Vehicles, Volume 6, Issue 3 (September 2024) – 10 articles

The automotive industry faces development challenges due to emerging technologies, regulatory demands, societal trends, and evolving customer mobility needs. These factors contribute to a wide range of vehicle variants and increasingly complex powertrains. The layout of a vehicle is usually based on standardized driving cycles such as WLTC, gradeability, acceleration test cases, and many more. In real-world driving cycles, however, this can lead to limitations under certain boundary conditions. To ensure that all customer requirements are met, vehicle testing is conducted under extreme environmental conditions, e.g., in Sweden or Spain. One way to reduce the development time while ensuring high product quality and cost-effectiveness is to use model-based methods for the comprehensive design of powertrains. This study presents a layout methodology using a top-down approach. Initially, powertrain-relevant requirements for an exemplary target customer are translated into a specification sheet with specific test cases. An overall vehicle model with detailed thermal sub-models is developed to evaluate the different requirements. A baseline design for a C-segment plug-in hybrid vehicle was developed as part of the FVV research project HyFlex-ICE using standardized test cases, highlighting the influence of customer profiles on the design outcome through varying weighting factors. The target customer’s design is analyzed in four real driving scenarios, considering variations in parameters such as the ambient temperature, traffic, driver type, trailer pulling, and battery state-of-charge, to assess their influence on the target variables. In the next step, the potential of hardware technologies and predictive driving functions is examined in selected driving scenarios based on the identified constraints of the baseline design. As a result, four application-specific technology packages (Cost neutral, Cold country, Hot country, and Premium) for different customer requirements and sales markets are defined, which, finally, demonstrates the applicability of the holistic methodology. Full article

The design of an efficient thermal management system for a lithium-ion battery pack hinges on a deep understanding of the cells’ thermal behavior. This understanding can be gained through theoretical or experimental methods. While the theoretical study of the cells using electrochemical and numerical methods requires expensive computing facilities and time, the Equivalent Circuit Model (ECM) offers a more direct approach. However, upfront experimental cell characterization is needed to determine the ECM parameters. In this study, the behavior of a cell is characterized experimentally, and the results are used to build a second-order equivalent electrical circuit model of the cell. This model is then integrated with the cooling system of the battery pack for effective thermal management. The Equivalent Circuit Model estimates the internal heat generation inside the cell using instantaneous load current, terminal voltage, and temperature data. By extrapolating the heat generation data of a single cell, we can determine the heat generation of the cells in the pack. With the implementation of the ECM in the cooling system, the coolant flow rate can be adjusted to ensure the attainment of a safe operating cell temperature. Our study confirms that 14% of pumping power can be reduced when compared to the conventional constant flow rate cooling system, while still maintaining the temperature of the cells within safe limits. Full article

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology. Full article

System perception of the environment becomes more important as the level of automation increases, especially at the higher levels of automation (L3+) of Automated Driving Systems. As a consequence, scenario-based validation becomes more important in the overall validation process of a vehicle. Testing all scenarios with potential triggering conditions that may lead to hazardous vehicle behaviour is not a realistic approach, as the number of such scenarios tends to be unmanageable. Therefore, another approach has to be provided to deal with this problem. In this paper, we present our approach, which uses the injection of perception performance insufficiencies instead of directly testing the potential triggering conditions. Finally, a use case is described that illustrates the implementation of the proposed approach. Full article

In modern automobiles, the infotainment system is crucial for enhancing driver and passenger capabilities, offering advanced features such as music, navigation, communication, and entertainment. Leveraging Wi-Fi, cellular networks, NFC, and Bluetooth, the system ensures continuous internet connectivity, providing seamless access to information. However, the increasing complexity of IT connectivity in vehicles raises significant cybersecurity concerns, including potential data breaches and exposure of sensitive information. To enhance security in infotainment systems, this study applied component-level threat modeling to a proposed infotainment system using the Microsoft STRIDE model. This approach illustrates potential component-level security issues impacting privacy and security concerns. The study also assessed these impacts using SAHARA and DREAD risk assessment methodologies. The threat modeling process identified 34 potential security threats, each accompanied by detailed information. Moreover, a comparative analysis is performed to compute risk values for prioritizing treatment, followed by recommending mitigation strategies for each identified threat. These identified threats and associated risks require careful consideration to prevent potential cyberattacks before deploying the infotainment system in automotive vehicles. Full article

