The main contribution of this article is the development of a controller for tracking of the driver intended path of an integrated driver/vehicle system. The controller receives heading and lateral deviation errors as well as the driver input and determines a corrective steering angle and a direct yaw moment to be applied to the vehicle so that a desired path is achieved. A genetic algorithm procedure is utilized in order to adapt a set of optimized controller parameters suitable for various driving styles, road conditions and the initial errors of vehicle position and orientation. The sensitivity of driver/ vehicle system response to the driver model, road and vehicle initial conditions is investigated. Computer simulations are performed to study the effectiveness of the proposed controller in different driving conditions, namely single-and double-lane changes, J-turn and other desired tracks. Simulation results demonstrated that the proposed controller was able to effectively keep the vehicle path very close to the desired path even in the presence of the driver commands.
This paper describes a methodology to optimize the component sizes of a plug-in hybrid electric vehicle in a blended control strategy using a genetic algorithm. For this purpose, first, a fuzzy logic controller with two operating modes is developed for the energy management system of a parallel plug-in hybrid electric vehicle. Then, the battery requirements to drive the vehicle during the all-electric range are determined. In order to optimize the transmission components, multi-objective constrained optimization based on a genetic algorithm is developed, where the objective function consists of the weighted fuel consumption and the weighted exhaust emissions. Also, the driving performance requirements of the vehicle are considered as the constraint functions from which, when they exceed their allowable bounds, the penalty functions are evaluated and added to the objective function. Finally, the results of optimization of the component sizing in a blended control strategy are compared for the optimized data and their default component sizes. The simulation results demonstrate the effectiveness of the approach and noticeable reductions in the equivalent fuel consumption and in the exhaust emissions while ensuring a good longitudinal vehicle performance in various driving cycles.
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