Vehicular Applications Research Articles

Feedforward-based decoupling control of air supply for vehicular fuel cell system: Methodology and experimental validation

Insufficient and time-delayed air supply reduces the performance and durability of fuel cell system in vehicular application. Considering the strong coupling between mass flow and pressure, the air supply subsystem is a complex two-input-two-output control object. A decoupling control method based on feedforward structure is proposed to independently control the intake mass flow and pressure. Through look-up tables established based on experimental results of compressor speed and throttle angle versus mass flow and pressure, the proposed method is able to quickly provide feedforward-based control reference for actuators. The control error of feedforward part is further eliminated by feedback loops designed using diagonal matrix decoupling method, which compensates compressor speed and throttle angle in a coordinated manner. The proposed method has been comprehensively validated through bench and real-world experiments using a prototype fuel cell vehicle. The results show that the proposed method effectively reduces the interaction of the coupled loops, achieving higher accuracy and stability. Compared with the strategy without decoupling compensation in feedback loops, the relative error is controlled within [-1%, 1%] with smaller variance and less control effort. Besides, according to real-world vehicle experiments, the proposed method has better anti-disturbance performance to air speed turbulence under varying vehicle speed.

Reinforcement learning based scheme for on-demand vehicular fog formation

The high need of real-time vehicular applications for self-driving cars to maintain service availability and reachability, and to process huge amount of generated data within a small amount of time, rise the need to improve the vehicular network infrastructure. Fog computing has been introduced to reduce the amount of data sent to cloud by bringing processing power near the edge and reducing latency. In this paper, we overcome the aforementioned limitations by taking advantage of Istio service mesh and kubeadm clustering to handle inter-service communication and provide clusters to serve nearby vehicles, the evolvement of On-Board Units in vehicle supplies enough processing power to run reinforcement learning model in taking action of pushing microservices to cluster, and the efficiency of Hungarian Algorithm to assign microservices to vehicles. We propose in our scheme (1) a service mesh architecture that manages communication between multiple microservices across different clusters and tackle inter-service communication, (2) a Reinforcement Learning model deployed on road side units to predict on-demand placement of microservices on cluster or caching at RSU, and (3) Reformulating the vehicular container placement (VCP) problem as an assignment problem to map microservices to vehicles. Experiments and simulations show that the reinforcement learning model served 90% of requests when deploying microservices which is more efficient than current strategies in the literature. Given that only needed microservices are deployed in limited resource cluster, mesh network shows an improvement of 84% in the deployment time and inter-service communication across clusters, and the usage of Hungarian algorithm in solving the VCP improves Quality of Service (QoS).

A Steiner Tree based efficient network infrastructure design in 5G urban vehicular networks

Autonomous or assisted driving under next-generation vehicular applications requires a very high data rate with ultra-reliable low latency. The design of a proper network infrastructure plays a primary role in supporting these demands. This necessitates the network infrastructure nodes, i.e., the edge nodes and 5G RSUs, to be efficiently placed and connected across a smart city in a cost-effective manner. Such planning under a restricted capital expenditure budget presents a significant research challenge. We address this issue by formulating it as a Steiner Tree with Profits, Budget and Hop constraint problem for designing cost-efficient network infrastructure in an urban vehicular scenario. The optimization maximizes the long-term profits under the budget and hops constraint. The problem under consideration is a known NP-hard problem, and therefore, we have employed a Breakout Local Search based heuristic to solve it. Performance evaluation with actual traffic in real cities with diverse setups reveals crucial insights for designing cost-efficient network infrastructure in 5G urban vehicular networks.

Dispositivo para Medição de Parâmetros Internos, Diagnóstico de Falhas e Previsão de Vida Útil em Capacitores Eletrolíticos de Conversores Utilizados em Veículos Elétricos

This work addresses the dynamic modeling and the drive of a multiphase YASA-type electric machine for use within electric traction. The main technical aspects of the machine, which can be fed with three or five phases depending on the electrical connection of its windings, are presented. The dynamic model of the machine for the two different electrical connections (three or five phases) is given in the synchronous reference frame and the parameters of the lumped model are obtained through experimental tests. In order to consider the vehicular application, standard driving cycles are used to serve as a reference for the speed whereas vehicular dynamics are considered to define the load torque to be developed by the machine. Afterward, field orientation control (FOC) is implemented for the machine to operate with three or five phases and simulations were carried out to show that the dynamic behavior is similar in both cases. Experimental tests validated the model and the FOC for the machine with both electrical connections. In addition, the reference curves of the driving cycle were satisfactorily followed, indicating the possibility of using the multiphase YASA machine with the control developed in vehicular traction applications.

Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies

In this research work, a Neural Network (NN) and Uniform Robust Exact Differentiator (URED) observer-based Fast Terminal Integral Sliding Mode Control (FTISMC) has been proposed for Oxygen Excess Ratio (OER) regulation of a Polymer Electrolyte Membrane Fuel Cell (PEMFC) power systems for vehicular applications. The controller uses URED as an observer for supply manifold pressure estimation. NN is used to estimate the stack temperature which is unavailable. The suggested control method increased the PEMFC’s effectiveness and durability while demonstrating the finite-time convergence of system trajectories. By controlling the air-delivery system in the presence of uncertain current requirements and measurement noise, the approach ensures maximum power efficiency. The Lyapunov stability theorem has been used to confirm the stability of the presented algorithm. In addition, the suggested method eliminated the chattering phenomenon and improved power efficiency. Given these noteworthy characteristics, the research has the potential to decrease sensor dependence and production costs while also improving the transient and steady-state response in vehicular applications.

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks.

With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay-energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

Inverted Sierpinski Carpet fractal‐inspired dielectric resonator antenna for wideband applications

AbstractThis communication explains a novel approach for attaining wideband inverted Sierpinski Carpet fractal geometry with a rectangular shape dielectric resonator antenna (DRA). Dielectric Resonators (DRs) have advantages, like, high gain, efficiency, wide bandwidth, low conduction loss, and minimum surface waves. Rectangular shape DR (εrdr = 9.8, 14 × 12 × 8 mm3) made from alumina (Al2O3) is mounted on top of a primary square patch. Inverted Sierpinski fractal geometry provides the benefit to decrease the volume to the surface area without removing elements from the patch. The proposed fractal DRA is printed on an FR4 substrate (εr = 4.4), fed with a 50‐Ω microstrip feed line, and has a compact size of 38 × 38 × 9 mm3. At the backside of the antenna, an extended stub from the partial ground plane enhances impedance matching from 4 to 5 GHz. The prototype of the proposed DRA is fabricated and measured. The antenna functions with an impedance bandwidth of 98.84% for S11 < − 10 dB, covering 3.5–10.34 GHz of the ultrawideband range. The proposed DRA offers a stable omnidirectional radiation pattern, maximum measured gain of 7.01 dBi, and has an average efficiency above 97% over the region of operation. These features make the proposed DRA applicable for cellular communication repeaters, local area networks, and vehicular communication applications.

Hybrid mmWave-Li-Fi 5G Architecture for Reconfigurable Variable Latency and Data Rate Communications

Despite modern vehicles having the necessary advanced driver assistance systems (ADAS) for autonomous operation, current implementations rely solely on sensor information from the surrounding environment or data from smart infrastructure. However, shortcomings in current implementations and standards around cost-effective variable latency and data rate have prevented widespread adoption of the technology to enable autonomous operation. This paper presents a proof-of-concept (PoC) reconfigurable design and performance of a hybrid high-ultra-high bands [i.e., millimetre-Wave (mmWave)-light fidelity (Li-Fi)] fifth generation (5G) architecture for autonomous vehicular communication applications. The hybrid multiple input multiple output (MIMO) system architecture design is mathematically modelled and presented. The reported prelim PoC validation focuses on the Li-Fi experiment and results. The proposed hybrid system’s effectiveness was evaluated using the open-source “Model-based Autonomous Traffic Simulation” (MOBATSim). The simulation results of a potential Li-Fi system and a PoC prototype are presented to demonstrate the role of the Li-Fi system in the proposed hybrid mmWave-Li-Fi 5G architecture. Three models of LED/lamp and two models of photodiode were simulated at three different vehicle speeds to ascertain the potential of the system. Promising results are reported at low speeds with received power values of up to -8.39 dBm and signal to noise ratios of up to 29.39 dB. Practical prototype simulations showed auspicious results including received power of -24.6 dBm to -34.12 dBm at the vehicle speeds of 10 MPH to 30 MPH respectively. The implemented PoC Li-Fi technology component has demonstrated the ability to transmit information with a theoretical output of 333 bits per second at 1.92 m, without the use of any high-power processors, components, and modulation techniques. The proposed system yields high data rates due to reconfigurable high bandwidth channels; many simultaneous multimedia mmWave-Li-Fi connections enabled due to spatial reuse with narrow beams; and easier mmWave-Li-Fi ultra-low latency support occasioned by smaller packet sizes. This holds a great promise for the hybrid 5G/6G mmWave-Li-Fi autonomous vehicular communication use case and/or applications.

