Renewable Energy Technologies and Hybrid Electric Vehicle Challenges

This paper introduces the utilization of selected renewable energy technologies such as solar cell, battery, proton exchange membrane (PEM) fuel cell (FC) and super-capacitors (SCs) in the electrical vehicle industry. Combination of multiple energy resources is imperative to balance the different characteristic of each resource. Concomitantly, the need of an efficient energy management system arises within the industry. Thus, existing system from past and present undergoing research papers are summarized to give a compact overview on the technology and know-how technique to readers

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Energy management of hybrid and battery electric vehicles

This work focuses on improving the fuel economy of parallel Hybrid Electric Vehicles (HEVs) and dual-motor Electric Vehicles (EVs) through energy management strategies. Both vehicle models have two propulsion branches, each powering a separate axle: An engine and an electric motor in the HEV and two electric motors in the EV. This similarity in the vehicle models emphasises the need for similar energy management solutions. In Part Energy Management of HEVs of this thesis, a high-fidelity parallel Through-The-Road (TTR) HEV model is developed to study and test conventional control strategies. The traditional control strategies serve as a guide for developing novel heuristic control strategies. The Equivalent Consumption Minimisation Strategy (ECMS) is an optimisation-based control strategy used as the benchmark in this part of the work. A family of rule-based energy management strategies is proposed for parallel HEVs, including the Torque-levelling Threshold-changing Strategy (TTS) and its simplified version, the Simplified Torque-levelling Threshold-changing Strategy (STTS). The TTS applies a concept of torque-levelling, which ensures the engine works efficiently by operating with a constant torque as the load demand crosses a certain threshold, unlike the load-following approach commonly used. However, the TTS requires finely tuned constant torque and threshold parameters, making it unsuitable for real-time applications. To address this, two feedback-like updating laws are incorporated into the TTS to determine the constant torque and threshold online for real-time applications. Real-time versions of these strategies, Real-time Torque-levelling Threshold-changing Strategy (RTTS) and Real-time Simplified Torque-levelling Threshold-changing Strategy (RSTTS) are developed using a novel Driving Pattern Recognition (DPR) algorithm. The effectiveness of the RTTS is demonstrated by implementing it on a high-fidelity parallel hybrid passenger car and benchmarking it against ECMS. In Part Energy Management of EVs of the thesis, a low-fidelity model of a novel EV powertrain with two electric propulsion systems, one at each axle, has been developed to study and test its energy management with one of the main conventional optimal control methods, Dynamic Programming (DP). The EV model uses two differently sized traction motors at the front and rear axles. The thermal dynamics of the utilised Permanent Magnet Synchronous Motors (PMSMs) are studied. DP is first implemented onto the Baseline model that does not include any PMSM thermal dynamics, referred to as the Baseline DP, which acts as a benchmark since it is the conventional case. The thermal dynamics of the traction motors are then introduced in the second DP problem formulation, referred to as the Thermal DP, which is compared against the Baseline DP to evaluate the possible benefits of energy efficiency by the more informed energy management optimisation formulation. The best method is chosen to include these thermal dynamics in the overall energy management control strategy without significantly compromising computational time.Open Acces

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

IRS-assisted UAV Communications: A Comprehensive Review

Intelligent reflecting surface (IRS) can smartly adjust the wavefronts in terms of phase, frequency, amplitude and polarization via passive reflections and without any need of radio frequency (RF) chains. It is envisaged as an emerging technology which can change wireless communication to improve both energy and spectrum efficiencies with low energy consumption and low cost. It can intelligently configure the wireless channels through a massive number of cost effective passive reflecting elements to improve the system performance. Similarly, unmanned aerial vehicle (UAV) communication has gained a viable attention due to flexible deployment, high mobility and ease of integration with several technologies. However, UAV communication is prone to security issues and obstructions in real-time applications. Recently, it is foreseen that UAV and IRS both can integrate together to attain unparalleled capabilities in difficult scenarios. Both technologies can ensure improved performance through proactively altering the wireless propagation using smart signal reflections and maneuver control in three dimensional (3D) space. IRS can be integrated in both aerial and terrene environments to reap the benefits of smart reflections. This study briefly discusses UAV communication, IRS and focuses on IRS-assisted UAC communications. It surveys the existing literature on this emerging research topic and highlights several promising technologies which can be implemented in IRS-assisted UAV communication. This study also presents several application scenarios and open research challenges. This study goes one step further to elaborate research opportunities to design and optimize wireless systems with low energy footprint and at low cost. Finally, we shed some light on future research aspects for IRS-assisted UAV communication

Data driven techniques for on-board performance estimation and prediction in vehicular applications.

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Light on Physiological Sensors for Efficient Driver Drowsiness Detection System

International audienceThe significant advance in bio-sensor technologies hold promise to monitor human physiologicalsignals in real time. In the context of public safety, such technology knows notable research investigations toobjectively detect early stage of driver drowsiness that impairs driver performance under various conditions.Seeking for low-cost, compact yet reliable sensing technology that can provide a solution to drowsy stateproblem is challenging. While some enduring solutions have been available as prototypes for a while, many ofthese technologies are now in the development, validation testing, or even commercialization stages. Thecontribution of this paper is to assess current progress in the development of bio-sensors based driver drowsinessdetection technologies and study their fundamental specifications to achieve accuracy requirements. Existingmarket and research products are then ranked following the discussed specifications. The finding of this work isto provide a methodology to facilitate making the appropriate hardware choice to implement efficient yet lowcostdrowsiness detection system using existing market physiological based sensors

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio