Energy and power management in series hybrid vehicles

Official URL: http://192.168.1.20/record=b1276330 (Table of Contents)

Hybrid electric vehicles are characterized by the existence of an electric energy buffer in the powertrain. Compared to a conventional vehicle the existence of the buffer means an extra degree of freedom in the powertrain. The driver's request for a specific power demand can thus be met by a combination of power from the primary power unit (internal combustion engines or fuel cells) and power from the electric buffer (batteries or ultracapacitors). The subject of this thesis is the control of the load distribution between the power sources in the hybrid electric powertrain. The control problem is to choose the distribution of power from the electric buffer and primary power unit that minimizes the fuel consumption in the long run. To solve this problem the efficiency characteristics of the components in the powertrain must be considered. It is the advantage of hybrids to have the extra degree of freedom because of the buffer so that the primary power unit can be driven independent of the transient traction demand of the vehicle powertrain. The improvement in the fuel consumption is obtained by the operation of the engine in a more efficient region. Furthermore, when the vehicle is braking, the electric energy generated by the traction system can be stored back in the buffer. In conventional vehicles this braking energy is dissipated into the atmosphere. The problem is complicated due to the fact that the future driving demands are largely unknown. This uncertainty of the future driving makes it difficult, from a fuel efficiency viewpoint, to compare the cost of supplying the energy demand from the buffer or the fuel tank. In this thesis this problem is handled by using a prediction based information perspective. It allows utilization of a policy derived by Dynamic Programming. Using a simple model of the power flows, energy levels and a regression model of the future driving, the resulting policy minimizes the expected fuel consumption with respect to the prediction model of the future driving conditions. Additional information from GPS and digital maps or cooperation with the traffic infrastructure further enhances the optimization in terms of improved predictions and constraints and can be used to better schedule the use of the buffer so that further fuel consumption reductions are achieved.