Vehicles, Volume 5, Issue 3 (September 2023) – 28 articles

Light-duty vehicles are increasingly incorporating plastic materials to reduce production costs and achieve lightweight designs. On average, a conventional car utilizes over 200 kg of plastic, comprising more than 23 different types, which often present challenges for recycling due to their incompatibility. Consequently, the focus on plastic recycling in end-of-life vehicles has intensified. This study aims to analyze critical car parts based on the plastics used, employing a novel thermodynamic approach that examines the embodied exergy (EE) of different plastics. Six vehicles from various segments, years, and equipment levels were assessed to understand their plastic compositions. The findings reveal that, on average, a vehicle contains 222 kg of plastic, accounting for 17.7% of its total weight. Among these plastics, 47.5% (105 kg) are utilized in car parts weighing over 1 kg, with plastics comprising over 80% of the part’s weight. The identified critical car parts include the front door trim panel, front and rear covers, fuel tank, floor covering, front lighting, dashboard, rear door trim panel, plastic front end, backrest pad, door trim panel pocket, plastic foam rear seat, rear lighting, window guide, molded headliner, bulkhead sound insulation, foam seat part, and wheel trim. Regarding their contribution to EE, the plastics with the highest shares are polypropylene—PP (24.5%), polypropylene and ethylene blends—E/P (20.3%), and polyurethane- PU (15.3%). Understanding the criticality of these car parts and their associated plastics enables targeted efforts in design, material selection, and end-of-life management to enhance recycling and promote circularity within the automotive industry. Full article

This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and $k_2$, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing. Full article

A review of past, current, and emerging electric vehicle (EV) propulsion system technologies and their integration is the focus of this paper, namely, the matching of electric motor (EM) and transmission (TRM) to meet basic requirements and performance targets. The fundaments of EM and TRM matching from a tractive effort and a vehicle dynamics perspective are provided as an introductory context to available or near-production propulsion system products available from OEM and Tier 1 suppliers. Engineering data and details regarding EM and TRM combinations are detailed with a specific focus on volumetric and mass density. Evolutionary trends in EM and TRM technologies have been highlighted and summarized through current and emerging products. The paper includes an overview of the initial EV propulsion system’s sizing and selection for a set of simple requirements that are provided through an examination of three light-duty EV applications. An enterprise approach to developing electrified propulsion modules with suitable applicability to a range of light-duty EVs from compact cars to full-size trucks concludes the paper. Full article

This paper explores the use of driver-in-the-loop simulations to detect personalized driving styles in autonomous vehicles. The driving simulator used in this study is modular and adaptable, allowing for the testing and validation of control and data-collecting systems, as well as the incorporation and proof of car models. The selected scenario is a double lane change maneuver to overtake a stationary obstacle at a relatively high speed. The user’s behavior was recorded, and lateral accelerations during the maneuver were used as criteria to compare the user-driven vehicle and the autonomous one. The tuning parameters of the lateral and longitudinal controllers were modified to obtain different lateral accelerations of the autonomous vehicle. A neural network was developed to find the combination of the two controllers’ tuning parameters to match the driver’s lateral accelerations in the same double lane change overtaking action. The results are promising, and this study suggests that driver-in-the-loop simulations can help increase autonomous vehicles’ safety while preserving individual driving styles. This could result in creating more individualized and secure autonomous driving systems that consider the preferences and behavior of the driver. Full article

