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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.

A trip-based energy management strategy for electric vehicles (EVs) is proposed. It can use deterministic routing information obtained from, nowadays, available navigation systems and determines stochastic descriptions of process uncertainties such as stop events as unpredictable disturbances. A dynamic programming algorithm is used to calculate the optimal control trajectories required to reach the target destination safely and to suggest the driver an optimal driving style to maximize the battery range. The algorithm is implemented on a rapid prototyping platform using MATLAB/Simulink. Simulations and experimental results obtained from an EV prototype car are presented.

In this paper optimal control problems for hybrid powertrain vehicles with different drive-modes are considered and solved using numerical techniques. This leads to the formulation of hybrid optimal control problems. The aim is to find optimal controls and optimal switchings between the drive-modes to minimize a cost function resembling fuel consumption. The problem is nonlinear and subject to constraints concerning both controls and state. The techniques include indirect methods as well as direct optimization methods. Efficiency and accuracy are evaluated for all methods using simulation studies. An experimental test on a near mass-production vehicle confirms the usability of the direct optimization approach.

The precision of direct fuel injection systems of combustion engines is crucial for the further reduction of emissions and fuel consumption. It is influenced by the dynamic behavior of the fuel system, in particular the injection valves and the common rail pressure. As model based control strategies for the fuel system could substantially improve the dynamic behavior, an accurate model of the common rail injection system for gasoline engines - consisting of the main components high-pressure pump, common rail and injection valves - that could be used for control design is highly desirable. Approaches for developing such a model are presented in this paper. For each key component, two models are derived, which differ in temporal resolution and number of degrees of freedom. Experimental data is used to validate and compare the models. The data was generated on a test bench specifically designed and built for this purpose.

A physics-based simulation program developed by IAV is used to calibrate the torque structure and cylinder charge calculation in the electronic control unit of SI engines. The model calculates both the charge cycle and combustion phase based on flow mechanics and a fractal combustion model. Once the air mass in the charge cycle has been computed, a fractal combustion model is used for the ongoing calculation of cylinder pressure and temperature. The progression of cylinder pressure over the high and low-pressure phases also provides information on engine torque. Following the engine-specific calibration of the model using elemental geometric information and reduced test bench measurements, the physical engine properties can be simulated over the operating cycle. The calibrated model allows simulations to be carried out at all operating points and the results to be treated as virtual test bench measurements.

A physically based simulation program developed by IAV makes a notable reduction of test bed measurements for the calibration of the cylinder charge calculation possible. Based upon geometric engine parameters and camshaft profiles, the cylinder charge is calculated from thermodynamic relationships taking into account the contribution of residual gas. After successful engine-specific calibration of the simulation model on the basis of a reduced set of test bed measurements, it is possible to calculate the cylinder air mass over the entire range of valve timing settings and operating points (engine load and speed). The simulation-generated “virtual” measurements can then be used for calibration of the control unit software over the entire operating range.

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.

Most energy management systems for hybrid electric vehicles still use rule-based energy management systems that rely on information stored in lookup tables, to define the current mode of operation and set-points for the low-level control laws. Because of the high number of parameters, the calibration of such energy managements can be a cumbersome task for the engineers. Mathematical tools are therefore inalienable to the calibration process. In this paper, it will be demonstrated, how the theory of hybrid optimal control can be used to calculate an initial parameter set for the energy management of charge-sustaining hybrids. The calculation procedure includes the solution of a hybrid optimal control problem to determine the controls for the optimal operation of the vehicle over a given 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.