In the context of global efforts to achieve carbon neutrality, the development of new energy vehicles has become a strategic priority worldwide. As a leading market for electric vehicles, China has implemented numerous policies to promote the adoption of clean energy transportation. Electric vehicles, particularly pure electric vehicles, offer zero emissions and high energy efficiency, making them pivotal in reducing environmental pollution and transitioning to sustainable energy systems. The advancement of battery technology has significantly improved the energy density of power batteries, with mass-produced cells now exceeding 300 Wh/kg, enabling an average driving range of over 460 kilometers for modern electric vehicles. Additionally, the superior control performance and rapid response of drive motors facilitate the integration of electric vehicles with autonomous driving technologies, positioning China’s EV industry at the forefront of innovation. Dynamic performance, encompassing maximum speed, acceleration, and gradability, is a fundamental aspect of vehicle evaluation. Utilizing computational tools like MATLAB/Simulink for dynamic modeling and simulation allows for in-depth analysis and optimization of electric vehicle performance, thereby enhancing design efficiency and accelerating the development of China’s EV sector.
Dynamic modeling and simulation play a crucial role in automotive engineering, providing insights into vehicle handling, stability, and safety. For electric vehicles, these models also incorporate energy management and battery longevity considerations. With the integration of artificial intelligence, big data, and model predictive control, dynamic models are evolving into intelligent tools for precise vehicle analysis. In this study, I focus on evaluating the dynamic performance of a pure electric vehicle through a comprehensive model built in MATLAB/Simulink. The model consists of five subsystems: drive force, velocity, driving resistance, acceleration, and gradability. This approach enables a holistic assessment of key performance indicators, supporting the optimization of China EV designs. The primary technical parameters of the vehicle under study are summarized in Table 1.
| Parameter | Value | Parameter | Value |
|---|---|---|---|
| Total Vehicle Mass (kg) | 1550 | Rotational Mass Conversion Factor | 1.2 |
| Rolling Resistance Coefficient | 0.011 | Transmission Efficiency | 0.92 |
| Air Resistance Coefficient | 0.31 | Total Transmission Ratio | 8.4 |
| Frontal Area (m²) | 2.4 | Peak Power (kW) | 75 |
| Wheel Radius (m) | 0.32 | Peak Torque (Nm) | 220 |
| Rated Power (kW) | 36 | Rated Torque (Nm) | 110 |
The dynamic performance of an electric vehicle is primarily determined by the balance between drive force and driving resistance. The drive force originates from the motor’s output torque, while driving resistance includes rolling resistance and air resistance. To model this, I developed mathematical equations for each subsystem. The drive force \( F_t \) is calculated using the formula:
$$ F_t = \frac{T_m \cdot i \cdot \eta}{R} $$
where \( T_m \) is the motor output torque, \( i \) is the total transmission ratio, \( \eta \) is the transmission efficiency, and \( R \) is the wheel radius. The motor output torque \( T_m \) varies with motor speed \( n \) and is defined as:
$$ T_m = \begin{cases} T_{max} & \text{for } n \leq n_b \\ \frac{P_{max}}{n} & \text{for } n > n_b \end{cases} $$
Here, \( T_{max} \) is the peak torque, \( P_{max} \) is the peak power, and \( n_b \) is the base speed (rated speed) of the motor. The base speed is derived from the rated power and torque:
$$ n_b = \frac{P_e}{T_e} $$
For the given China EV parameters, \( n_b = \frac{36000}{110} \approx 327.27 \, \text{rad/s} \), which corresponds to approximately 3255 rpm when converted. The driving resistance \( F_r \) consists of rolling resistance \( F_f \) and air resistance \( F_w \), expressed as:
$$ F_r = F_f + F_w = m \cdot g \cdot f + \frac{1}{2} \cdot C_d \cdot A \cdot \rho \cdot u^2 $$
where \( m \) is the total vehicle mass, \( g \) is gravitational acceleration, \( f \) is the rolling resistance coefficient, \( C_d \) is the air resistance coefficient, \( A \) is the frontal area, \( \rho \) is air density, and \( u \) is the vehicle speed. The vehicle speed \( u \) in km/h is related to motor speed \( n \) in rpm by:
$$ u = \frac{2 \pi R n}{60 i} \times 3.6 $$
Simplifying, this becomes \( u = \frac{2 \pi R n}{i} \times 0.06 \). The acceleration \( a \) and gradability \( i_a \) are derived from the equation of motion, considering the drive force, resistance, and rotational inertia. The acceleration is given by:
$$ a = \frac{F_t – F_r}{\delta m} $$
where \( \delta \) is the rotational mass conversion factor. The gradability, representing the slope angle the vehicle can climb, is calculated as:
$$ i_a = \tan\left[\arcsin\left( \frac{F_t – F_f}{m \cdot g} \right)\right] \times 100\% $$
In this formula, \( F_f \) is the rolling resistance, and the result is expressed as a percentage. These equations form the basis of the dynamic model implemented in Simulink, as shown in the model diagram. The model integrates these subsystems to simulate and analyze the performance of the electric vehicle under various conditions.
