Power analysis and simulation of a vehicle under combined loads

This paper presents an analytical model of fuel consumption (AMFC) to coordinate the driving power and manage the overall fuel consumption for an internal combustion engine vehicle. The model calculates the different loads applied on the vehicle including road-slope, road-friction, wind-drag, accessories, and mechanical losses. Also, it solves the combustion equation of the engine under different working conditions including various fuel compositions, excess airs and air inlet temperatures. Then it determines the contribution of each load to signify the energy distribution and power flows of the vehicle. Unlike the conventional models in which the vehicle speed needs to be given as an input, the developed model can predict the vehicle speed and acceleration under different working conditions by allowing the speed to vary within a predefined range only. Furthermore, the model indicates the ways to minimises the vehicles' fuel consumption under various driving conditions. The results show that the model has the potential to assist in the vehicle energy management.

An intelligent energy management model for a parallel hybrid vehicle under combined loads

To exploit the benefits offered by parallel HEVs, an intelligent energy management model is developed and evaluated in this paper. Despite most existing works, the developed model incorporates combined wind/drag, slope, rolling, and accessories loads to minimise the fuel consumption under varying driving conditions. A slope prediction unit is also employed. The engine and the electric motor can output power simultaneously under a heavy-load or a slopped road condition. Two simulation were conducted namely slopped-windy-prediction and slopped-windy-prediction-hybrid. The results indicate that the vehicle speed and acceleration is smoother where the hybrid component was included. The average fuel consumption for the first and second simulations were 7.94 and 7.46 liter/100 km, respectively.

Do we challenge students enough? Model Solar vehicle project challenges potential engineers.

A major challenge to Australia and New Zealand is the perceived need to develop "knowledge economies" based on the expertise of university graduates,  especially engineers. However, many countries are finding less students are choosing to study engineering. At the same time, there is increasing concern about increased levels of greenhouse gases leading to global warming with species loss, rising sea levels and desertification being likely outcomes. Numerous competitions have been established aimed at attracting school students into science and engineering careers. Environmental groups have also sponsored educational activities to increase student awareness of alternative energy technologies. One activity which provides both a science and engineering challenge while also raising awareness of alternative energy and more efficient conversion of that energy for transport is the Model Solar Vehicle Challenge (MSVC). The Challenge, which provides a solar powered boat competition for younger students and a car race for the older ones, has involved thousands of Victorian school students since 1990 and students from all Australian states since 1993. Boats race in 2 or 3 lanes guided by an overhead wire in a 10 metre pool, and cars race 100 metres around a figure 8 track. Top boats average over 7 kph and cars reach speeds of 25 kph at the finish line. This paper will discuss the conduct of the Challenge, motivation of participants, the depth of learning which can be achieved and the effectiveness of the Challenge in encouraging students to continue with science subjects through school and to select engineering at university. It will also briefly discuss the lessons that can be learnt from the MSVC and applied to first year university courses.

A flow control methodology for the rapid generation of vehicle traffic models

This paper presents a conveyor-based methodology to model complex vehicle flows common to factory and distribution warehouse facilities. The AGV and human path modelling techniques available in many commercial discrete event simulation packages require extensive knowledge and time to implement even the simplest flow control rules for multiple vehicle interaction. Although discrete event simulation is accepted as an effective tool to model vehicle delivery movements, human paths and delivery schedules for modern assembly lines, the time to generate accurate models is a significant limitation of existing simulation-based optimisation methodologies. The flow control method has been successfully implemented using two commercial simulation packages. It provides a realistic visual representation, as well as accurate statistical results, and reduces the model development process cost.

Comparison of static and dynamic engine models on the transient performance of a passenger vehicle powertrain

This paper presents experimental and computational results obtained on the Ford Barra 190 4.0 litres I6 gasoline engine and on the Ford Falcon car equipped with this engine. Measurements of steady engine performance, fuel consumption and exhaust emissions were first collected using an automated test facility for a wide range of cam and spark timings vs. throttle position and engine speed. Simulations were performed for a significant number of measured operating points at full and part load by using a coupled Gamma Technologies GT-POWER/GT-COOL engine model for gas exchange, combustion and heat transfer. The fluid model was made up of intake and exhaust systems, oil circuit, coolant circuit and radiator cooling air circuit. The thermal model was made up of finite element components for cylinder head, cylinder, piston, valves and ports and wall thermal masses for pipes. The model was validated versus measured steady state air and fuel flow rates, cylinder pressure parameters, indicated and brake mean effective pressures, and temperature of metal, oil and coolant in selected locations. Computational results agree well with experiments, demonstrating the ability of the approach to produce fairly accurate steady state maps of BMEP and BSFC, as well as to optimize engine operation changing geometry, throttle position, cam and spark timing. Measurements of the transient performance and fuel consumption of the full vehicle were then collected over the NEDC cycle. Simulations were performed by using a coupled Gamma Technologies GT-POWER/GT-COOL/GT-DRIVE model for instantaneous engine gas exchange, combustion and heat transfer and vehicle motion. The full vehicle model is made up of transmission, driveshaft, axles, and car components and the previous engine model. The model was validated with measured fuel flow rates through the engine, engine throttle position, and engine speed and oil and coolant temperatures in selected locations. Instantaneous engine states following a time dependent demand for torque and speed differ from those obtained by interpolating steady state maps of BSFC vs. BMEP and speed. Computational results agree well with experiments, demonstrating the utility of the approach in providing a more accurate prediction of the fuel consumption over test cycles.

