Fleet-Based Reliability Framework for Low-Voltage Electrical Subsystems in Autonomous Vehicles
Journal of Engineering Research and Sciences, Volume 5, Issue 9, Page # 35-60, 2026; DOI: 10.55708/js0509003
Keywords: Autonomous vehicles, Fleet reliability, Low-voltage electrical subsystem, Functional safety, Assurance case
(This article belongs to the Section Robotics (ROB))
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Pawar, S. (2026). Fleet-Based Reliability Framework for Low-Voltage Electrical Subsystems in Autonomous Vehicles. Journal of Engineering Research and Sciences, 5(9), 35–60. https://doi.org/10.55708/js0509003
Shrikant Pawar. "Fleet-Based Reliability Framework for Low-Voltage Electrical Subsystems in Autonomous Vehicles." Journal of Engineering Research and Sciences 5, no. 9 (September 2026): 35–60. https://doi.org/10.55708/js0509003
S. Pawar, "Fleet-Based Reliability Framework for Low-Voltage Electrical Subsystems in Autonomous Vehicles," Journal of Engineering Research and Sciences, vol. 5, no. 9, pp. 35–60, Sep. 2026, doi: 10.55708/js0509003.
The low-voltage (LV) electrical subsystem of an autonomous vehicle (AV), spanning the auxiliary battery and management system, DC-DC conversion, power distribution, compute ECUs, in-vehicle communication networks, and harnesses, must operate continuously; its failures are a leading cause of in-service downtime even when propulsion is healthy. Yet no method connects fleet-derived reliability evidence to the assurance arguments AV safety cases require fleet-reliability methods are mature but not directed at AV LV electronics, while ISO 26262, ISO 21448, and UL 4600 specify what evidence a safety case needs, and ISO 26262 obliges field monitoring, none specifies how field evidence becomes an apportioned, sufficiency-justified assurance claim. Objective. This paper closes that gap with a direct bridge from field reliability estimates to standards-aligned assurance artefacts. Methodology. The bridge is an eight-layer, source-agnostic, continuous framework: component taxonomy; fleet data ingestion; duty-cycle-aware exposure modelling; reliability estimation reported in both per-hour (PMHF, MTBF, FIT) and per-mile (issues per million miles, IPMM) units; predictive health monitoring; safety-and-standards integration that apportions the item-level ISO 26262 target across the component register, classifies each fault, tests evidence sufficiency, and emits claim records rendered as Goal Structuring Notation fragments; containment; and governance. Contributions. (i) the first reliability framework scoped specifically to the AV LV electrical subsystem, including the communication architecture, connectors, and on-ECU power supply prior frameworks omit; (ii) a method translating field-derived failure intensities into safety-case evidence in both units, realized as a fourteen-field claim record, a PMHF apportionment, a fault-class transfer, an ES1-ES5 sufficiency criterion, and the conditions governing cross-operator comparison; and (iii) an AV-fleet containment ladder exploiting centralized dispatch and over-the-air channels. Because no public dataset exposes LV power-rail telemetry from a real AV fleet, validity is established by argument and illustrated with a worked example instantiated end to end for one safety goal; empirical validation is future work.
- Road vehicles — Functional safety, ISO Standard 26262, 2018.
- S. Wu, “Warranty data analysis: A review,” Quality and Reliability Engineering International, vol. 28, no. 8, pp. 795–805, 2012, doi:10.1002/qre.1282.
- L. Attardi, M. Guida, G. Pulcini, “A mixed-Weibull regression model for the analysis of automotive warranty data,” Reliability Engineering & System Safety, vol. 87, no. 2, pp. 265–273, 2005, doi:10.1016/j.ress.2004.05.003.
- Road vehicles — Safety of the intended functionality, ISO Standard 21448, 2022.
- Standard for Safety for the Evaluation of Autonomous Products, UL 4600, Underwriters Laboratories, 2020.
- R. Mariani, “An overview of autonomous vehicles safety,” in Proc. IEEE Int. Reliability Physics Symp. (IRPS), 2018, doi:10.1109/IRPS.2018.8353618.
