In recent years, the global shift toward sustainable transportation has accelerated, with electric cars leading the charge. As a key player in this domain, China EV manufacturers are pushing the boundaries of performance and efficiency, particularly in high-speed motor systems. Traditional test benches often fall short in meeting the rigorous demands of these advanced motors due to limitations in loading capacity, mechanical constraints, and insufficient speed ranges. This inadequacy hinders comprehensive and accurate performance evaluations, which are critical for optimizing electric car powertrains. To address these challenges, we have developed a novel test bench specifically designed for high-speed motor systems in electric cars. This system integrates advanced components such as a permanent magnet synchronous motor-based dynamometer, a battery simulator, a cooling system, a data acquisition and analysis unit, and an upper computer control system. By leveraging high-precision sensors and dynamic simulation capabilities, our test bench enables detailed assessments of energy efficiency, power performance, thermal stability, and heat dissipation—key factors driving the evolution of China EV technologies.
The core functionality of our test bench revolves around simulating real-world driving conditions to evaluate electric car motor systems under various operational scenarios. It captures critical parameters including rotational speed, torque, output power, energy consumption, motor winding temperature, coolant temperature, and environmental factors like humidity and atmospheric pressure. This holistic approach ensures that the system can validate performance across normal function tests, dynamic characteristic assessments, safety verifications, and long-term reliability checks. For instance, the dynamometer applies adjustable loads to mimic road resistances such as aerodynamic drag, friction, and inertial forces, while the battery simulator provides stable DC voltage to the motor controller, replicating the power supply from an electric car battery. This setup allows for precise control over motor speed, controller output, and load torque, facilitating a thorough analysis of the electric car powertrain’s behavior under diverse conditions. The integration of these elements not only enhances testing accuracy but also supports the rapid development and optimization of China EV components, contributing to improved vehicle range and durability.
At the heart of our test bench is the system principle, which employs a coordinated network of components to replicate actual electric car operating environments. The power dynamometer, based on a permanent magnet synchronous motor, replaces conventional three-phase asynchronous AC motors to overcome efficiency losses and synchronization issues at high speeds. This motor offers superior performance with a maximum speed of 20,000 r/min and a peak power of 400 kW, significantly outperforming traditional models that typically cap at 12,000 r/min and 200 kW. The dynamometer works in tandem with a Siemens S120 bidirectional converter, which manages energy flow during regenerative braking by feeding electricity back to the grid, thus creating an efficient energy闭环. Additionally, the cooling system, implemented via an environmental chamber, maintains temperatures between -40°C and 150°C with rapid adjustment rates, ensuring thermal management aligns with electric car requirements. Data acquisition is handled by a high-sampling power analyzer (10 MS/s, 1 MHz bandwidth) and a Kistler 4550A KiTorq torque-speed sensor, which provides high dynamic response (10 kHz cutoff frequency) and wear-free operation due to its bearingless design. Communication across the system relies on CAN bus protocols for real-time reliability, enabling seamless interaction between the upper computer control and other modules. This integrated architecture allows for precise simulation of complex driving cycles, essential for advancing China EV innovations.
To quantify the advantages of our test bench, we can compare key performance metrics using mathematical models. For example, the efficiency of an electric car motor system is calculated as the ratio of output power to input power, expressed as: $$\eta = \frac{P_{\text{out}}}{P_{\text{in}}} \times 100\%$$ where \(P_{\text{out}}\) is the mechanical power output and \(P_{\text{in}}\) is the electrical power input. In high-speed applications, losses due to iron and mechanical friction become significant, and our bench minimizes these through the use of permanent magnet synchronous motors. The torque-speed characteristics can be modeled as: $$T = K_t \cdot I$$ where \(T\) is torque, \(K_t\) is the torque constant, and \(I\) is current. This linear relationship is crucial for accurate load simulation in electric car testing. Furthermore, the dynamic response of the system is governed by differential equations representing mechanical motion: $$J \frac{d\omega}{dt} = T_{\text{applied}} – T_{\text{load}} – B\omega$$ where \(J\) is the moment of inertia, \(\omega\) is angular velocity, \(T_{\text{applied}}\) is the motor torque, \(T_{\text{load}}\) is the load torque, and \(B\) is the damping coefficient. Our test bench’s ability to solve these equations in real-time via the dynamometer controller ensures faithful replication of electric car driving scenarios.
