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Over the past few decades, advanced methods have been developed for the analysis of digital systems using mathematical reasoning, i.e., formal logic. These methods are supported by sophisticated software tools that can be used to perform analysis far beyond what is practically achievable using “paper and pencil” analysis. In December 2011, RTCA published RTCA DO-178C [1] along with a set of technical supplements including RTCA DO-333 [2] which provides guidance on the use of formal methods towards the certification of airborne software. Such methods have the potential to reduce the cost of verification by using formal analysis instead of conventional test-based methods to produce a portion of the verification evidence required for certification.

This paper is the third in the series of documents designed to record the progress on the SAE Plug-in Electric Vehicle (PEV) communication task force. The initial paper (2010-01-0837) introduced utility communications (J2836/1™ & J2847/1) and how the SAE task force interfaced with other organizations. The second paper (2011-01-0866) focused on the next steps of the utility requirements and added DC charging (J2836/2™ & J2847/2) along with initial effort for Reverse Power Flow (J2836/3™ & J2847/3). This paper continues with the following: 1. Completion of DC charging's 1st step publication of J2836/2™ & J2847/2. 2. Completion of 1st step of communication requirements as it relates to PowerLine Carrier (PLC) captured in J2931/1. This leads to testing of PLC products for Utility and DC charging messages using EPRI's test plan and schedule. 3. Progress for PEV communications interoperability in J2953/1.

Engine module performance trending and engine system anomaly detection and identification are core capabilities for any engine Condition Based Maintenance system. The genesis of on-condition monitoring can be traced back nearly 4 decades, and a methodology known as Gas Path Analysis (GPA) has played a pivotal role in its evolution. GPA is a general method that assesses and quantifies changes in the underlying performance of the major modules of the engine (compressors and turbines) which directly affect performance changes of interest such as fuel consumption, power availability, compressor surge margins, and the like. This approach has the added benefit in that it enables anomaly detection and identification of many engine system accessory faults (e.g., variable stator vanes, handling and customer bleeds, sensor biases and drift). Legacy GPA has been confined to off-board analysis of snapshot data averaged over a stable flight conditions when the engine is in steady state operation.