In recent years, the automotive industry has witnessed a significant shift towards electrification, driven by stringent environmental regulations and advancements in technology. Among various types of new energy vehicles, electric SUVs have gained prominence due to their versatility and growing consumer demand. As a researcher focused on automotive engineering, I have explored the economic performance of electric SUVs under different driving cycles, which is crucial for accurate range estimation and energy efficiency assessments. This article delves into the simulation and analysis of an electric SUV’s performance using MATLAB/Simulink, comparing results across standardized cycles like NEDC, WLTC, and CLTC-P. The importance of this study lies in its applicability to real-world conditions in China, where traditional international cycles may not fully capture local driving patterns. Through this work, I aim to provide insights that can guide the development of more efficient electric SUVs, emphasizing factors such as maximum speed and average speed that significantly impact range.
The transition to electric vehicles (EVs) is accelerating globally, with electric SUVs representing a key segment due to their balance of utility and efficiency. However, the economic performance of these vehicles, particularly their driving range and energy consumption, is highly influenced by the driving cycles used for testing. Driving cycles are standardized profiles that simulate typical vehicle speeds over time, and they vary across regions based on traffic conditions, road types, and driving behaviors. In this study, I focus on three prominent cycles adopted in China: NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Cycle), and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger cars). Each of these cycles has distinct characteristics that affect the energy usage of an electric SUV. For instance, cycles with higher average speeds or more aggressive acceleration phases tend to reduce range due to increased power demand. By building a detailed simulation model, I can analyze these effects systematically and derive practical recommendations for electric SUV design and optimization.
To begin, it is essential to understand the fundamental parameters of the electric SUV under investigation. This vehicle is based on a conventional compact SUV that has been electrified, incorporating key components such as a high-capacity battery pack and an efficient electric motor. The table below summarizes the main vehicle parameters, which form the basis for the simulation model. These parameters include dimensions, mass, aerodynamic properties, and powertrain specifications, all of which play a critical role in determining the performance of the electric SUV.
| Parameter | Value |
|---|---|
| Length × Width × Height (mm³) | 4510 × 1820 × 1650 |
| Curb Mass (kg) | 1675 |
| Maximum Total Mass (kg) | 2050 |
| Drag Coefficient | 0.37 |
| Frontal Area (m²) | 2.54 |
| Rolling Resistance Coefficient | 0.0085 |
| Wheel Radius (m) | 0.325 |
| Transmission Efficiency | 0.9 |
The powertrain of the electric SUV is another critical aspect, as it directly influences both dynamic and economic performance. The motor and battery specifications are designed to meet targets for top speed, acceleration, and range. Below is a table detailing the key powertrain parameters. The electric motor provides sufficient power and torque for various driving conditions, while the battery pack ensures adequate energy storage for extended range. This electric SUV is equipped with a lithium-ion battery system that offers high energy density and efficiency, which is essential for achieving competitive performance in the market.
| Parameter | Value |
|---|---|
| Peak Motor Power (kW) | 110 |
| Peak Motor Torque (N·m) | 250 |
| Maximum Motor Speed (r/min) | 9000 |
| Battery Nominal Voltage (V) | 372.3 |
| Battery Nominal Capacity (A·h) | 180 |
| Battery Configuration | 1P102S |
| Battery Nominal Energy (kW·h) | 67 |
| Gear Reduction Ratio | 7.791 |
| Average Transmission Efficiency (%) | 95 |
Driving cycles are standardized speed-time profiles used to evaluate vehicle performance under controlled conditions. For electric SUVs, these cycles help assess range and energy consumption, which are key indicators of economic performance. The NEDC cycle, historically used in Europe and China, consists of urban and extra-urban segments with a total duration of 1180 seconds and a distance of 11.007 km. It features a maximum speed of 120 km/h and an average speed of 33.68 km/h. However, NEDC has been criticized for its simplicity and lack of representativeness of real-world driving. In contrast, the WLTC cycle, developed globally, includes four parts: low, medium, high, and extra-high speed phases. It lasts 1800 seconds, covers 23.27 km, and has a higher maximum speed of 131.3 km/h and average speed of 46.54 km/h. The CLTC-P cycle, tailored for China, comprises three parts over 1800 seconds, with a total distance of 14.48 km, a maximum speed of 114 km/h, and an average speed of 28.96 km/h. The following table compares these cycles in detail, highlighting parameters that influence the electric SUV’s energy use.
