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Technical Paper

Model Based Optimization of Supervisory Control Parameters for Hybrid Electric Vehicles

Supervisory control strategy of a hybrid electric vehicle (HEV) provides target powers and operating points of an internal combustion engine and an electric motor. To promise efficient driving of the HEV, it is needed to find the proper values of control parameters which are used in the strategy. However, it is very difficult to find the optimal values of the parameters by doing experimental tests, since there are plural parameters which have dependent relationship between each other. Furthermore variation of the test results makes it difficult to extract the effect of a specific parameter change. In this study, a model based parameter optimization method is introduced. A vehicle simulation model having the most of dynamics related to fuel consumption was developed and validated with various experimental data from real vehicles. And then, the supervisory control logic including the control parameters was connected to the vehicle model.
Technical Paper

Real-Time Powertrain Control Strategy for Series-Parallel Hybrid Electric Vehicles

The series-parallel hybrid electric vehicle(HEV), which employs a planetary gear set to combine one internal combustion engine(ICE) and two electric motors(EMs), can take advantages of both series and parallel hybrid system. The efficient powertrain operating point of the system can be obtained by the instantaneous optimization of equivalent fuel consumption. However, heavy computational requirements and variable constraints of the optimization process make it difficult to build real-time control strategy. To overcome the difficulty, this study suggests the control strategy which divides the optimization process into 2 stages. In the first stage, a target of charge/discharge power is determined based on equivalent fuel consumption, then in the second stage, an engine operating point is determined taking power transfer efficiency into account.