Search Results

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.

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.

The number of electric vehicles is increasing in line with the global carbon reduction targets. More households are installing electric charging points to complement the existing charging infrastructure. The increasing electricity prices affected by the global energy/economic crisis however pushed more households towards coupling their charging points with renewable energy generation and storage systems to manage the supply and demand of energy more effectively. In this study, an electric charging station equipped with Photovoltaic panels and an electric storage system utilising second-life Electric Vehicles (EV) batteries is designed and analysed. Various electricity generation capacities are considered to be installed on the roof of the case study building ranging from 5m2 and 20m2. The second-life batteries are disposed from EVs with an 80% state of health. MATLAB Simulink is used for mathematical modelling of system.

A twofold improvement in penetration resistance of laminated safety glass for use in vehicle windshields has been achieved. A new test procedure has been established which will provide better correlation of test conditions to accident conditions than present tests do. Present windshield material and the new safety glazings are compared.

Lithium-ion cells operate under a narrow range of voltage, current, and temperature limits, which requires a battery management system (BMS) to sense, control, and balance the battery pack. The state of power (SOP) estimation is a fundamental algorithm of the BMS. It operates as a dynamic safety limit, preventing rapid ageing and optimizing power delivery. SOP estimation relies on predictive algorithms to determine charge and discharge power limits sustainable within a specified time frame, ensuring the cell design constraints are not violated. This paper explores various approaches for real-time deployment of SOP estimation algorithms for a high-power lithium-ion battery (LIB) with a low-cost microcontroller. The algorithms are based on a root-finding approach and a first-order equivalent circuit model (ECM) of the battery.

Soot formation and distribution inside the cylinder of a light-duty direct injection diesel engine, have been predicted using Kiva-3v CFD software. Pathlines of soot particles traced from specific in-cylinder locations and crank angle instants have been explored using the results for cylinder charge motion predicted by the Kiva-3v code. Pathlines are determined assuming soot particles are massless and follow charge motion. Coagulation and agglomeration have not been taken into account. High rates of soot formation dominate during and just after the injection. Oxidation becomes dominant after the injection has terminated and throughout the power stroke. Computed soot pathlines show that soot particles formed just below the fuel spray axis during the early injection period are more likely to travel to the cylinder wall boundary layer. Soot particles above the fuel spray have lesser tendency to be conveyed to the cylinder wall.

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.

Electric Vehicles (EVs) are considered to be a worthy alternative to automobiles powered by internal combustion engines to achieve the goal of sustainable transportation. For their many known advantages, Li-ion cells are considered to be the most practical energy storage solution for the purpose of EVs propulsion currently. The capability of Li-ion cells to store energy in extreme cold operating temperatures is significantly lower than that at nominal operating temperatures due to greater power losses at cold temperatures. Therefore, it leads to degradation of performance of EVs in sub-zero temperatures. The present work proposes a novel approach to use numerical simulation technique to build an EV model based on BMW i3 using GT Suite at sub-zero temperatures. The model is validated against experimental data obtained from Argonne National Laboratory for US06, HWY and UDDS legislative drive cycles.

Lithium-ion batteries are ubiquitous in modern energy storage applications, necessitating efficient methods for assessing their state and performance. This study explores a non-destructive approach to extract vital battery parameters using machine learning techniques applied to simulated Electrochemical Impedance Spectroscopy (EIS) data. EIS is a powerful diagnostic tool for batteries and provides a safe and repeatable alternative to the physical intrusion of battery dismantling, which could alter the batteries properties. The research focuses on the design and training of machine learning models for accurate prediction of battery parameters within the widely used P2D model. By leveraging the power of machine learning, this approach aims to accurately characterize the battery parameters using an electrochemical model as opposed to the less accurate equivalent circuit models, contributing to the reliability and longevity of lithium-ion batteries in diverse applications.

To decarbonize heavy-duty vehicles solely through electrification with batteries is challenging as large batteries are required for a meaningful range, severely impacting payload. Employment of hybrid electric powertrains where fuel cells are integrated with batteries can deliver increased range and payload. However, the energy balance between the fuel cell and the battery needs to be analyzed to optimize the sizing of the powertrain components. This study has performed a multi-objective optimization using genetic algorithm to obtain the optimum range and hydrogen consumption for a DAF 44 tons heavy-duty truck. The proposed truck powertrain has been numerically modelled in AVL CRUISE M software. The electric drive from Involution Technologies Ltd and Bramble Energy Ltd’s printed circuit board fuel cell (PCBFC) are used in the model.

