In recent years, the global shift in energy structures has accelerated, leading to an explosive growth in the electric car industry, particularly in China EV markets. As electric vehicles become more prevalent, faults in their electronic control systems have emerged as one of the most common issues faced by maintenance technicians. Service centers and repair shops now demand skilled professionals capable of handling these complexities. In vocational education, standards such as the “New Energy Vehicle Technology Teaching Standards” emphasize “fault diagnosis and exclusion” as a core competency. However, current practical training often highlights a significant disconnect: approximately 65% of training time is devoted to equipment operation, while only 12% focuses on constructing fault trees. Moreover, existing assessment systems prioritize correct detection outcomes, accounting for 80% of scores, with analysis process rationality making up just 15%. This imbalance contributes to problems like “theory-practice disconnection” and “weak analytical skills.” To address these issues, I have developed a project-based approach centered on real-world enterprise cases, such as communication line faults in the Vehicle Control Unit (VCU) of electric cars, to reform training curricula and enhance student capabilities.
The project-oriented reform path for electric car fault diagnosis training involves several key stages, beginning with scenario design that simulates actual repair orders. For instance, in a China EV model like the BYD Qin EV, students work in groups to validate faults on real vehicles and document related symptoms in work orders. A typical task might involve a VCU CAN H line break, requiring students to record at least three observable phenomena: no change in brake pedal height when the start button is activated, absence of vacuum pump operation sounds in the front compartment, normal instrument panel display with SOC values but no OK light, illumination of warning lights for the powertrain, ESP, and parking systems, a “check powert system” message, and inability to shift into drive or reverse gears. Additionally, the motor control unit (MCU) may store fault codes like “U014187 – Communication fault with VCU.” This immersive setup ensures that learners engage with authentic electric car scenarios, bridging gaps between theoretical knowledge and hands-on application.
Control strategy analysis forms the next critical phase, where students utilize circuit diagrams to discuss control logic related to fault symptoms, rather than merely performing point-by-point checks. For example, when analyzing the failure of the brake vacuum pump to operate upon ignition, students must consider potential causes such as pump or control line faults, VCU power supply issues, or communication line disruptions. By integrating fault code U014187 and the inability to communicate with the VCU via diagnostic tools, they narrow down possibilities to VCU power/ground faults, communication line problems, or VCU internal failures. To deepen analytical skills, students are tasked with plotting measured CAN signal waveforms and interpreting them, moving beyond simple resistance measurements. This approach fosters a systematic understanding of electric car systems, as illustrated by the following formula for signal integrity in CAN networks:
$$V_{diff} = V_{CAN_H} – V_{CAN_L}$$
where \( V_{diff} \) represents the differential voltage critical for reliable communication. In normal operation, \( V_{CAN_H} \) and \( V_{CAN_L} \) should mirror each other, but a break in the CAN H line causes \( V_{CAN_H} \) to drop, leading to communication failures. This mathematical representation helps students visualize and diagnose issues in China EV architectures.
Subsequently, students develop fault检修 schemes by constructing fault trees and outlining diagnostic pathways. They meticulously record each step’s data in work orders, adhering to a “receiver-channel-source” detection principle. For instance, in diagnosing a VCU CAN H break, students use an oscilloscope to measure waveforms at the VCU end, observing a downward flip in the CAN H signal. Normal operation requires mirrored waveforms between CAN H and CAN L, so a discrepancy indicates a break or poor connection. Further tests involve disconnecting the battery negative terminal and VCU connector GK49, then measuring resistance between the VCU and gateway; an infinite resistance confirms the CAN H line break. After repair, students verify normal vehicle operation, clearing fault codes and re-measuring waveforms to ensure resolution. This process not only reinforces practical skills but also encourages mechanistic analysis, such as understanding how a CAN H break disrupts module communication, preventing high-voltage system activation and OK light illumination in electric cars.
To quantify the impact of various fault factors in electric car systems, I introduce a probability-based model for fault diagnosis. Let \( P(F) \) represent the probability of a fault occurring, which can be decomposed using Bayesian inference:
$$P(F|E) = \frac{P(E|F) \cdot P(F)}{P(E)}$$
where \( P(F|E) \) is the posterior probability of fault \( F \) given evidence \( E \), \( P(E|F) \) is the likelihood, \( P(F) \) is the prior probability, and \( P(E) \) is the marginal probability of evidence. For example, in a China EV VCU communication fault, evidence \( E \) might include specific warning lights and fault codes, allowing students to calculate the most probable causes. This mathematical approach enhances diagnostic accuracy and reduces reliance on trial-and-error methods.
| Aspect | Traditional Training | Project-Based Training |
|---|---|---|
| Time Allocation for Equipment Operation | 65% | 40% |
| Time Allocation for Fault Tree Construction | 12% | 30% |
| Weight of Detection Correctness in Assessment | 80% | 50% |
| Weight of Analysis Rationality in Assessment | 15% | 30% |
| Student Proficiency in China EV Systems | Low to Moderate | High |
In the implementation phase, innovative applications such as virtual simulation provide a safe and efficient foundation for training. Given the high costs and risks associated with electric car fault diagnosis, I have integrated virtual仿真 software that allows students to practice on整车控制系统 in a controlled environment. The platform includes intelligent scoring systems that monitor操作流程 and step规范性, offering real-time feedback. For instance, students can simulate diagnosing a VCU communication fault in a China EV model, with the software evaluating their adherence to procedures. This not only builds confidence but also prepares them for hands-on work without the dangers of high-voltage components.
