Extracting bridge frequencies from dynamic responses of two passing vehicles

A conventional way to identify bridge frequencies is utilizing vibration data measured directly from the bridge. A drawback with this approach is that the deployment and maintenance of the vibration sensors are generally costly and time-consuming. One of the solutions is in a drive-by approach utilizing vehicle vibrations while the vehicle passes over the bridge. In this approach, however, the vehicle vibration includes the effect of road surface roughness, which makes it difficult to extract the bridge modal properties. This study aims to examine subtracting signals of two trailers towed by a vehicle to reduce the effect of road surface roughness. A simplified vehicle-bridge interaction model is used in the numerical simulation; the vehicle - trailer and bridge system are modeled as a coupled model. In addition, a laboratory experiment is carried out to verify results of the simulation and examine feasibility of the damage detection by the drive-by method.

Electric vehicles and displaced gaseous emission

Electric vehicles (EV) do not emit tailpipe exhaust fumes in the same manner as internal combustion engine vehicles. Optimal benefits can only be achieved, if EVS are deployed effectively, so that the tailpipe emissions are not substituted by additional emissions in the electricity sector. This paper examines the potential contributions that Plug in Hybrid Electric Vehicles can make in reducing carbon dioxide. The paper presents the results of the generation expansion model for Northern Ireland and the Republic of Ireland built using the dynamic programming based long term generation expansion planning tool called the Wien Automatic System Planning IV tool. The model optimizes power dispatch using hourly electricity demand curves for each year 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. In order to simulate the effect of PHEV, two distinct charging scenarios are applied based on a peak tariff and an off peak tariff. The importance and influence of the charging regime on the amount of energy used and gaseous emissions displaced is determined and discussed.

Quantifying the Energy & carbon emissions implications of a 10% electric vehicles target

EU Directive 2009/28/EC on Renewable Energy requires each Member State to ensure 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020 (10% RES-T target). In addition to the anticipated growth in biofuels, this target is expected to be met by the increased electrification of transport coupled with a growing contribution from renewable energy to electricity generation. Energy use in transport accounted for nearly half of Ireland’s total final energy demand and about a third of energy-related carbon dioxide emissions in 2007. Energy use in transport has grown by 6.3% per annum on average in the period 1990 – 2007. This high share and fast growth relative to other countries highlights the challenges Ireland faces in meeting ambitious renewable energy targets. The Irish Government has set a specific target for Electric Vehicles (EV) as part of its strategy to deliver the 10% RES-T target. By 2020, 10% of all vehicles in its transport fleet are to be powered by electricity. This paper quantifies the impacts on energy and carbon dioxide emissions of this 10% EV target by 2020. In order to do this an ‘EV Car Stock’ model was developed to analyse the historical and future make-up of the passenger car portion of the fleet to 2025. Three scenarios for possible take-up in EVs were examined and the associated energy and emissions impacts are quantified. These impacts are then compared to Ireland’s 10% RES-T target and greenhouse gas (GHG) emissions reduction targets for 2020. Two key findings of the study are that the 10% EV target contributes 1.7% to the 10% RES-T target by 2020 and 1.4% to the 20% reduction in Non-ETS emissions by 2020 relative to 2005.

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.

Catalyst Deactivation on a Two-Stroke Engine

With the legislative demands increasing on recreational vehicles and utility engined applications, the two-stroke engine is facing increasing pressure to meet these requirements. One method of achieving the required reduction is via the introduction of a catalytic converter. The catalytic converter not only has to deal with the characteristically higher CO and HC concentration, but also any oil which is added to lubricate the engine. In a conventional two-stroke engine with a total loss lubrication system, the oil is either scavenged straight out the exhaust port or is entrained, involved in combustion and is later exhausted. This oil can have a significant effect on the performance of the catalyst.
To investigate the oiling effect, three catalytic converters were aged using a 400cm₃ DI two-stroke engine. A finite level of oil was added to the inlet air of the engine to lubricate the internal workings. The oil flow rate is independent of the engine speed and load.

