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Energy optimal control theory (EOC) is applied to the energy flow control of a hybrid electric vehicle. Since the differential equation is solved analytically, the control law can be easily implemented in real time. Because the objective function is described in power form that permits negative value, not only the energy consumption is minimized but also the energy regeneration by the motor is maximized. In the simulation for the 10-mode driving, it is shown that the fuel cost of EOC is 15% lower than the rule based control (RBC).

In this study, the genetic algorithm so called GA is newly applied for the optimization of many engine mounting parameters, calculations of stiffness matrix and inverse matrix to obtain 6 degrees of freedoms displacements at mounting points and a center of gravity. As a result, the optimized result could be shortly obtained in a minute, and an inexperienced engineer could easily make the optimum engine mounting layout, which can satisfy the vibration isolation and the non-interference in an engine compartment.