Investigation into the potential of using micro gas turbines in plug-in hybrid electric vehicles

The transport sector is considered to be one of the most dependent sectors on fossil fuels. Meeting ecological, social and economic demands throughout the sector has got increasingly important in recent times. A passenger vehicle with a more environmentally friendly propulsion system is the hybrid electric vehicle. Combining an internal combustion engine and an electric motor offers the potential to reduce carbon dioxide emissions. The overall objective of this research is to provide an appraisal of the use of a micro gas turbine as the range extender in a plug-in hybrid electric vehicle. In this application, the gas turbine can always operate at its most efficient operating point as its only requirement is to recharge the battery. For this reason, it is highly suitable for this purpose. Gas turbines offer many benefits over traditional internal combustion engines which are traditionally used in this application. They offer a high power-to-weight ratio, multi-fuel capability and relatively low emission levels due to continuous combustion.

A techno-economic evaluation of changing from conventional vehicles to range extended electric vehicles

Transportation accounts for 22% of greenhouse gas emissions in the UK, and increases to 25% in Northern Ireland. Surface transport carbon dioxide emissions, consisting of road and rail, are dominated by cars. Demand for mobility is rising rapidly and vehicle numbers are expected to more than double by 2050. Car manufacturers are working towards reducing their carbon footprint through improving fuel efficiency and controlling exhaust emissions. Fuel efficiency is now a key consideration of consumers purchasing a new vehicle. While measures have been taken to help to reduce pollutants, in the future, alternative technologies will have to be used in the transportation industry to achieve sustainability. There are currently many alternatives to the market leader, the internal combustion engine. These alternatives include hydrogen fuel cell vehicles and electric vehicles, a term which is widely used to cover battery electric vehicles, plug-in hybrid electric vehicles and extended-range electric vehicles. This study draws direct comparisons measuring the differing performance in terms of fuel consumption, carbon emissions and range of a typical family saloon car using different fuel types. These comparisons will then be analysed to see what effect switching from a conventionally fuelled vehicle to a range extended electric vehicle would have not only on the end user, but also the UK government.

Electric vehicles and opportunities to displace tailgate emissions using Smart Charging controls

Under the European Union Renewable Energy Directive each Member State is mandated to ensure that 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020. The Irish Government intends to achieve this target with a number of policies including ensuring that 10% of all vehicles in the transport fleet are powered by electricity by 2020. This paper investigates the impact of the 10% electric vehicle target in Ireland in 2020 using a dynamic programming based long term generation expansion planning model. The model developed optimizes power dispatch using hourly electricity demand curves up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. Two distinct scenarios are analysed based on a peak and off-peak charging regimes in order to simulate the effects of the electric vehicles charging in 2020. The importance and influence of the charging regimes on the amount of energy used and tailgate emissions displaced is then determined.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Novel decentralised formation control for unmanned vehicles

This paper proposes a new methodology for solving the unmanned multi-vehicle formation control problem. It employs a unique “extension-decomposition-aggregation” scheme to transform the overall complex formation control problem to a group of sub-problems which work via boundary interactions. The H∞ robust control strategy is applied to design the decentralised formation controllers to reject the interactions and work jointly to maintain the stability of the overall formation. Simulation studies have been performed to verify its performance and effectiveness.

A co-operative formation-based collision avoidance approach for a group of autonomous vehicles (Special Issue on Adaptive Control and Signal Processing in Marine System)

This paper employs a unique decentralised cooperative control method to realise a formation-based collision avoidance strategy for a group of autonomous vehicles. In this approach, the vehicles' role in the formation and their alert and danger areas are first defined, and the formation-based intra-group and external collision avoidance methods are then proposed to translate the collision avoidance problem into the formation stability problem. The extension–decomposition–aggregation formation control method is next employed to stabilise the original and modified formations, whilst manoeuvring, and subsequently solve their collision avoidance problem indirectly. Simulation study verifies the feasibility and effectiveness of the intra-group and external collision avoidance strategy. It is demonstrated that both formation control and collision avoidance problems can be simultaneously solved if the stability of the expanded formation including external obstacles can be satisfied.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Hydrogel antimicrobial capture coatings for endotracheal tubes; a pharmaceutical strategy designed to prevent ventilator-associated pneumonia.

This paper presents a novel strategy for the prevention of ventilator-associatedpneumonia that involves coating poly(vinyl chloride, PVC) endotracheal tubes (ET) withhydrogels that may be subsequently used to entrap nebulized antimicrobial solutions. Candidatehydrogels were prepared containing a range of ratios of hydroxyethyl methacrylate (HEMA) andmethacrylic acid (MAA) from 100:0 to 70:30 using free radical polymerization and, whenrequired, simultaneous attachment to PVC was performed. The mechanical properties, glasstransition temperatures, swelling kinetics, uptake of gentamicin from an aqueous medium, andgentamicin release were characterized. Increasing the MAA content of the hydrogels significantlydecreased the ultimate tensile strength, % elongation at break, Young’s modulus, and increasedthe glass transition temperature, the swelling ratio, and gentamicin uptake. Microbial(Staphylococcus aureus and Pseudomonas aeruginosa) adherence to control (drug-free) hydrogelswas observed; however, while adherence to gentamicin-containing p(HEMA) occurred, noadherence occurred to gentamicin-containing HEMA:MAA copolymers. Antimicrobialpersistence of gentamicin-containing hydrogels was examined by determining the zone ofinhibition against each microorganism on successive days. Hydrogel composition affected the observed antimicrobial persistence,with the hydrogel composed of 70:30 HEMA:MAA exhibiting >20 days persistence against S. aureus and P. aeruginosa,respectively. To simulate clinical use, the hydrogels (coated onto PVC) were first exposed to a nebulized solution of gentamicin(4 mL, 80 mg for 20 min), and then to nebulized bacteria (4 mL ca. 1 × 109 colony forming units mL−1, 30 min). Viable bacteriawere not observed on the gentamicin-treated p(HEMA: MAA) copolymers, whereas growth was observed on gentamicin-treatedp(HEMA). In light of the excellent antimicrobial activity and physicochemical properties, p(HEMA: MAA) copolymerscomposed of ratios of 80:20 or 70:30 HEMA: MAA were identified as potentially useful coatings of endotracheal tubes to be usedin conjunction with the clinical nebulization of gentamicin and designed for the prevention of ventilator-associated pneumonia

