DETERMINATION OF OPTIMAL BLEND RATIOS OF CNG AND GASOLINE FOR IMPROVED FUEL ECONOMY AND TORQUE IN ON-HIGHWAY VEHICLES

State of the art modern gasoline drivetrains have seen improvements in fuel economy thru common methods that include increased static compression ratios, Direct injection, reduced pumping work thru variable cam timing, improved Knock Sensing approaches, engine downsizing, variable displacement, increased transmission gear ratios, & amp; lower numerical axle ratios. Unfortunately, fuel economy improvements for any of these mentioned technologies are limited by the detonation sensitivity of 87 octane regular fuel. This sensitivity results in reduced engine torque and higher exhaust temperatures from retarding spark timing to avoid engine damaging detonation. One cost effective method of enhancing the octane rating of gasoline can be accomplished by blending with CNG in realtime. In other words, from the perspective of best torque and lowest fuel costs, the optimum solution is to create new fuel types that are an optimum blend of gasoline and CNG. If gasoline and CNG are simultaneously injected as a dualfuel application then the best fuel properties of each can be combined to create new, standalone fuels, or Gamma Fuels, that have superior properties, to either gasoline or CNG in and of themselves. The strengths of each fuel can be mixed in an infinite number of ratios in real time to best accommodate any given operating condition. In order to implement simultaneous injection of both gasoline and CNG as a dualfuel application, this approach must be seamlessly integrated with the rest of the automotive subsystems. This is accomplished by linking a proprietary highspeed Alternative Fuel Control Module (AFCM) onto the vehicle CAN Network. This computer architecture assures that the determination of the GAMMA fuel has taken into consideration all of the other vehicle control subsystems. The ultimate goal of this PhaseI feasibility study is to determine how GAMMA affects Borderline Spark, flammability limits, combustion stability, fuel economy, emissions, torque, and horsepower in order to define the software requirements needed to design the computer algorithms that are capable of determining the optimum GAMMA for the given drive mode. PhaseII would involve invehicle proof of concept that would demonstrate that this GAMMA Fuel control system is capable of meeting EPA, CARB, and OBDII. These capabilities would be