Sensing and connection systems for assisted and autonomous driving and unmanned vehicles

The special issue, “Sensors, Wireless Connectivity and Systems for Autonomous Vehicles and Smart Mobility” on MDPI Sensors presents 12 accepted papers, with authors from North America, Asia, Europe and Australia, related to the emerging trends in sensing and navigation systems (i.e., sensors plus related signal processing and understanding techniques in multi-agent and cooperating scenarios) for autonomous vehicles, including also unmanned aerial and underwater ones

Critical Aspects of Electric Motor Drive Controllers and Mitigation of Torque Ripple - Review

Electric vehicles (EVs) are playing a vital role in sustainable transportation. It is estimated that by 2030, Battery EVs will become mainstream for passenger car transportation. Even though EVs are gaining interest in sustainable transportation, the future of EV power transmission is facing vital concerns and open research challenges. Considering the case of torque ripple mitigation and improved reliability control techniques in motors, many motor drive control algorithms fail to provide efficient control. To efficiently address this issue, control techniques such as Field Orientation Control (FOC), Direct Torque Control (DTC), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Intelligent Control (IC) techniques are used in the motor drive control algorithms. This literature survey exclusively compares the various advanced control techniques for conventionally used EV motors such as Permanent Magnet Synchronous Motor (PMSM), Brushless Direct Current Motor (BLDC), Switched Reluctance Motor (SRM), and Induction Motors (IM). Furthermore, this paper discusses the EV-motors history, types of EVmotors, EV-motor drives powertrain mathematical modelling, and design procedure of EV-motors. The hardware results have also been compared with different control techniques for BLDC and SRM hub motors. Future direction towards the design of EV by critical selection of motors and their control techniques to minimize the torque ripple and other research opportunities to enhance the performance of EVs are also presented.publishedVersio

Battery Aging-Aware Online Optimal Control: An Energy Management System for Hybrid Electric Vehicles Supported by a Bio-Inspired Velocity Prediction

In this manuscript, we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed of the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks

The present work deals with the application of coevolutionary algorithms and artificial neural networks to perform input selection and related parameter estimation for nonlinear black-box models in system identification. In order to decouple the resolution of the input selection and parameter estimation, we propose a problem decomposition formulation and solve it by a coevolutionary algorithm strategy. The novel methodology is successfully applied to identify a magnetorheological damper, a continuous polymerization reactor and a piezoelectric robotic micromanipulator. The results show that the method provides valid models in terms of accuracy and statistical properties. The main advantage of the method is the joint input and parameter estimation, towards automating a tedious and error prone procedure with global optimization algorithms

Design and Analysis of an adaptive λ-Tracking Controller for powered Gearshifts in automatic Transmissions

To meet the continuously increasing goals in vehicle fuel efficiency, a number of measures are taken in automotive powertrain engineering, such as the combination of electric drives and conventional combustion engines in hybrid vehicles or the increase in gear ratios. This development leads to more complex powertrain systems, such as automatic transmissions. At the same time, the need for complex control systems is increased to achieve this desired functionality. Automatic transmissions are controlled by an electro-hydraulic control unit that governs all operations such as gear shifting and starting. Since most of the control software is designed in the form of open-loop control, most of the operations have to be calibrated manually. Thus, there exists a large number of calibration parameters in the control software that have to be tuned individually for each combination of engine, transmission and vehicle model. This process is therefore time-consuming and costly. Hence, it would be advantageous to reduce the need for calibration and in the end shorten the development process for automatic transmissions by reducing software complexity while maintaining functionality and performance. The goal of this thesis is to replace parts of the control software responsible for conducting the gearshifts that require extensive tuning by implementing control systems that have no need for calibration: adaptive high-gain λ-tracking controllers. In order to obtain the control parameters, i.e., the feedback gains, without calibration, an adaption law is implemented that continuously computes these parameters during operation of the controller. Thus, calibration is no longer needed. Since the system has to be high-gain-stabilizable, an extensive system analysis is conducted to determine whether an adaptive λ-tracking controller can be implemented. A nonlinear model of the clutch system dynamics is formulated and investigated. As a result, high-gain stability is proven for the system class and validated in simulation. Following the stability analysis, the devised adaptive controller is implemented into the control software running on the series production transmission control unit. Extensive simulations with a comprehensive vehicle model running the extended transmission software are conducted to design and to test the adaptive controllers and their underlying parameters during transmission operation in order to evaluate the control performance. The control software containing the adaptive controller is then implemented in two distinct vehicles with different automatic transmissions equipped with series production control hardware for the purpose of hardware experiments and validation. The resulting reduction of calibration efforts is discussed