The rise in autonomous vehicles further impacts road networks and driving conditions over the road networks. Cameras and sensors allow these vehicles to gather the characteristics of their surrounding traffic. One crucial factor in this environment is the appearance of emergency vehicles, which require special rules and priorities. Machine learning and deep learning techniques are used to develop intelligent models for detecting emergency vehicles from images. Vehicles use this model to analyze regularly captured road environment photos, requiring swift actions for safety on road networks. In this work, we mainly developed a Generative Adversarial Network (GAN) model that generates new emergency vehicles. This is to introduce a comprehensive expanded dataset that assists emergency vehicles detection and classification processes. Then, using Convolutional Neural Networks (CNNs), we constructed a vehicle detection model demonstrating satisfactory performance in identifying emergency vehicles. The detection model yielded an accuracy of 90.9% using the newly generated dataset. To ensure the reliability of the dataset, we employed 10-fold cross-validation, achieving accuracy exceeding 87%. Our work highlights the significance of accurate datasets in developing intelligent models for emergency vehicle detection. Finally, we validated the accuracy of our model using an external dataset. We compared our proposed model’s performance against four other online models, all evaluated using the same external dataset. Our proposed model achieved an accuracy of 85% on the external dataset. Full article

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system’s responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology. Full article

Novel vehicle concepts are needed to meet the requirements of resource-conserving and efficient mobility in the future, especially in urban areas. In the automated, driverless electric vehicle concept U-Shift, a new form of mobility is created by separating a vehicle into a drive module and a transport capsule. The autonomous driving module, the so-called Driveboard, is able to change the transport capsules independently and is therefore used to transport both people and goods. The wide range of possible capsules poses major challenges for the development of the Driveboard and the chassis in particular. A lifting actuator integrated into the chassis concept enables levelling and, thus, the raising and lowering of the Driveboard and the capsules to ground level. This means that no additional lifting devices are required for changing the capsules or for lowering them to the ground, e.g., for loading and unloading the capsules. To realise this mechanism simply and efficiently, a fully electromechanical actuator is designed and constructed. The actuator consists primarily of a profile rail guide, a steel cable winch, an electric motor, a housing that connects the subsystems and a locking mechanism. The electric motor is used to lift the vehicle and regulate the weight force-driven lowering of the vehicle. This paper describes the design of the actuator and shows the dimensioning of all main components according to the boundary conditions. Finally, the prototype model of the realised concept is presented. Full article

Increasing urbanisation and growing environmental awareness in society require new and innovative vehicle concepts. In the present work, the design freedoms of additive manufacturing (AM) are used to develop a front-axle wheel suspension for a novel modular vehicle concept. The development of the suspension components is based on a new method using industry-standard load cases for the strength design of the components. To design the chassis components, the available installation space is determined, and a suitable configuration of the chassis components is defined. Furthermore, numerical methods are used to identify the component geometries that are suitable for the force flow. The optimisation setup is selected in such a way that it is possible to integrate information, energy, and material-carrying conductions into the suspension arms. High-strength light metals are used to minimise the component masses. Apertures are provided through the components for the routing of electrical conductors. The transport of fluids is realised by conductions integrated into the wishbones. The final geometries of the suspension components are then validated by a finite element analysis (FEA) of the overall suspension model. The results of the applied method are lightweight suspension components with a high degree of functional integration. This improves the vehicle package and achieves higher front-wheel clearance, increasing the possible steering angles and thus improving manoeuvrability. The saving of unsprung mass can improve handling and has a positive effect on the vehicle’s energy consumption. Furthermore, the sectional conduction integration is followed by a simplified assembly of the front-axle suspension. Full article

The primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle’s operational range by 7.45%. Full article