Vehicles, Volume 3, Issue 3 (September 2021) – 18 articles

Electric mobility is at the forefront of innovation. Cutting down greenhouse gases when low-carbon electricity sources are maintained has answered the concerns of skeptics when switching to electric mobility. This paper presents a life-cycle-based comparative study between the electric and conventional gasoline vehicles with respect to their environmental performance, taking the case of Qatar. A well-to-wheel life cycle assessment is used to understand the carbon footprint associated with the use of alternative mobility when powered by non-renewable energy sources such as natural gas for electricity production. A survey was also conducted to evaluate the economic and practical feasibility of the use of electric vehicles in Qatar. The analysis showed that electric vehicles (EVs) have passed conventional gasoline vehicles with a minimum difference between them of 12,000 gCO₂eq/100 km traveled. This difference can roughly accommodate two additional subcompact electric vehicles on the roads of Qatar. Even though Qatar is producing all of its electricity from natural gas, EVs are still producing much less carbon footprint into the atmosphere with the results showing that almost identical alternatives produce triple the amount of GHG emissions. The results of the survey showed that, despite promising results shown in switching to carbon-neutral mobility solutions, a lack of willingness prevails within the State of Qatar to incline towards electric mobility among users. This implies that Qatar has to spend a lot of time and resources to achieve its ambitious goal to decarbonize mobility on roads with 10% electric vehicles by 2030. This research highlights the need for more practical incentives and generous subsidies by the government of Qatar on e-mobility solutions to switch the transportation system into an eco-friendly one. Full article

The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to this last point, the rate of reduction in accidents is considerable when switching safety control tasks to machines from humans, which can be noted as having significantly slower response rates. This paper explores this thematic by focusing on the safety of AVs by thorough analysis of previously collected AV crash statistics and further discusses possible solutions for achieving increased autonomous vehicle safety. To achieve this, this technical paper develops a dynamic run-time safe assessment system, using the standard autonomous drive system (ADS), which is developed and simulated in case studies further in the paper. OpenCV methods for lane detection are developed and applied as robust control frameworks, which introduces the factor of vehicle crash predictability for the ego vehicle. The developed system is made to predict possible crashes by using a combination of machine learning and neural network methods, providing useful information for response mechanisms in risk scenarios. In addition, this paper explores the operational design domain (ODD) of the AV’s system and provides possible solutions to extend the domain in order to render vehicle operationality, even in safe mode. Additionally, three case studies are explored to supplement a discussion on the implementation of algorithms aimed at increasing curved lane detection ability and introducing trajectory predictability of neighbouring vehicles for an ego vehicle, resulting in lower collisions and increasing the safety of the AV overall. This paper thus explores the technical development of autonomous vehicles and is aimed at researchers and practitioners engaging in the conceptualisation, design, and implementation of safer AV systems focusing on lane detection and expanding AV safe state domains and vehicle trajectory predictability. Full article

This paper reviews the impact that can be presented by the immersion of generation sources and electric vehicles into the distribution network, with a technical, operational and commercial approach, given by the energy transactions between customer and operator. This requires a mathematical arrangement to identify the balance between congestion and the operating cost of a microgrid when the operation scheduling of the system a day ahead of horizon time it is required. Thus, this research is directed to the solution, using heuristic algorithms, since they allow the non-convex constraints of the proposed mathematical problem. The optimization algorithm proposed for the analysis is given by the Multi-Object Particle Swarm Optimization (MOPSO) method, which provides a set of solutions that are known as Optimal Pareto. This algorithm is presented in an IEEE 141-bus system, which consists of a radial distribution network that considers 141 buses used by Matpower; this system was modified and included a series of renewable generation injections, systems that coordinate electric vehicles and battery storage, and the slack node was maintained and assumed to have (traditional generation). In the end it can be shown that the algorithm can provide solutions for network operation planning, test system robustness and verify some contingencies comparatively, always optimizing the balance between congestion and cost. Full article

Electric Vehicle (EV) technologies offer a leading-edge solution for clean transportation and have evolved substantially in recent years. The growing market and policies of governments predict EV massive penetration shortly; however, their large deployment faces some resistances such as the high prices compared to Internal Combustion Engine (ICE) cars, the required infrastructure, the liability for novelty and standardisation. During winter periods of cold countries, since the use of heating systems increases, the peak power may produce stress to the grid. This fact, combined with EVs high penetration, during charging periods inside of high consumption hours might overload the network, becoming a threat to its stability. This article presents a framework to evaluate load shifting strategies to reschedule the EV charging to lower grid load periods. The undesirable “rebound” effect of load shifting strategies is confirmed, leading us to our EV local overnight charging strategy (EV-ONCS). Our strategy combines the forecast of residential demand using probabilistic distribution from historical consumption, prediction of the EV expected availability to charge and the charging strategy itself. EV-ONCS avoids demand rebound of classic methods and allows a peak-to-average ratio reduction demonstrating the relief for the grid with very low implementation cost. Full article

