As an emerging technology in the automotive industry, electric vehicles have gained significant traction globally, with China EV manufacturers leading innovations in powertrain systems. The dual-motor electric vehicle, featuring independent front and rear axle drives, offers enhanced power distribution and stability. However, maintaining optimal traction on varying road surfaces remains a critical challenge. This article explores a drive anti-slip control strategy for dual-motor electric vehicles, leveraging fuzzy logic for road identification and sliding mode control for real-time torque adjustment. By integrating these methods, the system ensures maximum longitudinal force and lateral stability, addressing common issues like wheel spin on low-adhesion surfaces. The approach is validated through co-simulation using Simulink and Carsim, demonstrating improved acceleration and handling in diverse scenarios. With the rapid growth of China EV markets, such advancements are pivotal for enhancing safety and performance in electric vehicle fleets.
The structure of a dual-motor electric vehicle typically involves two independent motors driving the front and rear axles, allowing for flexible power allocation. This configuration is common in China EV designs, as it optimizes weight distribution and torque response. For instance, the vehicle model referenced here employs two permanent magnet synchronous motors with distinct specifications to cater to different driving demands. The front motor might have a peak power of 80 kW and peak torque of 160 Nm, while the rear motor could offer 150 kW and 310 Nm, ensuring balanced performance during acceleration and cruising. A schematic representation of this setup highlights the integration of motors with reduction gears, enabling efficient power transmission without complex gearboxes. This simplicity is a hallmark of electric vehicle designs, reducing maintenance and improving reliability.
To quantify the vehicle parameters, consider the following table summarizing key specifications for the dual-motor electric vehicle. This includes motor ratings,整车质量, and传动比details, which are essential for subsequent control strategies. The use of such parameters ensures that the electric vehicle model accurately reflects real-world China EV applications, facilitating realistic simulations.
| Parameter | Rated Voltage (V) | Rated/Peak Power (kW) | Rated/Peak Torque (Nm) | Rated/Peak Speed (rpm) |
|---|---|---|---|---|
| Motor 1 | 320 | 35/80 | 70/160 | 4600/13500 |
| Motor 2 | 320 | 70/150 | 145/310 | 4600/16000 |
Additionally, the整车质量is estimated at 2250 kg, with a tire rolling radius of 0.337 m. The front and rear axle reduction ratios are set at 9.95 and 11.5, respectively, to match the power characteristics of the motors. These values are typical in China EV designs, where optimizing energy efficiency and dynamic response is paramount.
The fundamental principle of acceleration slip regulation (ASR) revolves around wheel dynamics and adhesion. For a driven wheel, the torque balance equation can be expressed as:
$$ I_{\omega} \frac{d\omega}{dt} = T_d – F_d \cdot r $$
where \( I_{\omega} \) is the wheel moment of inertia, \( \omega \) is the angular velocity, \( T_d \) is the drive torque, \( F_d \) is the longitudinal force, and \( r \) is the wheel radius. The utilization adhesion coefficient \( \mu \) is defined as the ratio of longitudinal force to vertical load:
$$ \mu = \frac{F_d}{F_z} $$
To account for load transfer during acceleration or cornering, the vertical loads on each wheel can be calculated using:
$$ F_{z,F,l} = \frac{mgb}{2L} – \frac{m a_x h}{2L} – \frac{m a_y h b}{d L} $$
$$ F_{z,F,r} = \frac{mgb}{2L} – \frac{m a_x h}{2L} + \frac{m a_y h b}{d L} $$
$$ F_{z,R,l} = \frac{mga}{2L} + \frac{m a_x h}{2L} – \frac{m a_y h a}{d L} $$
$$ F_{z,R,r} = \frac{mga}{2L} + \frac{m a_x h}{2L} + \frac{m a_y h a}{d L} $$
Here, \( m \) is the vehicle mass, \( g \) is gravity, \( a \) and \( b \) are distances from the center of gravity to the front and rear axles, \( L \) is the wheelbase, \( h \) is the height of the center of gravity, \( d \) is the track width, and \( a_x \) and \( a_y \) are longitudinal and lateral accelerations. These equations ensure accurate adhesion estimation, which is crucial for electric vehicle stability.
Wheel slip rate \( s \) is a key parameter in ASR, defined as:
$$ s = \frac{\omega r – v_x}{\omega r} \times 100\% $$
where \( v_x \) is the longitudinal vehicle velocity. The relationship between slip rate and adhesion coefficient varies with road conditions. On dry asphalt, the peak adhesion coefficient \( \mu_p \) can reach 1.17 at an optimal slip rate \( s_b \) of 0.17, while on ice, \( \mu_p \) drops to 0.05 at \( s_b = 0.03 \). Controlling slip rate near \( s_b \) maximizes traction and minimizes lateral instability, a common focus in China EV safety systems.
