In the rapidly evolving landscape of sustainable transportation, electric vehicles have emerged as a pivotal component, driving innovations in energy efficiency and safety. Among the core technologies, the braking system plays a critical role in ensuring vehicle stability and maximizing energy recovery. However, traditional braking systems in electric vehicles often face challenges such as uneven distribution of electric and hydraulic braking forces, leading to extended braking distances and compromised performance. This paper addresses these issues by proposing an automated control method for electro-hydraulic composite braking systems in electric vehicles. We focus on optimizing the coordination between regenerative braking from the motor and hydraulic braking to enhance safety, energy recovery, and driving comfort. The development of such systems is particularly relevant in the context of China EV markets, where advancements in electric vehicle technologies are accelerating to meet growing environmental and economic demands.
To lay the foundation for our control strategy, we begin by establishing a comprehensive model of the electro-hydraulic composite braking system. This model integrates vehicle dynamics and hydraulic system characteristics, enabling a detailed analysis of braking behavior under various conditions. The electric vehicle platform considered here includes a motor, transmission, and reduction gear, which are essential for simulating real-world scenarios. We assume idealized conditions, such as negligible suspension effects, smooth road surfaces, and absence of wind resistance, to simplify the model while capturing key dynamics. The longitudinal vehicle dynamics are described by the following equation:
$$ M \cdot \frac{dv}{dt} = F_p – F_b – F_r – F_d $$
where \( M \) represents the total vehicle mass, \( v \) is the longitudinal velocity, \( F_p \) denotes the driving force, \( F_b \) is the total braking force, \( F_r \) accounts for rolling resistance, and \( F_d \) signifies air resistance. The distribution of braking forces between the front and rear wheels is critical for stability and is given by:
$$ F_b = F_{wf} + F_{wr} $$
Here, \( F_{wf} \) and \( F_{wr} \) are the ground braking forces on the front and rear wheels, respectively. The wheel dynamics equation further elaborates on the relationship between angular velocity and torque:
$$ I \cdot \frac{d\omega}{dt} = T_{wi} – F_{wi} \times R $$
In this expression, \( I \) 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 wheels redistribute based on factors like the center of mass, wheelbase, and braking intensity. This redistribution is modeled as:
$$
\begin{aligned}
F_{zf} &= \frac{b \cdot M \cdot g}{L} – \frac{h_g \cdot M \cdot a_x}{L} \\
F_{zr} &= \frac{a \cdot M \cdot g}{L} + \frac{h_g \cdot M \cdot a_x}{L}
\end{aligned}
$$
where \( F_{zf} \) and \( F_{zr} \) are the normal forces on the front and rear wheels, \( a \) and \( b \) are distances from the center of mass to the front and rear axles, \( L \) 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 hydraulic braking system’s dynamic characteristics are equally important, as they influence the responsiveness and efficiency of the overall braking process. We model the hydraulic system by controlling solenoid valves for pressure increase and decrease operations. The dynamic behavior is represented by:
$$
\begin{aligned}
\frac{dp_c}{dt} &= \frac{1}{C \cdot (R_p + R_s)} \cdot (u_p – p_c) \\
\frac{dp_c}{dt} &= \frac{1}{C \cdot (R_r + R_s)} \cdot (p_r – u_r – p_c)
\end{aligned}
$$
In these equations, \( p_c \) is the brake cylinder pressure, \( C \) is the equivalent hydraulic capacitance, \( R_p \) and \( R_r \) are the equivalent hydraulic resistances during pressure increase and decrease, \( R_s \) is the system hydraulic resistance, \( u_p \) and \( u_r \) are control signals for pressure increase and decrease, and \( p_r \) is the pressure in the brake fluid reservoir. The hydraulic braking force can then be expressed as a function of cylinder pressure and other parameters:
$$ F_h = f_p(p_c, R) $$
By combining the vehicle dynamics and hydraulic system models, we derive the electro-hydraulic composite braking model:
$$ F_t = F_h + F_e $$
where \( F_e \) is the electric braking force. This integrated model serves as the basis for designing an efficient energy recovery mechanism and automated control strategy, which are essential for improving the performance of electric vehicles in diverse driving conditions.
Energy recovery during braking is a key advantage of electric vehicles, as it converts kinetic energy into electrical energy, thereby extending driving range and reducing energy consumption. We design a braking energy recovery mechanism that considers factors such as battery state, motor characteristics, vehicle speed, and braking intensity. To model the motor’s behavior, we use a first-order inertial system to describe the relationship between the target braking torque and the actual torque output:
$$ \tau \frac{dz_h}{dt} + z_h = z_{h_t} $$
Here, \( \tau \) 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 efficiency is quantified by the ratio of recovered electrical energy to the reduction in kinetic energy during braking:
$$ \eta = \frac{E_r}{E_l} $$
where \( \eta \) is the energy recovery efficiency, \( E_r \) is the recovered electrical energy, and \( E_l \) is the decrease in vehicle kinetic energy. The kinetic energy reduction is calculated from the vehicle dynamics model:
$$ E_l = \frac{1}{2} M (v_0^2 – v_{\text{end}}^2) $$
with \( v_0 \) as the initial braking speed and \( v_{\text{end}} \) as the final speed. The recovered energy depends on the battery’s charging voltage and the amount of charge regained:
$$ E_r = U_c \times Q_r $$
where \( U_c \) is the battery’s nominal charging voltage and \( Q_r \) is the recovered charge. This energy recovery mechanism is optimized through real-time adjustments based on vehicle state and driver inputs, ensuring maximum efficiency across various braking scenarios. For instance, in China EV applications, where urban driving involves frequent stops, this approach can significantly enhance overall energy economy.
