An Intelligent System for Improving the Efficiency of a PHEV for the EcoCar Challenge

Globally, significant efforts are being made to reduce green-house gases and decrease the demand of fossil fuels. Automotive manufacturers are offering significantly more “green” versions of their popular automobiles in order to combat the negative impact of rising fuel prices. The EcoCar Challenge is a college-level competition primarily sponsored by General Motors and the United States Department of Energy in an effort to provide global awareness of this effort and educate future engineers in the processes and technologies used to construct fuel economic hybrid vehicles. The program consists of 17 teams with a wide variety of hybrid vehicle types. Embry-Riddle Aeronautical University (ERAU) is implementing a plug-in hybrid electric vehicle (PHEV) through the modification of a stock 2009 Saturn Vue as described in [1]. This paper presents the Intelligent Drive Efficiency Assistant (IDEA) system a hardware/software component being added to ERAU’s EcoCar vehicle. The IDEA system uses artificial intelligence techniques to analyze the driving conditions ahead (terrain, traffic, and anticipated torque requirements) to select the best operating mode for the hybrid vehicle. The IDEA system submits its recommendation to a hybrid or supervisory control unit, presented in [2], which does the necessary work to transition the vehicle into that operating mode (so long as it deems the request safe). This preemptive strategy is believed to provide two key benefits. First, through learning algorithms, new control strategies may be developed based on the driving conditions and past experience. Second, by preemptively making recommendations ahead of a driving event such as an uphill climb, or a frequent stop in rush-hour traffic, it is believed that there will be less energy wasted by not waiting until the need arises to start making the transition between hybrid modes. Within this paper, the initial design of the IDEA system is be presented, and the evaluation plan using hardware-in-the-loop and software-in-the-loop simulation is discussed.