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence.

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.

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.

The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 11 years. In the past couple of years, significant attention has been directed toward a revision to the standard for vehicular hydrogen systems, SAE J2579(1). In addition to streamlining test methodologies for verification of Compressed Hydrogen Storage Systems (CHSSs) as discussed last year,(2) the working group has been considering the effect of vehicle fires, with the major focus on a small or localized fire that could damage the container in the CHSS and allow a burst before the Pressure Relief Device (PRD) can activate and safely vent the compressed hydrogen stored from the container.

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.

While the shift to vehicle electrification plays a pivotal role in governments’ targets towards carbon neutrality, there exists certain technical challenges that need to be addressed. The motorsport car industry is also affected by this policy with the electric cars being included in the formula SAE and formula E competitions as one of the main categories. Moreover, there is a gap in the literature in energy assessment of the electric powertrain used in Formula SAE (FSAE) and Formula Student (FS) cars. In this paper, a Formula Student electric car powertrain was designed as a case study for energy analysis. The proposed electric powertrain is equipped with a four-wheel drive system. The vehicle was modelled in AVL CRUISE M software using technical and measured lab data as input parameters. Simulations were run in a transient driving cycle for a real circuit layout used in previous SAE competitions.

A fuel cell (FC) powerplant is an electrochemical engine that converts fuel and an oxidant electrochemically into electric energy, water and other chemical byproducts. When hydrogen is used as the fuel, the only products of the electrochemical reactions are water and electric power. Other conventional and advanced powerplants for transportation, such as the internal combustion (IC) engine, the Diesel engine and others, are thermal combustion engines. The theoretical or thermodynamic efficiency of a fuel cell or electrochemical engine is much higher than the thermodynamic efficiency of a heat engine. The practical efficiency of a fuel cell is highest at partial load, whereas the practical efficiency of a heat engine is highest at maximum power. A survey is presented of the different fuel cell types and their characteristics. The proton-exchange-membrane (PEM) fuel cell is shown to be the best available fuel cell for transportation applications.

Autonomous vehicles have been shown to increase safety for drivers, passengers, and pedestrians and can also be used to maximize traffic flow, thereby reducing emissions and congestion. At the same time, governments around the world are promoting the usage of battery electric vehicles (BEVs) to reduce and control the emissions of CO2. This has made the development of autonomous vehicles and electric vehicles a very active research area and has prompted a significant amount of government funding. This article presents the detailed design of a low-cost platform for the development of an autonomous electric vehicle. In particular, it focuses on the design of the electrical architecture and the control strategy, tailored around the usage of affordable sensors and actuators. The specifications of the components are extensively discussed in relation to the performance target.

As vehicle fuel economy continues to grow in importance, the ability to accurately measure the level of efficiency on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently and provides data to make comparisons. In addition, the procedure offers a reliable process to assess enablers for efficiency improvements. Previous published studies have outlined the development of a comprehensive test procedure to measure transfer case speed-dependent parasitic losses at key speed, load, and environmental conditions. This paper will take the same basic approach for the Power Transfer Units (PTUs) used on Front Wheel Drive (FWD) based All Wheel Drive (AWD) vehicles. Factors included in the assessment include single and multi-stage PTUs, fluid levels, break-in process, and temperature effects.

Chrysler, Ford, General Motors, the U.S. Environmental Protection Agency (EPA) and the California Air Resources Board (CARB) have collaborated over the past two years to develop an efficiency test for mobile air conditioner (MAC) systems. Because the effect of efficiency differences between different MAC systems and different technologies is relatively small compared to overall vehicle fuel consumption, quantifying these differences has been challenging. The objective of this program was to develop a single dynamic test procedure that is capable of discerning small efficiency differences, and is generally representative of mobile air conditioner usage in the United States. The test was designed to be conducted in existing test facilities, using existing equipment, and within a sufficiently short time to fit standard test facility scheduling. Representative ambient climate conditions for the U.S. were chosen, as well as other test parameters, and a solar load was included.