In recent years, the rapid advancement of electric vehicle technology has positioned it as a pivotal solution to global energy crises and environmental degradation. The China EV market has experienced exponential growth, driving innovations in powertrain systems to enhance efficiency and range. Traditional single-motor drive systems in electric vehicles often struggle with optimizing efficiency across diverse operating conditions, particularly under high-load and high-speed scenarios. To address this, we propose a dual-motor drive system that leverages fuzzy control theory for energy management, ensuring a balance between dynamic performance and economic efficiency. This study focuses on the design and validation of a dual-motor test bench, incorporating parameter matching, efficiency optimization, and real-world simulation to demonstrate the superiority of dual-motor configurations in electric vehicles.
The dual-motor drive system utilizes two motors with distinct power ratings—a primary motor and an auxiliary motor—coupled through mechanical structures to adapt to varying driving conditions. This configuration expands the high-efficiency operating region, unlike single-motor systems where efficiency peaks only in moderate torque and speed ranges. For electric vehicles, especially in the context of China EV development, maximizing energy utilization is critical due to battery capacity constraints and cost considerations. We begin by deriving the power requirements for a pure electric vehicle based on key scenarios: maximum speed, maximum gradability, and rapid acceleration. The power demand equations are as follows:
$$ S1 = \frac{u_{a,max}}{3600 \eta_m} \left( mg \times 0.018 + \frac{C_d A u_{max}^2}{21.15} \right) $$
$$ S2 = \frac{u_a}{3600 \eta_m} \left( mg \times 0.018 \cos \alpha_{max} + mg \sin \alpha_{max} + \frac{C_d A u_p^2}{21.15} \right) $$
$$ S3 = \frac{1}{3600 \eta_m} \left( \delta m \frac{u_m^2}{7.2 t_m} + mg \times 0.018 \frac{u_m}{1.5} + \frac{C_d A u_m^3}{52.875} \right) $$
Here, $u_{a,max}$ represents the maximum vehicle speed, $m$ is the fully loaded mass, $C_d$ is the air resistance coefficient, $\eta_m$ is the transmission efficiency, $g$ is gravitational acceleration, $A$ is the frontal area, $u_a$ is the climbing speed, $\alpha_{max}$ is the maximum slope angle, $u_p$ is the speed factor for slope, $\delta$ is the rotational mass conversion coefficient, $u_m$ is the final speed during acceleration, and $t_m$ is the acceleration duration. The combined rated power of the dual motors must exceed the sum of these scenario-based power demands. The peak torque of the primary motor must satisfy:
$$ T \geq \frac{r}{i \eta_m} \left( mg f \cos \alpha_{max} + mg \sin \alpha_{max} + \frac{C_d A u_p^3}{52.875} \right) $$
where $i$ is the transmission ratio and $r$ is the wheel radius. For our target electric vehicle, with a maximum speed of 170 km/h, we matched the dual motors with rated powers of 18 kW and 56 kW, respectively. The efficiency Map diagrams, as shown in the analysis, indicate that the primary motor operates efficiently at higher speeds with lower torque, ideal for economic driving, while the auxiliary motor excels in high-speed, low-load conditions. This dual-motor setup allows for flexible mode switching—single-motor drive during low-speed high-load conditions and dual-motor coupling for enhanced performance.
The test bench design for the dual-motor system comprises six integral components: drive motor control, load motor control, data acquisition, power integration, mechanical structure, and software control. The mechanical section includes a power dynamometer, drive shafts, torque sensors, couplings, drive motors, and electromagnetic clutches, all arranged in a series configuration to ensure alignment of input/output shafts. Key specifications include drive motors with a rated voltage of 380 V and a power dynamometer capable of up to 8,000 r/min speed, 22 kW rated power, and a 0.01 s response time to simulate driving resistance. The electromagnetic clutch provides dynamic and static friction torques of 320 N·m and 280 N·m, respectively. The data acquisition system integrates inverters, transmission systems, data analyzers, and control units, with a power analyzer featuring multiple torque-speed channels for generating efficiency maps and enabling remote LAN control. We developed upper-computer software using LABVIEW for motor parameter programming, communication protocol handling, and real-time monitoring, while a programmable logic controller serves as the lower-level controller for executing operational commands based on input parameters and driving cycles.
