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

Ce mémoire de thèse présente une méthode permettant d'améliorer laconduite automobile. Le filtre de Kalman étendu est utilisé pour identifierun modèle de la voiture. Ce filtre est particulièrement étudié afin de prendreen compte la redondance des informations et leur mesure asynchrone.Un ensemble cohérent et bon marché de capteurs - incluant accéléromètres,GPS, temps d'ouverture des injecteurs et vitesse - a été développé et installédans plusieurs véhicules. Ces mesures sont utilisées afin de reconstituer lafonction d'efficacité du moteur. Cette méthodologie peut-être utilisée pourtoute étude requérant la connaissance de cette fonction.La fonction d'efficacité est approchée par une fonction polynomiale etle modèle obtenu est la base d'une optimisation utilisant le principe dumaximum de Pontryagin.Les résultats des tests en condition réelle sont donnés et montrent l'efficicacité de l'observateur et du contrôleur / 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