| Parameter | Traditional Three-Phase AC Motor | Permanent Magnet Synchronous Motor (Our Bench) |
|---|---|---|
| Maximum Speed (r/min) | 12,000 | 20,000 |
| Peak Power (kW) | 200 | 400 |
| Efficiency at High Speed | Lower due to励磁 losses | Higher, minimal losses |
| Dynamic Response | Slower, limited precision | Fast, high precision |
| Synchronization with Test Motor | Prone to errors | Accurate and stable |
The control software for our test bench is developed using LabVIEW, providing an intuitive virtual instrument interface for operators. The front panel incorporates strings, arrays, and drop-down lists to display test工况 tables, allowing users to set parameters and monitor results in real-time. Sub-VIs are dynamically called to optimize memory usage, with color-coded controls enhancing usability. This software facilitates the dynamic load simulation system, which employs a real-time vehicle dynamics model to compute resistances like aerodynamic drag (\(F_{\text{drag}} = \frac{1}{2} C_d \rho A v^2\)), where \(C_d\) is the drag coefficient, \(\rho\) is air density, \(A\) is frontal area, and \(v\) is velocity. By comparing预设 speed with simulated vehicle speed, the system adjusts throttle and brake inputs via digital-to-analog conversion and direct signal transmission to the powertrain controller. The resulting torque data from sensors is used to calculate operational loads, which the dynamometer applies as equivalent resistance. This闭环 control, managed through CAN bus communication, ensures accurate emulation of electric car driving conditions, from urban stop-and-go to highway cruising, thereby supporting the development of robust China EV systems.
In experimental validation, we conducted efficiency and temperature rise tests on a BT18000 permanent magnet synchronous motor, representative of those used in electric cars. The efficiency test was performed under controlled conditions (room temperature 20–25°C, humidity 50–65% RH), with the motor operating at an rated voltage of 800 V, rated power of 70 kW, and peak power of 160 kW. The test bench measured input and output power to generate an efficiency map, revealing a peak system efficiency of 94.74%, motor efficiency of 96.15%, and controller efficiency of 98.7%. These results demonstrate the bench’s capability to accurately assess energy conversion in electric car motors, a critical factor for extending the range of China EV models. The efficiency map data can be represented as a function of speed and torque: $$\eta(\omega, T) = f(P_{\text{in}}, P_{\text{out}})$$ where higher values indicate optimal performance regions for electric car applications.
| Parameter | Value |
|---|---|
| Voltage Level (V) | 550 |
| Peak Torque (Nm) | 121.36 |
| Peak Power (kW) | 115.69 |
| DC Side Max Current (A) | 191.23 |
| AC Side Max Current (A) | 191.03 |
| Max Controller Efficiency (%) | 98.7 |
| Max Motor Efficiency (%) | 96.15 |
| Max System Efficiency (%) | 94.74 |
For the temperature rise test, the motor was subjected to a high-speed condition of 18,000 r/min with a torque of -85 Nm at 800 V. The test bench monitored temperature changes using embedded sensors, recording an initial temperature of 83°C and a peak of 90°C after 51 seconds of loading, resulting in a temperature rise of 7°C. This minimal increase underscores the motor’s thermal stability and the effectiveness of the cooling system, which is vital for preventing overheating in electric car operations. The temperature dynamics can be described by the heat transfer equation: $$\frac{dT}{dt} = \frac{P_{\text{loss}} – hA(T – T_{\text{ambient}})}{mc}$$ where \(P_{\text{loss}}\) is power loss, \(h\) is heat transfer coefficient, \(A\) is surface area, \(T\) is temperature, \(T_{\text{ambient}}\) is ambient temperature, \(m\) is mass, and \(c\) is specific heat capacity. Our bench’s ability to track these parameters in real-time ensures reliable thermal management assessments for China EV components, aligning with industry standards like GB/T 18488-2024.
In conclusion, our designed test bench represents a significant advancement in the evaluation of high-speed motor systems for electric cars. By incorporating a permanent magnet synchronous dynamometer, high-precision sensors, and dynamic load simulation, it addresses the shortcomings of traditional setups and provides comprehensive performance data. The experimental results confirm its efficacy in measuring efficiency and thermal behavior, key metrics for enhancing the reliability and range of China EV models. As the electric car industry evolves, this test bench will play a pivotal role in accelerating innovation, ensuring that motors meet the demanding requirements of modern transportation while supporting global sustainability goals. Future work may focus on integrating AI-driven analytics for predictive maintenance and further optimizing energy recovery systems, solidifying its value in the ongoing development of electric car technologies.