Energy management strategy is pivotal for optimizing the dual-motor drive system in electric vehicles. We employed a fuzzy control approach to compute the demand torque and allocate power efficiently between the motors. The strategy involves identifying driving modes and distributing torque based on acceleration pedal opening and its rate of change. Two modes are defined: economy mode, which minimizes energy consumption by assigning smaller load coefficients at low pedal openings, and power mode, which prioritizes dynamic performance with higher load coefficients for low-speed scenarios. The target demand torque $T_{de}$ is derived as:
$$ T_{de} = T + \Delta T \left( po, \frac{dpo}{dt} \right) $$
where $T$ is the baseline torque, $po$ is the pedal opening, and $\Delta T$ is the compensation torque determined by fuzzy rules. To prevent excessive battery discharge, we constrain the demand torque using an over-voltage protection threshold:
$$ T_{de} \leq \frac{V_{ref}}{E_{ocv} – V_{de}} R_i / n $$
Here, $V_{de}$ is the battery output voltage, $E_{ocv}$ is the open-circuit voltage, $R_i$ is the battery internal resistance, and $n$ is the output speed of the dual-motor system. The power demand for dual-motor coupling is calculated as:
$$ P_{de} = \frac{T_1 n_1 / 9550}{\eta_1} + \frac{T_2 n_2 / 9550}{\eta_2} $$
where $T_1$ and $T_2$ are the torques of the primary and auxiliary motors, and $n_1$ and $n_2$ are their respective speeds. The torque distribution algorithm minimizes the total electrical power demand by iterating through possible torque combinations within motor limits. Driving mode rules are summarized in the table below, which dictates when to use single-motor or dual-motor drive based on speed and torque conditions.
| Speed Range (r/min) | Motor 1 Drive | Motor 2 Drive | Dual-Motor Drive |
|---|---|---|---|
| 0–1,800 | Torque < 125 N·m | Drive失效 | Torque ≥ 125 N·m |
| 1,800–2,000 | Torque < -0.3 × Speed + 665 | Drive失效 | Torque ≥ -0.3 × Speed + 665 |
| 2,000–3,200 | Drive失效 | Torque < 65 N·m | Torque ≥ 65 N·m |
| 3,200–4,000 | Drive失效 | Torque < -0.015 × Speed + 113 | Drive失效 |
| 4,000–5,000 | Drive失效 | Drive失效 | Drive Normal |
For validation, we conducted simulations using AVL CRUISE under two standard driving cycles: CLTC-P (China Light-Duty Vehicle Test Cycle-Passenger) and NEDC (New European Driving Cycle). The test electric vehicle parameters included a fully loaded mass of 900 kg, frontal area of 1.7 m², maximum speed of 120 km/h, acceleration time of 16 s, and a battery capacity of 13.9 kWh. The dual-motor system demonstrated superior performance in climbability and acceleration compared to a single-motor system. Specifically, at speeds above 60 km/h, the dual-motor configuration maintained a higher gradability, with a difference of up to 0.09 in slope capability, and achieved 0–100 km/h acceleration in under 14 seconds. Under NEDC conditions, the dual-motor system showed smoother efficiency curves, with average combined efficiencies of 0.889 and 0.894 for the primary and auxiliary motors, respectively, versus 0.805 for the single-motor system. The operating points of the dual-motor system were concentrated in high-efficiency regions (above 85% on average), whereas the single-motor points clustered around 80%. Additionally, in CLTC-P tests, the dual-motor electric vehicle extended the driving range by approximately 10 km and reduced energy consumption to below 7.5 kWh/100 km, compared to 8.95 kWh/100 km for the single-motor system. The response characteristics, including braking deceleration and torque response, confirmed the system’s stability and anti-interference capability, with settling times under 8 seconds for speed and 5 seconds for torque.
The economic and dynamic benefits of the dual-motor system are further quantified through efficiency analysis and range evaluation. The table below summarizes key performance metrics from our simulations, highlighting the advantages for electric vehicles in the China EV market.
| Metric | Single-Motor System | Dual-Motor System |
|---|---|---|
| Average Efficiency (NEDC) | 0.805 | 0.889 (Primary), 0.894 (Auxiliary) |
| Range at 10% SOC (km) | ~100 | ~111.35 |
| Energy Consumption (kWh/100 km, CLTC-P) | 8.95 | < 7.5 |
| Acceleration Time (0–100 km/h, s) | > 16 | < 14 |
| High-Speed Climbability (Slope at 60 km/h) | Lower | Higher by 0.09 |
In conclusion, our design of a dual-motor test bench for electric vehicles, integrated with fuzzy control-based energy management, effectively addresses the limitations of single-motor systems in terms of efficiency and performance. The dual-motor configuration ensures optimal torque distribution across various driving conditions, enhancing both dynamic capabilities and economic metrics such as range and energy consumption. This approach is particularly relevant for the evolving China EV industry, where improving battery utilization and reducing costs are paramount. Future work will focus on real-world implementation and exploring additional powertrain configurations to further advance electric vehicle technology.