This study reviews progresses on the application of Artificial Neural Networks (ANNs) in ground vehicle system modeling and diagnostics since 1997. Fundamentals of ANN-based system modeling are laid out, and utilized to frame the review. Areas covered in this research include modeling of vehicle components, prediction of vehicle dynamics, modeling of safety-related driver behaviors and prediction of vehicle rollover, as well as vehicle system prognostics. As this review shows, most applications addressed powertrain system and its components (includes engine, transmission parts such as friction components etc.). Besides characterizing different ANN modeling / prognostic techniques for ground vehicle applications, this study also points out future directions in this area, and serves as the foot step for the future applications of ANNs in ground vehicles.

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