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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).

A method to inspect the rattle generated in a vehicle cabin has been developed. In the method, the waveform of overall in-cabin noise is analyzed using Wigner distribution, a kind of time-frequency analysis, and the rattle component of the waveform is condensed and separated from the background shake noise. Then the rattle component is classified into three levels, strong, middle and not detected, using a neural network. Fuzzy inference is also used to select normal waveform data. Experimental results show that the correct classification ratio of the method is more than 90%, which equals the ability of skilled inspectors.

A very effective and practical technique for active control of engine noise in a car cabin has been developed. By utilizing the repetitiveness of the engine noise and introducing the idea of error scanning, a new control algorithm was developed that requires only 30-50% of the arithmetic operations of the filtered-x least mean square method. A system with only 3 microphones allocated on the basis of the spatial distribution of the noise is able to make the whole cabin space quiet. Experimental results show that a maximum noise attenuation of 8dB(A) has been achieved at each seat position.