The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign was performed in the wind tunnel, testing 16 helmets in two different configurations of neck stiffness. The dataset was collected within a convolutional neural network (CNN or ConvNet) of images, creating a ranking by identifying the best and the worst helmets. The results revealed that each helmet has unique aerodynamic characteristics. Depending on the ranking scale previously created, the aerodynamic comfort of each helmets can be classified within the scale. Full article

In this paper, we investigate the intersection traffic management for connected automated vehicles (CAVs). In particular, a decentralized autonomous intersection management scheme that takes into account both the traffic efficiency and scheduling flexibility is proposed, which adopts a novel intersection–vehicle model to check conflicts among CAVs in the entire intersection area. In addition, a priority-based collision-avoidance rule is set to improve the performance of traffic efficiency and shorten the delays of emergency CAVs. Moreover, a multi-objective function is designed to obtain the optimal trajectories of CAVs, which considers ride comfort, velocities of CAVs, fuel consumption, and the constraints of safety, velocity, and acceleration. Simulation results demonstrate that our proposed scheme can achieve good performance in terms of traffic efficiency and shortening the delays of emergency CAVs. Full article

In multi-drive electrified powertrains, the control strategy strongly influences the component load collectives. Due to this interdependency, the component sizing becomes a difficult task. This paper comprehensively analyses different electric drive system sizing methods for multi-drive systems in the literature. Based on this analysis, a new data-enhanced sizing approach is proposed. While the characteristic is depicted with a physics-based polynomial model, a data-enhanced limiting function ensures the parameter variation stays within a physically feasible range. Its beneficial value is demonstrated by applying the new model to a powertrain system optimization. The new approach enables a detailed investigation of the correlations between the characteristic of electric drive systems and the overall vehicle energy consumption for varying topologies. The application results demonstrate the accuracy and benefit of the proposed model. Full article

Electric vehicles (EVs) represent an important socio-economic development opportunity for islands and remote locations because they can lead to reduced fuel imports, electricity storage, grid services, and environmental and health benefits. This paper presents an overview of opportunities, challenges, and examples of EVs in islands and remote power systems, and is meant to provide background to researchers, utilities, energy offices, and other stakeholders interested in the impacts of electrification of transportation. The impact of uncontrolled EV charging on the electric grid operation is discussed, as well as several mitigation strategies. Of particular importance in many islands and remote systems is taking advantage of local resources by combining renewable energy and EV charging. Policy and economic issues are presented, with emphasis on the need for an overarching energy policy to guide the strategies for EVs growth. The key conclusion of this paper is that an orderly transition to EVs, one that maximizes benefits while addressing the challenges, requires careful analysis and comprehensive planning. Full article

With the improvement of lithium-ion battery (LIB) technology, safety is becoming increasingly urgent topic for battery electric vehicles (BEVs). Short circuits, overcharging, high temperatures and overheating can cause thermal runaway reactions and the release of the flammable electrolyte which makes fire suppression very difficult. This study focuses on the mechanism of thermal runaway and fire suppression applications of LIBs. In order to understand this, 10 experiments were carried out. The experiments were divided into as Exp. A and Exp. B. A manual water suppression system was used in Exp. A and an automatic boron-based suppression system (AUT-BOR) was used in Exp. B. LIBs were heated in a controlled manner with a heat source and the effects of thermal runaway and fire suppression were observed. In Exp. A, a large amount of water was required to extinguish the LIB fires. The holes and slits which formed in the LIB after a fire were useful for injecting water. A projectile effect of cylindrical cells was observed in Exp. A. The Exp. B results showed that AUT-BOR mitigates risks effectively and safely. Also, AUT-BOR provides an early fire warning system and spot cooling to prevent thermal runaway reactions while localizing and suppressing the fire. In Exp. B, fire detection and suppression occurred without any explosion. Full article

One of the most important goals in vehicle acoustics is to describe the NVH behavior of a vehicle at sound pressure level using simulation models at an early stage of development. Different simulation models and methods are used for this purpose. To balance the advantages and disadvantages of the different methods, it is important to combine the simulation models. For the virtual description of the road booming noise behavior of a vehicle passing a rough road, we use a multibody simulation model excited with the elevation profile of the road in the time domain. To calculate the sound pressure inside the vehicle, the internal chassis forces of the multibody simulation model are combined with a finite element body model including the air cavity inside the cabin. The methodology for combining the chassis forces and body transfer functions to calculate the sound pressure is first validated using test data and then applied to the simulation data. The correlation of the calculated sound pressure based on test data ($\rho =0.96$) and based on simulation data ($\rho =0.90$) compared to a microphone measurement is very high. Full article

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms. Full article

As long as road accidents happen, passive safety systems like the airbag control unit are an essential part of the whole automotive safety system. Within the airbag control unit, the event data recorder (EDR) is an integrated function. Recent developments in legislation show that an increasing number of EDR-related regulations are being introduced. They are mainly focusing on data recording, crash-data retrieval, and some of them define testing aspects. In the system testing of an airbag control unit with a focus on the event data recorder, the question arises of how to deal with the fact that real-world crash events are not “straightforward” but arbitrary and do not follow any rules and restrictions. The purpose of this work is to develop a robust test approach to these conditions—giving a tester the possibility to extend the test depth considering the common test design techniques and testing principles. The applied methodology is the use of optimization algorithms in an automated test environment. With this, the tester can steer the test execution in a predefined way with minimal interaction. The application of the developed test method automatically creates a set of test data which fulfill the predefined conditions by the user. The generated results show that a high number of test data are created at and close to the target condition. Consequently, this test approach provides an extension to the common test design techniques with regard to how test input data can be created, and especially how automated test data creation and test execution can be realized. Full article

