In the rapidly expanding market of electric vehicles (EV cars), mass production stages frequently encounter quality issues that directly impact user experience, safety, and reliability. As a leading manufacturer in the EV cars industry, we have developed and implemented a systematic methodology—the Six-Step Method for Quality Problem Analysis—to address these challenges efficiently. This approach, rooted in the “Five Principles” and PDCA (Plan-Do-Check-Act) cycle, provides a replicable framework for resolving core quality problems in EV cars during mass production. In this article, we delve into the application of this method to three critical areas: autonomous driving systems, three-electric (battery, motor, and electronic control) systems, and thermal management systems in EV cars. By leveraging data-driven analysis, quantitative metrics, and structured problem-solving, we demonstrate how this method enhances quality control, reduces defects, and fosters continuous improvement in EV cars production. We will incorporate tables and mathematical formulas to summarize key concepts and ensure clarity, while emphasizing the importance of EV cars in the modern automotive landscape.
The Six-Step Method comprises six iterative stages: Situation Grasping, Cause Analysis, Root Cause Identification, Countermeasure Formulation, Effect Confirmation, and Source Feedback. Each step builds upon the previous one, creating a closed-loop system that ensures thorough problem resolution. For instance, in EV cars, issues like sudden autonomous driving failures, battery range degradation, and thermal management inefficiencies can be systematically addressed. We begin by outlining the methodology and then apply it to real-world scenarios in EV cars production, using quantitative data and formulas to illustrate improvements. This approach not only resolves immediate issues but also prevents recurrence by embedding lessons into organizational standards. Throughout this discussion, we will highlight the role of EV cars in driving innovation and the necessity of robust quality management systems to support their widespread adoption.
Overview of the Six-Step Method for Quality Problem Analysis
The Six-Step Method is a structured approach designed to tackle quality issues in EV cars during mass production. It integrates the Five Principles—focus on facts, process orientation, systematic thinking, continuous improvement, and respect for people—with the PDCA cycle to ensure comprehensive problem-solving. Below, we describe each step in detail, supported by a summary table and relevant formulas commonly applied in EV cars quality management.
| Step | Key Activities | Tools and Techniques | Application in EV Cars |
|---|---|---|---|
| 1. Situation Grasping | Collect data on fault phenomena, VIN, time, location; use 5W2H framework | Data analysis, 5W2H | Identify patterns in EV cars failures, e.g., autonomous driving exits |
| 2. Cause Analysis | Compare standards vs. actuals; use fishbone diagram; identify variation sources | Fishbone diagram, failure mode analysis | Analyze components like cameras in EV cars for defects |
| 3. Root Cause Identification | Conduct reproduction tests; perform 5WHY analysis | 5WHY, experimental validation | Trace issues to material or process changes in EV cars |
| 4. Countermeasure Formulation | Develop temporary and permanent measures for occurrence and outflow | Action planning, responsibility assignment | Implement hardware fixes or process updates in EV cars |
| 5. Effect Confirmation | Quantify improvements using metrics like defect rate, CPK | Statistical analysis, KPIs | Monitor performance in EV cars post-implementation |
| 6. Source Feedback | Convert insights to standards; update procedures; conduct training | Standardization, cross-functional training | Prevent recurrence across EV cars models |
In EV cars production, this method relies on quantitative metrics to drive decisions. For example, the defect rate ($ P_{defect} $) is calculated as:
$$ P_{defect} = \frac{N_{defective}}{N_{total}} \times 100\% $$
where $ N_{defective} $ is the number of defective EV cars units, and $ N_{total} $ is the total units produced. Similarly, the process capability index (CPK) is used to assess consistency in EV cars manufacturing:
$$ CPK = \min \left( \frac{USL – \mu}{3\sigma}, \frac{\mu – LSL}{3\sigma} \right) $$
where $ USL $ and $ LSL $ are the upper and lower specification limits, $ \mu $ is the process mean, and $ \sigma $ is the standard deviation. These formulas help in evaluating the effectiveness of countermeasures in EV cars production lines.
