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This paper presents a method for analysing the characteristics of nano-scale particles emitted from a 1.6 Litre, 4-stroke, gasoline direct injection (GDI) and turbocharged spark ignition engine fitted with a three-way catalytic converter. Ensemble Empirical Mode Decomposition (EEMD) is employed in this work to decompose the nano-scale particle size spectrums obtained using a differential mobility spectrometer (DMS) into Intrinsic Mode Functions (IMF). Fast Fourier Transform (FFT) is then applied to each IMF to compute its frequency content. The results show a strong correlation between the IMFs of specific particle ranges and the IMFs of the total particle count at various speed and load operating conditions. Hence, it is possible to characterise the influence of specific nano-scale particle ranges on the total particulate matter signal by analysing the frequency components of its IMFs using the EEMD-FFT method.

Drive cycles have been the official way to create standardized comparisons of fuel economy and emission levels between vehicles. Since the 1970s these have evolved to be more representative of real-world driving, with today’s standard being the World Harmonized Light Vehicle Testing Procedure. The performance of battery electric vehicles which consist of electric drives, battery, regenerative braking and their management systems may differ when compared to that of vehicles powered by conventional internal combustion engines. However, drive cycles used for evaluating the performance of vehicles, were originally developed for conventional powered vehicles. Moreover, the kinematic parameters that can distinguish the real-world performance of the differently powered vehicles are not fully known. This work aims to investigate the difference between vehicles powered by pure internal combustion engine, electric hybrid and pure electric drive.

Formula (1) vehicles have transitioned from E5 to E10 fuel for the 2022 season to reduce carbon emissions and by 2026 the vehicles are required to use 100% sustainable fuels. The aim of this paper is to identify the operating envelope of the F1 power unit for E10-E100 fuel and the resulting emission levels for these fuel compositions using numerical simulations. To achieve this aim an F1 engine model has been developed in GT-Suite with reference to the FIA 2022 Technical Regulations. The combustion model has been validated using data obtained from literature relating to laminar and turbulent flame speed, friction and heat transfer characteristics within the combustion chamber. One of the main challenges of using ethanol-based fuels is the increased levels of formaldehyde in the tailpipe.

Internal combustion (IC) engines are the most common power unit technology found in road vehicles. The process of combustion within IC engines is linked to the output torque and overall powertrain performance. This work presents a method of analysing the parameters of cylinder pressure and crankshaft instantaneous speed signals obtained from a turbocharged, 4-stroke, 4-cylinder, 1.6 Litre, spark ignition, gasoline direct injection engine at various speed and load operating conditions. Whereas cepstrum analysis is used in the present work to extract critical features characterising the combustion process. Cepstrum analysis showed that the location of maximum heat release can be directly obtained from the quefrency of the instantaneous crank speed. This paper presents a systematic scheme for applying cepstrum for obtaining combustion features from the instantaneous crank speed signal.

Formula One (F1) is considered to be the forefront of innovation for the automotive and motorsport industry. One of the key provisions has been towards the inclusion of the Energy Recovery System (ERS) since 2014 in F1 regulations. ERS comprises Motor Generator Unit-Heat (MGU-H), Motor Generator Unit-Kinetic (MGU-K) and an Energy Storage (ES). This has not only converted the conventional powertrain into a hybrid power-split device, but also imposed constraints on the fuel energy available, energy recovered and deployed by MGU-K, and charge stored in ES, along with various other parameters. Although the objective for a F1 race is to minimize lap-time, it is obvious that there is no unique control path or decision to meet this objective. This builds up needs to optimally control the power-split and energy of the system.

Internal combustion (IC) engines are the current dominant power source used in the automotive industry for hybrid vehicles. The combustion process of IC engines involves various parameters, which are linked to the overall performance of the driveline. Therefore, finding the frequency coupling between the manifold pressure, in-cylinder pressure and output crankshaft speed will provide an insight into the reasons for torque fluctuations and its effect on driveline performance. The present work introduces a methodology to analyze cylinder pressure, manifold pressure and instantaneous crank speed signals measured from a 4 cylinder, 1.6 Litre, Gasoline Direct Injection Engine at different speed conditions to identify the frequency coupling between these signals. This work uses Ensemble Empirical Mode Decomposition (EEMD) as a de-noising method and Bispectral analysis for examining the presence of a frequency coupling from the signals.

The search for alternative fuel for transport vehicles and also replacement of internal combustion engines in order to reduce the harmful emissions have been forcing the vehicle manufacturers to innovate new technology solutions for meeting the stringent legislative targets. Mexico’s commitment for de-carbonisation of transport sector and meeting the environmental goals is shaping it especially, and with this, it favours the move towards electrification of the vehicles. The aim of the present work is to numerically evaluate the possibility of replacing the IC engine in the existing hybrid vehicles with the Hydrogen fuel cell system. This work modelled a Hydrogen fuel cell vehicle based on Toyota MIRAI and validated the fuel economy performance of the vehicle using experimental data. This validated model was used to estimate the fuel economy for real-world drive cycles generated in 2019 from Mexico City.