To achieve automated control of the electro-hydraulic composite braking system, we develop a coordinated control strategy that dynamically allocates braking forces between the electric motor and hydraulic system. This strategy is structured into three layers: information acquisition, intelligent decision-making, and execution. The information acquisition layer collects real-time data from sensors, including vehicle speed, brake pedal position, and battery state of charge (SOC). The intelligent decision-making layer processes this data to determine the required braking intensity, calculated as:
$$ z = \frac{a}{g} $$
where \( a \) is the measured deceleration and \( g \) is gravitational acceleration. Based on this braking intensity and other factors, the system selects appropriate working modes. For example, in emergency braking situations where the braking intensity exceeds a threshold \( z_t \), the system disables regenerative braking and relies solely on hydraulic braking to ensure safety. Under normal conditions, the control strategy adjusts the distribution of electric and hydraulic braking forces according to the battery SOC and braking intensity. When the SOC is high, hydraulic braking is prioritized to prevent overcharging, whereas at moderate SOC levels, regenerative braking is emphasized for energy recovery, especially at low braking intensities. As braking intensity increases, hydraulic braking gradually supplements the electric braking to maintain stability.
The coordination between electric and hydraulic braking is further refined through a compensation control algorithm, which addresses delays and hysteresis in the hydraulic system. This algorithm adjusts the electric braking force during the initial braking phase based on the difference between the actual and estimated hydraulic force outputs, ensuring a smooth and consistent braking feel. The compensation process is illustrated in the control diagram, where feedback loops continuously optimize force distribution. The execution layer then implements the commands from the decision-making layer, precisely controlling the motor and hydraulic components to achieve the desired braking performance. This automated approach not only enhances safety but also provides a comfortable driving experience, which is crucial for the adoption of electric vehicles in competitive markets like China EV.
To validate the proposed method, we conducted extensive testing using a specialized electro-hydraulic composite braking test bench. The test setup included a motor with a rated power of 50 kW and a maximum speed of 6000 rpm, an inertia wheel simulating a vehicle mass of 1000 kg, and a braking system capable of generating torques up to 1000 N·m. The hydraulic system operated at pressures between 10 and 20 MPa, with a response time of less than 30 ms. Data acquisition systems, comprising sensors for speed, pressure, and current, were used with a sampling frequency of 10 kHz. Control software such as MATLAB/Simulink and ControlDesk facilitated real-time monitoring and adjustments. Safety measures, including protective gear and inspection tools, were strictly adhered to during experiments. The table below summarizes the key equipment and parameters used in the testing process.
| Category | Parameters/Specifications |
|---|---|
| Electro-Hydraulic Braking Test Bench | Motor: rated power 50 kW, rated speed 6000 rpm; Inertia wheel: simulated mass 1000 kg, max speed 3000 rpm; Brake: electric and hydraulic braking, torque range 0–1000 N·m; EHB 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, accuracy ±0.5%; Data acquisition card: 32 channels, sampling frequency 10 kHz; Computer: Intel Core i7, 16GB RAM, 500GB SSD |
| Control Software | MATLAB/Simulink R2022a; RTW/RTI 2022b; ControlDesk 6.2 |
| Safety Equipment | Workwear, safety helmet, protective gloves, goggles, compliant with safety standards |
| Inspection Tools | Multimeter: accuracy 0.1%; Signal generator: frequency range 1 Hz–1 MHz; Oscilloscope: bandwidth 100 MHz, 4 channels |
The experimental results demonstrated the effectiveness of our automated control method in maintaining braking distances within acceptable limits. We set a maximum braking distance criterion of 35 m and recorded distances at different braking intensities. The table below presents the results, showing that as braking intensity increases, the braking distance grows, but remains well-controlled across all tested scenarios. For instance, at low braking intensity (Z=0.1), the distance was only 6.5 m, highlighting the system’s responsiveness. Even at higher intensities, such as Z=0.8, the distance was kept below 29.1 m, confirming the stability and reliability of the electro-hydraulic composite braking system. These findings underscore the potential of this approach to enhance the safety and performance of electric vehicles, particularly in the context of China EV development, where stringent standards are applied.
| Test No. | Braking Intensity (Z) | Braking Distance (m) |
|---|---|---|
| 1 | 0.1 | 6.5 |
| 2 | 0.2 | 10.3 |
| 3 | 0.3 | 15.1 |
| 4 | 0.4 | 17.8 |
| 5 | 0.5 | 21.5 |
| 6 | 0.6 | 25.2 |
| 7 | 0.7 | 27.9 |
| 8 | 0.8 | 29.1 |
In conclusion, this paper presents an automated control method for electro-hydraulic composite braking in electric vehicles, integrating vehicle dynamics modeling, energy recovery mechanisms, and coordinated control strategies. The proposed approach effectively addresses issues of braking force distribution and energy efficiency, as validated through experimental testing. By ensuring stable braking performance across varying intensities, our method contributes to the safety and comfort of electric vehicle operations. Future work will explore additional factors influencing braking, such as road conditions and driver behavior, to further refine the control algorithms. The ongoing advancements in electric vehicle technologies, especially in regions like China EV, highlight the importance of such innovations in achieving sustainable and intelligent transportation systems.