Vehicles, Volume 4, Issue 3 (September 2022) – 12 articles

Air pollution near schools is particularly problematic. Pollution emissions from vehicle idling at or around schools may have significant effects on children’s health including increased rates of asthma and childhood leukemia. Outdoor pollution emissions from idling vehicles can also infiltrate into the schools resulting in health hazards both in school drop-off zones as well as inside nearby buildings. An Idle-Free Campaign was enacted at an elementary school to reduce idling among parents dropping off and picking up students. The campaign involved a focus group, surveys, informational events and materials, and vehicle counting efforts before and after the campaign. The surveys found that regardless of gender or level of education, parents were very concerned about air pollution concerns associated with idling and were willing to take steps to reduce their children’s exposure. Furthermore, the vehicle counting efforts showed a 17% reduction in idling vehicles and a 37% reduction in idling time following the anti-idling campaign. These findings show that a multi-pronged approach involving parents, teachers, staff, bus drivers, and delivery truck drivers, may be an effective tool to reduce idling at schools thus reducing children’s exposure. Full article

This paper describes the development trends and prospects of green-energy-based off-road electric vehicles and robots in the agricultural sector. Today, the agriculture sector faces several challenges, such as population growth, increasing energy demands, labor shortages, and global warming. Increases in energy demand cause many challenges worldwide; therefore, many methods are suggested to achieve energy independence from fossil fuels and reduce emissions. From a long-term point of view, the electrification of agricultural vehicles and renewable energy sources appear to be an essential step for robotic and smart farming in Agriculture 5.0. The trend of technological growth using fully autonomous robots in the agricultural sector seems to be one of the emerging technologies to tackle the increased demand for food and address environmental issues. The development of electric vehicles, alternative green fuels, and more energy-efficient technologies such as hybrid electric, robotic, and autonomous vehicles is increasing and improving work quality and operator comfort. Furthermore, related digital technologies such as advanced network communication, artificial intelligence techniques, and blockchain are discussed to understand the challenges and opportunities in industry and research. Full article

The introduction of automated-driving functions provides passengers with the opportunity to engage in non-driving related tasks during the ride. However, this benefit might be compromised by an increased incidence of motion sickness. Therefore, we investigated the effectiveness of active seat belt retractions as a countermeasure against motion sickness during inattentive automated driving. We hypothesized that seat belt retractions would mitigate motion sickness by supporting passengers to anticipate upcoming braking maneuvers, by actively tensioning their neck muscles and, thereby, reducing the extent of forward head movement while braking. In a motion base driving simulator, 26 participants encountered two 30 min automated drives in slow-moving traffic: one drive with active seat belt retractions before each braking maneuver and a baseline drive without. The results revealed that there was no difference in perceived motion sickness between both experimental conditions. Seat belt retractions resulted in an increased activity of the lateral neck muscles and supported drivers to anticipate braking maneuvers. However, at the same time, the retractions led to an increased magnitude of head movement in response to braking. This research lays the groundwork for future research on active seat belt retractions as a countermeasure against motion sickness and provides directions for future work. Full article

In order to improve the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. This article presents the extension of a generic prediction approach already proposed in a previous paper, which allows a robust forecasting of all traction torque-relevant variables for such strategies. The extension primarily includes the proper utilization of map data in the case of an a priori known route. Approaches from Artificial Intelligence (AI) have proven to be effective for such proposals. With regard to this, Recurrent Neural Networks (RNN) are to be preferred over Feed-Forward Neural Networks (FNN). First, preprocessing is described in detail including a wide overview of both calculating the relevant quantities from global navigation satellite system (GNSS) data in several steps and matching these with data from the chosen map provider. Next, an RNN including Long Short-Term Memory (LSTM) cells in an Encoder–Decoder configuration and a regular FNN are trained and applied. The models are used to forecast real driving profiles over different time horizons, both including and excluding map data in the model. Afterwards, a comparison is presented, including a quantitative and a qualitative analysis. The accuracy of the predictions is therefore assessed using Root Mean Square Error (RMSE) computations and analyses in the time domain. The results show a significant improvement in velocity prediction with LSTMs including map data. Full article

