Abstract
Keywords - Energy Management, Prognosis, Advanced System Control, Signal Processing, Artificial Intelligence Control.
Abstract - Electrical-Electronics functionality in vehicles is increasing. Higher complexity control systems are needed for managing the new functions and their supervision requirements by means of suitable Energy, Power & Load Management algorithms. Forecasting functions behaviour trajectory in state-space is nowadays a powerful tool for high-complex control systems design. An Intelligent Embedded time-series signal processing approach, based on Artificial Neural Networks (ANN) for vehicle status definition, to implement Energy Power & Load management algorithms is presented. Supported by an ANN algorithm, a time-series prediction strategy is implemented by means of functions control algorithm review. Such a new function control module reports to the master algorithm an intelligent processed frame where on-line function diagnosis, report of short-time probable future function behaviour and its associated probability are included. These new intelligently processed inputs allow the system to distinguish clearly short-term function status and thus, self-inducing transient preventive work modes in order to well manage forthcoming functions events.