As electric vehicles gain prominence in the automotive industry, the electric SUV segment faces unique challenges due to its larger size and higher energy demands. In this study, I focus on analyzing the energy flow of a specific electric SUV model to identify inefficiencies and propose optimization strategies for improving driving range. The electric SUV under investigation is a mid-sized vehicle with a battery capacity of 62.78 kWh, designed for urban and suburban use. Through comprehensive testing and simulation, I aim to enhance energy utilization and extend the vehicle’s range by addressing control strategies, low-voltage accessory consumption, and driving resistance. This research is critical as electric SUVs often struggle with range limitations compared to smaller EVs, and optimizing their energy flow can significantly impact consumer adoption and environmental benefits.
The methodology involves detailed energy flow testing, including measurements of high and low-voltage component能耗, vehicle传动阻力, and overall efficiency under standard driving cycles. I conducted these tests in a controlled environment, replicating real-world conditions to gather accurate data. For instance, the electric SUV was subjected to coast-down tests to determine rolling resistance and aerodynamic drag, while low-voltage accessories like cooling pumps and brake vacuum pumps were monitored for their energy consumption. Additionally, I developed a simulation model using MATLAB/Simulink to replicate the vehicle’s energy dynamics and predict the impact of various optimizations. This model incorporates parameters such as battery characteristics, motor efficiency, and control logic, allowing for a thorough analysis of energy distribution. The primary goal is to provide actionable insights that can be applied to similar electric SUV designs, ultimately contributing to longer driving ranges and better energy management.
To begin, I outline the key parameters of the electric SUV, as summarized in Table 1. These parameters include vehicle mass, dimensions, aerodynamic coefficients, and powertrain specifications, which are essential for understanding the baseline performance. The electric SUV has a curb weight of 2040 kg, a frontal area of 2.21 m², and a drag coefficient of 0.419, all of which influence energy consumption. The powertrain consists of a permanent magnet synchronous motor with a peak power of 85 kW and a battery system using ternary lithium cells. These details form the foundation for subsequent energy flow analysis and optimization efforts.
| Parameter | Value |
|---|---|
| Curb Weight + Payload (kg) | 2040 + 100 |
| Vehicle Dimensions (mm³) | 4949 × 1820 × 1940 |
| Frontal Area (m²) | 2.21 |
| Drag Coefficient (Cd) | 0.419 |
| Rolling Resistance Coefficient (f) | 0.007675 |
| Wheel Radius (m) | 0.364 |
| Final Drive Ratio | 2.66 / 4.1 |
| Transmission Efficiency | 0.89 |
| Maximum Speed (km/h) | 100 |
| Motor Type | Permanent Magnet Synchronous |
| Rated/Maximum Power (kW) | 35 / 85 |
| Rated/Maximum Motor Speed (rpm) | 2800 / 9000 |
| Rated/Peak Torque (N·m) | 120 / 290 |
| Battery Type | Ternary Lithium |
| Cell Voltage/Capacity (V/Ah) | 3.65 / 4 |
| Pack Configuration | 86 series, 50 parallel |
| Battery Energy (kWh) | 62.78 |
| Cell Energy Density (Wh/kg) | 211.59 |
| Battery Pack Mass (kg) | 448 |
Next, I performed driving resistance tests to quantify the forces opposing the electric SUV’s motion. Using coast-down procedures based on standard methods, I measured the total resistance as a function of speed. The results, plotted in Figure 1, show that the resistance increases with speed due to aerodynamic and rolling components. The relationship can be expressed mathematically as:
$$ F_{\text{total}} = F_{\text{rolling}} + F_{\text{aero}} + F_{\text{drivetrain}} $$
where \( F_{\text{rolling}} \) is the rolling resistance, \( F_{\text{aero}} \) is the aerodynamic drag, and \( F_{\text{drivetrain}} \) represents losses in the transmission system. For this electric SUV, the rolling resistance coefficient is 0.007675, and the aerodynamic drag force is calculated using:
$$ F_{\text{aero}} = \frac{1}{2} \rho C_d A v^2 $$
Here, \( \rho \) is the air density (approximately 1.225 kg/m³ at sea level), \( C_d \) is the drag coefficient, \( A \) is the frontal area, and \( v \) is the vehicle speed. The total resistance curve was fitted to a quadratic equation, yielding:
$$ F_{\text{total}} = 0.0401v^2 + 4.2478v + 125.09 $$
This equation is used in the simulation model to accurately represent the driving resistance. Initial range tests under the NEDC cycle resulted in a driving range of 258 km, with the test terminating at a state of charge (SOC) of 18%. This baseline performance highlights the need for optimization in energy flow management for the electric SUV.
In addition to driving resistance, I analyzed the energy consumption of low-voltage accessories in the electric SUV. These components, powered by the battery via a DC-DC converter, include the cooling pump, brake vacuum pump, cooling fan, and instrumentation. Using Hall-effect current sensors and voltage isolators, I measured the current and power draw during NEDC cycles at different SOC levels (100%, 65%, and 52%). The results, summarized in Table 2, indicate that the cooling pump consumes significant energy, especially when activated after the initial cold start. The brake vacuum pump operates intermittently, correlating with braking events, while other accessories have minimal impact.
| Accessory | Current (A, Average) | Power Contribution (%) |
|---|---|---|
| Cooling Pump | 4.0 | 0.74 |
| Brake Vacuum Pump | 9.0 (intermittent) | 0.12 |
| Other Accessories | < 0.1 | Negligible |
The DC-DC converter’s performance was also evaluated, with input and output power measurements showing an average efficiency of 72.2%. This means that a substantial portion of energy is lost in conversion, contributing to overall inefficiency. The total power drawn by low-voltage accessories averaged 275.43 W, which accounts for a notable share of the battery’s energy output. Reducing this consumption is a key target for improving the electric SUV’s range.
