Connected and automated vehicles (CAV) are increasingly recognized as a critical component of intelligent transportation systems (ITS), contributing to advances in transportation safety and mobility. However, the implementation of CAV in a real-world environment comes with various threats, and cybersecurity is among the most vulnerable. As the technology becomes more advanced and complex, it is essential to develop a comprehensive cybersecurity framework that can address these concerns. This research proposes a novel framework based on complexity theory and employs the fuzzy set qualitative comparative analysis (fsQCA) technique to identify combinations of security attacks that lead to achieving cybersecurity in CAV. Compared to structural equation modelling (SEM), the fsQCA method offers the advantage of demonstrating all possible ways to achieve the outcome. The study’s findings suggest that in-vehicle networks and data storage security are the most crucial factors in ensuring the cybersecurity of CAV. The results can be useful for automotive designers in reducing the potential for attacks while developing secure networks.

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

This paper presents the design of a bidirectional wireless power and information transfer system. The wireless information transfer is based on near-field technology, utilizing communication coils integrated into power transfer coils. Compared with conventional far-field-based communication methods (e.g., Bluetooth and WLAN), the proposed near-field-based communication method provides a peer-to-peer feature, as well as lower latency, which enables the simple paring of a transmitter and a receiver for power transfer and the real-time updating of control parameters. Using the established communication, control parameters are transmitted from one side of the system to another side, and the co-control of the inverter and the active rectifier is realized. In addition, this work innovatively presents the communication-signal-based synchronization of an inverter and a rectifier, which requires no AC current sensing in the power path and no complex algorithm for stabilization, unlike conventional current-based synchronization methods. The proposed information and power transfer system was measured under different operating conditions, including aligned and misaligned positions, operating points with different charging powers, and forward and reverse power transfer. The results show that the presented prototype allows a bidirectional power transfer of up to 1.2 kW, and efficiency above 90% for the power ranges from 0.6 kW to 1.2 kW was obtained. Furthermore, the integrated communication is robust to the crosstalk from the power transfer and misalignment, and a zero BER (bit error rate) and ultra-low latency of 15.36 µs are achieved. The presented work thus provides a novel solution to the synchronization and real-time co-control of an active rectifier and an inverter in a wireless power transfer system, utilizing integrated near-field-based communication.

Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles.

In this work, a method is developed for the component design of chassis bushings with contoured inner cores, aided by artificial neural networks (ANNs) and design optimization. First, a model of a physical chassis bushing is generated using the finite element method (FEM). To determine the material parameters of the material model, a material parameter optimization is conducted. Based on the bushing model, different samples for a design study are generated using the design of experiments method. Due to invalid areas of the geometrical model definitions, constraints are established and the design parameter space is cleaned up. From the cleaned design parameter space, a database of several design parameter samples and three associated quasi-static stiffnesses, calculated with FEM simulations, is generated. The database is subsequently used for the training and hyper-parameter optimization of the ANN. Subsequently, the feed-forward ANN is employed in a design study, where stiffnesses are prescribed and design parameters identified. The design process is inverted with the help of a constrained design parameter optimization (DO), based on particle swarm optimization (PSO). Two usecases are defined for the evaluation of the design accuracy of the entire method. The design parameters found are validated by corresponding FEM simulations.

Electric vehicles and photovoltaic power stations can play an important role in replacing fossil fuels. This article presents a case study on the placement of charging stations powered by photovoltaic energy along an important highway in Brazil. A demand model was adopted to elaborate three scenarios for 2030 with different participation levels of electric vehicles in the Brazilian market. An optimized allocation model was used to derive the location and number of charging stations required to meet the charging demand. The results provided a list of adequate locations for installing the charging stations and offered insights into the consumed electricity and greenhouse gas emissions that could be mitigated by these actions. A financial analysis was conducted, and it was determined that the charging costs, based on the Internal Rate of Return calculation, were 10%. These costs were compared to the fueling costs of other traditional vehicles. The results showed that the costs can be 72% lower than the cost of refueling current conventional automobiles. The results of this study can serve as a reference in the public policy debate, as well as for investors in fast charging stations.