- W. M. D. Chia, S. L. Keoh, C. Goh, C. Johnson, “Risk assessment methodologies for autonomous driving: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 16923–16939, 2022, doi:10.1109/TITS.2022.3163747.
- K. D. Majeske, “A mixture model for automobile warranty data,” Reliability Engineering & System Safety, vol. 81, no. 1, pp. 71–77, 2003, doi:10.1016/S0951-8320(03)00073-5.
- S. Wu, “A review on coarse warranty data and analysis,” Reliability Engineering & System Safety, vol. 114, pp. 1–11, 2013, doi:10.1016/j.ress.2012.12.021.
- R. Khoshkangini, P. Sheikholharam Mashhadi, P. Berck, S. Gholami Shahbandi, S. Nowaczyk, T. Niklasson, T. Jonsson, “Early prediction of quality issues in automotive modern industry,” Information, vol. 11, no. 7, p. 354, 2020, doi:10.3390/info11070354.
- R. Khoshkangini, M. Tajgardan, J. Lundström, M. Rabbani, D. Tegnered, “A snapshot-stacked ensemble and optimization approach for vehicle breakdown prediction,” Sensors, vol. 23, no. 12, p. 5621, 2023, doi:10.3390/s23125621.
- M. A. Gosavi, B. B. Rhoades, “Application of functional safety in autonomous vehicles using ISO 26262 standard: A survey,” in Proc. IEEE SoutheastCon 2018, 2018, doi:10.1109/SECON.2018.8479057.
- P. Sinha, “Architectural design and reliability analysis of a fail-operational brake-by-wire system from ISO 26262 perspectives,” Reliability Engineering & System Safety, vol. 96, no. 10, pp. 1349–1359, 2011, doi:10.1016/j.ress.2011.03.013.
- A. Schnellbach, G. Grießnig, “Development of the ISO 21448,” in Systems, Software and Services Process Improvement, EuroSPI 2019, Springer CCIS, vol. 1060, 2019, doi:10.1007/978-3-030-28005-5_45.
- D. Kinalzyk, “SOTIF process and methods in combination with functional safety,” in Systems, Software and Services Process Improvement, EuroSPI 2021, Springer CCIS, vol. 1488, 2021, doi:10.1007/978-3-030-85521-5_41.
- Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, SAE J3016_202104, SAE International, 2021.
- M. Chelouati, A. Boussif, J. Beugin, E.-M. El Koursi, “Graphical safety assurance case using Goal Structuring Notation (GSN) — challenges, opportunities and a framework for autonomous trains,” Reliability Engineering & System Safety, vol. 230, p. 108933, 2023, doi:10.1016/j.ress.2022.108933.
- Y. Dong, W. Huang, V. Bharti, V. Cox, A. Banks, S. Wang, X. Zhao, S. Schewe, X. Huang, “Reliability assessment and safety arguments for machine learning components in system assurance,” ACM Transactions on Embedded Computing Systems, vol. 22, no. 3, pp. 1–48, 2023, doi:10.1145/3570918.
- M. Ahsan, S. Stoyanov, C. Bailey, “Prognostics of automotive electronics with data driven approach: A review,” in Proc. 39th Int. Spring Seminar on Electronics Technology (ISSE), 2016, doi:10.1109/ISSE.2016.7563205.
- A. Prisacaru, P. J. Gromala, M. B. Jeronimo, B. Han, G. Q. Zhang, “Prognostics and health monitoring of electronic system: A review,” in Proc. 18th Int. Conf. Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), 2017, doi:10.1109/EuroSimE.2017.7926248.
- A. Prisacaru, P. J. Gromala, B. Han, G. Q. Zhang, “Degradation estimation and prediction of electronic packages using data-driven approach,” IEEE Transactions on Industrial Electronics, vol. 69, no. 3, pp. 2996–3006, 2022, doi:10.1109/TIE.2021.3068681.
