As an essential component of sustainable transportation, electric cars have garnered widespread attention in research and development. Among the core technologies, the braking system plays a critical role in ensuring driving safety and enhancing energy efficiency. In recent years, electro-hydraulic composite braking systems have emerged as a novel solution in the electric car domain, leveraging advantages such as improved braking efficiency and energy recovery. These systems integrate regenerative braking from electric motors with traditional hydraulic braking, optimizing brake force distribution and enabling efficient energy recuperation. However, the complex coupling between the drive motor, battery, and braking system in electric cars poses challenges for automated control, necessitating advanced strategies to ensure safety, stability, and efficiency. This article addresses these challenges by proposing an automated control method for electro-hydraulic composite braking in electric cars, with a focus on China EV advancements. Through detailed modeling, energy recovery mechanisms, and coordinated control strategies, the method aims to enhance performance and contribute to the evolution of China EV technologies.
The electro-hydraulic composite braking system combines regenerative braking, where the motor generates braking torque to recover energy, and hydraulic braking, which relies on friction for reliable stopping power. Despite its potential, issues like uneven distribution of electric and hydraulic braking forces can lead to extended braking distances, undermining high-performance driving demands. To overcome this, I developed an automated control approach that analyzes working principles, establishes comprehensive models, and implements real-time optimization. By perceiving driver intent and vehicle states, the method dynamically allocates braking forces, facilitating efficient energy recovery and automated control. This not only shortens braking distances but also improves safety and comfort, aligning with the growing emphasis on China EV innovation.
To deeply understand the operational principles and performance characteristics of the electro-hydraulic composite braking system in electric cars, I first established a precise model. This model integrates vehicle dynamics and hydraulic system dynamics, providing a foundation for control strategy design. The vehicle dynamics model focuses on the longitudinal behavior, assuming simplified conditions such as ignoring suspension effects, straight and smooth road surfaces, and negligible wind and air resistance. A two-wheel vehicle dynamics model is adopted to describe the relationships between forces and motion.
The longitudinal dynamics model expresses the change in vehicle speed relative to applied forces, as shown in Equation (1):
$$ M \cdot \frac{dv}{dt} = F_p – F_b – F_r – F_d $$
where \( M \) is the total vehicle mass, \( v \) is the longitudinal velocity, \( F_p \) is the driving force, \( F_b \) is the total braking force, \( F_r \) is the rolling resistance, and \( F_d \) is the air resistance. The distribution of braking forces between the front and rear wheels is given by:
$$ F_b = F_{wf} + F_{wr} $$
where \( F_{wf} \) is the ground braking force on the front wheel and \( F_{wr} \) is the ground braking force on the rear wheel. The wheel dynamics equation describes the relationship between wheel angular velocity and torque:
$$ I \cdot \frac{d\omega}{dt} = T_{wi} – F_{wi} \times R $$
where \( I \) is the wheel’s moment of inertia, \( \omega \) is the angular velocity, \( T_{wi} \) is the sum of electric and hydraulic braking torques, \( F_{wi} \) is the ground braking force on a single wheel, and \( R \) is the effective rolling radius. During braking, the normal forces on the front and rear wheels redistribute based on the center of mass position, wheelbase, and braking intensity, as follows:
$$
\begin{align*}
F_{zf} &= \frac{b \cdot M \cdot g}{L} – \frac{h_g \cdot M \cdot a_x}{L} \\
F_{zr} &= \frac{a \cdot M \cdot g}{L} + \frac{h_g \cdot M \cdot a_x}{L}
\end{align*}
$$
where \( F_{zf} \) is the normal force on the front wheel, \( F_{zr} \) is the normal force on the rear wheel, \( a \) is the distance from the center of mass to the front axle, \( b \) is the distance to the rear axle, \( L \) is the wheelbase (\( L = a + b \)), \( g \) is gravitational acceleration, \( h_g \) is the height of the center of mass, and \( a_x \) is the longitudinal acceleration.