Currently, in the course of the German mobility transition, an increasing number of disused rail lines are already being or intended to be reactivated in order to increase capacities, decrease transport-related emissions and reconnect rural areas to passenger rail services, thus creating a more comprehensive rail service. However, the use of state-of-the-art regional railcars on old rural infrastructure often leads to problems since they are often worn out and do not meet today’s technical standards. This applies, for example, to the axle loads and dimensions of the vehicles but also to operational aspects, such as the vehicle’s passenger capacity and accessibility. First, this work gives an overview of the available rolling stock and the given infrastructure, as well as an analysis of the (system) interfaces. Subsequently, various challenges for the re-connection of peripheral areas to the rail network were identified through data research and comparison of the vehicle and infrastructure parameters. In addition, the requirements related to possible autonomous operation and the related absence of the driver and crew personnel in the vehicle, which require new solutions in terms of safety, were taken into consideration. Orientation of future rolling stock generations towards the existing infrastructure and the required transport needs, including lower axle loads, accessibility and smaller capacities, can contribute to the economic operation of low-capacity lines and bring more passengers to public transport. Full article

Modern power engine concepts and environmental restrictions demand oil-free lubrication of rotors, for example, by gas bearings. However, the stiffness and damping properties ruling the rotor’s dynamics are poorly documented for aerodynamic bearings and simple calculation methods are lacking. Based on the similarity between aerodynamic and hydrodynamic journal bearings, it is investigated to what extent the hydrodynamic bearing element types of the commercial FE program ANSYS are also suitable for air bearings. Within these elements, the compressibility of the gas is neglected. After verification of the ANSYS hydrodynamic element types with literature data for cylindrical hydrodynamic bearings, the stiffness and damping coefficients of a cylindrical aerodynamic bearing are calculated by using the ANSYS hydrodynamic element types. In the examined speed range, the results agree well with literature data that consider gas compressibility. Therefore, the FE elements designed for hydrodynamical journal bearings may also be used for simulating cylindrical aerodynamic bearings. The presented calculation approach provides a compact and easy-to-use method for rotordynamic simulations with cylindrical aerodynamic bearings in a single development environment. Full article

Innovative solutions are now being researched to manage the ever-increasing amount of data required to optimize the performance of internal combustion engines. Machine learning approaches have shown to be a valuable tool for signal prediction due to their real-time and cost-effective deployment. Among them, the architecture consisting of long short-term memory (LSTM) and one-dimensional convolutional neural networks (1DCNNs) has emerged as a highly promising and effective option to replace physical sensors. This architecture combines the capacity of LSTM to detect patterns and relationships in smaller segments of a signal with the ability of 1DCNNs to detect patterns and relationships in larger segments of a signal. The purpose of this work is to assess the feasibility of substituting a physical device dedicated to calculating the torque supplied by a spark-ignition engine. The suggested architecture was trained and tested using signals from the field during a test campaign conducted under transient operating conditions. The results reveal that LSTM + 1DCNN is particularly well suited for signal prediction with considerable variability. It constantly outperforms other architectures used for comparison, with average error percentages of less than 2%, proving the architecture’s ability to replace physical sensors. Full article

Vehicular Ad Hoc Networks (VANETs) have gained significant attention due to their potential to enhance road safety, traffic efficiency, and passenger comfort through vehicle-to-vehicle and vehicle-to-infrastructure communication. However, VANETs face resource management challenges due to the dynamic and resource constrained nature of vehicular environments. Integrating cloud-fog-edge computing and Software-Defined Networking (SDN) with VANETs can harness the computational capabilities and resources available at different tiers to efficiently process and manage vehicular data. In this work, we used this paradigm and proposed an intelligent approach based on Fuzzy Logic (FL) to evaluate the processing and storage capability of vehicles for helping other vehicles in need of additional resources. The effectiveness of the proposed system is evaluated through extensive simulations and a testbed. Performance analysis between the simulation results and the testbed offers a comprehensive understanding of the proposed system and its performance and feasibility. Full article