| Characteristic | NEDC | WLTC | CLTC-P |
|---|---|---|---|
| Duration (s) | 1180 | 1800 | 1800 |
| Distance (km) | 11.007 | 23.27 | 14.48 |
| Maximum Speed (km/h) | 120 | 131.3 | 114 |
| Average Speed (km/h) | 33.68 | 46.54 | 28.96 |
| Number of Parts | 2 | 4 | 3 |
| Maximum Acceleration (m/s²) | 1.042 | 1.67 | 1.47 |
| Maximum Deceleration (m/s²) | -1.389 | -1.5 | -1.47 |
| Idle Time Percentage (%) | 24.9 | 13.2 | 22.11 |
The simulation model for the electric SUV was developed in MATLAB/Simulink to accurately capture the vehicle’s dynamics and energy consumption. This environment allows for modular design, integrating components such as the driver model, controller, battery, electric motor, transmission, and vehicle dynamics. The model uses Simulink blocks to represent each subsystem, and MATLAB scripts define parameters and control logic. For instance, the driver model follows the target speed profile of each driving cycle, while the controller manages power flow from the battery to the motor. The battery model accounts for state-of-charge (SOC) variations, and the motor model converts electrical energy to mechanical torque. The overall simulation structure ensures that the electric SUV’s performance can be evaluated under various conditions, including different driving cycles and load scenarios. The model’s accuracy was verified by comparing simulated speed profiles with the target cycles, showing close alignment and thus validating the approach for range estimation.
In the simulation, the economic performance of the electric SUV is primarily assessed through driving range and energy consumption rate. The range is calculated based on the distance traveled until the battery SOC drops from 100% to a minimum threshold, typically around 20%. The energy consumption rate is derived from the total energy used over the distance. Mathematical formulations help in understanding these metrics. For example, the instantaneous power demand \( P_{\text{req}} \) of the electric SUV can be expressed as:
$$ P_{\text{req}} = \frac{1}{\eta_{\text{trans}}} \left( F_{\text{aero}} + F_{\text{roll}} + F_{\text{accel}} \right) v $$
where \( \eta_{\text{trans}} \) is the transmission efficiency, \( F_{\text{aero}} \) is the aerodynamic drag force, \( F_{\text{roll}} \) is the rolling resistance force, \( F_{\text{accel}} \) is the acceleration force, and \( v \) is the vehicle speed. The aerodynamic drag force is given by:
$$ F_{\text{aero}} = \frac{1}{2} \rho C_d A v^2 $$
with \( \rho \) as air density, \( C_d \) as drag coefficient, and \( A \) as frontal area. The rolling resistance force is:
$$ F_{\text{roll}} = \mu_{\text{roll}} m g $$
where \( \mu_{\text{roll}} \) is the rolling resistance coefficient, \( m \) is the vehicle mass, and \( g \) is gravitational acceleration. The acceleration force depends on the vehicle’s mass and acceleration \( a \):
$$ F_{\text{accel}} = m a $$
Integrating these forces over time allows the simulation to compute energy consumption. The total energy \( E_{\text{total}} \) used during a driving cycle is:
$$ E_{\text{total}} = \int P_{\text{req}} \, dt $$
and the driving range \( R \) can be estimated as:
$$ R = \frac{E_{\text{battery}} \cdot \text{SOC}_{\text{usable}}}{E_{\text{total}} / D} $$
where \( E_{\text{battery}} \) is the battery energy capacity, \( \text{SOC}_{\text{usable}} \) is the usable SOC range (e.g., 0.8 for 80% depth of discharge), and \( D \) is the distance of the driving cycle. This formula highlights how range is inversely proportional to energy consumption per kilometer.
Simulation results for the electric SUV under NEDC, WLTC, and CLTC-P cycles reveal significant variations in economic performance. The following table summarizes the key outcomes, including total driving range and energy consumption rate. These results demonstrate that the electric SUV achieves the longest range under CLTC-P and the shortest under WLTC, primarily due to differences in average speed and acceleration patterns. For instance, the higher average speed in WLTC leads to greater energy demand, reducing range. This analysis underscores the importance of selecting appropriate driving cycles for electric SUV development to meet performance targets.
| Performance Metric | NEDC | WLTC | CLTC-P |
|---|---|---|---|
| Total Driving Range (km) | 484 | 398 | 509 |
| Energy Consumption Rate (kW·h/100 km) | 13.8 | 16.8 | 13.2 |
Further analysis involves examining the SOC profiles over time for each driving cycle. The SOC decreases linearly with distance under ideal conditions, but real-world factors like regenerative braking and varying power demands cause deviations. The rate of SOC depletion \( \frac{d\text{SOC}}{dt} \) is related to the battery current \( I_{\text{batt}} \) and capacity \( C_{\text{batt}} \):
$$ \frac{d\text{SOC}}{dt} = -\frac{I_{\text{batt}}}{C_{\text{batt}}} $$
where \( I_{\text{batt}} \) depends on the power demand and battery voltage. In simulations, the electric SUV’s SOC drops faster in cycles with higher power requirements, such as WLTC, due to its aggressive acceleration and higher speeds. This effect is quantified by the energy consumption rate, which is higher for WLTC (16.8 kW·h/100 km) compared to NEDC (13.8 kW·h/100 km) and CLTC-P (13.2 kW·h/100 km). Consequently, the electric SUV’s range is most favorable under CLTC-P, making it a suitable cycle for certifications in China where average speeds are lower.
