چكيده به لاتين
Due to growing concerns about greenhouse gas emissions and limited sources of fossil fuels, hybrid electric vehicles have gained attention in the automotive industry. Plug-in hybrid-electric vehicles (PHEVs) have recently emerged as a promising alternative technology, due to two independent rechargeable power supplies and dramatically reduce fleet petroleum consumption. PHEV performance is mainly related to energy management, powertrain system components sizes and cost, which are the most effective in improving fuel economy and reducing emissions. Therefore, the idea of simultaneous optimizing of the PHEV sizing and energy management may improve fuel economy and reduce emissions.
In this thesis, simultaneous optimization of control strategy and components sizing is proposed and implemented over the real world driving cycle. For this purpose, at the first step, a parallel PHEV with regard to the functional modes, the use of the lithium battery model and engine map test data is modeled over the real driving cycle which verified by the test results of real PHEV model. In the next step, PHEV component sizing is carried out and a fuzzy logic controller (FLC) is designed for energy management system in different driving conditions. The simultaneous multi-objective constrained optimization algorithm of control strategy and sizing of the components by employing the Genetic Algorithm (GA) is performed in a way to reduce the fuel consumption, emissions and current vehicle costs, without satisfying the battery life and vehicle longitudinal dynamic requirements. The simultaneous simulation is implemented to enhance an optimal various coupling design parameters (optimization variables), conflicting design objectives (fuel economy, cost and emissions) and nonlinear constraints (vehicle dynamic performance). The results show that in simultaneous optimization algorithm despite increases in the optimization variables and computational time, the fuel consumption, emission and operational cost of the vehicle, by considering the battery life under different driving conditions, are reduced up to 9%, 6% and 7%, respectively. Finally, the effects of different vehicle parameters and traffic conditions on the simultaneous optimization are evaluated, which confirms the adaptability of the designed PHEV performance in various conditions.
