Abstract
The research in automotive field is pursuit of obtaining an important goal: lower polluting emissions vehicles with an increased fuel economy. To reach these targets, the most promising solution is given by hybridization of the powertrain [1], [2]. Because many Hybrid Electric Vehicles (HEVs) powertrain components are nonlinear uncertain dynamic systems, a traditional control system could work improperly. Consequently, they need a sophisticated and powerful control architecture that must be able to manage the different forms of on-board energy storage (chemical, mechanical, electrical) and the conversions among them. The proposed control system is based on previous knowledge of the characteristics of the powertrain system components, on measures of the system state variables and on input commands by operator.
All these data flow through in a control unit which, following adaptive control algorithms by means of Soft Computing (SC) advanced control techniques, chooses the most suitable on-board operating conditions that allow to work closed to the maximum global energy efficiency points for the entire system. Same techniques further are able to recognize the current driving conditions (urban, extra urban, etc ) and use them as further inputs.