The Third Generation Partnership Project (3GPP) has specified Cellular Vehicle-to-Everything (C-V2X) radio access technology in Releases 15–17, with an emphasis on facilitating direct communication between vehicles through the interface, sidelink PC5. This interface provides end-to-end network slicing functionality together with a stable cloud-native core network. The performance of direct vehicle-to-vehicle (V2V) communications has been improved by using the sidelink interface, which allows for a network infrastructure bypass. Sidelink transmissions make use of orthogonal resources that are either centrally allocated (Mode 1, Release 14) or chosen by the vehicles themselves (Mode 2, Release 14). With growing interest in connected and autonomous vehicles, the advancement in radio access technologies that facilitate dependable and low-latency vehicular communications is becoming more significant. This is especially necessary when there are heavy traffic conditions and patterns. We thoroughly examined the New Radio (NR) sidelink’s performance based on 3GPP Releases 15–17 under various vehicle densities, speeds, and distance settings. Thus, by evaluating sidelink’s strengths and drawbacks, we are able to optimize resource allocation to obtain maximum coverage in urban areas. The performance evaluation was conducted on Network Simulator 3 (NS3.34/5G-LENA) utilizing various network metrics such as average packet reception rate, throughput, and latency.

Climate change and air pollution are two significant challenges facing our society and represent a major driver for new developments in the transport sector. As a consequence, automotive manufacturers have focused on the electrification of vehicle propulsion systems and offer a wide range of hybrid and full-electric vehicles in different classes. However, in the world’s most densely populated metropolitan areas, small and lightweight vehicles are key for the mobility of millions. Traditionally these vehicles have provided cost-effective transportation which is difficult to preserve with vehicle electrification. Many of these light vehicles, such as scooters and all-terrain vehicles, use internal combustion engines in combination with a continuously variable rubber belt transmission which provides a simple, comfortable and cost-effective transmission technology but with poor efficiency and high maintenance costs. In this contribution, a novel full hybrid powertrain concept is proposed that offers a similar driving experience to conventional continuously variable transmissions while providing significantly improved performance and fuel economy combined with low system complexity. In its basic configuration, the hybrid powertrain can operate without active actuators and even with mechanical throttle control of the internal combustion engine. This minimalist approach reduces system costs and helps to create a competitive solution for price-sensitive markets. The hybrid system is based on a planetary gear set that combines the internal combustion engine and an electric motor. It is complemented by a centrifugal clutch and one-way clutch, resulting in different operating modes for low and high speeds as well as for electric driving. This paper describes the mechanical design and control approach of the proposed hybrid powertrain layout. In order to evaluate the basic functionalities, a prototype vehicle was built and tested. This contribution shows the integration of the hybrid powertrain concept in a prototype vehicle and proves the fulfilment of all required full hybrid functionalities.

Vehicle-to-grid means that electric vehicles are charged when electricity is plentiful and discharged when it is scarce. New battery-electric vehicles have an energy capacity above 60 kWh installed and practically always have a DC connector. With over 1 million of such vehicles in Germany alone already, the flexibility potential to balance out fluctuating renewable generation or compensate for grid constraints is large. While many actors are working to enable this market, the readiness of hardware and regulations as well as the potential volume are hard to grasp. This paper provides an overview of these factors for Europe with a special focus on Germany. We find that some countries started to implement regulatory frameworks but none are ready yet. Issues include taxation, the fulfillment of grid codes, and the lack of smart meters. In terms of vehicles, 25 manufacturers with bidirectional charging ability were identified, but most vehicles were only used in field tests or operate in island mode. In terms of charging infrastructure, the picture is brighter with at least 20 manufacturers that offer DC bidirectional charging stations and 2 offering an AC variant.

This paper presents methods for vehicle state estimation and prediction for autonomous driving. A round intersection is chosen for application of the methods and to illustrate the results as autonomous vehicles have difficulty in handling round intersections. State estimation based on the unscented Kalman filter (UKF) is presented in the paper and then applied to state estimation of vehicles in a round intersection. The microscopic traffic simulator SUMO (Simulation of Urban Mobility) is used to generate realistic traffic in the round intersection for the simulation experiments. Change point detection-based driving behavior prediction using a multipolicy approach is then introduced and evaluated for the round intersection. Finally, these methods are combined for vehicle trajectory estimation based on UKF and policy prediction and demonstrated using the round intersection.