Application of the Six-Step Method to Autonomous Driving Quality Issues in EV Cars
Autonomous driving systems are critical for the safety and user experience of EV cars. In mass production, issues like sudden functional exits can arise, necessitating a systematic approach. We applied the Six-Step Method to resolve such problems in a high-end EV cars model, focusing on data-driven analysis and reproducible solutions.
Situation Grasping
We identified a batch of EV cars produced over three months where autonomous driving functions frequently exited unexpectedly. Data collection included vehicle identification numbers (VINs), occurrence times (e.g., after 5000 km mileage), and locations (primarily humid regions). Fault phenomena, such as “sensor abnormality” displays, were recorded, along with repair logs from service centers. For instance, in EV cars from the E30 series produced in May 2024, batch B2405XX, we observed a high incidence in southern rainy areas. Initial analysis pointed to front-view camera-related faults, as replacements resolved issues in many EV cars. Factory inspections of unsold EV cars showed no similar problems, narrowing the focus to specific production batches.
Cause Analysis
We compared the actual performance of front-view cameras in EV cars against design standards. The standard requires image clarity ≥90% and algorithm recognition rate ≥0.85 under conditions of humidity ≤80% and light intensity ≥500 lux. Faulty EV cars exhibited blurred imaging with recognition rates of only 0.6. Using a fishbone diagram, we analyzed potential variation sources: human factors (assembly errors), machine (sealing test equipment calibration), materials (lens protection film adhesive), methods (cleaning procedures), and environment (production humidity). Changes in production were traced to a switch in protection film brand on April 15 and delayed equipment calibration on May 1, leading to manual inspections. Testing revealed that the new film brand caused adhesive residue under high temperatures, and manual checks missed sealing defects in EV cars.
Root Cause Identification
Reproduction tests validated our hypotheses. We subjected cameras with the new film to 60°C for 2 hours, resulting in adhesive residue and reduced clarity. Similarly, sealing-defective cameras simulated rainy conditions showed internal fogging and recognition rate drops, matching fault phenomena in EV cars. Through 5WHY analysis, we identified root causes: insufficient validation of supplier material changes and lack of alternative plans for temporary process adjustments in EV cars production. This highlighted systemic gaps in change management for critical components in EV cars.
Countermeasure Formulation
We developed both temporary and permanent countermeasures. For occurrence, temporary actions included recalling affected EV cars from May production for camera replacements and adhesive cleaning. Permanent measures involved discontinuing the problematic film brand, reverting to a validated alternative, and ensuring 100% detection coverage with backup calibration tools. For outflow, we added temporary ultraviolet light inspections for adhesive residue and updated incoming inspection standards for EV cars cameras to include sealing and residue checks. Responsibility was assigned to specific teams with clear deadlines.
Effect Confirmation
Quantitative metrics confirmed improvements. The fault rate in EV cars dropped from 12% in May to 0.3% in June. Image clarity sampling合格率 increased from 85% to 99.5%, and simulated rainy tests showed no fogging. Customer complaints decreased by 98%, demonstrating the effectiveness of the countermeasures in EV cars. We used the defect rate formula to compute the improvement:
$$ P_{defect,initial} = 12\%,\quad P_{defect,final} = 0.3\%,\quad \text{Improvement} = \frac{12 – 0.3}{12} \times 100\% = 97.5\% $$
This significant reduction underscores the method’s impact on EV cars quality.
Source Feedback
We institutionalized the lessons by updating supplier change management protocols, requiring full validation tests for any material alterations in EV cars components. A checklist for change verification was implemented across key parts like cameras and radars. Training programs on residue detection were conducted quarterly for quality staff, ensuring sustained improvements in EV cars production.