The aim of the present work is to propose a control strategy for maximizing the performance of a Formula One (F1) car through numerical simulation for 2021 regulations taking 2019 regulations as a benchmark. This study has developed an engine-powertrain model of an F1 car with real world driver data for estimating the vehicle’s full throttle performance. The maximum possible energy recovered, stored and deployed by the Energy Recovery System (ERS) was estimated for the first 10 circuits in the 2019 FIA Formula One World Championship® Race Calendar. A 1.6L V6 Internal Combustion Engine (ICE), as well as, a full vehicle was modelled according to the 2019 Federation Internationale de l'Automobile (FIA) Formula One technical regulations using GT-Suite software. The model was validated against the experimental data. The data for validation was extracted from On- Board videos using Optical Character Recognition (OCR) and FIA regulations.

Air pollution levels in an urban environment is a major concern for developed and developing countries alike. Governments around the world are constantly trying to control and reduce air pollution levels through regulations. Low emission zones are being designated in cities worldwide in order to reduce the level of pollutants in big cities. The automotive industry is affected by those regulations and they are becoming more demanding over the years. Present work is aimed at developing a control strategy for a hybrid vehicle in order to optimize the fuel economy and emission levels based on GPS information, driver specific driving characteristics and weather forecast data for a given route. It uses powertrain model of a hybrid vehicle for developing route and driver specific control strategy. The full vehicle model has two sub-models: a route selector and a powertrain optimization model.

The thermal management of a Formula 1 car is a challenging task as it involves multiple components, systems and multiple sources of thermal energy. The present work attempts to model a representative F1 car following 2018 F1 regulations directly linked to the cooling systems requirements and performance. The main purpose of this work is to simulate the steady and transient behaviour of the cooling system when the vehicle is in a qualifying lap, and during the entire race, including the wait in the starting grid and the pit stops. This model includes the sub-models representing internal combustion engine, hybrid powertrain, vehicle, driver and an appropriate cooling system composed of radiators, pumps and expansion tanks. This work validates the cooling system of a representative 2018 F1 car for the Silverstone Circuit. This model is capable of simulating the overall thermal performance of the F1 car for sizing the cooling system for most of the F1 circuits.

In the continuous search for technology to improve the fuel economy and reduce greenhouse gas emission levels from the automotive vehicle, the automotive industry has been evaluating various technological options. Since the introduction of stringent legislative targets in Europe as well as in the United States of America in late 20th Century, one of the viable options identified by the industry was the application of alternative powertrain. On the motorsport arena, changes introduced by the Formula 1 governing body (FIA) for the high-performance racing engines also focuses on fuel economy. FIA regulation for 2014 restricts the fuel-flow rate to a maximum of 100kg/hr beyond 10,500 rev/min and prescribe fuel flow rate below 10,500 rev/min operating conditions for the F1 Engines. In addition, Formula1 and Le Mans racing regulations actively promote the integration of the hybrid powertrain in order to achieve optimum fuel economy.

This study details the investigation into the hybridization of engine ancillary systems for 2014+ Le Mans LMP1-H vehicles. This was conducted in order to counteract the new strict fuel-limiting requirements governing the powertrain system employed in this type of vehicle. Dymola 1D vehicle simulation software was used to construct a rectilinear vehicle model with a map based 3.8L V8 engine and its associated ancillary systems, including oil pumps, water pump and fuel pump as well as a full kinetic energy recovery system (ERS). Appropriate validation strategy was implemented to validate the model. A validated model was used to study the difference in fuel consumption for the conventional ancillary drive off of the internal combustion engine in various situational tests and a hybrid-electric drive for driving engine ancillaries.

Stringent emission norms introduced by the legislators over the decades has forced automotive manufacturers to improve the fuel economy and emission levels of their engines continuously. Therefore, the emission levels of modern engines are significantly lower than pre-1990 engines. However, the improvement in fuel economy is marginal when compared to that of emission levels. For example, approximately 30% of total energy in the fuel is being wasted through the cooling systems in the modern engines. Therefore, thermal management systems are being developed to reduce these losses and offer new opportunities for improving the fuel economy of the vehicles. One of the new emerging technologies for thermal management is the use of nanofluids as coolant. Nanofluids are a mixture of nano-sized particles added to a base fluid to improve its thermal characteristics.