Three catalysts were aged for 50 hours, experiencing a constant space velocity and set engine conditions. The engine was fueled on petrol and later on propane to eliminate the effects, if any, of petrol additives on catalyst deactivation. The oiling rate was varied to investigated deactivation from oil contamination. Post-mortem analysis was performed on the three catalysts. This consisted of a controlled light-off test performed on a catalyst rig, during which period, temperatures were measured and recorded towere aged for 50 hours, experiencing a constant space velocity and set engine conditions. The engine was fueled on petrol and later on propane to eliminate the effects, if any, of petrol additives on catalyst deactivation. The oiling rate was varied to investigated deactivation from oil contamination. Post-mortem analysis was performed on the three catalysts. This consisted of a controlled light-off test performed on a catalyst rig, during which period, temperatures were measured and recorded to find out where deactivation of each catalyst was occurring. The recorded results were all analyzed and these showed that from the measured light-off temperatures the aged catalysts behaved similarly. However the pattern in the light-off was significantly different when the engine was fueled by propane as opposed to gasoline.

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.

Using Instrumented Vehicles To Detect Damage in Bridges

Bridge structures are subject to continuous degradation due to the environment, ageing and excess loading. Monitoring of bridges is a key part of any maintenance strategy as it can give early warning if a bridge is becoming unsafe. This paper will theoretically assess the ability of a vehicle fitted with accelerometers on its axles to detect changes in damping of bridges, which may be the result of damage. Two vehicle models are used in this investigation. The first is a two degree-of-freedom quarter-car and the second is a four degree-of-freedom halfcar. The bridge is modelled as a simply supported beam and the interaction between the vehicle and the bridge is a coupled dynamic interaction algorithm. Both smooth and rough road profiles are used in the simulation and results indicate that changes in bridge damping can be detected by the vehicle models for a range of vehicle velocities and bridge spans.

Using Instrumented Vehicles to Detect Damage in Bridges

This paper describes a ‘drive-by’ method of bridge inspection using an instrumented vehicle. Accelerometers on the vehicle are proposed as a means of detecting damage on the bridge in the time it takes for the vehicle to cross the bridge at full highway speed. For a perfectly smooth road profile, the method is shown to be feasible. Changes in bridge damping, which is an indicator of damage, are clearly visible in the acceleration signal of a quarter-car vehicle on a smooth road surface modelled using MatLab. When road profile is considered, the influence of changes in bridge damping on the vehicle acceleration signal is much less clear. However, when a half-car model is used on a road with a rough profile, it is again possible to detect changes in bridge damping, provided the vehicle has two identical axles.

Parametric study to extract bridge frequencies from the dynamic response of passing vehicles

A conventional way to identify bridge frequencies is utilizing vibration data measured directly from the bridge. A drawback with this approach is that the deployment and maintenance of the vibration sensors are generally costly and time-consuming. One way to cope with the drawback is an indirect approach utilizing vehicle vibrations while the vehicle passes over the bridge. In the indirect approach, however, the vehicle vibration includes the effect of road surface roughness, which makes it difficult to extract the bridge modal properties. One solution may be subtracting signals of two trailers towed by a vehicle to reduce the effect of road surface roughness. A simplified vehicle-bridge interaction model is used in the numerical simulation; the vehicle - trailer and bridge system are modeled as a coupled model. In addition, a laboratory experiment is carried out to verify results of the simulation and examine feasibility of the damage detection by the indirect method.

Sustainable non-automotive vehicles: The simulation challenges

Simulation is a well-established and effective approach to the development of fuel-efficient and low-emissions vehicles in both on-highway and off-highway applications.

The simulation of on-highway automotive vehicles is widely reported in literature, whereas research relating to non-automotive and off-highway vehicles is relatively sparse. This review paper focuses on the challenges of simulating such vehicles and discusses the differences in the approach to drive cycle testing and experimental validation of vehicle simulations. In particular, an inner-city diesel-electric hybrid bus and an ICE (Internal Combustion Engine) powered forklift truck will be used as case studies.

Computer prediction of fuel consumption and emissions of automotive vehicles on standardised drive cycles is well-established and commercial software packages such as AVL CRUISE have been specifically developed for this purpose. The vehicles considered in this review paper present new challenges from both the simulation and drive-cycle testing perspectives. For example, in the case of the forklift truck, the drive cycles involve reversing elements, variable mass, lifting operations, and do not specify a precise velocity-time profile. In particular, the difficulties associated with the prediction of productivity, i.e. the maximum rate of completing a series of defined operations, are discussed. In the case of the hybrid bus, the standardised drive cycles are unrepresentative of real-life use and alternative approaches are required in the development of efficient and low-emission vehicles.

Two simulation approaches are reviewed: the adaptation of a standard automotive vehicle simulation package, and the development of bespoke models using packages such as MATLAB/Simulink.