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This paper demonstrates a solution to the security problem for automotive wireless powertrain networking. That is, the security for wireless automotive networking requires a localization function before we allow a node to join the network. We explain why for powertrain wireless networking, this ability of identifying the precise location of a communicating wireless node is critical. In this paper, we explore existing methods that others have used to implement localization for wireless networking. Then, we apply machine-learning techniques to a dataset that has localization information associated with received signal strength indication. We reveal insights provided by our dataset though an exploration with statistics and visualization. We then present our problem in terms of pattern recognition via multiple techniques, including Naïve Bayes Classifier and Artificial Neural Networks.

The Battery Monitoring Integrated Circuit (BMIC) is a key technology for Battery Electronics in the electrification of vehicles. Generally speaking, every production hybrid, plug-in hybrid, and battery electric vehicle uses some type of BMIC to monitor the voltage of each lithium battery cell. In order to achieve Functional Safety for the traction battery packs for these electrified vehicles, most designs require higher ASIL ratings for the BMIC such as C or D. For the entire market of available BMIC’s, there is a generic feature set that can be found on almost every IC on the market, such as a front end multiplexer, one or more precision references, one or more Analog to Digital (A/D) converters, a power supply, communications circuits, and window comparators. There is also a fairly consistent suite of self-diagnostics, available on just about every available BMIC, to detect failures and enable achievement of the appropriate ASIL rating.