The PhD presents a combined approach to improving individual car efficiency. An optimal observer, the Extended Kalman Filter, is used to create an efficiency model for the car. Particular attention was paid to handling the asynchronous and redundant nature of the measurement data. A low-cost sensor suite developed to measure data is described. This sensor suite was installed on multiple vehicles to good success. It employsan accelerometer, gps, fuel injector timer, and Vss input to measure all the data necessary to reconstruct the car's state. This observer and sensor suite can be used as the base for any study which requires car efficiency maps, allowing research to proceed without manufacturer supplied data. Once the efficiency map is found, it is then curve-fitted in order to reduce model complexity. The simplified model is then used as a basis for optimal control through Pontryagin's Maximum Principle. Real-world test results are given, both for efficiency mapping, and for optimal control. Detailed discussion of the observer and controller is presented, in order to ease understanding and save implementation time
