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This book provides an introduction to basic thermodynamic engine cycle simulations, and provides a substantial set of results. Key features includes comprehensive and detailed documentation of the mathematical foundations and solutions required for thermodynamic engine cycle simulations. The book includes a thorough presentation of results based on the second law of thermodynamics as well as results for advanced, high efficiency engines. Case studies that illustrate the use of engine cycle simulations are also provided.          

With the increased power density of internal combustion engines (ICE) and growing demands for lightweight design, the connecting rod big-end bearings are subjected to significant alternating loads. Consequently, the interference–fit interfaces become susceptible to fretting damage, which can markedly shorten engine service life and impair reliability. In the present study, the effects of the big end manufacturing process, bolt preload, and bearing bush interference fit are considered to develop a coupled lubrication–dynamic model of the connecting rod big-end bearing. This model investigates the fretting damage issue in the bearing bush of a marine diesel engine’s connecting rod big end. The results indicate that the relatively low stiffness of the big end is the primary cause of bearing bush fretting damage. Interference fit markedly affects fretting wear on the bush back, whereas the influence of bolt preload is secondary; nevertheless, a decrease in either parameter enlarges the fretting distance. Based on these findings, an optimized design scheme is proposed.

This research investigates the optimization of a dry-type alkaline HHO kit for efficient oxyhydrogen (HHO) gas production, targeting applications in small (two-wheel vehicle) internal combustion engines (ICE). Key experimental parameters were evaluated to enhance gas production and system efficiency, including voltage, electrode configuration, electrolyte type, and concentration. Sodium hydroxide (NaOH) was identified as a more effective electrolyte than potassium hydroxide (KOH) due to its lower electronegativity, which contributes to accelerating HHO gas production. The highest overall efficiency, 24.6%, was achieved with a 0.1M NaOH solution using stainless steel (SS) as the anode and Titanium (Ti) as the cathode, and SS paired with graphite scored 23.1%. Voltage levels positively influenced gas production, although higher potentials resulted in electrode surface oxidation and decreased efficiency. The optimum voltage range of 4.5V to 5.2V for SS with graphite and 4.2V to 5.2V for SS with Ti configurations was provided. The study concludes that the SS-Ti and SS-Graphite configurations are optimal options for HHO gas production, minimizing heat generation and energy consumption while enhancing gas output. These findings suggest significant potential for improving fuel efficiency and reducing greenhouse gas emissions in two-wheel vehicle four-stroke gasoline engines (100cc to 150cc).

The tremendous growth in the transportation sector as a result of changes in our ways of transport and a rise in the level of prosperity was reflected directly by the intensification of energy needs. Thus, electric vehicles (EV) have been produced to minimise the energy consumption of conventional vehicles. Although the EV motor is more efficient than the internal combustion engine, the well to wheel (WTW) efficiency should be investigated in terms of determining the overall energy efficiency. In simple words, this study will try to answer the basic question – is the electric car really energy efficient compared with ICE-powered vehicles? This study investigates the WTW efficiency of conventional internal combustion engine vehicles ICEVs (gasoline, diesel), compressed natural gas vehicles (CNGV) and EVs. The results show that power plant efficiency has a significant consequence on WTW efficiency. The total WTW efficiency of gasoline ICEV ranges between 11–27 %, diesel ICEV ranges from 25 % to 37 % and CNGV ranges from 12 % to 22 %. The EV fed by a natural gas power plant shows the highest WTW efficiency which ranges from 13 % to 31 %. While the EV supplied by coal-fired and diesel power plants have approximately the same WTW efficiency ranging between 13 % to 27 % and 12 % to 25 %, respectively. If renewable energy is used, the losses will drop significantly and the overall efficiency for electric cars will be around 40–70% depending on the source and the location of the renewable energy systems.

Motor vehicles are the backbone of global transport. In recent years, due to the rising costs of fossil fuels and increasing concerns about their negative impact on the natural environment, the development of low-emission power supply systems for vehicles has been observed. In order to create a stable and safe global transport system, an important issue seems to be the diversification of propulsion systems for vehicles, which can be achieved through the simultaneous development of conventional internal combustion vehicles, electric vehicles (both battery and fuel cell powered) as well as combustion hydrogen-powered vehicles. This publication presents an overview of commercial vehicles (available on the market) powered by internal combustion hydrogen engines. The work focuses on presenting the development of technology from the point of view of introducing ready-made hydrogen-powered vehicles to the market or technical solutions enabling the use of hydrogen mixtures in internal combustion engines. The study covers the history of the technology, dedicated hydrogen and bi-fuel vehicles, and vehicles with an engine powered by a mixture of conventional fuels and hydrogen. It presents basic technology parameters and solutions introduced by leading vehicle manufacturers in the vehicle market.

Engine Testing is a unique, well-organized and comprehensive collection of the different aspects of engine and vehicle testing equipment and infrastructure for anyone involved in facility design and management, physical testing and the maintenance, upgrading and trouble shooting of testing equipment. Designed so that its chapters can all stand alone to be read in sequence or out of order as needed, Engine Testing is also an ideal resource for automotive engineers required to perform testing functions whose jobs do not involve engine testing on a regular basis. This recognized standard reference for the subject is now enhanced with new chapters on hybrid testing, OBD (on-board diagnostics) and sensor signals from modern engines. One of few books dedicated to engine testing and a true, recognized market-leader on the subjectCovers all key aspects of this large topic, including test-cell design and setup, data management, and dynamometer selection and use, with new chapters on hybrid testing, OBD (on-board diagnostics) and sensor signals from modern enginesBrings together otherwise scattered information on the theory and practice of engine testing into one up-to-date reference for automotive engineers who must refer to such knowledge on a daily basis

Biodiesel, an environmentally degradable and renewable biofuel derived from organic matter, has exhibited its capacity as a viable and sustainable substitute for traditional diesel fuel. Numerous comprehensive investigations have been conducted to assess the effects of biodiesel on internal combustion engines (ICEs), with particular emphasis on diesel engine performance metrics, combustion dynamics, and emission profiles. Biodiesel demonstrates a significant decrease in emissions of particulate matter (PM), hydrocarbon (HC), and carbon monoxide (CO) in diesel engines. The addition of biodiesel has shown a minor decrease in power output and a slight increase in fuel consumption and nitrogen oxide (NOx) emissions. Nevertheless, the extensive implementation of biodiesel, despite its potential to effectively reduce detrimental emissions, has encountered obstacles stemming from external influences including restricted availability of feedstock, volatile petroleum oil prices, and inadequate governmental backing. This review presents a concise summary of significant advancements in the global adoption of biodiesel from a sustainability perspective. This review provides valuable insights into the challenges and opportunities associated with the advancement of sustainable biofuel technologies by synthesizing the current state of palm biodiesel and examining global trends in biodiesel implementation. The wider adoption of biodiesel can be facilitated by addressing concerns pertaining to feedstock availability, price stability, and policy support. This would allow for the realization of significant environmental advantages and contribute to a more environmentally friendly and sustainable biofuel.

In-cylinder pressure provides critical insights for analyzing and optimizing combustion in internal combustion engines, yet its acquisition across the full operating space requires extensive testing, while physics-based models are computationally demanding. Machine learning (ML) offers an alternative, but its application to direct reconstruction of full pressure traces remains limited. This study evaluates three strategies for reconstructing cylinder pressure under unmeasured operating conditions, establishing a machine learning-assisted framework that generates the complete pressure–crank angle (P–CA) trace. The framework treats crank angle and operating conditions as inputs and predicts either pressure directly or apparent heat release rate (HRR) as an intermediate variable, which is then integrated to reconstruct pressure. In all approaches, discrete pointwise predictions are combined to form the full P–CA curve. Direct pressure prediction achieves high accuracy for overall traces but underestimates HRR-related combustion features. Training on HRR improves combustion representation but introduces baseline shifts in reconstructed pressure. A hybrid approach, combining non-combustion pressure prediction with combustion-phase HRR-based reconstruction delivers the most robust and physically consistent results. These findings demonstrate that ML can efficiently reconstruct in-cylinder pressure at unmeasured conditions, reducing experimental requirements while supporting combustion diagnostics, calibration, and digital twin applications.

Accurate prediction of the performance of an internal combustion engine is an essential step towards achieving efficiency and complying with emission standards. This study presents an artificial neural network (ANN) model that uses sensor-derived parameters, such as design power, wheel power, torque, and rotational speed, to predict engine load. Data were collected from a dynamometer and a hardware-in-the-loop (HiL) setup to ensure realistic, sensor-based measurements. The proposed ANN architecture achieved high accuracy (99%) in multiclass classification and strong regression performance (R2≈0.98), demonstrating its ability to model complex engine load relationships under normal operating conditions. Performance was validated using 5-fold stratified cross-validation, achieving an average accuracy of 0.988±0.011, macro-F1 of 0.984±0.011, and regression R2 of 0.962±0.052, confirming strong generalization and robustness. The model can be extended to include additional sensor inputs and adapted for use with other powertrain systems, allowing it to be used in a range of automotive and industrial applications.

Nowadays hydrogen, especially if derived from biomass or produced by renewable power, is rising as a key energy solution to shift the mobility of the future toward a low-emission scenario. It is well known that hydrogen can be used with both internal combustion engines (ICEs) and fuel cells (FCs); however, hydrogen-fuelled ICE represents a robust and cost-efficient option to be quickly implemented under the current production infrastructure. In this framework, this article focuses on the conversion of a state-of-the-art 3.0L diesel engine in a hydrogen-fuelled Spark Ignition (SI) one. To preliminarily evaluate the potential of the converted ICE, a proper simulation methodology was defined combining zero-, one-, and three-dimensional (0D/1D/3D) Computational Fluid Dynamics (CFD) approaches. First of all, a detailed kinetic scheme was selected for both hydrogen combustion and Nitrogen Oxides (NOx) emission predictions in a 3D-CFD environment. Afterward, to bring the analysis to a system-level approach, a 1D-CFD predictive combustion model was firstly optimized by implementing a specific laminar flame speed correlation and, secondly, calibrated against the 3D-CFD combustion results. The combustion model was then integrated into a complete engine model to assess the potential benefit derived from the wide range of flammability and the high flame speed of hydrogen on a complete engine map, considering NOx formation and knock avoidance as priority parameters to control. Without a specific modification of turbocharger and combustion systems, a power density of 34 kW/L and a maximum brake thermal efficiency (BTE) of about 42% were achieved, thus paving the way for further hardware optimization (e.g., compression ratio reduction, turbocharger optimization, direct injection [DI]) to fully exploit the advantages enabled by hydrogen combustion.