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A novel driver-assist stability system for ail-wheel-drive Electric Vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a Fuzzy logic system that independently controls wheel torques to prevent vehicle spin. Another Fuzzy wheel slip controller is used to enhance the vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of Fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease to implement Fuzzy controllers gives a potential for vehicle stability enhancement. Index Terms--Direct yaw-moment control, electric vehicle, fuzzy logic control, vehicle stability.

This paper proposes analytics for reliability assessment of non‐isolated conventional pulse width modulation DC‐DC (NIDC‐DC) converters. This class of converters consists of conventional Buck, Boost, Buck–Boost, Cuk, Sepic and Zeta topologies. The proposed analytics are founded based on the Markov process principles and can effectively capture the effects of duty cycle, input voltage, output power, voltage gain, components characteristics and aging on the overall reliability performance and mean time to failure of the NIDC‐DC converters. Furthermore, the suggested framework takes both continuous and discontinuous conduction modes of each converter into account, with which the open and short circuit faults in the components are analysed. As an important outcome, the most reliable operation region of the NIDC‐DC converters are obtained with respect to different operational parameters, which is useful in design procedure. Eventually, extensive thermal experiments with an appropriate reflection of reliability metric are conducted to measure the components' temperatures and verify their performance in different operating conditions. [ABSTRACT FROM AUTHOR]

Position sensorless control of ac machines at zero and low speed is possible using high-frequency carrier injection methods. These methods utilize anisotropic properties of rotor. Therefore, they may lose their efficiency for nonsalient machines or machines with small rotor saliency. In these machines, measurement noise and offset, existing delays, as well as model uncertainties may lead to inaccurate estimation of rotor position. Stator resistance which is usually neglected in these methods may also lead to a considerable error especially in machines with small rotor saliency. In this paper, a new position estimation method is presented, and it is shown that in comparison to conventional methods, the proposed scheme minimizes the estimation error caused by disturbances like stator resistance and delays of the system. [ABSTRACT FROM AUTHOR]