Neural Network Based Modeling and Parameter Identification of Switched Reluctance Motors

Abstract -Phase windings of switched reluctance machines are modeled by a nonlinear inductance and a resistance that can be estimated from standstill test data. During online operation, the model structures and parameters of SRM's may differ from the standstill ones because of saturation and losses, especially at high current. To model this effect, a damper winding is added into the model structure. This paper proposes an application of artificial neural network to identify the nonlinear model of SRM's from operating data. A 2-layer recurrent neural network has been adopted here to estimate the damper currents from phase voltage, phase current, rotor position and rotor speed. Then the damper parameters can be identified using maximum likelihood estimation techniques. Finally the new model and parameters are validated from operating data

Connected Vehicles: Solutions and Challenges

Abstract-Providing various wireless connectivities for vehicles enables the communication between vehicles and their internal and external environments. Such a connected vehicle solution is expected to be the next frontier for automotive revolution and the key to the evolution to next generation intelligent transportation systems (ITSs). Moreover, connected vehicles are also the building blocks of emerging Internet of Vehicles (IoV). Extensive research activities and numerous industrial initiatives have paved the way for the coming era of connected vehicles. In this paper, we focus on wireless technologies and potential challenges to provide vehicle-to-x connectivity. In particular, we discuss the challenges and review the state-of-the-art wireless solutions for vehicle-to-sensor, vehicleto-vehicle, vehicle-to-Internet, and vehicle-to-road infrastructure connectivities. We also identify future research issues for building connected vehicles