The transition from hardware-centric automobiles to software-defined electric vehicles demands a new generation of validation methodologies capable of addressing complex interactions among thermal systems, charging infrastructure, battery management, and energy optimization. In this research paper, Abhishek Devgan introduces a Reliability-Centered Test Design (RCTD) framework that fundamentally redefines conventional automotive testing by integrating Reliability-Centered Maintenance (RCM), Failure Mode and Effects Analysis (FMEA), modified Risk Priority Number (RPN) modelling, and customer-impact analytics into a unified validation strategy. Rather than treating every requirement equally, the framework prioritizes engineering effort toward high-consequence failure modes, enabling automotive manufacturers to improve software quality while reducing unnecessary validation cycles. The study validates this methodology using 114 failure modes, 20 structured engineering case studies, and 20 real-world electric vehicle incidents, demonstrating an estimated 33% reduction in mandatory testing effort while maintaining a 90% retrospective defect detectability rate.
Engineering an Advanced Multi-Domain Risk-Based Validation Framework
One of the most significant technical contributions of this work is the development of a domain-integrated reliability engineering framework that simultaneously evaluates HVAC systems, AC/DC charging infrastructure, battery thermal management, and vehicle energy management instead of validating each subsystem independently. The proposed methodology systematically identifies 47 HVAC failure modes, 38 charging-system failure modes, and 29 energy-management failure modes, followed by a modified Risk Priority Number (RPN = Severity × Occurrence × Detection × Customer Frustration Factor) calculation that extends traditional FMEA by incorporating customer experience into engineering decision-making. The framework further introduces comprehensive validation methodologies including Hardware-in-the-Loop (HIL) simulation, CAN fault injection, battery chemistry emulation (LFP, NMC, and NCA), thermal chamber testing, extreme environmental validation (-25°C to +45°C), pilot signal boundary testing, connector thermal integrity, BMS handshake validation, and OCPP interoperability testing, creating a complete reliability validation ecosystem aligned with ISO 26262 and SAE J1739 principles.
Demonstrating Measurable Improvements in Electric Vehicle Software Reliability
The research establishes strong quantitative evidence supporting the effectiveness of the proposed methodology through comprehensive statistical and engineering analysis. Among the 114 identified failure modes, only 22 high-priority failures (19%) contributed nearly 79% of the cumulative system risk, validating the application of Pareto-based engineering prioritization for software validation. The framework successfully correlates 18 out of 20 documented production electric vehicle failures (90%) with pre-defined RCTD validation scenarios, demonstrating its capability to identify critical defects before customer exposure. The study further evaluates technically demanding scenarios involving 800V DC fast charging, battery thermal runaway precursors, Kalman-filter State-of-Charge estimation drift, AI-based diagnostic robustness, grid-interactive charging, battery aging under repeated fast-charging cycles, regenerative braking calibration, cloud-connected charging architectures, and software mode-conflict validation. These findings illustrate how predictive, reliability-driven validation can significantly enhance software quality while reducing validation complexity and development cost.
Establishing a Scalable Validation Blueprint for Future Electric Vehicle Platforms
This publication extends beyond theoretical research by proposing a practical validation architecture capable of supporting future generations of software-defined vehicles. The RCTD framework introduces a structured five-phase validation process that integrates risk modelling, structured FMEA, modified RPN prioritization, environmental stress testing, HIL validation, retrospective field-failure mapping, and standards-based traceability into a repeatable engineering workflow. By emphasizing battery thermal management, DC fast-charging interoperability, software-driven energy optimization, AI-assisted diagnostics, predictive defect detection, and customer-focused reliability engineering, the framework addresses several critical gaps that remain insufficiently covered by traditional automotive validation practices. The research provides automotive OEMs with a technically rigorous roadmap for reducing warranty risk, improving customer satisfaction, accelerating software release confidence, and advancing the reliability of next-generation electric vehicle ecosystems.
Key Technical Contributions
- Developed a Reliability-Centered Test Design (RCTD) methodology specifically for software-defined electric vehicles.
- Engineered a modified RPN algorithm by introducing a Customer Frustration Factor into traditional FMEA-based prioritization.
- Designed validation coverage for 114 EV failure modes across HVAC, Charging, and Energy Management domains.
- Created a five-phase engineering validation framework integrating FMEA, HIL simulation, environmental testing, Pareto analysis, and field-failure correlation.
- Proposed advanced validation strategies covering 800V DC fast charging, battery thermal management, CAN fault injection, OCPP interoperability, Kalman-filter SoC estimation, AI diagnostic robustness, and battery chemistry validation (LFP/NMC/NCA).
- Demonstrated 33% reduction in mandatory validation effort while achieving 90% real-world defect detectability, highlighting both engineering efficiency and practical applicability.