Impact of weather conditions on electric vehicle performance

Seasonal and day-to-day variations in travel behaviour and performance of private passenger vehicles can be partially explained by changes in weather conditions. Likewise, in the electricity sector, weather affects energy demand. The impact of weather conditions on private passenger vehicle performance, usership statistics and travel behaviour has been studied for conventional, internal combustion engine, vehicles. Similarly, weather-driven variability in electricity demand and generation has been investigated widely. The aim of these analyses in both sectors is to improve energy efficiency, reduce consumption in peak hours and reduce greenhouse gas emissions. However, the potential effects of seasonal weather variations on electric vehicle usage have not yet been investigated. In Ireland the government has set a target requiring 10% of all vehicles in the transport fleet to be powered by electricity by 2020 to meet part of its European Union obligations to reduce greenhouse gas emissions and increase energy efficiency. This paper fills this knowledge gap by compiling some of the published information available for internal combustion engine vehicles and applying the lessons learned and results to electric vehicles with an analysis of historical weather data in Ireland and electricity market data in a number of what-if scenarios. Areas particularly impacted by weather conditions are battery performance, energy consumption and choice of transportation mode by private individuals.

An Analysis of the Effect of Electric Vehicle Charging on the Operation of the Single Electricity Market

To meet European Union renewable energy and greenhouse gas emissions reduction targets the Irish government set a target in 2008 that 10% of all vehicles in the transport fleet be powered by electricity by 2020. Similar electric vehicle targets have been introduced in other countries. However, reducing energy consumption and decreasing greenhouse gas emissions in transport is a considerable challenge due to heavy reliance on fossil fuels. In fact, transport in the Republic of Ireland in 2009 accounted for 29% of non-emissions trading scheme greenhouse gas emissions, 32% of energy-related greenhouse gas emissions, 21% of total greenhouse gas emissions and approximately 50% of energy-related non-emission trading scheme greenhouse gas emissions. In this paper the effect of electric vehicle charging on the operation of the single wholesale electricity market for the Republic of Ireland and Northern Ireland is analysed. The energy consumed, greenhouse gas emissions generated and changes to the wholesale price of electricity under peak and off-peak charging scenarios are quantified and discussed. Results from the study show that off-peak charging is more beneficial than peak charging.

Transport Properties Investigation of Aqueous Protic Ionic Liquid Solutions through Conductivity, Viscosity, and NMR Self-Diffusion Measurements

We present a study on the transport properties through conductivity (s), viscosity (?), and self-diffusion coefficient (D) measurements of two pure protic ionic liquids—pyrrolidinium hydrogen sulfate, [Pyrr][HSO4], and pyrrolidinium trifluoroacetate, [Pyrr][CF3COO]—and their mixtures with water over the whole composition range at 298.15 K and atmospheric pressure. Based on these experimental results, transport mobilities of ions have been then investigated in each case through the Stokes–Einstein equation. From this, the proton conduction in these PILs follows a combination of Grotthuss and vehicle-type mechanisms, which depends also on the water composition in solution. In each case, the displacement of the NMR peak attributed to the labile proton on the pyrrolidinium cation with the PILs concentration in aqueous solution indicates that this proton is located between the cation and the anion for a water weight fraction lower than 8%. In other words, for such compositions, it appears that this labile proton is not solvated by water molecules. However, for higher water content, the labile protons are in solution as H3O+. This water weight fraction appears to be the solvation limit of the H+ ions by water molecules in these two PILs solutions. However, [Pyrr][HSO4] and [Pyrr][CF3COO] PILs present opposed comportment in aqueous solution. In the case of [Pyrr][CF3COO], ?, s, D, and the attractive potential, Epot, between ions indicate clearly that the diffusion of each ion is similar. In other words, these ions are tightly bound together as ion pairs, reflecting in fact the importance of the hydrophobicity of the trifluoroacetate anion, whereas, in the case of the [Pyrr][HSO4], the strong H-bond between the HSO4– anion and water promotes a drastic change in the viscosity of the aqueous solution, as well as on the conductivity which is up to 187 mS·cm–1 for water weight fraction close to 60% at 298 K.

Impacts of Electric Vehicle charging under electricity market operations

The Irish government set a target in 2008 that 10% of all vehicles in the transport fleet be powered by electricity by 2020. Similar electric vehicle targets have been introduced in other countries. In this study the effects of 213,561 electric vehicles on the operation of the single wholesale electricity market for the Republic of Ireland and Northern Ireland is investigated. A model of Ireland’s electricity market in 2020 is developed using the power systems market model called PLEXOS for power systems. The amount of CO2 emissions associated with charging the EVs and the impacts with respect to Ireland’s target for renewable energy in transport is also quantified. A single generation portfolio and two different charging scenarios, arising from a peak and off-peak charging profile are considered. Results from the study confirm that offpeak charging is more beneficial than peak charging and that charging EVs will contribute 1.45% energy supply to the 10% renewable energy in transport target. The net CO2 reductions are 147 and 210 kt CO2 respectively.

A drive-by inspection system via vehicle moving force identification

This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

Theoretical investigation of the use of a moving vehicle to identify bridge dynamic parameters

Pavements and bridges are subject to a continuous degradation due to traffic aggressiveness, ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure in order to provide adequate maintenance and guarantee the required levels of transport service and safety. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamics of bridges. A simplified quarter carbridge interaction model is used in theoretical simulations and the natural frequency of the bridge is extracted from the spectra of the vehicle accelerations. The accuracy is better at lower speeds and for smooth road profiles. The structural damping of the bridge was also monitored for smooth and rough road profiles. The magnitude of peaks in the power spectral density of the vehicle accelerations decreased with increasing bridge damping and this decrease was easier to detect the smoother the road profile.

