Exhaust emissions, which count among the most common causes of premature death worldwide, can cause irreversible changes in cells, leading to their damage or degeneration. In this research, L929 line cells were observed after exposure in the BAT-CELL chamber to exhaust gases emitted from a Euro 6 compression-ignition engine. Real road traffic conditions were simulated, taking into account air resistance while driving at speeds of 50 km/h, 120 km/h and idling engine. Morphological analysis of the cells was performed using an environmental scanning electron microscope. It has been observed that diesel exhaust fumes can cause inflammation, which can induce apoptosis or leads to necrotic cell death. The impact of the vehicle exhaust gases can inhibit cell proliferation by almost three times. Moreover, a correlation has been observed between the speed of the inflammatory reaction in cells and the presence of specific hydrocarbon compounds that determine the toxicity of exhaust gases. Research has shown that the toxicity of the emitted exhaust gases has been the highest at the driving speed of 120 km/h. In order to reduce the harmful effects of exhaust emissions, ecological alternatives and the supplementation of legal provisions regarding the compounds subject to limitation are necessary.

This study presents performance and emissions data of an Otto cycle mono-cylinder combustion engine operating with two different compression rates and several mixtures of anhydrous ethanol fuel and water. The instrumented engine was mounted on a dynamometer with the ignition point and injection fuel advance calibrated to obtain the maximum torque and mixture in stoichiometric conditions. Characteristic engine performance parameters and emission fractions from its exhaust system were acquired from 2,000 rpm to 4,000 rpm with fuel mixtures of up to 50% water content. To our knowledge, data on this extreme operating condition are not available in the literature.

Load growth puts pressure on existing electric infrastructure and impacts on the system\'s performance parameters which may necessitate network expansion. Conventionally, electric network expansion is done by building new substations or reinforcing the existing ones with new transformers and upgrading the network feeders. However, optimal allocation of combined heat and power distributed generators (CHP-DGs) on distribution networks (DNs) can be adopted for network expansion planning problem. Optimal DG allocation is an optimisation problem which requires an efficient optimisation approach. In this paper, an improved particle swarm optimisation (IPSO) based on weighted randomised acceleration coefficient and adaptive inertia weight is proposed for optimal DG allocation problem. The considered CHP-DGs include internal combustion engine (ICE) and fuel cells (FCs) powered by biogas obtained from anaerobic digestion of food wastes. The proposed IPSO is tested on IEEE 69 bus radial distribution network under single and multi-objectives considering constant and mixed seasonal voltage-dependent load models. Some of the key findings show that integrating ICE-based CHP-DGs operating at optimal power factor in winter day mixed voltage dependent load in base year achieves 97.63 % active power loss reduction in comparison to 77.14 % loss reduction for unity power factor operating FC-based CHP-DG. Economic and environmental evaluation indicate that FC-based CHP-DG records a net present value of over 29.29 million $, levelised cost of energy of 0.0493 $/kWh and zero pollutant emission in comparison with 28.40 million $, 0.0501 $/kWh and 0.2817 million kg pollutant emission for ICE-based CHP-DG over the planning horizon. In comparison with the standard PSO, the proposed IPSO performs better in terms of solution quality, convergence speed and statistical results.

The U.S. EPA MOVES3 model was used to assess the impact of the large-scale introduction of electric vehicles on emissions of criteria pollutants (CO, hydrocarbons [HC], NOx, and particulate matter [PM]) and CO2 from the U.S. light-duty vehicle fleet. Large reductions in emissions of these criteria pollutants occurred in 2000-2020. These trends are expected to continue through 2040 driven by turnover of the conventional fleet with old vehicles being replaced by battery electric vehicles (BEVs) and by new internal combustion engine vehicles (ICEVs) with modern emission control systems. Without the introduction of BEVs, the absolute emissions of CO, NOx, HC, and PM2.5 from the U.S. light-duty vehicle fleet are expected to decrease by approximately 61, 88, 55, and 20% from 2020 to 2040. Introduction of BEVs with market share increasing linearly to 100% in 2040 provides additional benefits, which, combined with ICEV fleet turnover, would lead to decreases of absolute emissions of CO, NOx, HC, and PM2.5 of approximately 77, 94, 71, and 37% from 2020 to 2040. Reductions in CO2 emissions follow a similar pattern. Large decreases in criteria pollutant and CO2 emissions from light duty vehicles lie ahead.

In the realm of internal combustion engines, there is a growing utilization of alternative renewable fuels as substitutes for traditional diesel and gasoline. This surge in demand is driven by the imperative to diminish fuel consumption and adhere to stringent regulations concerning engine emissions. Sole reliance on experimental analysis is inadequate to effectively address combustion, performance, and emission issues in engines. Consequently, the integration of engine modelling, grounded in machine learning methodologies and statistical data through the response surface method (RSM), has become increasingly significant for enhanced analytical outcomes. This study aims to explore the contemporary applications of RSM in assessing the feasibility of a wide range of renewable alternative fuels for internal combustion engines. Initially, the study outlines the fundamental principles and procedural steps of RSM, offering readers an introduction to this multifaceted statistical technique. Subsequently, the study delves into a comprehensive examination of the recent applications of alternative renewable fuels, focusing on their impact on combustion, performance, and emissions in the domain of internal combustion engines. Furthermore, the study sheds light on the advantages and limitations of employing RSM, and discusses the potential of combining RSM with other modelling techniques to optimise results. The overarching objective is to provide a thorough insight into the role and efficacy of RSM in the evaluation of renewable alternative fuels, thereby contributing to the ongoing discourse in the field of internal combustion engines.

Exhaust pollutants from diesel vehicles constitute an important portion of air pollution. In addition to conventional pollutants such as carbon and nitrogen oxides, persistent free radicals (PFRs) exist on exhaust particles could also pose a health risk by inducing oxidative stress. However, recently there is a dearth of comprehensive studies addressing this concern. In this study, the exhaust particles emitted by tractors adhering to two prominent emission standards, namely GB III and GB I, that currently hold the largest tractor stocks, were collected under various working conditions. For the first time, this study dynamically monitored the characteristics of PFRs in exhaust particles emitted by internal combustion engines using biodiesel as fuel during driving on rural actual roads. Due to the stricter emission standard of GB III, which resulted in lower particle emissions, the concentration of PFRs emitted under the same fuel consumption was ultimately reduced. Noteworthily, while advancements like fuel atomization under engine electronic control unit (ECU) and the utilization of oxidation catalysts with low ignition temperature successfully decreased polycyclic aromatic hydrocarbons (PAHs) emission by altering combustion in the engine, they also resulted in heightened carbon structure defects, leading to a higher concentration of PFRs emitted per unit mass of particles. Additionally, compared to non-plowing driving conditions, localized hypoxia during plowing that could cause excessive fuel injection and uneven formation of fuel-air mixture resulted in the emission of a significant amount of carbon-containing substances with unstable structures. Consequently, this scenario led to an increased concentration of PFRs during plowing conditions. The results of this study demonstrated that the stricter emission standards and optimized technology could better reduce the concentration and types of PFRs in exhaust particles, reducing the environmental risk of exhaust particle, which is also of great significance for the realization of pollution reduction and carbon reduction goals.

The research paper mainly deals with waste heat recovery from internal combustion engines (ICE) using the organic Rankine cycle (ORC) and Thermoelectric generator (TEG). Simultaneously recovering the wasted heat of both exhaust gases and coolant, a novel configuration named two-stage is proposed. Then a comprehensive thermo-economic analysis and optimization are conducted. Produced power and total cost rate are selected as the objective function of the optimization. Also, the first and second stage pressures of the ORC system are considered as decision variables. Finally, a sensitivity analysis is performed to study the effect of expander inlet temperature, pumps isentropic efficiency, and expander isentropic efficiency on the objective function.

In recent years, the interest in generating power through hybrid power generation systems has increased. In this study, a hybrid power generation system including an internal combustion engine (ICE) and a solar system based on flat plate collectors to generate electricity is investigated. To benefit from the thermal energy absorbed by solar collectors, an organic Rankine cycle (ORC) is considered. In addition to the solar energy absorbed by the collectors, the heat source of the ORC is the wasted heat through exhaust gases and the cooling system of the ICE. A two-pressure configuration for ORC is proposed for optimal heat absorption from the three available heat sources. The proposed system is installed to produce power with a capacity of 10 kW. A bi-objective function optimization process is carried out to design this system. The objective of the optimization process is to minimize the total cost rate and maximize the exergy efficiency of the system. The design variables of the present problem include the ICE rated power, the number of solar flat plate collectors (SFPC), the pressure of the high-pressure (HP) and low-pressure (LP) stage of the ORC, the degree of superheating of the HP and LP stage of the ORC, and its condenser pressure. Finally, it is observed among the design variables the most impact on total cost and exergy efficiency is related to the ICE rated power and the number of SFPCs.

Real-time estimation of the in-cylinder pressure of combustion engines is crucial to detect failures and improve the performance of the engine control system. A new estimation scheme is proposed based on the Extended Kalman Filter, which exploits measurements of the engine rotational speed provided by a standard phonic wheel sensor. The main novelty lies in a parameterization of the combustion pressure, which is generated by averaging experimental data collected in different operating points. The proposed approach is validated on real data from a turbocharged compression ignition engine, including both nominal and off-nominal working conditions. The experimental results show that the proposed technique accurately reconstructs the pressure profile, featuring a fit performance index exceeding 90% most of the time. Moreover, it can track changes in the engine operating conditions as well as detect the presence of cylinder-to-cylinder variations.

Pollutants in exhaust gases and the high fuel consumption of internal combustion engines remain key issues in the automotive industry despite the emergence of electric vehicles. Engine overheating is a major cause of these problems. Traditionally, engine overheating was solved using electric pumps and cooling fans with electrically operated thermostats. This method can be applied using active cooling systems that are currently available on the market. However, the performance of this method is undermined by its delayed response time to activate the main valve of the thermostat and the dependence of the coolant flow direction control on the engine. This study proposes a novel active engine cooling system incorporating a shape memory alloy-based thermostat. After discussing the operating principles, the governing equations of motion were formulated and analyzed using COMSOL Multiphysics and MATLAB. The results show that the proposed method improved the response time required to change the coolant flow direction and led to a coolant temperature difference of 4.90 °C at 90 °C cooling conditions. This result indicates that the proposed system can be applied to existing internal combustion engines to enhance their performance in terms of reduced pollution and fuel consumption.