To simulate the dynamic behavior, I configured the Simulink model with the parameters from Table 1 and executed the simulation. The output data was processed in MATLAB to generate performance curves. The drive force–driving resistance balance graph illustrates the relationship between drive force, resistance, and vehicle speed. At the point where the drive force curve intersects the driving resistance curve, the vehicle reaches its maximum speed, as acceleration becomes zero. The simulation results indicate a maximum speed of 183.50 km/h for this China EV model. The acceleration curve shows how acceleration decreases with increasing speed, peaking at low speeds and dropping to zero at maximum speed. Similarly, the gradability curve demonstrates the vehicle’s ability to climb slopes, with maximum gradability occurring at low speeds due to higher torque availability.
The drive motor’s operational characteristics are divided into two regions: constant torque and constant power. Below the base speed, the motor operates in the constant torque region, where torque remains constant and power increases linearly with speed. Above the base speed, the motor switches to the constant power region, where power remains constant, and torque decreases hyperbolically with speed. This behavior directly influences the drive force, acceleration, and gradability curves. For instance, at speeds below approximately 46.75 km/h (corresponding to the base speed), the curves are nearly linear, reflecting constant torque. Beyond this point, the curves exhibit a hyperbolic decline, aligning with the constant power operation. The driving resistance curve, dominated by air resistance at high speeds, shows a quadratic increase with speed, which impacts the overall dynamic performance.
To validate the model, I compared the simulation results with experimental data from physical tests. The comparison, summarized in Table 2, reveals close agreement between simulated and measured values, with minor errors attributable to uncertainties in parameters like air resistance coefficient and rolling resistance coefficient. Such discrepancies are acceptable in engineering applications, confirming the model’s practicality for evaluating electric vehicle performance.
| Performance Metric | Simulation Value | Experimental Value | Error |
|---|---|---|---|
| Maximum Speed (km/h) | 183.50 | 181 | 1.38% |
| Maximum Acceleration (m/s²) | 2.75 | 2.6 | 5.8% |
| Maximum Gradability (%) | 36 | 35 | 1% |
In-depth analysis of the simulation curves provides insights into the electric vehicle’s behavior. The drive force–resistance balance graph (Figure 2) highlights the equilibrium point at maximum speed. The acceleration curve (Figure 3) shows a peak acceleration of about 2.75 m/s² at low speeds, decreasing to zero at 183.50 km/h. The gradability curve (Figure 4) indicates a maximum slope capability of 36%, which is crucial for assessing the vehicle’s performance in hilly terrains. These results are consistent with the motor’s characteristics, where high torque at low speeds enables strong acceleration and gradability, while power limitations affect high-speed performance. The integration of these aspects underscores the importance of dynamic modeling in the design and optimization of China EV systems.
Furthermore, the model accounts for real-world factors such as energy consumption and efficiency. For instance, in the constant power region, the reduction in torque affects the vehicle’s ability to maintain high acceleration or climb steep slopes at elevated speeds. This has implications for battery usage and thermal management, as sustained high-power demand can impact battery life. By simulating these scenarios, the model aids in developing control strategies to enhance the durability and efficiency of electric vehicles. The use of MATLAB/Simulink also allows for parameter sweeps and sensitivity analyses, enabling engineers to identify critical parameters and optimize designs for specific conditions, such as urban driving or highway cruising in China’s diverse environments.
In conclusion, dynamic modeling and simulation using MATLAB/Simulink are invaluable tools for analyzing the performance of pure electric vehicles. The model developed in this study accurately captures the key dynamic characteristics, including maximum speed, acceleration, and gradability, and aligns well with experimental data. The analysis of simulation curves, coupled with the drive motor’s operational regions, provides a comprehensive understanding of vehicle behavior. This approach not only facilitates the evaluation of existing designs but also supports the development of next-generation China EV technologies. As the automotive industry evolves, such models will continue to integrate advanced features like machine learning and real-time data processing, further enhancing their utility in creating efficient, reliable, and high-performance electric vehicles for global markets.
The proliferation of electric vehicles in China and worldwide underscores the need for robust simulation methodologies. By leveraging tools like MATLAB/Simulink, researchers and engineers can accelerate innovation, reduce development costs, and contribute to the sustainable transformation of transportation. Future work may involve extending the model to include battery dynamics, thermal effects, and regenerative braking, thereby providing a more holistic view of electric vehicle performance. Ultimately, the insights gained from dynamic simulation will play a pivotal role in shaping the future of mobility, ensuring that China EV remains at the cutting edge of automotive technology.