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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.

With a push to continuously develop traditional engine technology efficiencies and meet stringent emissions requirements, there is a need to improve the precision of injection rate measurement used to characterise the performance of the fuel injectors. New challenges in precisely characterising injection rate present themselves to the Original Equipment Manufacturers (OEMs), with the additional requirements to measure multiple injection strategies, increased injection pressure and rate features. One commonly used method of measurement is the rate tube injection analyser; it measures the pressure wave caused by the injection within a column of stationary fluid. In a rate tube, one of the significant sources of signal distortion is a result of the injected fluid pressure waves reflected back from the tube termination.

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 COVID-19 pandemic has forced governments to implement rigorous containment measures on reduction or cessation of human mobility, transportation and economic activities, to control the spread of the virus. This is considered as a unique opportunity to study the impact of local lockdowns periods, especially, on the vehicle emission levels, and urban air quality in cities with high pollution levels, such as Bogota, Colombia. The first case was confirmed in Colombia on March 6, 2020, since then to prevent its propagation, the government declared a national lockdown starting from March 20 until August 31, 2020. Therefore, this study attempted to analyse the air quality in Bogota by assessing the concentrations of the atmospheric pollutants NO₂, SO₂, O₃, CO, PM₂.₅ and PM₁₀ during the lockdown period and the corresponding concentrations levels during the same period in 2018 and 2019. The data for this pilot study was obtained from the air quality monitoring stations of Bogota.

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.

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.

Formula 1 has always played a major role in technological advancements within the automotive and motorsport sectors. The adaptive changes introduced for the Power Unit (PU) in 2014 forced constructors, in collaboration with industry partners, to invent technologies for exceeding 50% brake thermal efficiency within a short span of time, demonstrating that technology-forcing regulations through motorsport is the favorable route to achieve transformative changes within the automotive sector. Therefore, in an attempt to address arising global warming and health concerns, the present work analytically examines the ambient air quality in track stadia during F1 race events to identify potential PU exhaust emission targets. It models the volume of air contained within the circuits located near heavily built-up areas assuming stagnant air conditions and uniform mixing.

This study analyzes the performance of the Energy Recovery System (ERS) of a Formula One car (F1) based on the qualification performance of 19 drivers for the first calendar race of the 2019 FIA Formula One World Championship®. In this study, the race circuit analysed was split into different sectors to examine the energy transfer between the Motor Generator Unit-Kinetic (MGU-K) and the Energy Storage (ES) systems. Positive Kinetic Energy (PKE) concept was used for estimating the energy deployment potential of the ERS along with numerical simulations for estimating the energy recovering potential. This investigation highlights the strategies used by different drivers and the effect of driver to driver variation on their ERS performance during qualification. The methodology demonstrated in this study is able to identify the correlation between the unique driving style of individual drivers and the ERS strategies used by the teams for maximizing the performance of their car.

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.

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.