Taking the carbon out of the car

Effective measures are being taken to reduce emissions from cars, which are now emerging as a major contributor to climate change. Developed countries will need to reduce emissions by at least 80% by 2050 to achieve stabilization of atmospheric CO2 concentration between 450 and 550 ppm, and have a unique opportunity to avoid the most damaging effects of climate change. The UK is aiming at completely decarbonising transport by 2050 through a combination of more efficient vehicles, cleaner fuels, and smart driving choices. The European Commission has proposed a mandatory CO2 target on new car CO 2 efficiency, which is an urgent needed development. The nation is also using regulatory targets for local schemes, such as free parking or congestion charging, break points for company car tax, and vehicle excise duty. Car ownership and use should thereby continue to drive economic growth and enhance quality of life around the world without destroying the planet.

Unravelling the theories of pre-eclampsia:Are the protective pathways the new paradigm?

Pre-eclampsia is a vascular disorder of pregnancy where anti-angiogenic factors, systemic inflammation and oxidative stress predominate, but none can claim to cause pre-eclampsia. This review provides an alternative to the 'two-stage model' of pre-eclampsia in which abnormal spiral arteries modification leads to placental hypoxia, oxidative stress and aberrant maternal systemic inflammation. Very high maternal soluble fms-like tyrosine kinase-1 (sFlt-1 also known as sVEGFR) and very low placenta growth factor (PlGF) are unique to pre-eclampsia; however, abnormal spiral arteries and excessive inflammation are also prevalent in other placental disorders. Metaphorically speaking, pregnancy can be viewed as a car with an accelerator and brakes, where inflammation, oxidative stress and an imbalance in the angiogenic milieu act as the 'accelerator'. The 'braking system' includes the protective pathways of haem oxygenase 1 (also referred as Hmox1 or HO-1) and cystathionine-γ-lyase (also known as CSE or Cth), which generate carbon monoxide (CO) and hydrogen sulphide (H2S) respectively. The failure in these pathways (brakes) results in the pregnancy going out of control and the system crashing. Put simply, pre-eclampsia is an accelerator-brake defect disorder. CO and H2S hold great promise because of their unique ability to suppress the anti-angiogenic factors sFlt-1 and soluble endoglin as well as to promote PlGF and endothelial NOS activity. The key to finding a cure lies in the identification of cheap, safe and effective drugs that induce the braking system to keep the pregnancy vehicle on track past the finishing line.

Impact of driver behaviour on availability of electric vehicle stored energy for STOR

As take up of low carbon vehicles increase, there is interest in using the energy stored in the vehicles to help maintain system frequency through ancillary services on the electricity grid system. Research into this area is generally classed as vehicle-to-grid research. In theory, the energy available from electric vehicles could be directly correlated to the vehicle's state of charge (SoC) and battery capacity during the time the car is parked and plugged in. However, not all the energy in the vehicle may be used, as some capacity is required by the driver for their next journey. As such, this paper uses data captured as part of a large scale electric vehicle trial to investigate the effect of three different types of driver routine on vehicle-to-grid availability. Each driver's behaviour is analysed to assess the energy that is available for STOR, with follow on journey requirements also considered.

The evolution of the car-making industry

Mass-production, cars, pollution – they all have long become well known and well connected phenomena of the modern life. Nowadays the people can also add to the list such items like awareness, scientific approach, long-term thinking, and environmental responsibility. They are surrounded by a multitude of consumer goods, most of which are produced in a scientific manner, and all of which will more sooner than later end up in the garbage. Cars are the most noticeable – both by size and by numbers – and also the most expensive of all the mass products in people’s view. For many of them they are a clear target for reprimand and regulation, and, as a result, the automotive industry is being increasingly brought under bureaucratic control, together with its whole supplier and distributor network. The author started writing this article in an attempt to place the above process under scrutiny, because it is his firm belief that similar measures, similar tough governmental control will inevitably spill over to other industries, which at the moment are producing more inconspicuous, but still polluting products. The present paper shows the relationship between car-making, supply chain management and the efforts of public administration to protect the environment – a connection with clear practical implications.

