In recent years, the global shift toward sustainable transportation has accelerated the development of electric vehicles, with China EV markets leading in innovation and adoption. As a key component in these vehicles, the motor controller plays a critical role in managing the performance and efficiency of the powertrain. In this article, we present the design and implementation of an off-line test system specifically tailored for motor controllers in range-extended electric vehicles. This system addresses the need for comprehensive functional and safety testing, ensuring that controllers meet stringent standards in the rapidly evolving electric vehicle industry. We focus on the integration of hardware and software components to automate testing processes, enhance accuracy, and adapt to various voltage and current levels commonly found in China EV applications. Through detailed analysis and experimental validation, we demonstrate the system’s capability to perform static and dynamic tests, including potential equilibrium checks, CAN communication verification, current sensor accuracy assessments, peak condition evaluations, and passive discharge tests. The results highlight the system’s reliability and efficiency, contributing to the advancement of electric vehicle technologies.
The range-extended electric vehicle represents a significant innovation in the electric vehicle landscape, combining the benefits of pure electric drive with an onboard generator to extend driving range. This hybrid approach is particularly relevant in China EV markets, where infrastructure and consumer demands vary widely. The motor controller in such vehicles consists of two main units: the Motor Control Unit (MCU) for driving the traction motor and the Generator Control Unit (GCU) for managing the integrated starter generator (ISG). These controllers regulate power conversion, torque output, and energy regeneration, making their performance crucial for overall vehicle efficiency. In our research, we aimed to develop a test system that could rigorously evaluate these controllers under simulated real-world conditions, thereby supporting the growth of the electric vehicle sector. The system leverages modular design principles, utilizing National Instruments (NI) virtual instruments and embedded technologies to create a flexible and scalable platform. By incorporating CAN communication, customized power supplies, and load simulations, we enable thorough testing of motor controllers across different operational scenarios, ensuring they comply with international standards like GB 18384-2020 and GB/T 18488.2-2015. This article details the system’s architecture, testing methodologies, and empirical results, emphasizing its applicability to the diverse needs of the electric vehicle industry.
To understand the testing requirements, we first analyzed the structure and operation of range-extended electric vehicles. These vehicles utilize a powertrain that includes a battery system, drive system, range extender, and vehicle control unit. The MCU and GCU interact with the vehicle controller to manage energy distribution based on factors such as battery state of charge (SOC) and driver inputs. For instance, the MCU converts electrical energy from the battery into mechanical power to drive the wheels, while the GCU controls the ISG to start the engine or generate electricity for battery charging. This complexity necessitates a test system that can verify multiple parameters, including voltage, current, and communication integrity. In the context of China EV development, where manufacturers strive for high power density and integration, our test system provides a vital tool for quality assurance. We designed it to handle a wide voltage range of 250–450 V, with rated values of 350 V, and power levels up to 120 kW for MCU and 80 kW for GCU, reflecting typical specifications in the electric vehicle market. The following sections elaborate on the test system’s design, implementation, and validation, showcasing its role in advancing electric vehicle reliability and performance.
The test system’s hardware architecture is built around an industrial computer serving as the core, which coordinates various modules through data acquisition cards, CAN interfaces, and programmable logic controllers (PLCs). We selected components such as the NI PCI-6323 data acquisition card for high-speed signal processing and the Peak-CAN PCIe card for robust communication, ensuring accurate data handling in electric vehicle applications. The power management module employs a DC feedback electronic load capable of delivering up to 500 V and 600 A, simulating the battery’s role in an electric vehicle. This module is controlled via LAN interface, allowing precise adjustment of voltage and current levels to mimic different driving conditions. Additionally, the fixture module includes pneumatic cylinders for automated connection of interfaces, such as three-phase, busbar, and low-voltage ports, facilitated by PLC-controlled valves. The cooling system, managed by a chiller unit, maintains temperatures at 45°C with a flow rate of 10 L/min, critical for preventing overheating during peak load tests in electric vehicle controllers. To mitigate interference from high-voltage signals, we implemented shielding for all cables and separated high and low-voltage wiring layers, enhancing measurement accuracy. This hardware setup enables comprehensive testing of motor controllers, aligning with the demands of the China EV industry for efficient and safe operation.
For the software component, we developed a user-friendly interface using the C# platform, which supports test sequence configuration, real-time monitoring, and data logging. The software allows operators to set parameters, select test items, and view results through a graphical interface, making it adaptable to various electric vehicle controller models. It integrates CAN communication protocols to send and receive messages, enabling dynamic control of the motor controller’s internal functions. Test sequences can include loops, conditions, and retries, providing flexibility for different testing scenarios. Data acquired during tests, such as current and voltage readings, are processed and stored for analysis, with reports generated in standard formats. This software design emphasizes modularity, allowing easy updates and customization for future electric vehicle technologies. In China EV applications, where rapid prototyping and production are common, such software capabilities reduce testing time and improve reliability.
