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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.

Automatic Crash Notification (ACN) technology provides an opportunity to rapidly transmit crash characteristics to emergency care providers in order to improve timeliness and quality of care provided to occupants in the post crash phase. This study evaluated the relative value of crash attributes in providing useful information to assist in the identification of crashes where occupants may be seriously injured. This identification includes an indication of whether a crash is likely to require a level of emergency response with higher priority than is needed for most crashes reported by ACN Systems. The ability to predict serious injury using groupings of variables has been determined. In this way, the consequence of not transmitting each variable can be estimated. In addition, the incremental benefit of voice communication is shown.

To reduce vehicle development cycles it is necessary to perform numerical durability analyses in an early development phase. Typically there is no physical prototype available at that time hence there are no measured data, either from the proving ground or from test rigs. This paper presents an alternative method to predict the required loads. Using Multi-Body Simulation (MBS), the loads prediction process is performed for an unconstrained vehicle, which means that vehicle body position and orientation are allowed to change. Of particular interest are the time series of the loads acting at components of the front-and the rear-suspension, as well as on the body structure of the vehicle. For the loads prediction BMW uses the so called Hybrid-Road-Approach developed by LMS. After an initial pilot project demonstrating that approach's feasibility and potential, the project presented below is the first run of that approach by BMW in their productive environment.