Branislav Šarkan

This study proposes a Hidden Semi-Markov Model (HSMM)-based framework for identifying and comparing traction-battery energy-flow regimes in a hybrid electric vehicle under route-controlled real-world driving conditions. Vehicle operating data were acquired through the OBD-II interface during repeated ECO, NORMAL, and SPORT trips performed along the same road route. Instantaneous battery power was reconstructed from measured voltage and current, while empirical charge–discharge episode durations were first analyzed to assess the validity of the memoryless assumption inherent in conventional Hidden Markov Models. The resulting dwell-time distributions supported the use of an explicit-duration semi-Markov formulation. A six-state HSMM identified physically interpretable regimes corresponding to mild charging/coasting, EV propulsion, hybrid propulsion with electric assistance, stationary/low-power operation, strong regenerative braking, and ICE-supported battery charging. State occupancy, transition structure, dwell time, and episode-level energy reward were subsequently combined into a battery dynamic signature. Repeated NORMAL-mode trips exhibited highly reproducible transition structures despite substantial variability in their vehicle-speed profiles, while ECO and SPORT operation produced distinct redistributions of the same underlying regimes. The proposed framework demonstrates that hybrid-battery operation can be characterized more comprehensively through its temporal and energetic organization than by conventional trip-level indicators such as SOC change or total energy balance alone.

The stability of vehicles is one of the most important factors of safety. When assessing the conditions for the stability of the vehicle, on-road and off-road vehicles differ in many aspects, and because of this, specific methods are needed to describe the stability conditions for terrain vehicles. In this paper, the aspects of off-road vehicle stability are assessed, and terrain vehicle-specific stability modeling methods are shown. After that, LiDAR-based terrain mapping technology and the measurement and remote sensing possibilities for the variables affecting stability, both from the vehicle and terrain, are introduced. Field measurements conducted with a small-scale remote-controlled vehicle are presented, the measured and model-predicted stability values are compared, and it is concluded that the theoretical model does describe the actual values accurately enough that the differences can be mitigated by introducing a safety factor. At the end, some further possibilities are proposed for the improvement of the stability modeling method.

This paper presents a modelling approach for assessing the environmental impacts of passenger vehicle traffic and demonstrates its application to regional transport planning using a labor mobility case study. The analysis focuses on the Strážný–Philippsreut model route, a 6.8 km regional commuter corridor linking the South Bohemian Region (Czech Republic) with Bavaria (Germany). The volume of emissions was quantified using the EEA/COPERT 5.5 methodology, based on actual traffic data collected during traffic surveys. Calculations took into consideration fuel type, Euro emission standard, route terrain characteristics, and round-trip (journey) distances. The study evaluated pollutant emissions of CO, HC, NOₓ, PM, and CO₂. On the first day of data collection, total round-trip emissions reached 5.78 kg CO, 0.83 kg HC, 1.77 kg NOₓ, 0.13 kg PM, and 865.5 kg CO₂, whilst lower traffic intensity on the second day of conducting the traffic survey reduced emissions' volume by more than 60%, resulting in 2.20 kg CO, 0.31 kg HC, 0.68 kg NOₓ, 0.05 kg PM, and 330 kg CO₂. Approximately 61% of pollutant emissions were generated on the Czech part of the route and 39% on the German part. Vehicles equipped with spark-ignition engines were the primary source of CO and HC, whereas those with compression-ignition engines dominated NOₓ and PM emissions. The designed model serves as a practical tool for evaluating the environmental impacts of regional mobility and can support evidence-based planning to reduce the transport-related emissions burden.

The manuscript focuses on the quantification of exhaust emission load generated by commuters' road vehicles along a selected cross-border section between the Czech Republic (South Bohemian region) and Bavaria, which is heavily used by Czech residents for daily work-related commuters' mobility. An examined model route – Volary–Passau – was defined for the investigation, characterized by its length (i.e., travel distance), traffic load, and operational circumstances. In regard to a territorial perspective, approximately 25–30% of emissions produced along the route are attributable to the Czech route section and 70–75% to German section, which corresponds to the distance ratio of the given route. The quantification was conducted based on emission factors according to the EEA/COPERT 5.5 methodology, when taking into account the actual vehicle numbers and emission categories recorded during traffic surveys on 26 May 2025 and 30 May 2025. Emission indicators were expressed as “on-road” values in units of g·km⁻¹ and subsequently converted to the total travel distance and both travel directions (“round-trip”). Specifically, along the examined model route (Volary–Passau), the following values of emission indicators were recorded for the total daily balance: 9.2 t CO₂, 18.9 kg NOₓ, and 1.4 kg PM. The quantified attributes of the model route confirm that gasoline vehicles dominate the production of carbon monoxide (CO) and hydrocarbons (HC), diesel vehicles are the primary source of nitrogen oxides (NOₓ) and particulate matter (PM), and carbon dioxide (CO₂) accounts for more than 99% of the total mass of all calculated emissions.

Stability of vehicles is one of the most important factors of safety. When assessing the conditions for the stability of the vehicle, on-road and off road vehicles differ in many perspectives, and because this, specific methods are needed to describe the stability conditions for terrain vehicles. In this paper, the aspects of off-road vehicle stability are assessed, terrain vehicle specific stability modeling methods are shown. After that, the LiDAR based terrain mapping technology and the measurement and remote sensing possibilities for the variables affecting the stability, both from the side of the vehicle and terrain, are introduced. At the end, field measurements conducted with a small scale remote controlled vehicle are presented.

