In recent years, the rapid growth of the electric car industry, particularly in regions like China EV markets, has highlighted the need for effective vocational training. As an educator specializing in automotive technology, I have observed that many secondary vocational schools rely on teaching aids such as electric car demonstration racks for practical training in new energy vehicle courses. While these racks provide visual demonstrations, they often fall short for students with minimal electrical foundation, leading to suboptimal learning outcomes. The primary issues include the lack of integration with entire vehicle systems, inability to address historical faults, and oversimplified fault scenarios that hinder comprehensive problem-solving skills. To address these challenges, I have leveraged existing teaching conditions to conduct research on fault diagnosis for drive motors in electric cars. This study focuses on enhancing teaching equipment, adjusting instructional models, optimizing methods, and reforming assessment approaches. Rather than isolating drive motor instruction, I integrate it with other components and controllers in a full vehicle context, aiming to improve educational quality and prepare students for real-world scenarios in the China EV sector.
The current training equipment used in electric car education typically includes computer simulation platforms and motor test benches. Computer simulation platforms reduce instructor workload and offer hands-on operation experiences, but they lack the realism of actual operational issues, making measurement results less reliable for practical applications. On the other hand, motor test benches, which consist of small motors, lightweight power batteries, control components, and data acquisition modules, provide a closer approximation to real electric car conditions by collecting and analyzing data during motor operation. However, these setups often fail to replicate the interconnected nature of vehicle systems, where faults in one component, such as the drive motor, battery, or control units, can trigger chain reactions. In my research, I explored how to modify these benches to simulate common faults, transmit fault information via CAN bus to vehicle controllers, and record vehicle state data, thereby enabling students to diagnose and address issues more effectively in China EV contexts.
To improve the training equipment, I designed an enhanced setup based on a permanent magnet synchronous motor system test bench. The hardware structure includes a low-speed electric car drive motor with a rated power of 4 kW and operating voltage of 72 V, coupled with a motor controller. Key sensors in the controller, such as current, temperature, and voltage sensors, provide real-time data to the CAN bus for fault analysis. Additionally, simulation switches for ignition, throttle, brake, and gear selection mimic actual driving operations in an electric car. This allows students to observe motor behavior under various fault conditions and perform measurements to identify and resolve issues. The table below summarizes the core components of the hardware setup:
| Component | Specification | Function |
|---|---|---|
| Permanent Magnet Synchronous Motor | 4 kW, 72 V | Simulates drive motor operation in electric car |
| Motor Controller | Includes current, temperature, voltage sensors | Monitors and controls motor parameters |
| CAN Bus Module | SJA1000 controller, AT89C51 microcontroller | Facilitates data communication and fault transmission |
| Operation Switches | Ignition, throttle, brake, gear | Replicates driving scenarios for China EV training |
The establishment of CAN bus communication is crucial for replicating real electric car environments. The CAN bus module consists of a controller supporting CAN 2.0B protocol (e.g., SJA1000), a transceiver for differential signal handling, an AT89C51 microcontroller for data processing, a termination resistor to prevent signal reflection, and data transmission lines for inter-unit communication. This setup enables the transmission of fault data to the vehicle controller, allowing students to analyze historical faults and develop diagnostic skills. The data acquisition process utilizes a USBCAN second-generation interface card for stable data transfer, and the program structure for CAN bus data can be represented mathematically. For instance, the data frame error rate can be modeled using the formula: $$ P_e = \frac{1}{2} \text{erfc} \left( \sqrt{\frac{E_b}{N_0}} \right) $$ where \( P_e \) is the probability of error, \( E_b \) is the energy per bit, and \( N_0 \) is the noise power spectral density. This emphasizes the importance of reliable communication in electric car systems, particularly in China EV applications where accuracy is paramount.
For data monitoring, I implemented a software platform based on LabVIEW, which provides a graphical interface for real-time data visualization and analysis. The development involved setting up the LabVIEW environment, connecting it to data reading modules, programming basic functions, designing front panels and program diagrams, studying data types, and constructing the program structure. This allows students to monitor motor parameters and fault indicators dynamically, enhancing their understanding of electric car drive systems. The relationship between sensor inputs and fault outputs can be expressed using linear equations, such as: $$ V_{\text{out}} = k \cdot I_{\text{sensor}} + b $$ where \( V_{\text{out}} \) is the output voltage, \( I_{\text{sensor}} \) is the sensor current, and \( k \) and \( b \) are constants derived from calibration. This mathematical approach helps in quantifying fault conditions and improving diagnostic accuracy in China EV training.