The dynamic characteristics of the hydraulic braking system are crucial for braking performance. By controlling the switch states of solenoid valves, pressure increase and decrease operations are achieved. The dynamics can be represented as:
$$
\begin{align*}
\frac{dp_c}{dt} &= \frac{1}{C \cdot (R_p + R_s)} \cdot (u_p – p_c) \\
\frac{dp_c}{dt} &= \frac{1}{C \cdot (R_r + R_s)} \cdot (p_r – u_r – p_c)
\end{align*}
$$
where \( p_c \) is the brake cylinder pressure, \( C \) is the equivalent hydraulic capacitance, \( R_p \) is the equivalent hydraulic resistance during pressure increase, \( R_r \) is the equivalent resistance during decrease, \( R_s \) is the system hydraulic resistance, \( u_p \) is the pressure increase control signal, \( u_r \) is the pressure decrease control signal, and \( p_r \) is the pressure in the brake fluid reservoir. The hydraulic braking force is expressed as:
$$ F_h = f_p(p_c, R) $$
Integrating the vehicle dynamics and hydraulic system dynamics, the electro-hydraulic composite braking model for electric cars is formulated as:
$$ F_t = F_h + F_e $$
where \( F_e \) is the electric braking force. This model enables a holistic analysis of braking behavior, essential for developing effective control strategies in China EV applications.
To enhance energy efficiency and extend the driving range of electric cars, I designed a braking energy recovery mechanism. This system converts kinetic energy into electrical energy during braking, storing it back into the battery. A simplified motor model, represented as a first-order inertial element, describes the relationship between the target and actual braking torques:
$$ \tau \frac{dz_h}{dt} + z_h = z_{h_t} $$
where \( \tau \) is the motor response time constant, \( z_h \) is the actual motor speed, \( \frac{dz_h}{dt} \) is the rate of change of motor speed, and \( z_{h_t} \) is the target speed. The energy recovery strategy accounts for battery state, motor characteristics, vehicle speed, and braking intensity. For instance, when the state of charge (SOC) is high, energy recovery is reduced to prevent overcharging; when SOC is low, recovery is maximized to improve range. The efficiency of the regenerative braking system is measured by:
$$ \eta = \frac{E_r}{E_l} $$
where \( \eta \) is the energy recovery efficiency, \( E_r \) is the recovered electrical energy, and \( E_l \) is the reduction in vehicle kinetic energy during braking. \( E_l \) is calculated from the vehicle dynamics model:
$$ E_l = \frac{1}{2} M (v_0^2 – v_{\text{end}}^2) $$
where \( v_0 \) is the initial braking speed and \( v_{\text{end}} \) is the speed at the end of braking. The recovered energy \( E_r \) depends on the battery’s charging nominal voltage and the recovered charge:
$$ E_r = U_c \times Q_r $$
where \( U_c \) is the charging nominal voltage and \( Q_r \) is the charge recovered during braking. This mechanism underscores the potential of electric cars, particularly in China EV markets, to achieve sustainability through intelligent energy management.
In the electro-hydraulic composite braking system for electric cars, coordinated control between electric and hydraulic braking is vital for optimal performance and energy recovery. I devised an integrated automated control method that optimizes brake force distribution based on real-time driver intent and vehicle state. When the driver presses the brake pedal, sensors collect data on vehicle speed and pedal position, which are analyzed by the vehicle control unit to determine braking demand. The braking intensity is computed as:
$$ z = \frac{a}{g} $$
where \( a \) is the measured deceleration. To address varying braking intensities, multiple operational modes are designed. In emergency braking scenarios, regenerative braking is disabled to ensure prompt and safe stopping via hydraulic brakes alone. A braking intensity threshold \( z_t \) is set; if exceeded, the system switches to pure mechanical hydraulic braking. For non-emergency cases, the automated coordination strategy is more refined. Based on SOC levels, protective measures are implemented: at high SOC, hydraulic braking predominates to avoid overcharging; at moderate SOC, electric and hydraulic forces are allocated according to braking intensity. At low intensities, regenerative braking is prioritized for energy recovery, while hydraulic braking gradually engages as intensity increases to maintain stability and safety.