In terms of testing methodology, we divided the process into static and dynamic phases. Static tests, conducted without high-voltage activation, include potential equilibrium verification and CAN communication checks. For potential equilibrium, we used a four-wire resistance measurement method to minimize errors from lead and contact resistances. The resistance $$ R $$ between test points is calculated as $$ R = \frac{V}{I} $$, where $$ V $$ is the voltage difference and $$ I $$ is the excitation current. The relative deviation $$ R_d $$ is given by $$ R_d = \frac{|x_i – \bar{x}|}{\bar{x}} \times 100\% $$, where $$ x_i $$ is the individual measurement and $$ \bar{x} $$ is the average. A deviation within 5% indicates acceptable consistency, crucial for safety in electric vehicles. CAN communication tests involve sending and receiving extended frame messages at 250 kbit/s, verifying the controller’s ability to handle data traffic typical in electric vehicle networks.
| Test Point | Average Resistance (Ω) | Relative Deviation (%) | Judgment |
|---|---|---|---|
| A | 0.0063 | 2.21 | Pass |
| B | 0.0062 | 3.71 | Pass |
| C | 0.0064 | 2.68 | Pass |
| D | 0.0062 | 4.19 | Pass |
Dynamic tests assess the controller’s performance under operational conditions. Current sensor accuracy is evaluated by comparing measured values from test equipment with those reported via CAN messages. The relative error is computed as $$ \frac{|I_1 – I_2|}{I_2} \leq \text{sensor accuracy} $$, where $$ I_1 $$ is the CAN-reported current and $$ I_2 $$ is the actual measured current. For peak condition tests, the system drives the MCU and GCU to their maximum outputs—480 A and 230 A, respectively—for 30 seconds, using inductive loads calculated with $$ Z = \frac{\sqrt{3} \cdot U}{I} $$ and $$ L = \frac{\sqrt{3} \cdot U}{2\pi f I} $$, where $$ U $$ is voltage, $$ I $$ is current, and $$ f $$ is frequency. Passive discharge tests monitor the time for capacitor voltage to drop from 450 V to below 60 V, with a requirement of less than 120 seconds for electric vehicle safety.
| Current Setpoint (A) | CAN Current (A) | Measured Current (A) | Relative Error (%) | Judgment |
|---|---|---|---|---|
| 100 | 100.012 | 99.218 | 0.80 | Pass |
| 200 | 200.121 | 203.279 | 1.55 | Pass |
| 300 | 300.134 | 299.534 | 0.20 | Pass |
| 400 | 400.402 | 399.747 | 0.16 | Pass |
| 480 | 480.021 | 482.101 | 0.43 | Pass |
During testing, we conducted multiple runs to ensure repeatability. For instance, in peak condition tests, the three-phase currents remained within ±3% of the target values, demonstrating the system’s precision. The passive discharge tests showed consistent discharge times around 70 seconds, well under the 120-second limit, confirming the controller’s compliance with safety standards for electric vehicles. Overall, the test system completed evaluations in under 7 minutes per controller, making it highly efficient for production lines in the China EV sector. The integration of automated sequences and real-time data acquisition reduces human error and enhances throughput, addressing the scalability needs of electric vehicle manufacturers.
In conclusion, our off-line test system for motor controllers in range-extended electric vehicles offers a robust solution for verifying performance and safety in the electric vehicle industry. By combining advanced hardware and software, we have created a platform that adapts to various testing scenarios, from static checks to dynamic simulations. The system’s ability to handle high voltages and currents, coupled with accurate data processing, ensures that motor controllers meet the rigorous demands of China EV markets. Empirical results validate its reliability, with all test items showing high repeatability and compliance with international standards. As electric vehicle technologies continue to evolve, this test system provides a foundation for future enhancements, such as integrating artificial intelligence for predictive maintenance or expanding to other components. We believe that such innovations will play a pivotal role in accelerating the adoption of electric vehicles worldwide, contributing to a sustainable transportation ecosystem.
Looking ahead, we plan to further optimize the test system for emerging trends in the electric vehicle sector, including higher power densities and faster charging capabilities. The modular design allows for easy upgrades, ensuring that it remains relevant as China EV standards evolve. Additionally, we aim to incorporate more sophisticated algorithms for data analysis, enabling proactive fault detection and performance forecasting. By continuing to refine this system, we hope to support the global transition to electric mobility, reducing carbon emissions and enhancing energy efficiency. The success of this project underscores the importance of interdisciplinary collaboration in advancing electric vehicle technologies, and we are committed to contributing to this exciting field through ongoing research and development.