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Automobile is one of the most widely distributed cyber-physical systems. Over the last few years, the electronic explosion in automotive vehicles has significantly increased the complexity, heterogeneity and interconnectedness of embedded systems. Although designed to sustain long life, systems degrade in performance due to gradual development of anomalies eventually leading to faults. In addition, system usage and operating conditions (e.g., weather, road surfaces, and environment) may lead to different failure modes that can affect the performance of vehicles. Advanced diagnosis and prognosis technologies are needed to quickly detect and isolate faults in network-embedded automotive systems so that proactive corrective maintenance actions can be taken to avoid failures and improve vehicle availability. This paper discusses an integrated diagnostic and prognostic framework, and applies it to two automotive systems, viz., a Regenerative Braking System (RBS) in hybrid electric vehicles and an Electric Power Generation and Storage (EPGS) system.