The impact of driving cycle characteristics on the electric SUV’s performance can be generalized using correlation analysis. For example, a multiple regression model can relate range \( R \) to cycle parameters such as maximum speed \( v_{\text{max}} \), average speed \( v_{\text{avg}} \), and idle ratio \( r_{\text{idle}} \):
$$ R = \beta_0 + \beta_1 v_{\text{max}} + \beta_2 v_{\text{avg}} + \beta_3 r_{\text{idle}} + \epsilon $$
where \( \beta \) coefficients represent the sensitivity of range to each factor, and \( \epsilon \) is the error term. Based on the simulation data, \( v_{\text{avg}} \) has a strong negative correlation with range, as higher average speeds increase energy consumption. Similarly, \( v_{\text{max}} \) affects range by influencing peak power demands. This mathematical insight helps in optimizing the electric SUV for specific markets by focusing on key cycle parameters.
In addition to range, the electric SUV’s dynamic performance is evaluated through metrics like top speed, acceleration, and gradability. The maximum speed \( v_{\text{max}} \) is determined by the power balance at steady state:
$$ P_{\text{motor}} = \frac{1}{\eta_{\text{trans}}} \left( \frac{1}{2} \rho C_d A v_{\text{max}}^3 + \mu_{\text{roll}} m g v_{\text{max}} \right) $$
where \( P_{\text{motor}} \) is the motor’s peak power. Acceleration performance, such as 0-100 km/h time, depends on the torque-speed characteristics of the motor and vehicle mass. The time \( t \) to accelerate from speed \( v_1 \) to \( v_2 \) can be approximated by:
$$ t = \int_{v_1}^{v_2} \frac{m}{F_{\text{traction}} – F_{\text{resist}}} \, dv $$
where \( F_{\text{traction}} \) is the traction force at the wheels, and \( F_{\text{resist}} \) is the total resistive force. For the electric SUV, simulations confirm that it meets typical targets, such as a top speed above 150 km/h and 0-100 km/h acceleration under 10 seconds, ensuring competitiveness in the market.
Another aspect considered in the simulation is the effect of environmental factors on the electric SUV’s performance. Temperature variations can impact battery efficiency and thus range. For instance, at low temperatures, battery internal resistance increases, reducing available energy. This can be modeled by adjusting the battery capacity \( C_{\text{batt}} \) based on temperature \( T \):
$$ C_{\text{batt}}(T) = C_{\text{batt,ref}} \left[ 1 + \alpha (T – T_{\text{ref}}) \right] $$
where \( \alpha \) is a temperature coefficient, and \( T_{\text{ref}} \) is the reference temperature. Although not included in the core simulations, this factor is important for real-world applications of electric SUVs in diverse climates.
Regenerative braking is a key feature in electric SUVs that improves energy efficiency by recovering kinetic energy during deceleration. The amount of energy recovered \( E_{\text{regen}} \) depends on the braking force and system efficiency:
$$ E_{\text{regen}} = \eta_{\text{regen}} \int F_{\text{brake}} v \, dt $$
where \( \eta_{\text{regen}} \) is the regeneration efficiency, and \( F_{\text{brake}} \) is the braking force. In the simulation, regenerative braking is integrated into the controller model, and it contributes to higher range in cycles with frequent deceleration events, such as CLTC-P. This highlights the synergy between driving cycles and vehicle technology in enhancing the economic performance of electric SUVs.
To further illustrate the simulation process, the model includes subsystems for ancillary loads, such as air conditioning and electronics, which add to the total energy consumption. The power drawn by ancillaries \( P_{\text{anc}} \) is typically constant or speed-dependent, and it reduces the available energy for propulsion. Thus, the net energy for driving \( E_{\text{drive}} \) is:
$$ E_{\text{drive}} = E_{\text{battery}} – \int P_{\text{anc}} \, dt $$
In practice, for an electric SUV, ancillaries can account for 5-10% of total energy use, emphasizing the need for efficient auxiliary systems in design.
The validation of the simulation model involves comparing results with empirical data or established software like ADVISOR. However, as ADVISOR uses outdated cycles, the custom MATLAB/Simulink approach provides more relevant insights for current electric SUV development. The close match between simulated and target speed profiles in NEDC, WLTC, and CLTC-P cycles, as shown in earlier plots, confirms the model’s reliability. This allows for confident application in product development, where accurate range prediction is critical for customer satisfaction and regulatory compliance.
In conclusion, this study demonstrates the significant influence of driving cycles on the economic performance of electric SUVs. Through detailed simulation in MATLAB/Simulink, I have shown that cycles like CLTC-P, with lower average speeds, yield longer ranges compared to WLTC. The electric SUV’s range varies from 398 km under WLTC to 509 km under CLTC-P, highlighting the importance of cycle selection in performance targets. Key factors such as maximum speed and average speed are identified as primary drivers of energy consumption. These findings provide valuable guidance for manufacturers in optimizing electric SUVs for specific markets, ensuring that vehicles meet both regulatory standards and consumer expectations. Future work could explore additional cycles or real-world driving data to further enhance the accuracy and applicability of simulations for electric SUV development.