Big Data Analysis in Social Networks : Extracting Food Preferences of Vegans from Twitter

Market research is often conducted through conventional methods such as surveys, focus groups and interviews. But the drawbacks of these methods are that they can be costly and timeconsuming. This study develops a new method, based on a combination of standard techniques like sentiment analysis and normalisation, to conduct market research in a manner that is free and quick. The method can be used in many application-areas, but this study focuses mainly on the veganism market to identify vegan food preferences in the form of a profile. Several food words are identified, along with their distribution between positive and negative sentiments in the profile. Surprisingly, non-vegan foods such as cheese, cake, milk, pizza and chicken dominate the profile, indicating that there is a significant market for vegan-suitable alternatives for such foods. Meanwhile, vegan-suitable foods such as coconut, potato, blueberries, kale and tofu also make strong appearances in the profile. Validation is performed by using the method on Volkswagen vehicle data to identify positive and negative sentiment across five car models. Some results were found to be consistent with sales figures and expert reviews, while others were inconsistent. The reliability of the method is therefore questionable, so the results should be used with caution.

An Economic Approach to Motor Vehicle Theft

Motor vehicle theft costs dearly to the Australian economy. Conservative estimates have put the annual cost of this form of illegal activity at 654 million during 1996. A number of initiatives aimed at reducing the incidence and cost of car theft have been implemented in recent years, yet statistics indicate that car theft is on the increase. Several authors have proposed an integrated approach to the regulation of markets for stolen property. Understanding property crime as a market is central to identifying approaches to its control. This paper discusses an industry model of crime and develops it on Australian data. Our model is an adaptation of one originally proposed by Vandeale (1978). It considers a production sector that uses inputs from a market of illegal labour to generate a supply of illegal goods that are traded in a product market. These sectors interact with each other and with a criminal justice sector. The model is applied to the analysis of car theft in Queensland.

Towards sustainable transport: A comparison of demographic and behavioural characteristics of Finnish and international car sharing users

Strong evidence suggests that the climate is changing and that these changes are largely caused by human activities. A consensus exists among researchers that human activity is causing global warming and that actions to mitigate global warming need to be taken swiftly. The transportation sector, which relies heavily on fossil fuel burning and primarily oil, is one of the big contributors to air pollution problems at local, regional and global levels. It is the fastest growing source of greenhouse gas emissions and is estimated to be responsible for nearly a quarter of global energyrelated carbon dioxide emissions. Car sharing is a mobility solution encouraging its users to decrease private car usage in favour of communal transit and environmental goals. The idea of car sharing originates from the aspiration to decrease personal car ownership and to reduce vehicle distance travelled. This thesis seeks to complement the understanding of Finnish car sharing users and their usage through better categorization. Through better categorization and segmentation of Finnish car sharing users the thesis seeks to provide information for improved marketing insight. Research is done on the demographic and behavioural characteristics of Finnish car sharing users and they are compared with international findings about the characteristics of International car sharing users. The main research problem is Are Finnish car sharing users similar to international ones? A theoretical research framework on the determinants of individual car sharing usage is built based on international research about demographic and behaviouristic characteristics. After this a quantitative survey is performed to the customers of a Finnish car sharing organization. The data analysed in the thesis consist out of 532 answers received from the car sharing organizations customers. The data is analysed with descriptive and other exploratory methods, which create an understanding of Finnish car sharing users. At the end of the analysis the demographic and behavioural characteristics of Finnish car sharing users are compared with international ones. The research findings of the thesis indicate that the demographic and behavioural characteristics of Finnish car sharing usage largely follow those of their international counterparts. Thanks to the thesis results the car sharing organization is able to better target their customers through improved marketing insight.

Fire analysis of car park building structures

The thermal loading of an open car park building structure is going to be analysed, based on different fire scenarios that depend on the type of vehicle (different heat release rate). The compartment is going to be fixed and the thermal effect on beams is going to be analysed, depending on the vehicle position. The result of simple calculation method will be used to determine several temperature-time curves. The simple calculation method (Hasemi method) is also to be compared with the calculations of the Elefir-EN calculation program to analyse the thermal effect of the localized fire on beams.