Using Unmanned Aerial Vehicles (UAVs), commonly referred to as “drones”, as a supplementary mode for last-mile deliveries has been a research focus for some years now. Motivation lies in the reduced dependency on Conventional Vehicles (CVs) and fossil fuels and in serving remote areas and underprivileged populations. We are building a flexible, modular framework for integrated CV-UAV parcel delivery operations planning that is responsive to infrastructure and demand and offers an open and practical tool for future adaptations. The entire model and solution methodology are practical tools for decision making and strategic planning, with novelties such as the variable Launch Site types for Launch and Recovery Operations (LAROs), the tailored Assignment and Routing Optimization nested GA, the consideration of airspace restrictions of any shape and size, the inclusion of GIS tools in the process, the modularity of the platform, and most importantly, the inclusion of all the above in a single, comprehensive, and holistic approach. Because of the need for safe UAV deployment sites and the high presence of restricted airspace zones in urban environments, the intended field of application is assumed to be the delivery of small packages in rural and under-connected areas, the execution of inter-city deliveries, and the expansion of a city’s original service range. A single CV is equipped onboard with UAVs, while special locations, such as Remote Depots (RDs) with UAVs and Virtual Hubs (VHs) for UAV deployment facilitation, are introduced. The framework considers the presence of Restricted Zones (RZs) for UAV flights. Part of the methodology is implemented in a GIS environment, taking advantage of modern tools for spatial analysis and optimal path planning. We have designed a tailored nested GA method for solving the occurring mode assignment and vehicle routing optimization problems and have implemented our workflow on a devised case study with benchmark characteristics. Our model responds well to unfavorable network types and demand locations, while the presence of RZs notably affects the expected solution and should be considered in the decision-making process. Full article

Computer vehicle simulators are used to model real-world situations to overcome time and cost limitations. The vehicle simulators provide virtual scenarios for real-world driving. Although the existing simulators precisely observe movement on the basis of good-quality graphics, they focus on a few driving vehicles instead of accident simulation. In addition, it is difficult to represent vehicle collisions. We propose a vehicle crash simulator with simulation and animation components. The proposed simulator synthesizes and simulates models of vehicles and environments. The simulator animates corresponding to the simulation through the execution results. The simulation results validate that the proposed simulator provides collision and non-collision results according to the speed of two vehicles at an intersection. Full article

The ride comfort provided by a vehicle to the driver and the passengers is an important feature, directly correlated to the technical characteristics of the suspension system of the vehicle. In the literature, several lumped-parameter models simulating the vehicle and the driver are proposed for the computational evaluation of ride comfort. In order to quantify ride comfort, other than the values of acceleration, metrics such as seat effective amplitude transmissibility (SEAT) and seat-to-head transmissibility (STHT) are utilized. In this paper, a quarter car model is coupled with a six-degree-of-freedom lumped-parameter model, consisting of the driver’s seat and the driver. A sensitivity analysis is performed on the values of the lumped parameters of the seated human body with regard to ride comfort in order to evaluate the effect of their accuracy relative to the ride comfort evaluation. The results of the sensitivity analysis revealed that the values of the mass, the stiffness and the damping parameters of the seated human model influence the ride-comfort metrics to a different extent. Furthermore, it was depicted that ride-comfort metrics were affected in different manners depending on the characteristics of the excitation of the vehicle, yet less than 10% Finally, the importance of the consideration of single-disturbance excitations in such sensitivity studies emerged. Full article

Autonomous ground vehicles (AGVs) operating in complex environments face the challenge of accurately following desired paths while accounting for uncertainties, external disturbances, and initial conditions, necessitating robust and adaptive control strategies. This paper addresses the critical path-tracking task in AGVs through a novel control framework for multilevel speed AGVs, considering both structured and unstructured uncertainties. The control system introduced in this study utilizes a nonlinear adaptive approach by integrating integral backstepping with terminal sliding mode control (IBTSMC). By incorporating integral action, IBTSMC continuously adjusts the control input to minimize tracking errors, improving tracking performance. The hybridization of the terminal sliding mode method enables finite time convergence, robustness, and a chatter-free response with reduced sensitivity to initial conditions. Furthermore, adaptive control compensators are developed to ensure robustness against unknown but bounded external disturbances. The Lyapunov stability theorem is employed to guarantee the global asymptotic stability of the closed-loop system and the convergence of tracking errors to the origin within finite time. To validate the effectiveness of the proposed control scheme, high-fidelity cosimulations are conducted using CarSim and MATLAB. Comparative analysis is performed with other methods reported in the literature. The results confirm that the proposed controller demonstrates competitive effectiveness in path-tracking tasks and exhibits strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances. Full article