Analysis of electric vehicle charging using the traditional generation expansion planning analysis tool WASP-IV

Electric vehicles (EV) are proposed as a measure to reduce greenhouse gas emissions in transport and support increased wind power penetration across modern power systems. Optimal benefits can only be achieved, if EVs are deployed effectively, so that the exhaust emissions are not substituted by additional emissions in the electricity sector, which can be implemented using Smart Grid controls. This research presents the results of an EV roll-out in the all island grid (AIG) in Ireland using the long term generation expansion planning model called the Wien Automatic System Planning IV (WASP-IV) tool to measure carbon dioxide emissions and changes in total energy. The model incorporates all generators and operational requirements while meeting environmental emissions, fuel availability and generator operational and maintenance constraints to optimize economic dispatch and unit commitment power dispatch. In the study three distinct scenarios are investigated base case, peak and off-peak charging to simulate the impacts of EV’s in the AIG up to 2025.

Arsenite transport into paddy rice (Oryza sativa) roots

Here the mechanism of arsenite transport into paddy rice (Oryza sativa) roots, uptake of which is described by Michaelis-Menten kinetics, is reported. A recent study on yeast (Saccharomyces cerevisiae) showed that undissociated arsenite (its pK_a is 9.2) was transported across the plasma membrane via a glycerol transporting channel. To investigate whether the same mechanism of transport was involved for rice, competitive studies with glycerol, which is transported into cells via aquaporins, were performed. Glycerol competed with arsenite for transport in a dose-dependent manner, indicating that arsenite and glycerol uptake mechanisms were the same. Arsenate transport was unaffected by glycerol, confirming that arsenate and arsenite are taken up into cells by different mechanisms. Antimonite, an arsenite analogue that is transported into S. cerevisiae cells by aquaporins, also competed with arsenite transport in a dose-dependent manner, providing further evidence that arsenite is transported into rice roots via glycerol transporting channels. Mercury (Hg²⁺) inhibited both arsenite and arsenate uptake, suggesting that inhibition of influx was due to general cellular stress rather than the specific action of Hg²⁺ on aquaporins. Arsenite uptake by pea (Pisum sativum) and wheat (Triticum aestivum) was also described by Michaelis-Menten kinetics.

Extracellular DNA impedes the transport of vancomycin in Staphylococcus epidermidis biofilms pre-exposed to sub-inhibitory concentrations of vancomycin

Staphylococcus epidermidis biofilm formation is responsible for the persistence of orthopedic implant infections. Previous studies have shown that exposure of S. epidermidis biofilms to sub-MICs of antibiotics induced an increased level of biofilm persistence. BODIPY FL-vancomycin (a fluorescent vancomycin conjugate) and confocal microscopy were used to show that the penetration of vancomycin through sub-MIC-vancomycin-treated S. epidermidis biofilms was impeded compared to that of control, untreated biofilms. Further experiments showed an increase in the extracellular DNA (eDNA) concentration in biofilms preexposed to sub-MIC vancomycin, suggesting a potential role for eDNA in the hindrance of vancomycin activity. Exogenously added, S. epidermidis DNA increased the planktonic vancomycin MIC and protected biofilm cells from lethal vancomycin concentrations. Finally, isothermal titration calorimetry (ITC) revealed that the binding constant of DNA and vancomycin was 100-fold higher than the previously reported binding constant of vancomycin and its intended cellular D-Ala-D-Ala peptide target. This study provides an explanation of the eDNA-based mechanism of antibiotic tolerance in sub-MIC-vancomycin-treated S. epidermidis biofilms, which might be an important factor for the persistence of biofilm infections.

Identification of Road Irregularities via Vehicle Accelerations

A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.

Detection of Bridge Dynamic Parameters using an Instrumented Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure to provide adequate maintenance and guarantee the required levels of transport service and safety. In Europe, this is now a legal requirement - a European Directive requires all member states of the European Union to implement a Bridge Management System. However, the process is expensive, requiring the installation of sensing equipment and data acquisition electronics on the bridge. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges as an indicator of its structural condition. This approach eliminates the need for any on-site installation of measurement equipment. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the possibility of extracting the dynamic parameters of the bridge from the spectra of the vehicle accelerations. The effect of vehicle speed, vehicle mass and bridge span length on the detection of the bridge dynamic parameters are investigated. The algorithm is highly sensitive to the condition of the road profile and simulations are carried out for both smooth and rough profiles

Monitoring Bridge Dynamic Behaviour using an Instrumented Two Axle Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing, loading and environmental factors. A rational transport policy must monitor and provide adequate maintenance to this infrastructure to guarantee the required levels of transport service and safety. Increasingly in recent years, bridges are being instrumented and monitored on an ongoing basis due to the implementation of Bridge Management Systems. This is very effective and provides a high level of protection to the public and early warning if the bridge becomes unsafe. However, the process can be expensive and time consuming, requiring the installation of sensors and data acquisition electronics on the bridge. This paper investigates the use of an instrumented 2-axle vehicle fitted with accelerometers to monitor the dynamic behaviour of a bridge network in a simple and cost-effective manner. A simplified half car-beam interaction model is used to simulate the passage of a vehicle over a bridge. This investigation involves the frequency domain analysis of the axle accelerations as the vehicle crosses the bridge. The spectrum of the acceleration record contains noise, vehicle, bridge and road frequency components. Therefore, the bridge dynamic behaviour is monitored in simulations for both smooth and rough road surfaces. The vehicle mass and axle spacing are varied in simulations along with bridge structural damping in order to analyse the sensitivity of the vehicle accelerations to a change in bridge properties. These vehicle accelerations can be obtained for different periods of time and serve as a useful tool to monitor the variation of bridge frequency and damping with time.