Following virtual training, students progress to standardized skill reinforcement using实训台架 that mirror enterprise production standards. These台架 are equipped with professional tools and detailed repair manuals, enabling tasks like system parameter measurement, fault code reading, and line connection checks. By incorporating interchangeable fault modules, the台架 simulate common electric car control system failures, such as those in VCU or battery management systems. This flexibility allows students to practice logical reasoning and fault exclusion, translating theoretical knowledge into practical skills. For example, a typical exercise might involve measuring CAN bus resistance and voltage levels to identify breaks, using formulas like:
$$R_{total} = R_{line} + R_{termination}$$
where \( R_{total} \) is the total resistance measured, \( R_{line} \) is the line resistance, and \( R_{termination} \) is the termination resistance, typically 120 Ω in CAN networks. Deviations from expected values indicate faults, reinforcing the importance of precise measurements in China EV maintenance.
Real-world能力进阶 is achieved through实车操作 in校企共建实训基地, where students handle actual售后维修工单 for electric cars. They encounter complex issues like battery thermal management anomalies, VCU communication faults, and drive motor performance degradation, all under the guidance of enterprise engineers. This exposure to authentic China EV scenarios cultivates problem-solving skills and familiarity with industry trends. For instance, in one case, students diagnosed a recurring communication fault by analyzing waveform distortions and applying the following signal-to-noise ratio (SNR) formula to assess signal quality:
$$SNR = 10 \log_{10} \left( \frac{P_{signal}}{P_{noise}} \right)$$
where \( P_{signal} \) and \( P_{noise} \) represent the power of the signal and noise, respectively. A low SNR indicated interference, leading to targeted repairs in the communication lines. Such experiences bridge the gap between classroom learning and professional practice, enhancing employability in the electric car sector.
The construction of a project-based teaching evaluation system is crucial for assessing student progress. I have designed a comprehensive framework that combines process and summative evaluations, quantitative and qualitative measures, and values individual growth. The evaluation指标体系 spans five dimensions: understanding and application of theoretical control logic, fault diagnosis analysis ability, practical operation skills, innovative thinking, and teamwork with professional素养. Each dimension is weighted differently to emphasize key areas, such as fault diagnosis analysis, which carries a higher proportion to align with industry demands for electric car technicians.
| Dimension | Weight (%) | Key Indicators |
|---|---|---|
| Theoretical Control Logic | 20 | Accuracy in explaining systems like VCU communication |
| Fault Diagnosis Analysis | 35 | Ability to construct fault trees and interpret data |
| Practical Operation Skills | 25 | Proficiency in using tools like oscilloscopes |
| Innovative Thinking | 10 | Proposing novel solutions for China EV faults |
| Teamwork and Professionalism | 10 | Collaboration and adherence to safety protocols |
The evaluation mechanism employs a tripartite approach involving teacher assessments, peer reviews, and enterprise mentor feedback. Teachers evaluate students based on理论知识运用, fault analysis logic, and操作规范性, while peers focus on communication and collaboration during group tasks. Enterprise mentors, drawing from industry experience, assess工程实践能力 and innovative thinking in real-world electric car projects. This multifaceted system ensures a balanced development of skills, preparing students for challenges in the rapidly evolving China EV landscape.
In conclusion, the project-oriented reform of electric car fault diagnosis training has proven effective in addressing the overemphasis on operation at the expense of analysis. By developing integrated projects like整车控制系统 diagnostics, I have fostered systematic thinking and innovation among students. The use of virtual仿真,实训台架, and实车操作 creates a progressive learning environment that enhances adaptability to industry needs. However, there is room for improvement, such as expanding the range of fault points in virtual software to cover more scenarios in China EV models. Overall, this approach not only improves fault cause analysis and job readiness but also contributes to the sustainable growth of the electric car sector by cultivating a skilled workforce capable of tackling emerging challenges.
Through this reform, I have observed significant advancements in students’ abilities to handle complex electric car systems. For instance, in one project, students applied differential equation modeling to predict fault propagation in VCU networks:
$$\frac{dI}{dt} = \frac{V – I \cdot R}{L}$$
where \( I \) is current, \( V \) is voltage, \( R \) is resistance, and \( L \) is inductance, helping them understand transient behaviors in communication lines. Such analytical tools, combined with hands-on experience, ensure that graduates are well-equipped to contribute to the innovation and maintenance of China EV technologies, driving the industry forward in a competitive global market.