Current research with Vehicular Ad-hoc Networks (VANETs) has focused on adapting an efficient consensus mechanism and reducing the blockchain size while maintaining security. Care must be taken when implementing blockchains within VANET applications to leverage the chains’ strengths while mitigating their weaknesses. These chains can serve as distributed ledgers that provide storage for more than financial transactions. The security provided by longer blockchains constitutes a nearly immutable, decentralized data structure that can store any data relevant to the applications. However, these chains must be adapted to the ad-hoc, resource-constrained environments found in VANETs. In the absence of abundant resources and reliable network connections, chain operation and maintenance must address the challenges presented by highly mobile nodes in novel ways, including situations such as emergency messaging that require real-time responses. Researchers have included different mechanisms to realize lightweight blockchains, such as adding reputation to existing consensus mechanisms, condensing the consensus committees, using geographical information, and monitoring a nodes behavior in attempts to adapt blockchains to these domains. This paper analyzes the challenges and gives solutions for these different mechanisms to realize lightweight blockchains for VANETs. Full article

The lack of general algorithms for the control of nonlinear systems is a generalized problem, especially when attempting to stabilize systems such as ground vehicles, which have uncertainties and are usually linearized under the assumption of small angles. To solve this problem, in this work, the implementation of a suboptimal discrete control is developed to stabilize an autonomous automobile. We assume the system is affine for the optimization procedure of finite horizon that allows us to find a solution while avoiding solving the Ricatti-type equation, commonly encountered in this kind of algorithm. This procedure is applied to the dynamical model of the lateral displacement and orientation errors of the vehicle that was discretized through the method of Euler. These nonlinear models discretized to compute a bounded control. The control is tested in different simulated scenarios to show the efficiency of the system for solving typical tasks for the path planning of an autonomous vehicle. Full article

On behalf of MÁV Hungarian State Railways Ltd., the authors carried out a research and development (R&D) project on behalf of the Budapest University of Technology and Economics, Department of Highway and Railway Engineering, on the subject of “Research and investigation of the causes of gauge narrowing by finite-element modeling in running track and turnout, and under operational and laboratory conditions”. The main objective of the research was to investigate the causes of localized defects of gauge narrowing in railway tracks based on machine and manual track measurements, laboratory measurements, and theoretical considerations. The measures proposed as a consequence of identifying the causes could significantly contribute to reducing the number and extent of local defects in the future. Furthermore, the research aims to develop new theories in less scientifically mature areas and provide procedures and instructions that professional engineers and practitioners can easily apply. The main areas of research, which are not exhaustive, are as follows: (i) the evaluation of the measurement results provided by track geometry measuring and recording cars; (ii) on-site investigations in the railway track in terms of gauge and rail profile measurements; and, based on these, (iii) the selection of concrete sleepers, which were removed from the track and subjected to more detailed geometrical investigations in the laboratory, together with the components of the rail reinforcement; (iv) the track–vehicle connection, tight running in straight and curved track sections under track confinement; (v) modeling of the stability and deflection of the rail when the rail fastenings lose part of their supporting function; and (vi) finite element modeling of the concrete sleepers under operating conditions such as slow deformation of the concrete, temperature variation effects, and lateral support on the ballast. In the already-narrowed track section, the tight vehicle running is not the cause of the track gauge narrowing but a consequence, so it is not investigated in this paper. Full article