Road identification using fuzzy control involves estimating the maximum adhesion coefficient and optimal slip rate in real-time. The Burckhardt model describes the adhesion-slip relationship:
$$ \mu(s) = c_1 (1 – e^{-c_2 s}) – c_3 s $$
where \( c_1 \), \( c_2 \), and \( c_3 \) are road-specific parameters. The optimal slip rate and maximum adhesion coefficient are derived as:
$$ s_b = \frac{1}{c_2} \ln \left( \frac{c_1 c_2}{c_3} \right) $$
$$ \mu_p = c_1 \left( 1 – \frac{c_3}{c_1 c_2} (1 + \ln \left( \frac{c_1 c_2}{c_3} \right) \right) $$
A table of parameters for standard road surfaces aids in fuzzy inference:
| Road Surface | c1 | c2 | c3 | s_b | μ_p |
|---|---|---|---|---|---|
| Dry Asphalt | 1.28 | 23.99 | 0.52 | 0.17 | 1.17 |
| Dry Concrete | 1.19 | 25.16 | 0.537 | 0.16 | 1.09 |
| Wet Asphalt | 0.85 | 33.82 | 0.347 | 0.13 | 0.801 |
| Cobblestone | 0.40 | 33.70 | 0.12 | 0.14 | 0.34 |
| Snow | 0.19 | 94.12 | 0.064 | 0.06 | 0.19 |
| Ice | 0.05 | 306.3 | 0.001 | 0.03 | 0.05 |
The fuzzy controller uses inputs like identified maximum adhesion coefficient and actual slip rate, with membership functions for模糊化. Fuzzy rules correlate these inputs to similarity weights for each road type. For example, if the adhesion coefficient is low and slip rate is small, the surface is highly similar to ice. Outputs are defuzzified to obtain the current \( \mu_p \) and \( s_b \) through weighted averages:
$$ \mu_p = \frac{\sum_{i=1}^{6} x_i \mu_{p,i}}{\sum_{i=1}^{6} x_i} $$
$$ s_b = \frac{\sum_{i=1}^{6} x_i s_{b,i}}{\sum_{i=1}^{6} x_i} $$
where \( x_i \) are similarity weights. This method ensures robust road adaptation for electric vehicles, particularly in variable China EV operating environments.
The ASR control strategy employs sliding mode control to regulate motor torque based on slip rate error. The sliding surface is defined as \( \sigma = s – s_b \), and the reaching law uses an exponential approach with a saturation function to reduce chattering:
$$ \dot{\sigma} = -\epsilon \text{sat}(\sigma) – k \sigma $$
where \( \epsilon \) and \( k \) are control gains, and sat(σ) is the saturation function. The control torque \( T_{\text{SMC}} \) is derived from the wheel dynamics equation:
$$ T_{\text{SMC}} = I_{\omega} \left( \frac{s_b \dot{v_x}}{r} + \frac{v_x \dot{s_b}}{r} \right) + r F_d – I_{\omega} \frac{\dot{v_x}}{r} + I_{\omega} \frac{v_x}{r} \left( \epsilon \text{sat}(\sigma) + k \sigma \right) $$
This torque replaces the driver’s demand when the slip rate exceeds \( s_b \), ensuring optimal traction. The ASR system disengages when the driver’s torque request is lower, prioritizing energy efficiency in electric vehicle operation.
Simulation results validate the control strategy under various conditions. On low-adhesion surfaces like snow, the ASR system maintains slip rate near \( s_b \), improving acceleration and distance covered compared to uncontrolled cases. For instance, over a fixed time, the controlled electric vehicle achieves speeds up to 48 kph and covers 44 m, versus 44 kph and 41 m without control. This demonstrates a 9% improvement in speed and 7.4% in distance, highlighting the benefits for China EV performance in adverse weather.
In split-μ scenarios, where left and right wheels encounter different surfaces, the ASR applies a low-select principle, controlling torque based on the lower-adhesion side. This minimizes lateral deviation and steering correction. For example, the maximum lateral displacement and steering wheel angle are reduced to 0.018 m and 16 degrees, compared to 0.045 m and 110 degrees without ASR. Such enhancements are crucial for electric vehicle stability, especially in China EV applications where road conditions can vary abruptly.
Lane change maneuvers on low-adhesion surfaces further illustrate the system’s efficacy. With ASR, the electric vehicle closely follows the desired path, with a maximum deviation of 0.29 m and steering angle of 112 degrees. Without control, the vehicle loses stability, underscoring the importance of real-time slip regulation. These findings align with the growing emphasis on safety in the electric vehicle sector, particularly as China EV manufacturers integrate advanced control systems.
In conclusion, the integration of fuzzy-based road identification and sliding mode control for ASR in dual-motor electric vehicles significantly enhances traction and stability. Simulations confirm reductions in slip rate errors under 7%, with notable improvements in acceleration and lateral control. As the electric vehicle industry evolves, such strategies will be instrumental in advancing China EV technologies, ensuring reliable performance across diverse driving conditions. Future work could explore adaptive gains and machine learning for even finer control, paving the way for smarter electric vehicle systems.