Reducing carbon dioxide emissions in transportation has become a priority for achieving emission targets. Transitioning to electric vehicles significantly decreases global CO2 emissions and reduces urban noise and air pollution. The selection of efficient charging strategies for electric bus fleets substantially influences their environmental impact. This study analyzes the charging strategy for electric bus fleets based on real operational data from Győr, Hungary. It evaluates the impact of different charging times and strategies on CO2 emissions, considering the energy mixes of Hungary, Poland, Germany, and Sweden. A methodology has been developed for defining sustainable and environmentally friendly charging strategies by incorporating operational conditions as well as daily, monthly, and seasonal fluctuations in emission factors. Results indicate substantial potential for emission reduction through the recommended alternative charging strategies, although further studies regarding battery lifespan and economic feasibility of infrastructure investments are recommended. The novelty of this work lies in integrating real charging data with hourly country-specific emission intensity values to assess environmental impacts dynamically. A comparative framework of four charging strategies provides quantifiable insights into emission reduction potential under diverse national energy mixes.

Addressing the critical challenge of reducing local emissions through the electrification of urban public transport, this research specifically focuses on integrating electric buses. The primary objectives are to evaluate energy efficiency and ensure battery cell supervision. Introducing electric buses plays a significant role in reducing emissions, contributing to more sustainable urban transport systems. However, this transition introduces a set of new challenges, including the complexities of electric charging logistics, the establishment of new consumption standards, and the intricate relationships between distance traveled, ambient temperature, passenger load, and battery health. Methodologically, this study collects and examines factors impacting energy consumption, including external temperatures, bus conditions, road conditions, and driver behavior. By analyzing these variables, a baseline for actual consumption can be established, allowing for the calculation of an energy balance to identify energy inefficiencies. This enables the optimization of route planning, the strategic selection of stops, and the efficient scheduling of charging times, along with ensuring the proper scaling of the bus battery system. This study found that energy consumption peaked at 116.73 kWh/100 km in the lowest temperature range of −5 °C to 0 °C. Consumption decreased significantly with rising temperatures, dropping by 25 kWh between 5 °C and 10 °C and by an additional 10 kWh between 10 °C and 15 °C. Beyond 20 °C, variations were more influenced by route and driving style than by temperature. Route and driver variability significantly influenced energy consumption, with up to threefold differences across routes due to factors such as road type and traffic volume. Additionally, there was a 31.85% difference between the most and least efficient drivers, highlighting the critical impact of driving style. Furthermore, this study explores the assessment of battery systems through cell-level diagnostics to detect potential faults. Considering that buses are equipped with significantly more batteries than typical electric vehicles, detecting and localizing faults at the cell level is crucial to avoid the substantial costs and environmental impact associated with replacing large battery systems. Utilizing the results of this research and the applied examination methods, it is possible to enhance energy efficiency and extend battery life, thereby contributing to the development of more sustainable and cost-effective urban transport solutions.

The increasingly stricter environmental regulations and standards aim to reduce the ecological impact of vehicles and promote the sustainable use of natural resources. Improving the energy efficiency of vehicles has become a priority in recent decades. This is a key issue for vehicle development, production, and operation. Several studies and measurements have been conducted to accurately determine vehicles’ energy consumption. This research has investigated and categorized the factors according to external impacts, losses due to vehicle properties, and the effects of vehicle control and energy reduction methods. A better understanding of these factors is crucial for improving energy efficiency.

Towards the introduction of autonomous vehicles, studying their functionality is becoming increasingly important. Detecting the environment in a self-driving vehicle is a very complex issue. The combination of different sensors is essential for safe and reliable operation. Detection enables the vehicle to accurately recognize and track surrounding objects, understand changes in the dynamic environment, and adapt to different situations. Improving environmental sensing and object recognition is essential for the widespread deployment of self-driving vehicles. In addition to real-world tests, simulation environments provide an opportunity to investigate the operation of autonomous vehicles. Simulations are cost-effective methods for examining the processing of information from the vehicle environment and identifying the current limitations and problems of these technologies. In the CARLA simulator environment, object detection is reproduced in realistic traffic situations. Based on the results, the detection performance was analyzed using interference matrices, F1 scores, accuracy, and coverage metrics.

Energy-efficient driving is essential for reducing the environmental impacts of road transport, especially for electric passenger vehicles. This research aims to build a data-driven behavioral analysis and energy-consumption evaluation model. The model relies on sensor data from the vehicle’s on-board communication network, primarily the CAN (Controller Area Network) bus. We analyze patterns of key powertrain and battery parameters—such as current, voltage, state of charge (SoC), and power—in relation to driver inputs, such as the accelerator pedal position. In the first stage, we review the literature with a focus on machine learning and clustering methods used in behavioral and energy analysis. We also examine the role of on-board telemetry systems. Next, we develop a controlled measurement architecture. It defines reference consumption maps from dynamometer data across operating points and environmental variables, including SoC, temperature, and load. The longer-term goal is a multidimensional behavioral map and profiling framework that can predict energy efficiency from real-time driver inputs. This work lays the foundation for a future system with adaptive, feedback-based driver support. Such a system can promote intelligent, sustainable, and behavior-oriented mobility solutions.