Road authorities worldwide can leverage the advances in vehicle technology by continuously monitoring their roads’ conditions to minimize road maintenance costs. The existing methods for carrying out road condition surveys involve manual observations using standard survey forms, performed by qualified personnel. These methods are expensive, time-consuming, infrequent, and can hardly provide real-time information. Some automated approaches also exist but are very expensive since they require special vehicles equipped with computing devices and sensors for data collection and processing. This research aims to leverage the advances in vehicle technology in providing a cheap and real-time approach to carry out road condition monitoring (RCM). This study developed a deep learning model using the You Only Look Once, Version 5 (YOLOv5) algorithm that was trained to capture and categorize flexible pavement distresses (FPD) and reached 95% precision, 93.4% recall, and 97.2% mean Average Precision. Using vehicle built-in cameras and GPS sensors, these distresses were detected, images were captured, and locations were recorded. This was validated on campus roads and parking lots using a car featured with a built-in camera and GPS. The vehicles’ built-in technologies provided a more cost-effective and efficient road condition monitoring approach that could also provide real-time road conditions. Full article

Automotive control functions are becoming increasingly complex and their development is becoming more and more elaborate, leading to a strong need for automated solutions within the development process. Here, reinforcement learning offers a significant potential for function development to generate optimized control functions in an automated manner. Despite its successful deployment in a variety of control tasks, there is still a lack of standard tooling solutions for function development based on reinforcement learning in the automotive industry. To address this gap, we present a flexible framework that couples the conventional development process with an open-source reinforcement learning library. It features modular, physical models for relevant vehicle components, a co-simulation with a microscopic traffic simulation to generate realistic scenarios, and enables distributed and parallelized training. We demonstrate the effectiveness of our proposed method in a feasibility study to learn a control function for automated longitudinal control of an electric vehicle in an urban traffic scenario. The evolved control strategy produces a smooth trajectory with energy savings of up to 14%. The results highlight the great potential of reinforcement learning for automated control function development and prove the effectiveness of the proposed framework. Full article

Reducing costs and time spent in experiments in the early development stages of vehicular technology such as off-road and agricultural semi-autonomous robots could help progress in this research area. In particular, evaluating path tracking strategies in the semi-autonomous operation of robots becomes challenging because of hardware costs, the time required for preparation and tests, and constraints associated with external aspects such as meteorological or weather conditions or limited space in research laboratories. This paper proposes a methodology for the real-time hardware-in-the-loop emulation of path tracking strategies in low-cost agricultural robots. This methodology enables the real-time validation of path tracking strategies before their implementation on the robot. To validate this, we propose implementing a path tracking strategy using only the information of motor’s angular speed and robot yaw velocity obtained from encoders and a low-cost inertial measurement unit (IMU), respectively. This paper provides a simulation with MATLAB/Simulink, hardware-in-the-loop with Qube-servo (Quanser), and experimental results with an Agribot platform to confirm its validity. Full article

Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are presented so that articulated vehicles can automatically perform perpendicular parking on a reverse path. By analyzing the empirical parking methods of professional drivers, these path plans were constructed by appropriately combining several standardized simple basic motions to facilitate implementation in real vehicles. In addition, the path plans included appropriate complementary motions to effectively respond to uncertainties arising from articulation angles, etc. The complementary motions developed in this study are based on the results of qualitative analysis on the behavior of articulated vehicles. The usefulness of the proposed articulated vehicle parking method has been proven through hundreds of experimental tests using a scaled model automated vehicle. Full article

The purpose of this study is to investigate the uncertainty of the design variables of a front suspension lower control arm under fatigue-loading circumstances to estimate a reliable and robust product. This study offers a method for systematic uncertainty quantification (UQ), and the following steps were taken to achieve this: First, a finite element model was built to predict the fatigue life of the control arm under bump-loading conditions. Second, a sensitivity scheme, based on one of the global analyses, was developed to identify the model’s most and least significant design input variables. Third, physics-based and data-driven uncertainty quantification schemes were employed to quantify the model’s input parameter uncertainties via a Monte Carlo simulation. The simulations were conducted using 10,000 samples of material properties and geometrical uncertainty variables, with the coefficients of variation ranging from 1 to 3%. Finally, the confidence interval results show a deviation of about 21.74% from the mean (the baseline). As a result, by applying systematic UQ, a more reliable and robust automobile suspension control arm can be designed during the early stages of design to produce a more efficient and better approximation of fatigue life under uncertain conditions. Full article