Epothilone-d rescues cognition and attenuates alzheimer’s disease-like pathology in APP/PS1 mice

AIMS: Cognitive decline in Alzheimer's disease (AD) patients has been linked to synaptic damage and neuronal loss. Hyperphosphorylation of tau protein destabilizes microtubules leading to the accumulation of autophagy/vesicular material and the generation of dystrophic neurites, thus contributing to axonal/synaptic dysfunction. In this study, we analyzed the effect of a microtubule-stabilizing compound in the progression of the disease in the hippocampus of APP751SL/PS1M146L transgenic model. METHODS: APP/PS1 mice (3 month-old) were treated with a weekly intraperitoneal injection of 2 mg/kg epothilone-D (Epo-D) for 3 months. Vehicle-injected animals were used as controls. Mice were tested on the Morris water maze, Y-maze and object-recognition tasks for memory performance. Abeta, AT8, ubiquitin and synaptic markers levels were analyzed by Western-blots. Hippocampal plaque, synaptic and dystrophic loadings were quantified by image analysis after immunohistochemical stainings. RESULTS: Epo-D treated mice exhibited a significant improvement in the memory tests compared to controls. The rescue of cognitive deficits was associated to a significant reduction in the AD-like hippocampal pathology. Levels of Abeta, APP and ubiquitin were significantly reduced in treated animals. This was paralleled by a decrease in the amyloid burden, and more importantly, in the plaque-associated axonal dystrophy pathology. Finally, synaptic levels were significantly restored in treated animals compared to controls. CONCLUSION: Epo-D treatment promotes synaptic and spatial memory recovery, reduces the accumulation of extracellular Abeta and the associated neuritic pathology in the hippocampus of APP/PS1 model. Therefore, microtubule stabilizing drugs could be considered therapeutical candidates to slow down AD progression. Supported by FIS-PI12/01431 and PI15/00796 (AG),FIS-PI12/01439 and PI15/00957(JV)

Effect of Pavement-Vehicle Interaction on Highway Fuel Consumption and Emission

Vehicle fuel consumption and emission are two important effectiveness measurements of sustainable transportation development. Pavement plays an essential role in goals of fuel economy improvement and greenhouse gas (GHG) emission reduction. The main objective of this dissertation study is to experimentally investigate the effect of pavement-vehicle interaction (PVI) on vehicle fuel consumption under highway driving conditions. The goal is to provide a better understanding on the role of pavement in the green transportation initiates. Four study phases are carried out. The first phase involves a preliminary field investigation to detect the fuel consumption differences between paired flexible-rigid pavement sections with repeat measurements. The second phase continues the field investigation by a more detailed and comprehensive experimental design and independently investigates the effect of pavement type on vehicle fuel consumption. The third study phase calibrates the HDM-IV fuel consumption model with data collected in the second field phase. The purpose is to understand how pavement deflection affects vehicle fuel consumption from a mechanistic approach. The last phase applies the calibrated HDM-IV model to Florida’s interstate network and estimates the total annual fuel consumption and CO2 emissions on different scenarios. The potential annual fuel savings and emission reductions are derived based on the estimation results. Statistical results from the two field studies both show fuel savings on rigid pavement compared to flexible pavement with the test conditions specified. The savings derived from the first phase are 2.50% for the passenger car at 112km/h, and 4.04% for 18-wheel tractor-trailer at 93km/h. The savings resulted from the second phase are 2.25% and 2.22% for passenger car at 93km/h and 112km/h, and 3.57% and 3.15% for the 6-wheel medium-duty truck at 89km/h and 105km/h. All savings are statistically significant at 95% Confidence Level (C.L.). From the calibrated HDM-IV model, one unit of pavement deflection (1mm) on flexible pavement can cause an excess fuel consumption by 0.234-0.311 L/100km for the passenger car and by 1.123-1.277 L/100km for the truck. The effect is more evident at lower highway speed than at higher highway speed. From the network level estimation, approximately 40 million gallons of fuel (combined gasoline and diesel) and 0.39 million tons of CO2 emission can be saved/reduced annually if all Florida’s interstate flexible pavement are converted to rigid pavement with the same roughness levels. Moreover, each 1-mile of flexible-rigid conversion can result in a reduction of 29 thousand gallons of fuel and 258 tons of CO2 emission yearly.

The Take Home Car Advantage

This paper explores the idea of police officers from Conway being able to take their police cars home instead of leaving them at the station and having an assigned vehicle instead of a shared one.

