As a researcher in automotive education, I have observed the rapid global energy transition driving the EV car industry to new heights, with production and sales leading the world for eight consecutive years. By 2024, market penetration exceeded 35%, fueling an explosive growth in industry scale and creating a massive talent gap. However, traditional training for EV cars faces three major pain points: high-voltage electrical safety risks limiting hands-on practice, high equipment update costs due to rapid iterations in intelligent connectivity, and difficulties in replicating core modules like battery management in teaching scenarios. The maturity of virtual simulation technology offers a promising solution, and its deep integration with real-world training is reshaping vocational education paradigms. In this article, I explore the theoretical logic, implementation pathways, and practical outcomes of a virtual-real integrated teaching model, aiming to build a “competency-oriented, virtual-real collaborative, dynamically optimized” training system for cultivating skilled EV car technicians who excel in craftsmanship, intelligence, and innovation.
In the context of EV cars, the theoretical framework for virtual-real integrated teaching is built on core concepts and a robust technical foundation. From my perspective, virtual-real integrated teaching leverages technologies like virtual reality and digital twins to combine virtual simulation environments with real training scenarios. Its essence lies in creating a bidirectional interaction system where virtual elements map reality and reality feeds back into the virtual, breaking through the limitations of time, space, equipment, and safety in traditional education. This enables the concretization of theoretical knowledge, pre-evolution of practical operations, and intelligentization of the teaching process. For EV cars, virtual simulation training is characterized by technical precision, safety enhancement, and adaptability. Technical precision involves accurate modeling of complex systems such as high-voltage battery management and motor control; safety is achieved by avoiding risks like electric shock and thermal runaway in virtual settings; and adaptability allows for dynamic updates to keep pace with intelligent connectivity advancements.
The technical support system for EV cars is multifaceted, comprising various virtual simulation technologies and platform architectures. I classify these technologies into virtual reality (VR), augmented reality (AR), digital twins (DT), and simulation software like MATLAB/Simulink. VR and AR are ideal for immersive operational scenarios, enhancing student interaction; DT enables real-time mapping and fault simulation of entire EV car systems; and simulation software excels in algorithm verification for abstract principles like control strategies. Together, these technologies complement each other in teaching applications. The platform architecture consists of hardware, software, and data layers. The hardware layer includes VR terminals, training benches, and sensors; the software layer integrates 3D modeling, simulation computing, and teaching management systems; and the data layer aggregates equipment operation data, student logs, and industry standards, supporting an integrated “teach-learn-practice-assess” workflow.
Traditional training for EV cars is plagued by several issues, which I have identified as equipment update lags, safety risks, and limited teaching scenarios. For instance, core components like intelligent connectivity controllers have short iteration cycles, leading to high procurement and maintenance costs. High-voltage systems pose electrocution hazards, and extreme scenarios such as battery thermal runaway are hard to replicate safely. Additionally, teaching scenarios are constrained by factors like location and weather, hindering practices like onboard network protocol analysis. The virtual-real integrated model addresses these by reducing costs through reusable virtual environments, enhancing safety by allowing repeated practice of high-risk scenarios, and expanding scenarios via digital twins that simulate full operating conditions. This innovation overcomes the “invisible, untouchable, unpracticable” challenges of traditional methods, providing new pathways for visualizing complex EV car principles.
In designing the virtual-real integrated training model for EV cars, the core philosophy revolves around technology empowerment and deep industry-education coupling. I aim to realign training with the “electrification, connectivity, and intelligence” trends of the EV car industry, transforming cutting-edge technologies—such as Tesla’s 4680 battery production processes and BYD’s blade battery thermal management—into teaching scenarios. This ensures that content stays synchronized with real-world production, addressing the typical 3-5 year lag in traditional training. By leveraging digital twins and VR, we create a teaching environment where physical devices are mirrored virtually, scenarios are infinitely expandable, and data drives intelligence, enabling controlled risks and traceable technological updates. This shifts the focus from device operation to holistic competency development, fostering skills in both hardware fault diagnosis and software strategy optimization for EV cars.