As automated driving has not yet been established, on narrow roads where there is no separation between pedestrians and vehicles, it is essential to switch to manual driving. However, when the driver turns the steering wheel from one hand to another on narrow roads, it causes steering burdens and operational errors if the steering feel or burden is not proper. Thus, this study aims to construct an active steering wheel system that provides an appropriate steering feel or burden by controlling the steering reaction torque, driving position and steering gear ratio for each driver. In this paper, we focused on and examined the driving position among these. A two-dimensional steering model that considers the size of the arms for each driver was established to evaluate steering burden. In addition, a basic study was conducted on the appropriate driving position. Then, based on the joint movements and angles calculation, the appropriate driving position that considers the size of the arms was studied by evaluating the joint power. As a result, it was found that if the steering wheel position is too close to the driver, the amount of joint movement increases, and if it is too far away, the joint movement decreases. Therefore, it was found that the appropriate steering wheel position for each driver’s arm length can be considered by using the joint power. Full article

Electric vehicles (EVs) are experiencing explosive growth in public adoption, causing a major shift in research and development priorities by OEMs toward electrified powertrains. To verify EV drivetrain platforms and software models in the design phase, testbeds with specific capabilities are essential. Full-scale vehicle testbeds are expensive, bulky, dissipative, and not easily reconfigurable or movable, making scaled testbeds more attractive, especially for education and research institutes. To support this cause, this paper reports on the development of a small-scale, modular, hardware-in-the-loop (HIL) testbed platform for the drivetrain of EVs that is cost-effective, efficient, and easily movable and reconfigurable and allows integration of a battery pack. The testbed is comprised of two directly coupled electric machines. The first machine emulates the traction motor and is used to control vehicle speed according to a specified drive cycle. The second machine is used to impose a torque profile on the first machine’s shaft—based on the vehicle’s parameters and driving environment—and emulates a gearbox (if necessary). A systematic two-way scaling approach is adopted to downscale the parameters and driving environment of full-size EVs to a level that can be handled by the testbed and to upscale the test results obtained from the testbed to the full-size vehicle level. The power consumption of the testbed is limited to system losses. A case study involving a full-size EV was performed and the HIL simulation results were compared to the computer simulation results to verify the performance of the testbed. Full article

Integration, testing, and release of complex Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) is one of the main challenges in the field of automated driving. In order for the systems to be accepted by customers and to compete in the market, they have to feature functional, comfortable, safe, efficient, and natural driving behavior. The calibration process acquires increasing importance in the achievement of this objective. Complex ADAS/ADS require the optimization of interacting calibration parameters in a large number of different scenarios—a task that can hardly be performed with feasible effort and cost using conventional calibration methods. Virtual calibration in simulation enables reproducible and automated testing of different data sets of calibration parameters in various scenarios. These capabilities facilitate different use cases to extend the conventional calibration process of ADAS/ADS through virtual testing. This paper discusses the different use cases of virtual calibration and methods to achieve the desired objectives. A special focus is on a multi-scenario-level method that can be used to iteratively calibrate ADAS/ADS for optimal behavior in a variety of scenarios, resulting in a more comfortable, safe, and natural behavior of the system and still a feasible number of test cases. The presented methods are implemented for the virtual calibration of an Adaptive Cruise Control model for evaluation. Full article