Active Control of the Acoustic Field in a Vehicle Cabin

In this thesis, a thorough investigation on acoustic noise control systems for realistic automotive scenarios is presented. The thesis is organized in two parts dealing with the main topics treated: Active Noise Control (ANC) systems and Virtual Microphone Technique (VMT), respectively. The technology of ANC allows to increase the driver's/passenger's comfort and safety exploiting the principle of mitigating the disturbing acoustic noise by the superposition of a secondary sound wave of equal amplitude but opposite phase. Performance analyses of both FeedForwrd (FF) and FeedBack (FB) ANC systems, in experimental scenarios, are presented. Since, environmental vibration noises within a car cabin are time-varying, most of the ANC solutions are adaptive. However, in this work, an effective fixed FB ANC system is proposed. Various ANC schemes are considered and compared with each other. In order to find the best possible ANC configuration which optimizes the performance in terms of disturbing noise attenuation, a thorough research of \gls{KPI}, system parameters and experimental setups design, is carried out. In the second part of this thesis, VMT, based on the estimation of specific acoustic channels, is investigated with the aim of generating a quiet acoustic zone around a confined area, e.g., the driver's ears. Performance analysis and comparison of various estimation approaches is presented. Several measurement campaigns were performed in order to acquire a sufficient duration and number of microphone signals in a significant variety of driving scenarios and employed cars. To do this, different experimental setups were designed and their performance compared. Design guidelines are given to obtain good trade-off between accuracy performance and equipment costs. Finally, a preliminary analysis with an innovative approach based on Neural Networks (NNs) to improve the current state of the art in microphone virtualization is proposed.

Analysis and improvements of four-wire power converters for electric vehicle chargers

A robust and well-distributed backbone charging network is the priority to ensure widespread electrification of road transport, providing a driving experience similar to that of internal combustion engine vehicles. International standards set multiple technical targets for on-board and off-board electric vehicle chargers; output voltage levels, harmonic emissions, and isolation requirements strongly influence the design of power converters. Additionally, smart-grid services such as vehicle-to-grid and vehicle-to-vehicle require the implementation of bi-directional stages that inevitably increase system complexity and component count. To face these design challenges, the present thesis provides a rigorous analysis of four-leg and split-capacitor three-phase four-wire active front-end topologies focusing on the harmonic description under different modulation techniques and conditions. The resulting analytical formulation paves the way for converter performance improvements while maintaining regulatory constraints and technical requirements under control. Specifically, split-capacitor inverter current ripple was characterized as providing closed-form formulations valid for every sub-case ranging from synchronous to interleaved PWM. Outcomes are the base for a novel variable switching PWM technique capable of mediating harmonic content limitation and switching loss reduction. A similar analysis is proposed for four-leg inverters with a broad range of continuous and discontinuous PWM modulations. The general superiority of discontinuous PWM modulation in reducing switching losses and limiting harmonic emission was demonstrated. Developments are realized through a parametric description of the neutral wire inductor. Finally, a novel class of integrated isolated converter topologies is proposed aiming at the neutral wire delivery without employing extra switching components rather than the one already available in typical three-phase inverter and dual-active-bridge back-to-back configurations. The fourth leg was integrated inside the dual-active-bridge input bridge providing relevant component count savings. A novel modified single-phase-shift modulation technique was developed to ensure a seamless transition between working conditions like voltage level and power factor. Several simulations and experiments validate the outcomes.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

In-car sound linear simulation

In recent years, vehicle acoustics have gained significant importance in new car development: increasingly advanced infotainment systems for spatial audio and sound enhancement algorithms have become the norm in modern vehicles. In the past, car manufacturers had to build numerous prototypes to study the sound behaviour inside the car cabin or the effect of new algorithms under development. Nowadays, advanced simulation techniques can reduce development costs and time. In this work, after selecting the reference test vehicle, a modern luxury sedan equipped with a high-end sound system, two independent tools were developed: a simulation tool created in the Comsol Multiphysics environment and an auralization tool developed in the Cycling ‘74 MAX environment. The simulation tool can calculate the impulse response and acoustic spectrum at a specific position inside the cockpit. Its input data are the vehicle’s geometry, acoustic absorption parameters of materials, the acoustic characteristics and position of loudspeakers, and the type and position of virtual microphones (or microphone arrays). The simulation tool can also provide binaural impulse responses thanks to Head Related Transfer Functions (HRTFs) and an innovative algorithm able to compute the HRTF at any distance and angle from the head. Impulse responses from simulations or acoustic measurements inside the car cabin are processed and fed into the auralization tool, enabling real-time interaction by applying filters, changing the channels gain or displaying the acoustic spectrum. Since the acoustic simulation of a vehicle involves multiple topics, the focus of this work has not only been the development of two tools but also the study and application of new techniques for acoustic characterization of the materials that compose the cockpit and the loudspeaker simulation. Specifically, three different methods have been applied for material characterization through the use of a pressure-velocity probe, a Laser Doppler Vibrometer (LDV), and a microphone array.