The target orientation is to build a three-dimensional competency matrix aligned with vocational standards, such as the National Vocational Skills Standard for EV car inspection and repair. I define this as a triad of technical skills, safety literacy, and innovative thinking. Students should master core skills like diagnosing high-voltage battery management system (BMS) faults, calibrating motor controller parameters, and parsing intelligent connectivity terminal protocols. They must also develop the ability to simulate and debug entire EV car control systems in virtual environments and analyze real vehicle data. Safety training involves immersive drills in over 30 risk scenarios, including high-voltage leakage and thermal runaway, aiming for 100% compliance and a 50% improvement in emergency response. Innovation is encouraged through open virtual platforms where students design algorithms for motor energy savings and battery balancing, validated across multiple standard driving cycles.
The layered architecture of the teaching model for EV cars includes hardware, software, and application layers, which I have designed to ensure seamless integration. In the hardware layer, we combine virtual interaction devices with real training equipment to form a dual-field support system. This involves deploying VR/AR terminals, smart training benches, and data sensors in the physical space, while the digital space uses high-performance computing and immersive projection for real-time rendering. Data exchange via industrial buses and IoT protocols creates an environment where operations are perceivable, states are monitorable, and faults are reproducible. For the software layer, I develop a hierarchical resource library with basic simulation tools and customized teaching modules. The base layer includes vehicle dynamics models and BMS simulation software, while the application layer offers interactive courseware for virtual assessments and parameter debugging. Digital twin technology facilitates a lifecycle management system that collects training data to iteratively update resources, keeping EV car education current with industry advances.
In the application layer, I design a dynamic “three-stage progressive” teaching process. Before class, students use virtual platforms for预习 tasks, simulating principles and pre-training operations; this reduces error rates by up to 60% in EV car scenarios. During class, a hybrid approach of virtual demonstration, real operation, and virtual-real verification is employed, with AR annotations highlighting key parameters and simulation systems validating logic. After class, virtual expansion scenarios allow students to independently design control strategies and test them under various conditions, fostering innovation. Throughout, intelligent learning analytics adjust difficulty and resource推送 based on student data.
The support systems for implementing this model in EV car training include a dual-teacher team, process evaluation, and quality monitoring. I establish a collaborative mechanism between theoretical teachers and enterprise engineers, where teachers need digital teaching skills like platform architecture understanding, and engineers contribute practical expertise in EV car technologies. Through joint training and project participation, the team develops integrated teaching capabilities. For evaluation, I design a three-dimensional system assessing operation standardization, innovative thinking, and collaboration effectiveness. Quantitative data from virtual platforms—such as action fluency and fault resolution time—are combined with qualitative assessments of strategy optimization. Blockchain technology records this data for traceable growth profiles. Quality monitoring involves a closed-loop system of teaching implementation, effect assessment, and resource iteration. Machine learning analyzes multi-source data to identify weaknesses, leading to optimized virtual scenarios and adjusted real equipment parameters. Regular industry-education meetings ensure that EV car teaching content evolves with technological standards.
In my practical exploration of virtual-real integrated training for EV cars, I draw on experiences from institutions that have implemented such models. For example, one vocational college addressed challenges like high safety risks and slow equipment updates by partnering with leading EV car manufacturers. They established a dedicated training center with VR safety cabins and intelligent training benches, connected via low-latency networks to digital twin platforms. This enabled simulation of extreme conditions, such as low-temperature charging and adverse weather driving, with high accuracy in fault replication. The three-stage teaching process was applied: virtual pre-training ensured 100% safety certification before real operations, in-class AR and simulation improved diagnostic efficiency by 40%, and post-class innovation sandboxes allowed students to submit optimization schemes, some of which were adopted by industry. Outcomes included competition awards, reduced training times, lower equipment costs, and higher graduate employability, demonstrating the model’s effectiveness for EV cars.