Furthermore, I characterized the electric SUV’s motor and powertrain efficiency through dynamometer testing. The motor’s torque-speed curve and efficiency map are shown in Figure 2 and Figure 3, respectively. The efficiency map reveals that the motor operates at high efficiency (above 90%) in certain regions but drops at low loads and high speeds. The powertrain’s overall efficiency, including the transmission, is approximately 89% under optimal conditions. However, losses occur during energy regeneration, particularly when the battery’s SOC is high, limiting regenerative braking effectiveness. This is captured in the control strategy, where regenerative braking is disabled at SOC levels above 91%, leading to missed energy recovery opportunities.
To simulate the electric SUV’s energy flow, I developed a model in MATLAB/Simulink that integrates the vehicle’s dynamics, battery behavior, and control logic. The model uses equations of motion, such as:
$$ P_{\text{req}} = \frac{v}{\eta_{\text{total}}} \left( F_{\text{total}} + m a \right) $$
where \( P_{\text{req}} \) is the required power, \( \eta_{\text{total}} \) is the total efficiency, \( m \) is the vehicle mass, and \( a \) is the acceleration. The battery model includes open-circuit voltage (OCV) and internal resistance as functions of SOC, derived from empirical data:
$$ V_{\text{batt}} = \text{OCV}(SOC) – I_{\text{batt}} R_{\text{int}}(SOC) $$
Here, \( V_{\text{batt}} \) is the battery voltage, \( I_{\text{batt}} \) is the current, and \( R_{\text{int}} \) is the internal resistance. The simulation outputs, such as motor torque and battery current, were validated against experimental data, showing a close match with the measured driving range of 258 km. For instance, the simulated range was 255 km, confirming the model’s accuracy. This model serves as a tool for evaluating optimization strategies for the electric SUV.
One major optimization area is the control strategy, particularly for regenerative braking and SOC management. In the baseline setup, regenerative braking is inactive at SOC levels above 91%, and power is limited at SOC below 18%. However, analysis shows that the battery cells can safely discharge to lower voltages without damage. By modifying the strategy to allow regenerative braking with a current limit of 40 A (0.2C) at high SOC and extending the discharge limit to SOC 10%, the simulation predicts a range increase to 284.1 km. This represents a gain of 29 km, demonstrating the potential of control optimization for the electric SUV.
Another optimization target is the reduction of low-voltage accessory consumption. Currently, these accessories draw an average of 275.43 W, with the DC-DC converter operating at 72.2% efficiency. By improving converter efficiency to 90% and minimizing accessory loads through better component selection (e.g., high-efficiency pumps), the total power draw can be reduced to 100 W. Implementing this in the simulation results in a range of 263.3 km, an improvement of 8.3 km. This highlights the importance of addressing auxiliary systems in energy flow management for the electric SUV.
Driving resistance optimization is also critical for the electric SUV. I decomposed the total resistance into components: drivetrain resistance, rear axle resistance, cardan shaft resistance, and brake caliper resistance. Tests using a five-motor dynamometer and component-level measurements revealed that the cardan shaft and rear axle contribute significantly to losses. For example, the cardan shaft resistance decreases linearly with speed due to friction, while the rear axle resistance increases with speed because of oil churning. The brake caliper resistance, caused by pre-load friction, can be reduced by adjusting caliper tension. Table 3 summarizes the resistance components and proposed optimizations.
| Component | Resistance at 50 km/h (N) | Optimization Method | Target Resistance (N) |
|---|---|---|---|
| Cardan Shaft | 17.1 | Reduce universal joints and angles | 11.3 (at 100 km/h) |
| Rear Axle | 66.5 | Use low-friction bearings and lubricants | 66.5 (max) |
| Brake Caliper | 14.3 | Adjust pre-load tension | 8.1 (43% reduction) |
After applying these optimizations, the total driving resistance is reduced, as described by the new equation:
$$ F_{\text{total, optimized}} = 0.0401v^2 + 4.2478v + 125.09 $$
Simulating this updated resistance in the model yields a range of 270.2 km, an increase of 15 km over the baseline. This shows that mechanical improvements can substantially enhance the electric SUV’s efficiency.
In conclusion, this study demonstrates a comprehensive approach to optimizing the energy flow of an electric SUV. By combining control strategy adjustments, low-voltage accessory reductions, and driving resistance minimizations, the driving range can be extended significantly. The simulation model serves as a reliable tool for predicting these improvements, with total potential gains exceeding 50 km under the NEDC cycle. These findings underscore the importance of holistic energy management in electric SUVs, which can lead to better performance and wider adoption. Future work could explore real-world validation and additional factors like thermal management, further advancing the capabilities of electric SUVs.