Though the current wave of electric vehicles is transforming the on-road passenger and commercial vehicle fleets, similar attempts in the off-road equipment sector appear to be lacking. Because of the diverse equipment categories and varied applications, electrifying off-road equipment requires significant research and development. A successful electrification of such equipment can offer an array of benefits, including reduced air and noise pollution, higher energy efficiency, and increased productivity. This paper provides a review of the current state of technology in off-road equipment electrification, with a focus on the equipment used in construction and agricultural applications. The paper also discusses advantages of, and challenges associated with, electrifying off-road construction and agricultural equipment. In addition, potential solutions for overcoming these challenges as well as opportunities to facilitate the electrification of off-road construction and agricultural equipment are identified. Full article

Wrong-way driving (WWD) crashes lead to severe injuries and fatalities, especially when a large truck is involved. This study investigates the factors associated with crash-injury severity in large-truck WWD crashes in Florida. Various driver, roadway, weather, and traffic characteristics were explored as explanatory variables through a random parameter ordered logit model. The study also accounted for heterogeneity by identifying random parameters in the model and introducing interaction effects as potential sources of such heterogeneity. The findings indicate that not using a seatbelt, driving under the influence of drugs, and a driving speed of 50–74 mph were more likely to result in fatal crashes. On the contrary, female drivers, private roadways, and sideswipe collisions showed negative impacts on crash-injury severity. The model identified two random parameters, including a speed of 25–49 mph and early-morning crashes. The interaction effects showed that when driving at a speed of 25–49 mph, young drivers (under 20 years old) and middle-aged drivers (36–50 years old) were the sources of heterogeneity, decreasing crash-injury severity. Understanding the contributing factors of large-truck WWD crashes can help policymakers develop safety countermeasures to reduce the associated injury severity and improve truck safety. Full article

Road surface friction, or in other words, a pavement’s skid resistance, is an essential attribute of highway safety, acting as a liaison between the infrastructure condition and the driver’s response to it through proper vehicle maneuvering. The present study reviews aspects related to the tire–road friction, including affecting factors, monitoring systems and related practices, and demonstrates the efficacy of using smart tires, or tires embedded with sensors, for the purpose of evaluating roadway friction levels in real-time while traveling. Such an approach is expected to assist drivers in adjusting their behavior (i.e., lowering their speed) in the event that signs of reduced skid resistance are observed in favor of road safety. The current challenges and research prospects are highlighted in terms of tire manufacturers’ perspectives as well as future mobility patterns with autonomous driving modes. Overall, smart tires are commented as a tool able to enhance drivers’ safety for both current and future mobility patterns, help to control pavement deterioration and complement existing practices for infrastructure condition assessment. Full article

Shorter development times, increased standards for vehicle emissions and a greater number of vehicle variants result in a higher level of complexity in the vehicle development process. Efficient development of powertrain and driver assistance functions under comparable and reproducible operating conditions is possible on vehicle test benches. Yet, the realistic simulation of real driving environments on test benches is a challenge. Current test procedures and new technologies, such as Real Driving Emission tests and Autonomous Driving, require a reproducible and even more detailed simulation of the driving environment. Due to this, the simulation of curve driving in particular is gaining in importance. This results from its significant influence on energy consumption and Autonomous Driving functions with lateral guidance, such as lane departure and evasion assistance. Reproducibility can be additionally increased by using a driving robot. At today’s vehicle test benches, pedal and shift robots are predominantly used for longitudinal dynamic tests in the performed test procedures. In order to meet these new test automation requirements for vehicle test benches, the cooperative operation of pedal and steering robots is needed on a test bench setup suitable for this purpose. In this publication, the authors present the setup of a vehicle test bench to be used in automated and reproducible vehicle-in-the-loop tests during steering events. The focus is on the test-bench-specific setup with steerable front wheels, the actuators for simulating the wheel steering torque around the steering axle and the robots used for pedals and steering wheel. Results from various test series are presented and the potential of the novel test environment is shown. The results are reproducible in various test series due to the closed-loop operation without human driving influences at the test bench. Full article

