Development of Robust Fault Signatures for Battery and Starter Failure Prognosis
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Abstract
Battery and starter are crucial vehicle components, whose failures may cause customers to be stranded. To enhance customer satisfaction and improve dealership serviceability, the failure prognosis and fault isolation for battery or starter are very important. In order to develop a robust diagnostic and prognostic solution, in this work, the feature extraction algorithms are developed to extract two fault signatures, namely battery charging resistance equivalent and battery cranking resistance ratio. The algorithms are based on the equivalent circuit model for the battery and starter system, the battery empirical model, and the field knowledge about the driver’s behavior and battery management system. The proposed solution is a passive approach, and does not require any additional sensors for GM vehicles, or expensive computing hardware. Therefore, it is suitable for both onboard and off-board implementation. The solution has been validated with large fleet of vehicles under different scenarios, and implemented for selected GM vehicles through the OnStar™ Proactive Alerts service.
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battery, prognosis, starter
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