Application of the Six-Step Method to Three-Electric System Quality Issues in EV Cars
The three-electric system—battery, motor, and electronic control—is fundamental to the performance of EV cars. In mass production, issues like battery range degradation can severely affect user satisfaction. We applied the Six-Step Method to address such problems in a specific EV cars model, emphasizing technical analysis and long-term solutions.
Situation Grasping
We focused on a batch of EV cars (Model A, March 2024 production) with 65 kWh lithium iron phosphate batteries, where multiple complaints indicated over 20% range reduction after 15,000 km, especially in high-temperature regions (above 35°C). Data showed battery capacity retention dropping from 500 km to under 400 km in常温 conditions, with worse performance in low temperatures. Factory tests on unused EV cars batteries showed 98% capacity retention, pointing to issues under high-mileage, high-temperature use in EV cars.
Cause Analysis
We examined the failure mechanism: lithium iron phosphate batteries suffer capacity fade due to positive electrode material collapse and electrolyte decomposition under heat. A fishbone diagram revealed variation sources: a change in positive material supplier’s grinding equipment in March led to uneven particle size, causing material powdering, while a reduction in laser welding power from 300W to 280W increased contact resistance and heat generation during charging. These changes, combined, accelerated degradation in EV cars batteries under high temperatures. We quantified the impact using the capacity fade rate formula:
$$ \text{Fade Rate} = \frac{C_{initial} – C_{current}}{C_{initial}} \times 100\% $$
where $ C_{initial} $ is the initial capacity and $ C_{current} $ is the current capacity. For faulty EV cars, this rate exceeded 20%.
Root Cause Identification
Reproduction tests involved subjecting batteries to 40°C environments with 100 charge-discharge cycles, resulting in 22% capacity fade (normal ≤15%), 30% positive material powdering, and oxidation at welding points. 5WHY analysis pinpointed root causes: lack of particle size inspection for incoming materials, insufficient high-temperature validation for process changes, and absence of dynamic condition tests in outgoing quality checks for EV cars.
Countermeasure Formulation
We formulated comprehensive countermeasures. For occurrence, temporary actions included free replacements of heat dissipation modules in delivered EV cars and OTA updates to adjust charging cut-off voltage, while permanent measures involved supplier switching with strict material specifications and process restorations. For outflow, temporary high-temperature testing was added, and permanent updates to inspection standards were made. Responsibilities were assigned, such as the procurement manager for material changes and the quality manager for test enhancements. The following table summarizes the countermeasures for EV cars:
| Type | Temporary Measures | Permanent Measures | Responsible Party | Timeline |
|---|---|---|---|---|
| Occurrence | Replace heat dissipation modules; OTA updates | Switch suppliers; restore welding power; update BMS algorithms | Service Director, Procurement Manager | 4 weeks, 1 month |
| Outflow | Add high-temperature cycle tests | Revise inspection standards; automate testing | Quality Manager, Equipment Department | 3 days, 2 months |
Effect Confirmation
Post-implementation, range degradation in EV cars under 40°C dropped from 22% to 12%, complaints reduced to zero within four weeks, and defect detection rates improved from 15% to 1.2%. New battery batches showed 93% capacity retention after 100 high-temperature cycles, compared to 78% previously. Extreme condition tests (45°C with continuous climbing) confirmed stable performance in EV cars, with battery temperatures peaking at 42°C instead of 50°C. We used the CPK formula to assess process stability:
$$ CPK_{initial} = 0.8,\quad CPK_{final} = 1.5,\quad \text{indicating improved capability in EV cars production} $$
Source Feedback
We updated supplier management manuals and process change protocols, incorporating high-temperature validation requirements. Training programs on battery degradation prevention were institutionalized, with quarterly refreshers for production and quality teams involved in EV cars manufacturing.
Application of the Six-Step Method to Thermal Management Quality Issues in EV Cars
Thermal management systems are vital for maintaining optimal performance in EV cars, especially under extreme conditions. Issues like power attenuation during high-temperature and frequent start-stop scenarios were addressed using the Six-Step Method, focusing on design and material improvements.
