Recent studies on autonomous vehicles focus on improving driving efficiency and ignore driving comfort. Because acceleration and jerk affect driving comfort, we propose a comfort regenerative braking system (CRBS) that uses artificial neural networks as a vehicle-control strategy for braking conditions. An autonomous vehicle driving comfort is mainly determined by the control algorithm of the vehicle. If the passenger’s comfort is initially predicted based on acceleration and deceleration limits, the control strategy algorithm can be adjusted, which would be helpful to improve ride comfort in autonomous vehicles. We implement numerical analysis of the control strategy, ensuring reduced jerk conditions. In addition, backward propagation was applied to estimate the braking force limits of the regenerative braking systems more accurately. The developed algorithm was verified through the Car Sim and MATLAB/Simulink simulations by comparing them with the conventional braking system. The proposed CRBS offers effective regenerative braking within limits and ensures increased driving comfort to passengers.

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In this study, the authors proposed a method of controlling harmonic parasitic torque in concentrated winding induction motor and validated its theory's practicality through the experiment. To control harmonic, the rotor was skewed at an angle and to determine optimum skew angle, three dimensional electromagnetic analysis was performed. The result of analysis demonstrated larger than 20 degree skew angle, which was not conventional in distributed winding, was favorable in the proposed concentrated winding induction motor. In this investigation, 27 degree skew angle was optimal for the proposed designed motor. Additionally, in the case AC motor was designed per the proposed theory in this study, the proposed design can be manufactured more cost effectively than conventional distributed Winding .

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