On the Role of Thermal Imaging in Automotive Applications: A Critical Review

For decades, the number of automobiles in urban areas around the world has been increasing. It causes serious challenges such as traffic congestion, accidents, and pollution, which have a social, economic, and environmental impact on widespread urban cities. To overcome these challenges, we need to explore smart AI-based perception systems for vehicular applications. Such types of systems can provide improved situational awareness to the driver and generate early alarm about upcoming obstacles and road incidents. In this study, we have presented the effective use of uncooled thermal IR sensors for designing smart thermal perception systems as an alternative to CMOS visible imaging by presenting state-of-the-art studies for in-cabin and out-cabin vehicular applications with potential long-term benefits for the automotive industry. The key rationale for selecting thermal IR sensors over conventional image sensors is that visible cameras are highly dependent on lighting conditions and performance is degraded significantly in low-lighting scenarios and harsh weather conditions. Contrary to this, thermal sensors remain largely unaffected by external lighting conditions or most environmental conditions, making it a perfect optical sensor choice for all-weather and harsh environmental conditions. This study presents a review of the current state of the art for automotive thermal imaging with a focus on the contributions and advances achieved by the EU-funded project ‘HELIAUS’ in the domain of AI-based thermal imaging pipelines for safer and reliable road journeys.

Vehicular Applications of Koopman Operator Theory—A Survey

Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.

Star Shaped Fractal Conformal MIMO Antenna for WLAN, Vehicular and Satellite Applications

Star Shaped Fractal Conformal MIMO Antenna for WLAN, Vehicular and Satellite Applications

A Polarization and Frequency Reconfigurable Microstrip Antenna for Vehicular Communication System Application

To face a complex wireless communication scenario, a flexible and tunable multi-functional antenna system should be employed to guarantee system quality. This paper presents a polarization and frequency reconfigurable microstrip patch antenna with a wide 3-dB axial ratio beam-width for vehicle communication systems. By controlling the p-i-n diodes, the proposed design can achieve four operating states: linear polarization (LP) in lower frequency bands between 1.45 and 1.47 GHz, and LP, left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP) in higher frequency bands between 1.50 and 1.53 GHz. The proposed antenna has an exceptionally low profile (0.02 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda _{0}$</tex-math></inline-formula> at 1.5 GHz) and a simple structure, consisting of a single-layer square patch with two annular ring slots and four switches placed at appropriate locations in the slots. P-i-n diodes, as the switchs, change the current distribution on the radiating patch to achieve the antenna's polarization and frequency reconfigurability. The proposed antenna was fabricated and tested, and the observed results are identical to those predicted by the simulation. The antenna has outstanding reconfigurable capabilities independent of frequency or polarization in the operational frequency range. Therefore, it can be well used for vehicular communication systems because of its multi-reconfigurability and stable mechanical property.

Applications and Services Using Vehicular Exteroceptive Sensors: A Survey

Modern vehicles are equipped with a myriad of sensors. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Proprioceptive</i> sensors monitor the vehicle status and operation, whereas <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exteroceptive</i> ones sense the external environment, i.e., outside the vehicle’s mechanics domain. A myriad of vehicular applications, from monitoring to autonomous driving, rely on the information provided by sensors. In this article, we focus those which depend on exteroceptive sensors, which are critical for modern Advanced Driving Assistance Systems (ADAS) and autonomous vehicles, regardless of their automation level. Initially, we provide an overview of vehicular sensors and Off-the-Shelf (OTS) devices. Then, we focus on exteroceptive sensors, identifying different technologies, usages, pros and cons, as well as their availability in existing OTS devices. Next, we review the services and applications using exteroceptive sensors, especially regarding safety, navigation, and driving profiling. Besides referencing the most relevant literature on exteroceptive sensors, we also identify the datasets available in the literature. We conclude with open challenges using exteroceptive sensors from both industry and research perspectives.

Uncertainty-aware Task Offloading for Multi-vehicle Perception Fusion over Vehicular Edge Computing

Multi-vehicle perception fusion is an emerging advanced vehicular application providing Vehicle Users (VUs) with comprehensive driving assistance. This involves each VU's individual vehicular perception tasks and additional fusion tasks at the end. However, performing these perception fusion applications on some VUs may not be feasible due to the applications' high computing complexity. Vehicular Edge Computing (VEC) server, which is collocated with a Road-side Unit, together with VUs' Vehicular Local Computing (VLC) units can be used to support these perception fusion applications through task offloading. Achieving this edge-collaborated perception fusion with task offloading involves multiple challenges: (i) frequently varying uplink channel conditions, (ii) sequential and parallel task dependencies, and (iii) uncertain task composition induced by unknown object detection outputs in the dynamic driving environment. In this paper, a real-time mechanism is proposed to minimize the end-to-end delay of an edge-collaborated multi-vehicle perception fusion application by addressing these challenges. Based on the real-time channel conditions, VEC and VLC server capacities, and number of detected objects, this algorithm jointly determines the bandwidth allocation, task partitioning, offloading, and execution scheduling for all the involved tasks of the multi-vehicle perception fusion application. Numerical results show that the proposed approach can significantly reduce the end-to-end fusion application latency compared to existing techniques.