Situation Grasping
We investigated a batch of EV cars (Model B, April 2024 production) where users reported power reduction under high temperatures (above 38°C) and frequent start-stop cycles. Data indicated motor temperatures reaching 120°C (exceeding the 110°C limit) and coolant flow at 75% of design value. Unsold EV cars showed normal static performance, highlighting dynamic operational issues in EV cars.
Cause Analysis
We analyzed the thermal management design: cooling pipes had excessive right-angle bends and small diameters, causing turbulent flow and reduced efficiency. Radiators were positioned too close to condensers, disrupting airflow. Material-wise, coolant boiling point was 108°C, below the 110°C standard. A change in radiator fin spacing from 1.5mm to 1.2mm in April, without full validation, further reduced散热效率 by 15%. Combined, these factors impaired cooling in EV cars under stress. The heat dissipation efficiency ($ \eta $) can be expressed as:
$$ \eta = \frac{Q_{actual}}{Q_{design}} $$
where $ Q_{actual} $ is actual heat dissipation and $ Q_{design} $ is design value. For faulty EV cars, $ \eta $ was as low as 0.75.
Root Cause Identification
Simulation tests under 38°C with 30 start-stops per hour showed flow rates dropping to 70% of design and motor temperatures hitting 125°C, triggering power limits. 5WHY analysis revealed root causes: design omissions for extreme conditions, insufficient validation of component changes, and lack of dynamic testing in quality checks for EV cars.
Countermeasure Formulation
We implemented targeted countermeasures. Design teams optimized pipe diameters and reduced bends, while structural teams adjusted radiator positions and reverted fin spacing. Software upgrades improved temperature control algorithms, and procurement switched to higher-boiling-point coolant. Responsibilities and timelines were clearly defined, as shown in the table below for EV cars:
| Area | Countermeasure | Responsible Party | Timeline |
|---|---|---|---|
| Design | Increase pipe diameter to 18mm; reduce bends | Design Department | 45 days |
| Structure | Adjust radiator position; revert fin spacing | Structure Department | 30 days |
| Software | Upgrade temperature control algorithms | Software Department | 30 days |
| Materials | Switch to coolant with boiling point ≥112°C | Procurement Department | 20 days |
Effect Confirmation
After improvements, motor temperatures in EV cars under 38°C and frequent start-stops decreased to 105°C, and coolant flow reached 98% of design. Third-party evaluations showed thermal stability scores rising from 72 to 91, and customer complaints vanished within a month. The improvement in heat dissipation efficiency was calculated as:
$$ \eta_{initial} = 0.75,\quad \eta_{final} = 0.98,\quad \text{Enhancement} = \frac{0.98 – 0.75}{0.75} \times 100\% = 30.7\% $$
This confirms the effectiveness of the countermeasures in EV cars.
Source Feedback
We revised thermal management design standards to include simulation requirements for extreme conditions, such as high-temperature and start-stop scenarios. A checklist for component change validation was established, and historical case databases were created to facilitate knowledge sharing. Training on flow simulation and dynamic testing was conducted regularly for EV cars development teams, ensuring sustained quality improvements.
Conclusion
The Six-Step Method for Quality Problem Analysis has proven highly effective in resolving critical issues in EV cars during mass production. By applying this structured approach to autonomous driving, three-electric systems, and thermal management, we have demonstrated significant reductions in defect rates, enhanced performance metrics, and improved user satisfaction. The method’s emphasis on data-driven analysis, root cause identification, and systematic countermeasures ensures that solutions are not only immediate but also sustainable. As the EV cars industry continues to grow, adopting such methodologies will be crucial for maintaining high quality standards and fostering innovation. We recommend that manufacturers of EV cars integrate similar frameworks into their quality management systems to address the evolving challenges of mass production. Through continuous improvement and source feedback, the reliability and safety of EV cars can be consistently elevated, supporting the global transition to electric mobility.