This study presents a data-driven analysis of electric powertrain energy flow and traction battery behavior in a modern battery electric vehicle based on real-world on-board diagnostic (OBD) measurements. Time-resolved signals acquired during an urban trip by a Renault 5 E-Tech Electric were processed to reconstruct instantaneous energy exchange between the traction system and the battery, identify distinct operating regimes, and derive physically interpretable empirical models of selected drivetrain relationships. The analysis focused on the traction power, battery current, battery voltage, state of charge, accelerator pedal position, and cell voltage imbalance. The recorded data were decomposed into propulsion, regenerative, and auxiliary-load-dominated operating regimes, which improved the interpretability of the measured signals and the quality of the regression-based models. A second-order model was used to describe the relationship between traction power and accelerator pedal position, while a linear current-voltage model provided a locally accurate approximation of battery electrical behavior. In addition, the dependence of the cell voltage imbalance on the battery current was analyzed as a diagnostic indicator of load-dependent battery response. The results show that auxiliary loads, especially cabin and battery heating under winter conditions, introduce a significant baseline power demand that affects the apparent drivetrain response. Within the analyzed single-trip dataset, the recorded battery signals showed a low cell-voltage imbalance and a consistent local current–voltage trend over the observed operating range. These findings should be interpreted as preliminary and vehicle-specific, since they were obtained from one short winter urban trip and from a restricted set of OBD-accessible signals. Although the study is limited to a single vehicle and a single short trip, it demonstrates that accessible real-world OBD data can support physically interpretable, exploratory analysis of electric powertrain operation and battery response under practical measurement constraints.

This paper presents a methodological framework for inferring energy-related operating states of plug-in hybrid electric vehicles (PHEVs) under conditions of limited and incomplete on-board diagnostic (OBD) data. The proposed approach is illustrated using a single short real-world urban trip recorded for one PHEV operating in electric mode. Unsupervised clustering based on k-means is applied in progressively expanded state spaces (3D–5D) to decompose the driving process into physically interpretable operating states, despite the absence of direct measurements of key variables such as regenerative braking power. Cluster validity indices, per-cluster silhouette values, temporal segmentation, and robustness checks are employed to support the interpretability and internal consistency of the results. The study demonstrates that even a single, non-representative OBD time series contains sufficient internal structure to recover meaningful energy-related information when appropriate state-space decomposition is applied. While no statistical generalization is intended, the results highlight the potential of the proposed framework for analyzing real-world vehicle operation under constrained data availability.

This article aims to identify changes in the components of pellet supply chain management (SCM). The following research question is explored: To what extent are pellet supply chains changing? A research gap was identified in the use of pellets for energy and the analysis of safe management of logistics processes in the pellet supply chain (PSC). The study uses theoretical and empirical research methods: literature analysis and statistical methods covering the years 2017–2023 and scientific observation to obtain information about the facts, phenomena, and components of safe management of logistics processes in the PSC. The results of the study suggest that supply chains play a role as one of the main drivers of energy transition, and the PSC may be one of them. A modified PSC can contribute to more environmentally friendly procurement, leaner logistics, and tighter scheduling, reducing waste and emissions in existing energy systems without immediately changing the electricity/fuel mix.

This article addresses an underexplored yet important factor—the influence of road geometry and characteristics on the energy demands and environmental footprint of road vehicle operation. Based on a case study using real-world data from the operation of a petrol powered passenger car, differences in fuel consumption and CO₂ and NOₓ emissions when driving on rural and urban roads are analyzed. The results indicate that factors such as road gradient, traffic flow characteristics, and stop frequency can significantly affect operational efficiency. The study provides evidence that traffic engineering applications have a measurable impact on fuel consumption and pollutant emissions and, as such, should not be overlooked. Based on these findings, the study proposes that road infrastructure characteristics and management should be actively integrated into strategic sustainable transport policy measures, particularly within the framework of the European Union’s Green Deal objectives.

The waste collection in the territory of urban agglomerations, as a part of city logistics approaches, has become one of the key topics due to growing demands for logistic operations in cities burdened by high traffic volumes, worsening the quality of life and the environment in cities. Congested traffic ails České Budějovice, especially during peak hours. Congestions should partially be avoided by the planned construction of new roads to divert transit traffic from the city centre. Despite these remedial measures, the current transport and logistic situation requires further improvement. The paper focuses on waste collection, which significantly affects the traffic flow in České Budějovice. Currently, the city uses costly CNG-propelled trucks, which release a lot of harmful emissions. The paper aims to consider and evaluate new approaches to waste collection that could work in compliance with ecological principles and sustainable urban development of cities. This matter embraces waste collection trucks propelled by hydrogen, electricity, biogas and CNG. In other words, the decision needs to be made on whether it makes sense to invest in up-to-date and sustainable technologies to fuel waste collection trucks in a territory under investigation. To this end, multicriteria decision-making methods are applied, namely, Saaty and Fuller methods – to quantify the criteria weights, and the PROMETHEE and the ORESTE methods – to identify a compromise (ideal) variant. The findings encompass the decision-making process and a draft methodology concerning the determination of rank of the considered waste collection trucks based on relevant criteria set identified by the expert evaluation.