Dielectric characteristics of electric vehicle traction motor winding insulation under thermal ageing

The electric motor is the heart of the electric vehicle. It is crucial that any occurring faults are detected promptly so that a catastrophic failure is avoided. At the same time, deep knowledge of the degradation mechanisms is required to allow maximum performance at minimum cost. This paper focuses on this balance. Statistical results from measurements of unaged and accelerated aged winding insulation samples provide information about the degradation processes, enabling steps toward a reliable prognosis model of the motor's remaining life

The Impact of Thermal Degradation on Properties of Electrical Machine Winding Insulation Material

Inter-turn stator short circuits can develop quickly leading to serious damage of an electric machine. However, degradation mechanisms of winding insulation material are not yet fully understood. Therefore, the main contribution of this article is analysis of the impact of thermal ageing on the electrical properties of the thin film winding insulation. The insulation samples have been aged thermally at 200–275 °C and for 100–1600 hours. After ageing, impedance spectroscopy measurements were undertaken on the samples and equivalent circuit model (ECM) parameters fitted for each measurement. This allows the impact of thermal ageing on ECM parameters to be analysed, giving insight into the changes of the electrical properties of the insulation. Finally, high voltage was applied to the samples aiming to identify the breakdown voltage characteristics of the insulation material

A control systems approach to input estimation with hydrological applications

This paper demonstrates the feasibility of a new approach to system inversion and input signal estimation based on the exploitation of non-minimal state space feedback control system design methods that can be applied to non-minimum phase and unstable systems. The real and simulated examples demonstrate its practical utility and show that it has particular relevance in a hydrological systems context

Dielectric Characteristics of Electric Vehicle Traction Motor Winding Insulation Under Thermal Aging

The electric motor is the heart of the electric vehicle. It is crucial that any occurring faults are detected promptly so that a catastrophic failure is avoided. At the same time, deep knowledge of the degradation mechanisms is required to allow maximum performance at minimum cost. This paper focuses on this balance. Statistical results from measurements of unaged and accelerated aged winding insulation samples provide information about the degradation processes, enabling steps toward a reliable prognosis model of the motor's remaining life

A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses

Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities