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.

In order to reduce the emissions caused by internal combustion engine vehicles, the industry is introducing more and more electric or hybrid vehicles to the market nowadays. The battery cells and modules of these vehicles require a lot of care, as improper or improperly maintained battery units can cause serious problems inside vehicles and can be extremely dangerous. The safest solution is to keep this unit of a vehicle under constant supervision so that it can be repaired immediately in case of an issue. Since all necessary data can be extracted from a vehicle’s communication network(s) through standard communication protocols, it is advisable to use them for continuous monitoring and diagnostics of units, while also considering cost-effectiveness and simplicity. The data received from here can also be used for measurement of electric powertrains and other parameters. However, since these data go through many conversions and computers (ECUs) before reaching us, their accuracy is questionable. In this study, we present our own custom battery diagnostic tool based on data extracted from a communication network. With the help of commercially available diagnostic tools, we also compare several measurements of the extent of the error limits of the data arriving at the communication network, how far they differ from the real values, and with the help of these, we analyze the accuracy of the device we have made. We present the commonly used Controller Area Network (CAN) communication protocol for passenger vehicles and briefly describe the construction of the high-voltage battery unit of the test vehicle.

Environmental protection and the reduction of greenhouse gas (GHG) emissions are becoming top priorities in the mobility sector especially in heavy-duty truck (HDT) sector. In recent years, numerous regulations, targets, and initiatives have been introduced, all of which strongly promote the...

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour.

The deployment of automated driving features demands stringent compliance with ISO 26262, particularly concerning data integrity over in-vehicle networks. This study effectively addresses the computational challenges of quantifying the residual error probability of Classical CAN and CAN FD communication under high-order fault profiles. By introducing a GPU-accelerated residual error analysis framework utilizing OpenCL, it overcomes the mathematical barriers of traditional brute-force simulation, elevating execution speeds from 150,000 to 4.2 million iterations per second. Empirical evaluations demonstrate that native data-link layer CRC protection may be bypassed under specific multi-bit physical-layer corruption patterns involving stuff-bit cascading effects. Conversely, for the evaluated application-layer End-to-End (E2E) protected configurations, no residual errors were observed. However, from a functional safety management perspective, these observations must be interpreted within the scope of the investigated configurations: the localized payload regions between Bytes 12 and 16 exhibiting an elevated susceptibility to masking failures apply exclusively to the investigated radar payload structures and selected real-world BLF traces. Because these masking failures are highly dependent on the evaluated frame structures and specific fault-injection scenarios, the conclusions should be limited accordingly to the evaluated configurations.

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.