This work investigates the effect of a three-way catalytic converter and sampling dilution ratio on nano-scale exhaust particulate matter emissions from a gasoline direct-injection engine during cold-start and warm-up transients. Experimental results are presented from a four cylinder in-line, four stroke, wall-guided direct-injection, turbo-charged and inter-cooled 1.6 litre gasoline engine. A fast-response particulate spectrometer for exhaust nano-particle measurement up to 1000 nm was utilised. It was observed that the three-way catalytic converter had a significant effect on particle number density, reducing the total particle number by up to 65 % over the duration of the cold-start test. The greatest change in particle number density occurred for particles less than 23 nm diameter, with reductions of up to 95 % being observed, whilst the number density for particles above 50 nm diameter exhibited a significant increase.

Improving the thermal efficiency of internal combustion engines over the engine operating range is essential for achieving optimum fuel economy. The thermal efficiency of the engine during cold start is one of the areas where significant improvement can be made if a suitable thermal management strategy is identified and implemented. Thermal management strategy in an engine can allow the engine to work at different operating temperatures in order to reduce the heat transfer loss by ensuring optimum volumetric efficiency, efficient combustion and adequate safety margin for the durability of mechanical components. The aim of the present work was to numerically model the warm-up characteristics of a 4 cylinder, 1.6 litre, turbocharged and intercooled, Euro IV, gasoline direct injected engine. It used a fully validated engine model which works based on the predictive combustion model.

This work investigates the effect of engine operating conditions and exhaust sampling conditions (i.e. dilution ratio) on engine-out, nano-scale, particulate matter emissions from a gasoline direct-injection engine during cold-start and warm-up transients. The engine used for this research was an in-line four cylinder, four stroke, wall-guided direct-injection, turbo-charged and inter-cooled 1.6 l gasoline engine. A fast-response particulate spectrometer for exhaust nano-particle measurement up to 1000 nm was utilized, along with a spark-plug mounted pressure transducer for combustion analysis. It was observed that the total particle count decreases during the cold-start transient, and has a distinct relationship with the engine body temperature. Tests have shown that the engine body temperature may be used as a control strategy for engine-out particulate emissions.

A semi phenomenological and global chemical kinetic model is adopted and applied to predict soot formation in gasoline-fueled spark ignition engines. The adopted model considers acetylene produced from gasoline pyrolysis process as the main precursor for soot inception. The adopted soot model was initially proposed for diffusion flames and this work tries to apply and modify it to gasoline fueled (premixed flame) spark ignition engines. The burned mass fraction and burn rate are used to estimate the instantaneous acetylene, oxygen and Hydroxyl (OH) radical mass fractions at each crank angle of the engine. Experimental data from a single point throttle body injected spark ignition engine is used for validating total particle numbers at different engine operating conditions. The simulation results agree reasonably with the experimental results. Both experimental and predicted results showed that the inception rate increases with the engine load in an exponential form.

The search for next generation transportation fuels in order to fully or partially replace petrol based fuels has resulted in use of varieties of fuels and fuel blends in internal combustion engines. However, the engine management systems are fuel specific and therefore, every major change in fuel composition requires significant amount of calibration work to optimize the operating variables in order to meet legislative emission targets and reduce the real-world emission and improve fuel economy levels. The current work has successfully devised a numerical simulation for the operation of a modern 4-cylinder turbocharged engine using an adaptive combustion modelling methodology that identifies a fuel type during engine start itself, and adapts engine operating parameters for optimum performance. A strategy was devised to use commercially available sensors to obtain and correlate measurable cylinder pressure based information for fuel identification.

This study aims to identify the factors that control particulate matter (PM) formation and size distribution in direct-injection spark-ignition (DISI) engines. The test engine used for this research was a 1.6 litre, wall-guided DISI, turbocharged, intercooled, in-line 4 cylinder, Euro IV engine. The exhaust sampling point was before the catalytic converter, i.e. engine-out emissions were measured. The first part of this paper investigates the characteristics of PM number and size distribution of DISI and throttle body injected (TBI) engines. The second part investigates the effect of combustion characteristics of DISI engines on the number of 5nm and 10nm (nucleation) and 200nm (accumulation) PM. A statistical analysis of the coefficient of variance (COV) of the maximum rate of pressure rise (RPmax) over 100 cycles was performed against the COV of 5nm, 10nm and 200nm total particle number.

Improving fuel economy and reducing greenhouse gas emissions from vehicle sources have been major research themes in recent times. One of the ways to achieve this is to use alternative fuels that can fully or partly replace petroleum-derived fuels using existing internal combustion engine technology so that the benefit from the alternative fuels can be realized immediately without delay. The present work attempted to investigate the performance and emission characteristics of a diesel engine using conventional diesel fuel with mixtures of hydrogen and oxygen generated from water at the point of use. Small amounts of hydrogen and oxygen were introduced in the air stream at the time of induction so that no extra injection system or additional modifications to the existing engine were required.