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In the past decade, various Plug-in Hybrid Electric Vehicles have been demonstrated which offer the potential of a significant reduction in fuel consumption and emission. However, this capability strongly depends on the sizing of the components, driver's usage profile and the quality of the energy management. These challenges require new optimization procedures for a systematical exploration of the design space with the objective of an optimal powertrain configuration. A novel optimization strategy based on a multi-objective problem formulation is proposed. The optimization procedure consists of a multi-objective genetic algorithm for determining the best design parameters with respect to fuel consumption and driving performance. The approach is combined with an analytical optimal control problem to find the optimal continuous and discrete control trajectories for the energy management.

In this paper an energy management is proposed which is optimal to certain driving scenarios which can be clustered into freeway, rural and urban situations. This strategy is non-predictive but uses information about the current driving situation provided by modern navigation systems to identify the current road type. Based on this information a set of simplified optimal control problems are solved offline via an indirect shooting algorithm. By relaxation of the problem formulation, the solutions of these optimal control problems can be stored into easily implementable maps. The energy management control is then determined from these maps during vehicle operation using the current road type, the vehicle speed and the required wheel-torque. The strategy is implemented in a dSPACE MicroAutoBox and validated on a near mass-production vehicle. The proposed methodology has shown fuel savings on a real world drive cycle.

In this paper, a hybrid optimal control problem (HOCP) for the optimal heating of the three-way catalytic converter is solved. We propose a model for a hybrid vehicle that beneath State of Charge and fuel consumption includes thermal system states like engine cooling water temperature and catalytic converter temperature. Since models for noxious emissions with appropriate computational demand are not yet available for optimization purposes, an artificial state that resembles the emissions produced is introduced. A hybrid optimal control problem is then formulated for the beginning of the FTP-75 drive cycle whose target is to minimize the energy loss during the catalytic converter and engine cooling water heating phase. The corresponding input values to be optimized are continuous variables as ignition angle and cylinder charge as well as discrete decisions such as different injection schemes. As additional constraint, an upper limit is imposed on the artificial emissions state.