Characterisation and modelling of the thermorheological properties of pharmaceutical polymers and their blends using capillary rheometry: Implications for hot melt processing of dosage forms.

Given the growing interest in thermal processing methods, this study describes the use of an advanced rheological technique, capillary rheometry, to accurately determine the thermorheological properties of two pharmaceutical polymers, Eudragit E100 (E100) and hydroxypropylcellulose JF (HPC) and their blends, both in the presence and absence of a model therapeutic agent (quinine, as the base and hydrochloride salt). Furthermore, the glass transition temperatures (Tg) of the cooled extrudates produced using capillary rheometry were characterised using Dynamic Mechanical Thermal Analysis (DMTA) thereby enabling correlations to be drawn between the information derived from capillary rheometry and the glass transition properties of the extrudates. The shear viscosities of E100 and HPC (and their blends) decreased as functions of increasing temperature and shear rates, with the shear viscosity of E100 being significantly greater than that of HPC at all temperatures and shear rates. All platforms were readily processed at shear rates relevant to extrusion (approximately 200–300 s−1) and injection moulding (approximately 900 s−1). Quinine base was observed to lower the shear viscosities of E100 and E100/HPC blends during processing and the Tg of extrudates, indicative of plasticisation at processing temperatures and when cooled (i.e. in the solid state). Quinine hydrochloride (20% w/w) increased the shear viscosities of E100 and HPC and their blends during processing and did not affect the Tg of the parent polymer. However, the shear viscosities of these systems were not prohibitive to processing at shear rates relevant to extrusion and injection moulding. As the ratio of E100:HPC increased within the polymer blends the effects of quinine base on the lowering of both shear viscosity and Tg of the polymer blends increased, reflecting the greater solubility of quinine within E100. In conclusion, this study has highlighted the importance of capillary rheometry in identifying processing conditions, polymer miscibility and plasticisation phenomena.

Immunological determination of the pharmaceutical diclofenac in environmental and biological samples

A highly sensitive and specific competitive ELISA on 96-microwell plates was developed for the analysis of the nonsteroidal anti-inflammatory drug diclofenac. Within the water cycle in Europe, this is one of the most frequently detected pharmaceutically active compounds. The LOD at a signal-tonoise ratio (S/N) of 3, and the IC ₅₀, were found to be 6 ng/L and 60 ng/L respectively in tap water. In a comparative study using ELISA and GC-MS, diclofenac levels in wastewater from 21 sewage treatment plants were determined and a good correlation between these methods was found (ELISA vs. GCMS: r = 0.70, slope = 0,90, intercept = 0.37, n = 24). An average degradation rate of -25% can be calculated. Labscale-experiments on the elimination of diclofenac in continuous pilot sewage plants revealed a removal rate of only 5% over a period of 13 weeks. In a further study, the ELISA was applied to a number of extracts of various animal tissues from a range of species, and again a very good relationship between ELISA and LC-ESI/MS data sets was obtained (r = 0.90, p<0.0001; n = 117). The ELISA has proven to be a simple, rapid, reliable and affordable alternative to otherwise costly and advanced techniques for the detection of diclofenac in matrix diverse water samples and tissue extracts after only relatively simple sample preparation. © 2007 American Chemical Society.

A Reliability Assessment Approach For Integrated Transportation and Electrical Power Systems Incorporating Electric Vehicles

With the increasing utilization of electric vehicles (EVs), transportation systems and electrical power systems are becoming increasingly coupled. However, the interaction between these two kinds of systems are not well captured, especially from the perspective of transportation systems. This paper studies the reliability of integrated transportation and electrical power system (ITES). A bidirectional EV charging control strategy is first demonstrated to model the interaction between the two systems. Thereafter, a simplified transportation system model is developed, whose high efficiency makes the reliability assessment of the ITES realizable with an acceptable accuracy. Novel transportation system reliability indices are then defined from the view point of EV’s driver. Based on the charging control model and the transportation simulation method, a daily periodic quasi sequential reliability assessment method is proposed for the ITES system. Case studies based on RBTS system demonstrate that bidirectional charging controls of EVs will benefit the reliability of power systems, while decrease the reliability of EVs travelling. Also, the optimal control strategy can be obtained based on the proposed method. Finally, case studies are performed based on a large scale test system to verify the practicability of the proposed method.