The automated control strategy is structured into three layers: information acquisition, intelligent decision-making, and execution. The information layer collects critical data, the decision layer applies精细化 control measures to automatically adjust braking performance, ensuring consistent brake feel. During mode transitions, specific algorithms account for differences in response characteristics between electric and hydraulic braking to achieve smooth handovers. Compensation control algorithms are incorporated to mitigate hysteresis and delay effects in the hydraulic system, enhancing the braking experience. These algorithms adjust the retained electric braking force in the initial phase based on the discrepancy between actual and estimated hydraulic force outputs, as illustrated in compensation schematics. The execution layer precisely controls the motor and hydraulic systems according to decision layer commands, realizing optimal braking outcomes. This comprehensive approach exemplifies the advancements in automation for electric cars, driving the future of China EV technology.
To validate the feasibility of the proposed method, I conducted experimental verification. The preparation phase involved assembling a complete set of test equipment, including an electro-hydraulic composite braking test bench, hydraulic circuits, data acquisition systems, control software, safety gear, and inspection tools. The test bench featured high precision and sensitivity, requiring careful operation to prevent damage. Key equipment details are summarized in Table 1.
| Category | Parameters/Specifications |
|---|---|
| Electro-Hydraulic Composite Braking Test Bench | Motor: rated power 50 kW, rated speed 6000 r/min; Inertial wheel: simulated mass 1000 kg, max speed 3000 r/min; Brake: electric and hydraulic braking, torque range 0–1000 N·m; EHB braking system: working pressure 16 MPa, response time <50 ms |
| Hydraulic Circuit | Working pressure: 10–20 MPa; Flow range: 5–20 L/min; Response time: <30 ms |
| Data Acquisition System | Sensors: speed, pressure, current, etc., accuracy ±0.5%; Data acquisition card: 32 channels, sampling frequency 10 kHz; Computer: Intel Core i7 processor, 16 GB RAM, 500 GB SSD |
| Control Software | MATLAB/Simulink: version R2022a; RTW/RTI: version 2022b; ControlDesk: version 6.2 |
| Safety Protective Equipment | Work clothes, safety helmet, protective gloves, goggles, etc., compliant with relevant standards |
| Inspection Tools | Multimeter: accuracy 0.1%; Signal generator: frequency range 1 Hz–1 MHz; Oscilloscope: bandwidth 100 MHz, 4 channels |
Given the coordination between electric and hydraulic braking systems, seamless communication and precise synchronization were ensured during tests. The electro-hydraulic composite braking test bench, with its high accuracy, was handled meticulously to avoid damage, especially during sensor and brake installation or adjustment. To evaluate method effectiveness, a maximum braking distance of 35 m was set as the criterion. Experiments were conducted according to the designed method and preparation requirements, recording braking distances at different braking intensities. Results are presented in Table 2.
| No. | Braking Intensity | Braking Distance (m) |
|---|---|---|
| 1 | Z = 0.1 | 6.5 |
| 2 | Z = 0.2 | 10.3 |
| 3 | Z = 0.3 | 15.1 |
| 4 | Z = 0.4 | 17.8 |
| 5 | Z = 0.5 | 21.5 |
| 6 | Z = 0.6 | 25.2 |
| 7 | Z = 0.7 | 27.9 |
| 8 | Z = 0.8 | 29.1 |
From Table 2, it is evident that braking distance increases gradually with rising braking intensity. At low intensities, such as Z = 0.1, the braking distance is only 6.5 m, indicating that the electro-hydraulic composite braking system responds quickly under low braking demands, effectively shortening distances and ensuring vehicle stability and safety. As intensity increases, braking distance grows, but not linearly; in medium intensity ranges, the increase is steady, demonstrating the system’s adaptability and stability across different conditions. Notably, at higher intensities like Z = 0.8, the braking distance remains controlled within 29.1 m, highlighting the system’s卓越 performance under demanding scenarios. These results validate the method’s ability to maintain stable and efficient braking in electric cars, reinforcing its applicability in China EV development for enhanced safety and comfort.
In summary, through the design of control strategies and algorithms, I have achieved efficient energy recovery and optimized braking processes for electric cars. Simulation and experimental results confirm the method’s effectiveness and feasibility. The automated control approach ensures stable and high-performance braking across various intensities, providing robust support for the safe operation of electric cars. This contributes significantly to the advancement of China EV technologies, paving the way for smarter, more efficient transportation solutions. Future research will delve deeper into other potential influencing factors during braking in electric cars, leading to the development of more precise and comprehensive control strategies to further elevate performance in the evolving landscape of China EV innovation.