Energy storage plays a fundamental role in balancing the power fluctuations induced by the distributed generation of renewable energy sources. In this scenario, electric vehicles can strongly contribute to exchange power with the grid through their on-board batteries. When the vehicle is parked, the battery can be discharged, injecting active power into the grid, provided that its state of charge will be restored before vehicle utilization. This paper presents a comprehensive step-by-step design of a wireless charger for a Vehicle-to-Home application. The design procedure begins from the constraints disposed by the Italian reference technical rules for Low Voltage utilities and by the standard SAE J2954 for Wireless Power Transfer for electric vehicles. The selection of the output power of the battery is followed by the power sizing of each stage of the bidirectional wireless charger. Full article

Today, design engineers engaged in the development of a high-performance electrical drive-train are challenged by the multitude of possible topological choices and numerous mutually interconnected physical phenomena. Development teams around the globe struggle with this challenge; usually they employ several tools for simulation and topology optimization and transfer multiple versions of their product models in a mainly manual process. The research presented in this paper aims to explore a holistic possibility to realize a sensible analysis-synthesis cycle that takes into consideration current developments in design, simulation and optimization processes. This kind of process can enhance the transparency of design decisions, can reduce the risk of design and process flaws and can support the approach toward a holistic optimum. The investigation starts with the development of the topological concept of the drive-train and continues over the interconnected simulation of several decisive properties of the drive-train. Obviously, these properties concern several domains (mechanical, electrical, thermal and the control domain). The optimization of the drive-train takes into consideration the main requirement—in the investigated example, which is a formula student drive-train—the lap time. The result is a holistic concept for a design, simulation and optimization approach that considers topological variety, interconnected multi-domain simulation and a continuous connection to the decisive requirements. Full article

A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques. Full article

Active safety systems for three-wheeled vehicles seem to be in premature development; in particular, delta types, also known as tuk-tuks or sidecars, are sold with minimal protection against accidents. Unfortunately, the risk of wheel lifting and lateral and/or longitudinal vehicle roll is high. For instance, a tripped rollover occurs when a vehicle slides sideways, digging its tires into soft soil or striking an object. Unfortunately, research is mostly aimed at un-tripped rollovers while most of the rollovers are tripped. In this paper, models for lateral skid tripped and un-tripped rollover risks are presented. Later, independent braking and accelerating control actions are used to develop a dynamic stability control (DSC) to assist the driver in mitigating such risks, including holes/bumps road-scenarios. A common Lyapunov function and an LMI problem resolution ensure robust stability while optimization allows tuning the controller. Numerical and HIL tests are presented. Implementation on a three-wheeled vehicle requires an inertial measurement unit, and independent ABS and propulsion control as main components. Full article

Modern hybrid electric vehicles (HEVs) like the fourth generation of Toyota Prius incorporate multiple planetary gears (PG) to interconnect various power components. Previous studies reported that increasing the number of planetary gears from one to two reduces energy consumption. However, these studies did not compare one PG and two PGs topologies at their optimal operation. Moreover, the size of the powertrain components are not the same and hence the source of reduction in energy consumption is not clear. This paper investigates the effect of the number of planetary gears on energy consumption under optimal operation of the powertrain components. The powertrains with one and two PGs are considered and an optimal simultaneous torque distribution and mode selection strategy is proposed. The proposed energy management strategy (EMS) optimally distributes torque demands amongst the power components whilst also controlling clutches (i.e., mode selection). Results show that increasing from one to two PGs reduces energy consumption by 4%. Full article

The COVID-19 pandemic has affected public transportation worldwide, and its implications need to be evaluated and study deeply on all public transportation aspects. Therefore, an analysis has been created to examine the effects of the pandemic on public transportation service quality decisions to have a better vision of the different stakeholders’ needs to keep the system functioning in a profitable way. Stakeholder participation in complex, multi-criteria decision-making often produces very different results in prioritizing the decision attributes. Rank correlation techniques generally measure the degree of agreement or non-agreement among the evaluator groups. However, the multi-criteria methodology can determine not only ordinal but also cardinal priorities. Consequently, except for the attributes’ positions, the weight values are also significant in the final decision. This paper aims to apply a more sophisticated measure of group agreement than rank correlation. First, the Fuzzy-hierarchical analytical process (FAHP) has been used to find out the aggregated weights, then the Kendall correlation values are computed to reveal stakeholder opinions. Finally, the agreement measure approach has been tested in a real-world case study: the public transport development decision of Amman, Jordan. The analysis shows that by applying the Kendall technique, Kendall could gain a more profound insight into the priority characteristics of different evaluator groups. Full article