The increasing interest in employing wide-bandgap (WBG) drive systems has brought about very high power, high-frequency inverters enjoying switching frequencies up to hundreds of kilohertz. However, voltage surges with steep fronts, caused by turning semiconductor switches on/off in inverters, travel through the cable and are reflected at interfaces due to impedance mismatches, giving rise to overvoltages at motor terminals and in motor windings. The phenomena typically associated with these repetitive overvoltages are partial discharges and heating in the insulation system, both of which contribute to insulation system degradation and may lead to premature failures. In this article, taking the mentioned challenges into account, the repetitive transient overvoltage phenomenon in WBG drive systems is evaluated at motor terminals and in motor windings by implementing a precise multiconductor transmission line (MCTL) model in the time domain considering skin and proximity effects. In this regard, first, a finite element method (FEM) analysis is conducted in COMSOL Multiphysics to calculate parasitic elements of the motor; next, the vector fitting approach is employed to properly account for the frequency dependency of calculated elements, and, finally, the model is developed in EMTP-RV to assess the transient overvoltages at motor terminals and in motor windings. As shown, the harshest situation occurs in turns closer to motor terminals and/or turns closer to the neutral point depending on whether the neutral point is grounded or floating, how different phases are connected, and how motor phases are excited by pulse width modulation (PWM) voltages. Full article

The wireless charging electric tram system is presently receiving attention as an eco-friendly means of transportation. The conventional electric tram system has a similar advantage in regards to environmental pollution, but it has several problems that are caused by the overhead power supply line. The battery-type electric tram system should be considered carefully, because the battery itself is an environmentally harmful material. Therefore, the wireless charging electric tram system is regarded as an alternative means of transportation. The adequate battery capacity and the location of the wireless charging infrastructure are investigated in this study, which consider the dual operation policy, and the objective is to minimize the total investment cost. The variation of the battery capacity and the location of the wireless charging infrastructure are examined that compare Case 1, which involves the electric trams operating only in normal operations, and Case 2, which includes the electric trams operating in normal and express operations. Full article

The rapid conversion of conventional powertrain technologies to climate-neutral new energy vehicles requires the ramping of electrification. The popularity of fuel cell electric vehicles with improved fuel economy has raised great attention for many years. Their use of green hydrogen is proposed to be a promising clean way to fill the energy gap and maintain a zero-emission ecosystem. Their complex architecture is influenced by complex multiphysics interactions, driving patterns, and environmental conditions that put a multitude of power requirements and boundary conditions around the vehicle subsystems, including the fuel cell system, the electric motor, battery, and the vehicle itself. Understanding its optimal fuel economy requires a systematic assessment of these interactions. Artificial intelligence-based machine learning methods have been emerging technologies showing great potential for accelerated data analysis and aid in a thorough understanding of complex systems. The present study investigates the fuel economy peaks during an NEDC in fuel cell electric vehicles. An innovative approach combining traditional multiphysics analyses, design of experiments, and machine learning is an effective blend for accelerated data supply and analysis that accurately predicts the fuel consumption peaks in fuel cell electric vehicles. The trained and validated models show very accurate results with less than 1% error. Full article

In recent decades, the trend of using zero-emission vehicles has been constantly evolving. This trend brings about not only the pressure to develop electric vehicles (EVs) or hybrid electric vehicles (HEVs) but also the demand for further developments in battery technologies and safe use of battery systems. Concerning the safe usage of battery systems, Battery Management Systems (BMS) play one of the most important roles. A BMS is used to monitor operating temperature and State of Charge (SoC), as well as protect the battery system against cell imbalance. The paper aims to present hardware and software designs of a BMS for unmanned EVs, which use Lithium multi-cell battery packs. For higher modularity, the designed BMS uses a distributed topology and contains a master module with more slave modules. Each slave module is in charge of monitoring and protecting a multi-cell battery pack. All information about the state of each battery pack is sent to the master module which saves and sends all data to the control station if required. Controlled Area Network (CAN) bus and Internet of Things technologies are designed for requirements from different applications for communications between slave modules and the master module, and between the master module and control station. Full article