Automated guided vehicles undertake complex transportation tasks, for instance, in production and storage systems. In recent years, an increased focus on sustainability has occurred as the effects of ongoing climate change have become more apparent. Engineers are searching intensively for ways to design technical systems that are not only environmentally sustainable, but are also resilient to the challenges of the changing climate and other environmental conditions. The production of automated guided vehicles requires considerable resources; therefore, a long operation time is desirable for overall sustainability. The performance of transportation tasks requires certain processes, such as control, path planning, coordination/synchronization, and maintenance and update processes—the latter are also very important for a long operation time. This article proposes understanding these processes as services and to explore product service systems with automated guided vehicles. Due to their complexity, the efficient and safe operation of such systems can be at risk because of several factors, such as component faults, external attacks and disturbances. For several years both resilient control and resilience engineering have been researched as possible remedies. An extension of these two concepts to the early stages of system development processes and including the system’s hardware is proposed in this article. This extension is referred to as resilient design. A primary purpose of resilient design is sustainability through extended usability and planned updates. The main intention of this article is to provide a comprehensive understanding of resilient design through application to product service systems with automated guided vehicles. The basis for this contribution is an extensive literature review and detailed system analyses on different levels. The main research results include novel application modes for product development methods. The explanation of the results is supported by means of an illustrative example based on a product service system with automated guided vehicles. Full article

The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order to improve the public charging infrastructure, this paper aims to develop a model capable of analyzing the current situation of a stretch of highway, identifying the congestion points, created by the formation of queues at the charging points. A specific section of a highway in Spain was selected as a case study to evaluate the performance of the model, allowing for rigorous testing and thorough analysis of its performance in a real-world scenario. The first step is to define and evaluate the effects of factors affecting EV consumption, such as the slope of the road, weather conditions, and driving style. Subsequently, a simulation model is developed using the agent-based simulation software AnyLogic, which simulates the journey of a fleet of electric vehicles, taking into account the battery charging and discharging process. Based on the obtained results, the charging infrastructure is improved to minimize the total travel time of an electric vehicle on a long-distance trip. Full article

Long uphill stretches of single-carriageway rural roads with one lane per travel direction may reduce the Level of Service (LoS), due to the decreased speed of heavy vehicles. In those circumstances, a slowdown of traffic, resulting in the formation of platoons, may be generated due to the difficulty of performing overtaking maneuvers safely. To solve this critical issue, an additional (climbing) lane for slow vehicles may be included in the road platform. This study aims to evaluate the effectiveness of such climbing lanes in a real case in Italy (National Road n. 4 “Via Salaria”—around 44+000 km). Using a microsimulation model implemented in VISSIM, the study analyzes speeds and travel times, delays, and queuing waiting times, comparing the Actual Scenario (AS) without climbing lanes, with two counterfactual scenarios: the first one (CS1) with three stretches of climbing lanes, and the second one (CS2), with just two stretches, in which the first two additional lanes of CS1 are merged together. The obtained results confirm the effectiveness of installing climbing lanes on road sections with the described characteristics, and the potential of microsimulation models also to carry out such kind of evaluations. Full article

In today’s automotive industry, digital technology trends such as Big Data, Digital Twin, and Hardware-in-the-loop simulations using synthetic data offer opportunities that have the potential to transform the entire industry towards being more software-oriented and thus more effective and environmentally friendly. In this paper, we propose generative models to synthesize car features related to vehicle speed: brake pressure, percentage of the pressed throttle pedal, engaged gear, and engine RPM. Synthetic data are essential to digitize Hardware-in-the-loop integration testing of the vehicle’s dashboard, navigation, or infotainment and for Digital Twin simulations. We trained models based on Multilayer Perceptron and bidirectional Long-Short Term Memory neural network for each feature. These models were evaluated on a real-world dataset and demonstrated sufficient accuracy in predicting the desired features. Combining our current research with previous work on generating a speed profile for an arbitrary trip, where Open Street Map data and elevation data are available, allows us to digitally drive this trip. At the time of writing, we are unaware of any similar data-driven approach for generating desired speed-related features. Full article