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Contemporary research on electric vehicle (EV) consumption is predominantly focused on the vehicle’s powertrain and battery technology. However, the analyses indicate that the actual state of the various electrical subsystems in the vehicle can have a significant impact on the overall consumption figures. The primary objective of this article is to demonstrate the capabilities of our vehicle simulation model, which was developed with a particular focus on the electrical subsystems of vehicles, when employed in a 3D digital representation of a real environment. The central scientific contribution of this work is the systematic quantification of subsystem-level energy usage in real-world scenario simulation. This provides a novel framework for the evaluation of EV energy distribution, thereby informing future strategies and models.

This study investigates the optimal photo overlap for documenting and reconstructing road accident sites using drone imagery. While a general recommendation for drone imagery overlap stands at 60–80%, this research aims to determine the minimum acceptable overlap required to generate a precise 3D point cloud suitable for forensic road accident simulation. A DJI Mavic Air 2 drone captured images at 2-meter intervals over a junction and a connecting road segment from varying altitudes, following the same flight path. The experiment systematically excluded images from the original dataset, processing photo sets taken at 2, 4, 6, 8, and 10-meter intervals. The corresponding point clouds were evaluated for accuracy and fragmentation. Comparisons were made regarding the number of images, the size of image sets and processing times. Additionally, 3D mesh surfaces were generated in the Virtual Crash software, and their quality was assessed. Results revealed that a 50% overlap was adequate for generating satisfactory 3D simulation environments, thereby reducing size of the raw data, the point cloud and processing time considerably. This finding is significant for forensic experts seeking efficient methods of road accident scene reconstruction, emphasizing the practicality of lower photo overlap in such scenarios.

This paper presents the development and implementation of a digital twin model of a Mitsubishi i-MiEV vehicle aimed at accurately estimating electric energy consumption along predefined routes. The model is based on empirical data collected from an i-MiEV battery electric vehicle at the ZalaZONE proving ground in Hungary, and it focuses on simulating the vehicle's electric drive system, power distribution network, and major electrical loads. By leveraging real-time digital twin technology, the simulation reproduces subsystem behavior with high fidelity, offering valuable insights into system-level power dynamics and energy usage. The results demonstrate that digital twin-based modeling is highly effective for evaluating and forecasting electric power consumption, and it holds significant potential for predictive diagnostics. In particular, the ability to replicate electrical subsystems in real time enables proactive monitoring and failure prediction for safety-critical components–areas where traditional diagnostic tools may fall short due to increasing system complexity. Looking ahead, our research envisions a shift towards estimator-based digital twin models capable of operating at faster-than-real-time speeds, ultimately facilitating fault detection and mitigation before failures manifest. This work highlights the transformative potential of digital twin technology in electric vehicle energy management and safety assurance.

A pivotal moment in the leap toward autonomous vehicles in recent years has revealed the need to enhance vehicle-to-everything (V2X) communication systems so as to improve road safety. A key challenge is to integrate real-time pedestrian detection to permit the use of timely alerts in situations where vulnerable road users, especially pedestrians, might pose a risk. Seeing that, in this article, a YOLO-based object detection model was used to identify pedestrians and extract key data such as bounding box coordinates and confidence levels. These data were encoded afterward into decentralized environmental notification messages (DENM) using ASN.1 schemas to ensure compliance with V2X standards, allowing for real-time communication between vehicles and infrastructure. This research identified that the integration of pedestrian detection with V2X communication brought about a reliable system wherein the roadside unit (RSU) broadcasts DENM alerts to vehicles. These vehicles, upon receiving the messages, initiate appropriate responses such as slowing down or lane changing, with the testing demonstrating reliable message transmission and high pedestrian detection accuracy in simulated–controlled environments. To conclude, this work demonstrates a scalable framework for improving road safety by combining machine vision with V2X communication.

The main objective of this paper is to present a methodology for the reliable estimation of the energy consumption of electric vehicles, focusing on the main electrical subsystems of passenger cars. This paper presents a comparative analysis of the available regression models and the results of our simulation experiments. While numerous regression models have been documented in the literature, their accuracy is not always satisfactory. Consequently, there is a need to develop a sufficiently accurate and comprehensive generalized simulation framework, which is presented in the paper. Currently, most of the major vehicle manufacturers have developed pure electric vehicle platforms and are using them in the production of many models available on the market. The estimation of consumption data for these vehicles is still based on traditional techniques, namely, prediction from historical operation data. To overcome this problem, in this article, we have constructed a multi-element, model-based simulation for the purpose of implementing an energy consumption monitoring system. In order to create a simulation that reflects real-life vehicle behavior, the input data are based on empirical measurements, while the simulation model is based on actual electric vehicle parameters. In the main simulation model, it is possible to simulate the energy consumption of the vehicle’s drive system and to extract the requisite input data for the simulation of the other vehicle subsystems. In regard to the simulation, the subsystems that have been incorporated are the electric vehicle steering system, the vehicle lighting system and the HVAC system. After running the simulation, the total system consumption for a given trip segment is obtained by running each vehicle subsystem simulation. The findings were validated with real data and compared with two relevant regression models. Our preliminary expectation is that, given the level of detail of our simulation, the developed model can be considered validated if the error of the estimate remains below 4% and if the simulation model in question yields superior results in comparison to other regression models.