A Magnetic Switch Sensor Based Inductive Power Transfer System With Power Control and Efficiency Maximization for Vehicular Applications

A Magnetic Switch Sensor Based Inductive Power Transfer System With Power Control and Efficiency Maximization for Vehicular Applications

Power distribution methods of ultra-capacitor/battery hybrid power source for vehicular applications

Power distribution methods of ultra-capacitor/battery hybrid power source for vehicular applications

Challenges and Solutions for Service Continuity in Inter-PLMN Handover for Vehicular Applications

The reliability and availability of network connectivity, which significantly varies with mobility, is crucial in Connected, Cooperative and Automated Mobility (CCAM). Handover and roaming are the most challenging situations in terms of connectivity of cellular networks, which require switching across cells of the same cellular network or between Public Land Mobile Networks (PLMNs). This paper proposes a set of solutions for vehicular applications to mitigate the impact of mobility in service continuity, including a dual modem solution that reduces the interruption time when switching PLMNs, an adaptive bitrate mechanism for media streaming that increases reliability, a WebRTC server acting as a gateway in media streaming sessions between vehicles, and a MEC discovery and handover method. The proposed solutions have been evaluated executing an Extended Sensors application in several commercial and experimental 5G Non-Standalone (NSA) and Stand Alone (SA) setups with different Multi-access Edge Computing (MEC), edge-cloud and cloud infrastructures to host services. It can be concluded from the results obtained that 5G networks have not yet achieved the required performance for CCAM, and that practitioners need to implement solutions and workarounds, such as the ones proposed in this work, to mitigate the issues. As lessons learnt from the deployment and experimentation, this paper also overviews a detailed set of problems and the proposed solutions that CCAM industry and cellular network stakeholders need to consider.

Power distribution methods of ultra-capacitor/battery hybrid power source for vehicular applications

Power distribution methods of ultra-capacitor/battery hybrid power source for vehicular applications

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility characteristics of vehicles, it is necessary to cache the user data in advance and learn the most popular and interesting contents for vehicular users. Since user data usually contains privacy information, users are reluctant to share their data with others. To solve this problem, traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy. However, vehicles may frequently drive out of the coverage area of the VEC before they achieve their local model trainings and thus the local models cannot be uploaded as expected, which would reduce the accuracy of the global model. In addition, the caching capacity of the local RSU is limited and the popular contents are diverse, thus the size of the predicted popular contents usually exceeds the cache capacity of the local RSU. Hence, the VEC should cache the predicted popular contents in different RSUs while considering the content transmission delay. In this paper, we consider the mobility of vehicles and propose a cooperative Caching scheme in the VEC based on Asynchronous Federated and deep Reinforcement learning (CAFR). We first consider the mobility of vehicles and propose an asynchronous FL algorithm to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay. Extensive experimental results have demonstrated that the CAFR scheme outperforms other baseline caching schemes.

Comprehensive Control Strategy and Verification for PEM Fuel Cell/Battery/Supercapacitor Hybrid Power Source

Using renewable energy is becoming a new tendency for vehicular applications to reduce fossil fuel consumption and minimize greenhouse gas emissions. Well-known as an eco-friendly energy source, the proton exchange membrane fuel cell (PEMFC) is extensively used in hybrid power systems to achieve the objective of zero-emission and air protection. However, this type of fuel cell offers slow dynamics and cannot adapt to abrupt load variations when used as a primary energy source. To overcome this shortcoming, battery (BAT) and/or supercapacitor (SC) are supplemented as auxiliary sources. In this paper, an innovative energy management strategy (EMS) for a PEMFC/BAT/SC hybrid power source (HPS) is proposed to improve the accuracy of power distribution from energy sources to the load. In detail, according to different characteristics of energy sources, a frequency decoupling (FD) method is designed to determine the required currents for PEMFC, BAT, and SC based on the load power demand. Besides, an adaptive DC bus control loop is utilized to guarantee a stable DC output voltage by using the BAT. The proposed EMS is simulated in a MATLAB/Simulink environment and experimentally implemented with a real-time DSP TMS320F28379D controller board. Subsequently, a test bench of a 200 W PEMFC, 24 V–12 Ah battery, and 25 V–60 F supercapacitor is conducted for experimental validation. The obtained results show that the proposed EMS is effective to coordinate energy flows between the three used sources and enhance the fuel cell performance in a hybrid power system.