Adjustments to the teaching methodology were essential to address the gaps in traditional approaches. First, I strengthened the theoretical foundation by introducing basic concepts through practical examples, such as using simple automotive lighting systems to demonstrate short circuits and open circuits. Students learn to use multimeters for fault identification and develop standardized troubleshooting procedures. Second, I enhanced the authenticity of teaching cases by incorporating 18 common drive motor faults based on real-world scenarios in electric cars. These faults, validated by industry professionals, include issues like phase current sampling faults and motor overtemperature, which are prevalent in China EV operations. Third, I reformed the assessment model by eliminating final theoretical exams and adopting a process-based evaluation system. Grades are now derived from project-based assessments, comprehensive fault exclusion tasks, and group performance, as summarized in the table below:
| Assessment Type | Weight | Description |
|---|---|---|
| Project-Based Assessments | 40% | Evaluation of individual fault diagnosis tasks |
| Comprehensive Fault Exclusion | 40% | Practical exams on resolving multiple faults in electric car systems |
| Group Performance | 20% | Collaboration and participation in China EV-related scenarios |
The implementation of this teaching research involved conducting practical activities with a focus on safety and non-destructive testing. Students first connect all hardware and software components of the fault testing system, initiate the monitoring interface, and compare data with normal motor operation values. Pre-set faults are introduced without damaging the motor structure, such as by disrupting circuits or adding simulated fault points. After starting the motor, students observe external conditions and data changes, record findings, and analyze fault conditions and relevant values. Based on the impact, faults are categorized into severity levels, and appropriate pre-processing plans are developed, including responses from the vehicle controller and motor controller. This hands-on approach fosters logical thinking and problem-solving skills essential for maintaining electric cars in China EV environments.
A key aspect of the implementation is the definition of specific faults, which I derived from common issues in electric car drive systems. The table below outlines these faults, their CAN bus communication IDs, controller responses, and handling strategies, emphasizing their relevance to China EV applications:
| Fault Type | CAN Bus ID | Motor Controller Response | Vehicle Controller Response | Technician Handling | Severity Level |
|---|---|---|---|---|---|
| Phase Current Sampling Fault | 0x012 | Disable motor enable, reset torque and speed | Assess battery and motor state for high-voltage shutdown | Restart vehicle; if persistent, inspect | Critical |
| Motor Overtemperature | 0x012 | Disable motor enable, reset commands | Evaluate for immediate high-voltage system closure | Restart; inspect if needed | General |
| Bus Undervoltage | 0x012 | Reduce power output | No action | Monitor and warn user | Warning |
| IGBT Overtemperature | 0x012 | Disable motor enable, reset commands | Assess for system shutdown | Restart; inspect if needed | General |
| Overspeed Fault | 0x012 | Reduce power output | No action | Monitor and warn user | Warning |
Each of these 18 faults serves as a teaching task, where students use resources like technical manuals and online materials to research, test with tools, identify fault points, and report findings. Group collaboration is emphasized, with students following standardized procedures and completing technical assessment forms. After fault exclusion, groups engage in self and peer evaluations, discuss problems, and consolidate knowledge. This methodology not only deepens understanding of electric car drive motors but also cultivates professional habits suited for China EV industries. The mathematical modeling of faults, such as the relationship between temperature and resistance in overtemperature scenarios, can be described by: $$ R(T) = R_0 [1 + \alpha (T – T_0)] $$ where \( R(T) \) is the resistance at temperature \( T \), \( R_0 \) is the reference resistance, \( \alpha \) is the temperature coefficient, and \( T_0 \) is the reference temperature. This equation helps students quantify thermal effects and improve diagnostic precision.
In conclusion, the redesigned equipment and adjusted teaching model have transformed student learning outcomes in electric car technology education. By integrating fault diagnosis with full vehicle systems, students gain practical skills and develop a systematic approach to problem-solving. This research underscores the importance of adaptive vocational training in supporting the growth of the China EV market, equipping future technicians with the expertise to handle complex real-world challenges. The iterative process of testing and refinement has proven that hands-on, context-rich education is key to mastering electric car drive motor fault diagnosis, ultimately contributing to a skilled workforce in the evolving automotive landscape.