Non-dimensional design approach for electrodynamic bearings

Electrodynamic bearings (EDBs) are passive magnetic bearings that exploit the interaction between eddy currents developed in a rotating conductor and a static magnetic field to generate forces. Similar to other types of magnetic suspensions, EDBs provide contactless support, thus avoiding problems with lubrication, friction and wear. The most interesting aspect of EDBs is that levitation can be obtained by passive means, hence, no electronic equipment, such as power electronics or sensors, are necessary. Despite their promising characteristics, rotors running on EDBs are still lacking a design procedure; furthermore, at present the static behavior of a bearing can only be defined by means of finite element analyses. The aim of the present paper is to present a methodology that allows performing a first approximation design without resorting to detailed FE analyses. The methodology is based on the use of non-dimensional parameters, similar to the analysis of fluid bearings (Sommerfeld number). The non-dimensional quantities are derived using dimensional analysis, and contain the main geometrical and physical parameters determining the EDBs' performance. The relation between the non-dimensional quantities characterizing the static performance of the EDB is derived using FE simulations and is presented in the form of graph

Model and design of a double frequency piezoelectric resonator

A novel design of a multifrequency mechanical resonator with piezoelectric materials for energy harvesting is presented. The electromechanical response is described by a finite element model, which predicts the output voltage and the generated powe

Model predictive control for comfort optimization in assisted and driverless vehicles

This paper presents a method to design a Model Predictive Control to maximize the passengers’ comfort in assisted and self-driving vehicles by achieving lateral and longitudinal dynamic. The weighting parameters of the MPC are tuned offline using a Genetic Algorithm to simultaneously maximize the control performance in the tracking of speed profile, lateral deviation and relative yaw angle and to optimize the comfort perceived by the passengers. To this end, two comfort evaluation indexes extracted by ISO 2631 are used to evaluate the amount of vibration transmitted to the passengers and the probability to experience motion sickness. The effectiveness of the method is demonstrated using simulated experiments conducted on a subcompact crossover vehicle. The control tracking performance produces errors lower than 0.1 m for lateral deviation, 0.5 for relative yaw angle and 1.5 km/h for the vehicle speed. The comfort maximization results in a low percentage of people who may experience nausea (below 5%) and in a low value of equivalent acceleration perceived by the passenger (below 0.315 m=s 2 ‘‘not uncomfortable’’ by ISO 2631). The robustness at variations of vehicle parameters, namely vehicle mass, front and rear cornering stiffness and mass distribution, is evaluated through a sensitivity analysis

Study of the Impact of E-Machine in Hybrid Dual Clutch Transmission Powertrain

Recent studies show that hybrid powertrains with dual clutch transmission are becoming more common. This is mainly influenced by the fact that they have higher efficiency, allow power-on shifting, and are more compact. However, the electric motor attached to the primary shaft of the transmission might represent some critical issues in statics and dynamics due to additional torque, inertia, and working irregularities. This paper aims to investigate the influence of the electric motor integration in the transmission on the reliability of the overall system. The focus is on the investigation of potential over stresses of the ICE bearings due to the bending of the gearbox primary shaft. To achieve the aim, the static and the rotor dynamic analysis of the inner and outer primary shafts is performed. The numerical analyses show the potential critical issues that can arise from the excessive manufacturing and assembling tolerances, the interaction of the e-machine torque irregularities and inertia with the bending dynamics of the primary shaft

Vehicle Driveability: Dynamic Analysis of Powertrain System Components

The term driveability describes the driver's complex subjective perception of the interactions with the vehicle. One of them is associated to longitudinal acceleration aspects. A relevant contribution to the driveability optimization process is, nowadays, realized by means of track tests during which a considerable amount of driveline parameters are tuned in order to obtain a good compromise of longitudinal acceleration response. Unfortunately, this process is carried out at a development stage when a design iteration becomes too expensive. In addition, the actual trend of downsizing and supercharging the engines leads to higher vibrations that are transmitted to the vehicle. A large effort is therefore dedicated to develop, test and implement ignition strategies addressed to minimize the torque irregularities. Such strategies could penalize the engine maximum performance, efficiency and emissions. The introduction of the dual mass flywheel is beneficial to this end. Nevertheless, its role on the vehicle driveability, as well as that of other driveline components, is not yet so clear. The aim of the present work is to establish which are the main driveline components affecting the filtering behavior of the transmission and how their parameters can be tuned in order to improve the vehicle ability to respond to driver’s different demands without negative impact on his comfort. A complete nonlinear coupled torsional and longitudinal vehicle dynamic model is proposed to this end. The model is validated both in time and frequency domain and allows linearization of its nonlinear components

Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles

A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm

This paper presents a local trajectory planning and control method based on the Rapidly-exploring Random Tree algorithm for autonomous racing vehicles. The paper aims to provide an algorithm allowing to compute the planned trajectory in an unknown environment, structured with non-crossable obstacles, such as traffic cones. The investigated method exploits a perception pipeline to sense the surrounding environment by means of a LIDAR-based sensor and a high-performance Graphic Processing Unit. The considered vehicle is a four-wheel drive electric racing prototype, which is modeled as a 3 Degree-of-Freedom bicycle model. A Stanley controller for both lateral and longitudinal vehicle dynamics is designed to perform the path tracking task. The performance of the proposed method is evaluated in simulation using real data recorded by on-board perception sensors. The algorithm can successfully compute a feasible trajectory in different driving scenarios