Optimal Control Of ISG Hybrid System With Diesel Engines In Transient State

Integrated Starter Generator (ISG) parallel hybrid system set a new trend of vehicle powertrain systems. Most of the studies of ISG hybrid technology are conducted on gasoline engines and the focus is put on reducing fuel consumption. Diesel engines have the advantage of fuel efficiency over gasoline engines, but reduce emission especially in transient state has become more significant with the more stringent regulation on exhaust emission. For the purposes of reducing emission in transient state, optimal control of ISG hybrid system with diesel engines has been studied in this dissertation. The powertrain system being studied is composed of a turbocharged (non-EGR) common rail diesel engine, an ISG motor and ultracapacitors. The optimal control algorithm targeting at reducing emission in transient state is the core of the study, and the torque coordination control between the engine and the motor serves as a basis.First, a control oriented emission model for turbocharged diesel engines is built. The nonlinear static behavior of emission is described by a multi-layer neural network, which is trained by steady state experimental data. A linear auto-regression with exogenous input (ARX) model is implemented for dynamical correction of the steady model. The accuracy of the estimated smoke and NOx value by the model is validated by experiments.Then a mean value model based torque estimation model of the diesel engine is built. The accuracy of the estimation is examined by validation experiments. The total torque requirement is calculated by a nonlinear auto-regression with exogenous input (NARX) dynamic neural network model. Based on the above models, a complete torque coordination algorithm for ISG hybrid system is proposed, in which the dynamic limit on motor torque output and the delay in torque output is considered. The effectivness of the algorithm is validated by experiments. For the dynamic global optimization problem in transient state with multi-states, a receding local horizon solving algorithm is proposed. Firstly simulation analyse of the optimal control algorithm is carried out on computer. Then the algorithm is simplified, and an adaptive SOC sustaining energy management algorithm is designed. The algorithm is implemented on a microcontroller in real-time. The test results on engine dynamometer show that, the total output torque characteristic is kept unchanged and the adaptive sustaining of SOC is ensured. The deterioration of soot emission in transient state is successfully suppressed. In ETC test cycle, the peak smoke is reduced by about 77%. The total PM emission is reduced by about 15%, in which the total soot emission is reduced by about 20%. The fuel consumption, NOx and other emission compositions are kept unchanged. For the test cycle simulating the driving cycle of a middle-sized passenger car, with the help of regenerative braking, the fuel consumption can be reduced by about 9%.