The results from these practices show significant improvements: students in reformed programs excelled in national skills competitions, with high-voltage fault diagnosis times shortened by 35% and strategy optimization success rates rising to 82%. Graduates adapted faster to EV car industry roles, with independent work rates reaching 75% in the first month. Equipment costs dropped by 65%, virtual simulation substitution rates hit 80%, and high-risk training frequency increased from 4 to 25 times annually. Resource reuse exceeded 90%, supporting cross-disciplinary teaching and boosting laboratory utilization by 200%. These metrics underscore the value of virtual-real integration in addressing the talent gap for EV cars.
In conclusion, my research on virtual-real integrated training for EV cars addresses the urgent need for skilled professionals in the era of carbon neutrality. By constructing a theoretical framework, layered implementation architecture, and robust support systems, I have developed a dynamic and collaborative training model that overcomes traditional limitations in equipment, safety, and scenarios. Case studies validate its scientific basis and replicability, offering a new paradigm for vocational education. Looking ahead, I emphasize the need to enhance data interaction precision, strengthen industry-education resource sharing, and improve teacher digital literacy. This will ensure that the teaching model evolves in sync with technological advancements, providing sustained support for cultivating versatile technicians capable of driving the future of EV cars.
To summarize key aspects of virtual-real integrated training for EV cars, I present the following tables and formulas. Table 1 outlines the classification of virtual simulation technologies and their applicability in EV car education:
| Technology | Application in EV Cars | Benefits |
|---|---|---|
| Virtual Reality (VR) | Immersive high-voltage safety drills | Enhances engagement and risk-free practice |
| Augmented Reality (AR) | Real-time parameter annotation during operations | Improves accuracy and learning efficiency |
| Digital Twin (DT) | Whole-vehicle system simulation and fault mapping | Enables scenario expansion and real-time feedback |
| Simulation Software | Control strategy validation and algorithm testing | Supports abstract principle comprehension |
Table 2 details the evaluation指标体系 for assessing student performance in EV car training, incorporating both quantitative and qualitative measures:
| Dimension | Metrics | Data Sources |
|---|---|---|
| Operation Standardization | Action fluency, parameter accuracy, fault resolution time | Virtual platform logs, sensor data |
| Innovative Thinking | Strategy optimization scores, anomaly response quality | Project submissions, peer reviews |
| Collaboration Effectiveness | Task分工合理性, data sharing efficiency | Group activity records, instructor assessments |
In terms of performance improvement, I use formulas to quantify outcomes. For example, the reduction in training time for EV car fault diagnosis can be expressed as:
$$ \text{Time Reduction} = \frac{T_{\text{traditional}} – T_{\text{virtual}}}{T_{\text{traditional}}} \times 100\% $$
where \( T_{\text{traditional}} \) is the average diagnosis time in traditional training and \( T_{\text{virtual}} \) is that in virtual-real integrated training. In practice, this yielded a 35% improvement.
Similarly, the cost savings from virtual simulation for EV cars can be modeled as:
$$ \text{Cost Savings} = C_{\text{equipment}} \times (1 – R_{\text{virtual}}) $$
where \( C_{\text{equipment}} \) is the initial equipment cost and \( R_{\text{virtual}} \) is the virtual substitution rate. With \( R_{\text{virtual}} = 0.80 \), savings reach 65%.
For safety training efficacy in EV cars, the improvement in emergency response speed is calculated as:
$$ \text{Speed Gain} = \frac{S_{\text{after}} – S_{\text{before}}}{S_{\text{before}}} \times 100\% $$
where \( S_{\text{before}} \) and \( S_{\text{after}} \) represent response times before and after virtual drills, resulting in over 50% gain.
These elements collectively demonstrate how virtual-real integration transforms EV car education, fostering a skilled workforce ready for industry demands. As EV cars continue to evolve, this model will play a crucial role in sustaining innovation and competency development.