An optimal energy-based approach for driving guidance of full Electric Vehicles

The present work focuses on the development of computational algorithms to determine on-line energy-based driving guidance for an Electric Vehicle (EV) endowed with regenerative breaking system capabilities. A predictive decision support system is designed to optimally distribute the energy flow between the instantaneous power demand requested by the driver for the powertrain engine and the different auxiliaries relating to comfort performance, such as the heating system. The proposed methodology uses an on-line iterative optimization process algorithm to search for global optimum relatively to specific objective functions, which take into account the battery autonomy, driving comfort indexes and the travel time. Our methodology has been validated for a heavy motorized quadricycle vehicle using Hardware In the Loop (HIL) simulations, for which the Energy Management System has been implemented in a DSP board communicating through a CAN protocol.