When combined with a magnet having a magnetic field gradient (for example, a permanent magnet), a Y‐based oxide superconductor is capable of forming a noncontact bearing with a strong levitational force. Since this bearing exhibits low rotational loss, it is very likely to form a highly efficient power storage system in combination with a flywheel. In this paper, an 8‐MWh power storage system utilizing a flywheel was designed conceptually to examine its applicability and the possible effects of its introduction. It was found that this system was an effective power storage.

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In order to develop an efficient driving system for electric vehicle(EV), a testing system using motors has been built to simulate the driving performance of EVs. In the testing system, the PID controller is used to control rotating speed of motor when the EV drives. In this paper, in order to improve the performance of speed control, a neural network is applied to tuning parameters of PI controller. It is shown through experiments that a neural network can reduce output error effectively while the PI controller parameters are being tuned on-line.

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