This study compares the environmental impacts of diesel and LNG-powered tractors under varying loads, by utilizing a cost-effective measurement system on the ZalaZONE Proving Ground. The same test cycles and scenarios were conducted with both trucks simultaneously on the closed test track modules and the research highlights LNG’s advantages in reducing CO₂ emissions, particularly under lower load conditions. This innovative approach, based on the fuel consumption rather than expensive exhaust gas analyzers, underscores LNG’s potential in meeting EU emission targets. The results support LNG as a competitive and sustainable alternative to diesel, promoting greener freight transport solutions for the future.

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.

Tire pressure is a key factor influencing vehicle dynamic behavior, controllability, and handling performance. This study investigates the effect of tire pressure distribution on steady-state cornering and self-steering behavior near the handling limit. Experimental tests were performed on standardized constant-radius circular tracks at the ZalaZONE Dynamic Platform using winter and summer tires. Starting from the manufacturer-recommended reference pressure, the vehicle was tested at increasing speeds until the slip limit was approached. Symmetric and asymmetric front–rear tire pressure configurations were evaluated to assess their influence on steering demand, lateral acceleration, and handling balance. The results indicate pressure-dependent changes in steering angle demand, achievable lateral acceleration, and self-steering characteristics under the investigated test conditions. Asymmetric front–rear pressure distributions were found to modify the understeer–oversteer balance, highlighting the importance of tire pressure distribution in vehicle controllability near the handling limit. The findings provide practical trend-level insights for future studies on vehicle dynamics, stability control, and steering assistance functions, particularly in operating conditions where tire pressure may deviate from nominal values.

Electric and hybrid vehicles, similarly to combustion engine ones, can experience malfunctions, yet preventive diagnostics for their electric motors are underdeveloped. While many methods exist for testing electric motors in heavy industry, they are not commonly applied in the vehicle industry. Our study aims to develop a real-time, non-invasive fault detection procedure for electric motors in these vehicles. Previous research has focused on simulations, but our work involves real measurements conducted in a controlled laboratory using a two-axle chassis dyno. We present the hybrid vehicle’s drivetrain, our equipment, and the feasibility of simulated methods, and we also detail the evaluation method used.

Lane-Keeping Assist Systems (LKAS) are widely used in modern passenger vehicles to support lateral vehicle control and reduce the risk of unintended lane departure. Although LKAS performance is commonly evaluated in relation to perception, control, and sensor fusion, the observable vehicle response may also depend on the mechanical condition of the chassis. This study presents a qualitative, measurement-based evaluation of the influence of intentionally introduced front-wheel toe misalignment on the observable response of a production LKAS under controlled proving-ground conditions. Experimental tests were conducted on the highway module of the ZalaZONE proving ground using a Lexus RX 450h equipped with a factory-installed LKAS function. Three front-wheel toe configurations were investigated: factory-specified alignment, single-wheel toe misalignment, and severe toe misalignment affecting both front wheels. Measurements were performed at 70, 90, and 110 km/h on straight and curved road sections. Vehicle speed, steering angle, lateral acceleration, and GNSS-based position data were recorded using CAN- and GNSS/IMU-based data acquisition. The qualitative comparison of the measured signal profiles indicated that the misaligned configurations were associated with a shifted steering-angle operating range and less uniform steering and lateral-acceleration responses. The most pronounced visible differences occurred under the severe toe-misalignment condition, particularly at higher speeds and in the curved section. As the analysis did not include quantitative effect measures or statistical comparisons, these observations are interpreted as exploratory tendencies rather than statistically validated changes in LKAS performance. The findings suggest that front-wheel toe condition should be considered in the measurement-based assessment, maintenance, and calibration of ADAS-equipped vehicles.

Modern passenger vehicles can indicate the speed limit for a given road section using GPS, cameras, or both. Sign recognition systems must comply with specified testing procedures before operation. Our goal is to create a cost-effective lab environment for vehicle-level tests of these systems. Image recognition can be tested with various traffic signs in a simulation video placed in front of the vehicle’s camera while it runs on a chassis dyno. We tested this environment with factory-built systems, displaying recognized signs on the dashboard. The simulation software allows unlimited signs, eliminating the need for long-distance driving or extensive test track setups. While the lab environment does not replace final public road testing, it is a cost-effective solution for the development and testing of traffic sign recognition systems.