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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.

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.

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.

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.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

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.

Autonomous vehicles in formula student competition is a relatively new competition, with most of the teams testing new concepts every year with their challenger for the season. A background search conducted reflects the various concept changes offered by the FS teams in Formula student Germany each year. Hence, it can be concluded that the teams are uncertain about many concepts in an autonomous vehicle. This paper explores one such aspect; the choice of controller governs the steering capabilities of the autonomous vehicle. An MPC controller is used to build a basic controller model for the autonomous vehicle in the formula student competition. A bicycle model representative of the Oxford Brookes Racing team's electric vehicle is modeled, and the MPC controller is used to check various vehicle dynamic parameters in Simulink.

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.

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.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.

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.

Since their inception, the design of airbag sensing systems has continued to evolve. The evolution of air bag sensing system design has been rapid. Electromechanical sensors used in earlier front air bag applications have been replaced by multi-point electronic sensors used to discriminate collision mechanics for potential air bag deployment in front, side and rollover accidents. In addition to multipoint electronic sensors, advanced air bag systems incorporate a variety of state sensors such as seat belt use status, seat track location, and occupant size classification that are taken into consideration by air bag system algorithms and occupant protection deployment strategies. Electronic sensing systems have allowed for the advent of event data recorders (EDRs), which over the past decade, have provided increasingly more information related to air bag deployment events in the field.

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.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.