Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.

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The paper discusses the advanced spectral analysis performed on two important classes of electric drive systems, namely Switched Reluctance Motors (SRM) and Permanent Magnet Synchronous Machines (PMSM). In particular, the noise and vibration signatures as a function of operating speed are analyzed by using a waterfall analysis and an order analysis. The harmonic and modulation components attributable to the motor configuration, the motor operating principles and the Variable Speed Drives (VSD) are identified in two cases for each motor type. Furthermore, a number of approaches to improve the noise and vibration performance have been evaluated, in particular by acting on the controls of the variable speed drive. Some comments on additional noise source phenomena such as caused by phase unbalance are given.

Abstract-In this paper the authors present a comparison between optimised versions of three torque sharing functions, with different number of degrees of freedom, used for improving the efficiency in Switched Reluctance Motors. Starting from the basic rectangular variation, the ascending and descending flanks of the function are modeled using a cosine, exponential and a more general approach (further referred to as piecewise cubic sharing function). The degrees of freedom varying between two and an arbitrary number (for the general approach) are determined so as to minimize losses, when operating in smooth torque conditions. Based upon an optimisation procedure with both linear and nonlinear constraints, the total (copper and iron) losses are minimized considering a limited available DC bus voltage. Moreover, a smooth model is presented so as to be able to separate the two main causes for the torque ripple and it is shown that, neglecting the chopping ripple, a ripple-free torque can be obtained. Extensive studies have been conducted for determining the optimal number of degrees of freedom (DoFs) for the piecewise cubic function. The proposed methods were successfully implemented on a 8/6, 30kW peak power, SRM both numerically and experimentally-wise.

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In this study, the design optimisation of a synchronous reluctance machine for light electric vehicles is proposed, to increase efficiency and reduce torque ripples. The existing machine was structurally optimised, using dedicated genetic algorithms, replacing only the rotor and keeping the stator and it's winding untouched. Starting from the original design of the rotor implemented in Flux2D, a finite element analysis software, and the genetic algorithm optimisation implemented in Matlab, a complex co-simulation was accomplished to obtain a rotor architecture that increases the machine's performances and decreases the torque ripples. By this, performing rotor skewing is not needed any more, hence the torque loss due to it was cancelled. The optimised rotor design increases the machine performances by higher mean torque, no skewing, <8% torque ripples, higher efficiency and better inductance characteristics. Comparative results obtained both in simulations and experimental measurements prove positive outcomes of the optimisation process.

Cost reduction of any design process is always of interest for industries. Simulation work packages tackle this problem since they can quickly provide reliable results that permit detection of critical design issues prior to the prototype phase. A trade-off is then often made between model accuracy and computation speed. In the particular case of electric machines, homogenization techniques are used in order to keep high accuracy while running fast calculations. They are involved in multiple disciplines in which the machine performances are verified such as elec tromagnetic, mechanical, thermal and acoustic domains.This paper aims at defining whether these homogenization methods can be extended from one discipline to another by reviewing them independently of the physical domain.