- A. Kleyner, A. S. S. Vasan, M. Pecht, “A new application for failure prognostics — reduction of automotive electronics reliability test duration,” in Annual Conference of the PHM Society, vol. 9, no. 1, 2017, doi:10.36001/phmconf.2017.v9i1.2446.
- M. S. H. Lipu, S. Ansari, M. S. Miah, K. Hasan, S. T. Meraj, M. Faisal, T. Jamal, S. H. M. Ali, A. Hussain, M. M. Naser et al., “Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects,” Journal of Energy Storage, vol. 55, p. 105752, 2022, doi:10.1016/j.est.2022.105752.
- A. Aitio, D. A. Howey, “Predicting battery end of life from solar off-grid system field data using machine learning,” Joule, vol. 5, no. 12, pp. 3204–3220, 2021, doi:10.1016/j.joule.2021.11.006.
- D. Astigarraga, F. M. Ibáñez, A. Galarza, J. M. Echeverria, I. Unanue, P. Baraldi, E. Zio, “Analysis of the results of accelerated aging tests in insulated gate bipolar transistors,” IEEE Transactions on Power Electronics, vol. 31, no. 11, pp. 7953–7962, 2016, doi:10.1109/TPEL.2015.2512923.
- B. Ji, V. Pickert, W. Cao, B. Zahawi, “In situ diagnostics and prognostics of wire bonding faults in IGBT modules for electric vehicle drives,” IEEE Transactions on Power Electronics, vol. 28, no. 12, pp. 5568–5577, 2013, doi:10.1109/TPEL.2013.2251358.
- A. Forouzandeh Shahraki, S. Al-Dahidi, A. R. Taleqani, O. P. Yadav, “Using LSTM neural network to predict remaining useful life of electrolytic capacitors in dynamic operating conditions,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 236, no. 6, pp. 1069–1080, 2022, doi:10.1177/1748006X221087503.
- S.-F. Lokman, A. T. Othman, M.-H. Abu-Bakar, “Intrusion detection system for automotive Controller Area Network (CAN) bus system: A review,” EURASIP Journal on Wireless Communications and Networking, vol. 2019, no. 184, 2019, doi:10.1186/s13638-019-1484-3.
- S. Rajapaksha, H. Kalutarage, M. O. Al-Kadri, A. Petrovski, G. Madzudzo, M. Cheah, “AI-based intrusion detection systems for in-vehicle networks: A survey,” ACM Computing Surveys, vol. 55, no. 11, pp. 1–40, 2023, doi:10.1145/3570954.
- A. Uçar, M. Karaköse, N. Kırımça, “Artificial intelligence for predictive maintenance applications: Key components, trustworthiness, and future trends,” Applied Sciences, vol. 14, no. 2, p. 898, 2024, doi:10.3390/app14020898.
- J. Maier, H.-C. Reuss, “Design of zonal E/E architectures in vehicles using a coupled approach of k-means clustering and Dijkstra’s algorithm,” Energies, vol. 16, no. 19, p. 6884, 2023, doi:10.3390/en16196884.
- M. Gleißner, M.-M. Bakran, “Reliable & fault-tolerant DC/DC-converter structures,” in Proc. PCIM Europe 2014, 2014.
- H. Wang, M. Liserre, F. Blaabjerg, “Toward reliable power electronics: Challenges, design tools, and opportunities,” IEEE Industrial Electronics Magazine, vol. 7, no. 2, pp. 17–26, 2013, doi:10.1109/MIE.2013.2252958.
- H. Wang, M. Liserre, F. Blaabjerg, P. de Place Rimmen, J. B. Jacobsen, T. Kvisgaard, J. Landkildehus, “Transitioning to physics-of-failure as a reliability driver in power electronics,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 2, no. 1, pp. 97–114, 2014, doi:10.1109/JESTPE.2013.2290282.
- A. Gupta, R. Ayyanar, S. Chakraborty, “Novel electric vehicle traction architecture with 48 V battery and multi-input, high conversion ratio converter,” IEEE Open Journal of Vehicular Technology, vol. 2, pp. 448–463, 2021, doi:10.1109/OJVT.2021.3132281.
- A. Lahyani, P. Venet, A. Guermazi, A. Troudi, “Battery/supercapacitors combination in uninterruptible power supply (UPS),” IEEE Transactions on Power Electronics, vol. 28, no. 4, pp. 1509–1522, 2013, doi:10.1109/TPEL.2012.2210736.
- H. A. Gabbar, A. M. Othman, M. R. Abdussami, “Review of battery management systems (BMS) development and industrial standards,” Technologies, vol. 9, no. 2, p. 28, 2021, doi:10.3390/technologies9020028.
- M. Buffolo, D. Favero, A. Marcuzzi, C. De Santi, G. Meneghesso, E. Zanoni, M. Meneghini, “Review and outlook on GaN and SiC power devices: Industrial state-of-the-art, applications, and perspectives,” IEEE Transactions on Electron Devices, vol. 71, no. 3, pp. 1344–1355, 2024, doi:10.1109/TED.2023.3346369.
- G. Tshagharyan, G. Harutyunyan, S. Shoukourian, Y. Zorian, “Modeling and testing of aging faults in FinFET memories for automotive applications,” in Proc. IEEE International Test Conference (ITC), 2018, doi:10.1109/TEST.2018.8624890.
- A. Richter, T. Puig Walz, A. Marko, M. Rexer, F. Schnabel, “Components and their failure rates in autonomous driving,” in Proc. 33rd European Safety and Reliability Conference (ESREL 2023), 2023, doi:10.3850/978-981-18-8071-1_P398-cd.
- Failure Rates of Components — Expected Values, General, SN 29500, Siemens AG, 2018.
- Reliability data handbook — Universal model for reliability prediction of electronics components, PCBs and equipment, IEC Standard 62380, 2004.
- Failure Mechanisms and Models for Semiconductor Devices, JEDEC Publication JEP122H, JEDEC Solid State Technology Association, 2016.
- J. W. McPherson, H. C. Mogul, “Underlying physics of the thermochemical E model in describing low-field time-dependent dielectric breakdown in SiO2 thin films,” Journal of Applied Physics, vol. 84, no. 3, pp. 1513–1523, 1998, doi:10.1063/1.368217.
- Assurance Case Working Group, “Goal Structuring Notation Community Standard, Version 3,” SCSC-141C, Safety-Critical Systems Club, 2021.
- T. Kelly, R. Weaver, “The Goal Structuring Notation — a safety argument notation,” in Proc. Dependable Systems and Networks Workshop on Assurance Cases, 2004.
- W. Q. Meeker, L. A. Escobar, F. G. Pascual, Statistical Methods for Reliability Data, 2nd ed. Hoboken, NJ, USA: Wiley, 2021.
- M. E. Verma, R. A. Bridges, M. D. Iannacone, S. C. Hollifield, P. Moriano, S. C. Hespeler, B. Kay, F. L. Combs, “A comprehensive guide to CAN IDS data and introduction of the ROAD dataset,” PLOS ONE, vol. 19, no. 1, p. e0296879, 2024, doi:10.1371/journal.pone.0296879.
- Vaibhavi Tiwari, “Navigating the Autonomous Era: A Detailed Survey of Driverless Cars”, Journal of Engineering Research and Sciences, vol. 3, no. 10, pp. 21–36, 2024. doi: 10.55708/js0310003
- Raymond Ning Huang, Jing Ren, Hossam A. Gabbar, “The Current Trends of Deep Learning in Autonomous Vehicles: A Review”, Journal of Engineering Research and Sciences, vol. 1, no. 10, pp. 56–68, 2022. doi: 10.55708/js0110008
- Joshua Ogbebor, Xiangyu Meng, Xihai Zhang, “A Deep Reinforcement Learning Approach to Eco-driving of Autonomous Vehicles Crossing a Signalized Intersection”, Journal of Engineering Research and Sciences, vol. 1, no. 5, pp. 25–33, 2022. doi: 10.55708/js0105003