposite wheel fixed to account for drivetrain effects. This approach allows for a detailed assessment of internal resistance without requiring complex machinery like dynamometers, making it highly applicable for China EV development phases.
The data processing involves calculating internal resistance forces from the torque measurements. For non-driven wheels, the internal resistance force F_non_driven is derived as: $$ F_{\text{non\_driven}} = \frac{T_{\text{non\_driven}}}{R} $$ where T_non_driven is the sum of torques from all non-driven wheels, and R is the wheel rolling radius. For driven wheels, the internal resistance force F_driven is computed using: $$ F_{\text{driven}} = \frac{(T_{\text{driven, fixed}} + T_{\text{driven, free}} / 2)}{2 \times R} $$ where T_driven, fixed is the torque sum with opposite wheels fixed, and T_driven, free is the torque sum without fixation. Total internal resistance F_total is then: $$ F_{\text{total}} = F_{\text{non\_driven}} + F_{\text{driven}} $$ This method was applied to a developing electric car model and compared with a benchmark China EV to identify discrepancies.
| Wheel Position | Developing Model Torque (N·m) | Benchmark Model Torque (N·m) | Difference (N·m) |
|---|---|---|---|
| Front Left | 3.6 | 0.7 | 2.9 |
| Front Right | 2.2 | 1.0 | 1.2 |
| Rear Left | 6.7 | 3.3 | 3.4 |
| Rear Right | 2.9 | 2.0 | 0.9 |
| Driven Wheels (Fixed) | 11.4 | 6.5 | 4.9 |
Analysis of the results revealed that the developing electric car had an internal resistance approximately 8.9 N·m higher than the benchmark China EV, equivalent to a wheel force difference of 28.1 N. This excess was primarily attributed to brake caliper drag and wheel bearing friction, as indicated by consistent discrepancies across both driven and non-driven wheels. Further investigation identified non-standard brake pads in the test vehicle as the root cause. After replacing them with production-specification components, internal resistance decreased significantly, aligning with the benchmark model. This adjustment led to a 25.5 N reduction in total road coasting resistance, validating the testing method and enabling subsequent economy evaluations for the electric car.
To optimize internal resistance in electric cars, several measures can be implemented. For brake caliper drag, standardizing preload forces and adjusting clearances can reduce residual friction. Wheel bearing friction can be minimized through seal optimization and precise bearing fits. Additionally, improving electromagnetic compatibility in motors and managing operational loads can lower motor drag torque. These enhancements are essential for advancing China EV technologies, as they contribute directly to energy efficiency and reduced emissions. The testing method described here provides a practical tool for ongoing development, though it may have limitations in accuracy due to differences between test and real-world conditions.
In conclusion, this study presents an effective internal resistance testing method for electric cars, emphasizing its application in China EV contexts. By decomposing total resistance and employing straightforward equipment, we can identify and address inefficiencies early in the design phase. The results demonstrate that targeted optimizations, such as brake and bearing adjustments, can substantially lower energy consumption, supporting the broader goals of economic and environmental sustainability in the electric car industry. Future work could integrate dynamic testing scenarios to improve precision, further solidifying the role of internal resistance management in the evolution of electric cars.