An investigation on asset allocation and performance measurement for unit trust funds in Malaysia using multifactor model : a post crisis period analysis

This study examines the pattern of asset allocation and the performance of unit trust in Malaysia over the post crisis period by using risk-adjusted performance measures and multi-factor model from the year 2000 to 2004. Evidence from the statistics suggests that an active asset allocation strategy had been observed among Malaysian fund managers during the post Asian financial crisis. It is also suggested that investment allocation in equity remained a dominant vehicle for investment and asset allocation. Findings from multifactor model suggest that all funds of different objectives registered positive alphas except for income funds, with growth funds being among the top. While balanced funds registered highest diversification effectively, diversifying away about 70%-80% of unsystematic risk, the momentum factor is not among the important elements to explain unit trust performance in Malaysia.

What decision rules might pink-footed geese use to depart on migration? An individual-based model

Decisions taken during migration can have a large effect on the fitness of birds. Migration must be accurately timed with food availability to allow efficient fueling but is also constrained by the optimal arrival date at the breeding site. The decision of when to leave a site can be driven by energetics (sufficient body stores to fuel flight), time-related cues (internal clock under photoperiodic control), or external cues (temperature, food resources). An individual based model (IBM) that allows a mechanistic description of a range of departure decision rules was applied to the spring migration of pink-footed geese (Anser brachyrhynchus) from wintering grounds in Denmark to breeding grounds on Svalbard via 2 Norwegian staging sites. By comparing predicted with observed departure dates, we tested 7 decision rules. The most accurate predictions were obtained from a decision rule based on a combination of cues including the amount of body stores, date, and plant phenology. Decision rules changed over the course of migration with the external cue decreasing in importance and the time-related cue increasing in importance for sites closer to breeding grounds. These results are in accordance with descriptions of goose migration, following the “green-wave”: Geese track the onset of plant growth as it moves northward in spring, with an uncoupling toward the end of the migration if time is running out. We demonstrate the potential of IBMs to study the possible mechanisms underlying stopover ecology in migratory birds and to serve as tools to predict consequences of environmental change.

Backward modelling and look-ahead fuzzy energy management controller for a parallel hybrid vehicle

This paper focuses on a parallel hybrid electric vehicle. It first develops a model for the vehicle using the backward-looking approach where the flow of energy starts from wheels and spreads towards engine and electric motor. Next, a fuzzy logic-based strategy is developed to control the operation of the vehicle. The objectives of the controller include managing the energy flow from engine and electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines current vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. Then, it determines the best values for continuous variable transmission ratio, speed, and torque. A slope window scheme is also developed to take into account the look-ahead slope information and determine the best vehicle speed for better fuel economy. The developed model and control strategy are simulated. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces fuel consumption.

Look-ahead intelligent energy management of a parallel hybrid electric vehicle

Improving fuel efficiency in vehicles can reduce the energy consumption concerns associated with operating the vehicles. This paper presents a model for a parallel hybrid electric vehicle. In the model, the flow of energy starts from wheels and spreads toward engine and electric motor. A fuzzy logic based control strategy is implemented for the vehicle. The controller manages the energy flow from the engine and the electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines the vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. It then determines the best values for continuous variable transmission ratio, speed, and torque. A slope window method is formed that takes into account the look-ahead slope information, and determines the best vehicle speed. The developed model and control strategy are simulated using real highway data relating to Nowra-Bateman Bay in Australia, and SAE Highway Fuel Economy Driving Schedule. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces the fuel consumption of the vehicle.

Stochastic models of road geometry and wind condition for vehicle energy management and control

Modeling and simulation is commonly used to improve vehicle performance, to optimize vehicle system design, and to reduce vehicle development time. Vehicle performances can be affected by environmental conditions and driver behavior factors, which are often uncertain and immeasurable. To incorporate the role of environmental conditions in the modeling and simulation of vehicle systems, both real and artificial data are used. Often, real data are unavailable or inadequate for extensive investigations. Hence, it is important to be able to construct artificial environmental data whose characteristics resemble those of the real data for modeling and simulation purposes. However, to produce credible vehicle simulation results, the simulated environment must be realistic and validated using accepted practices. This paper proposes a stochastic model that is capable of creating artificial environmental factors such as road geometry and wind conditions. In addition, road geometric design principles are employed to modify the created road data, making it consistent with the real-road geometry. Two sets of real-road geometry and wind condition data are employed to propose probability models. To justify the distribution goodness of fit, Pearson's chi-square and correlation statistics have been used. Finally, the stochastic models of road geometry and wind conditions (SMRWs) are developed to produce realistic road and wind data. SMRW can be used to predict vehicle performance, energy management, and control strategies over multiple driving cycles and to assist in developing fuel-efficient vehicles.

The effect of plant tissue and vaccine formulation on the oral immunogenicity of a model plant-made antigen in sheep

Antigen-specific antibody responses against a model antigen (the B subunit of the heat labile toxin of enterotoxigenic Escherichia coli, LTB) were studied in sheep following oral immunisation with plant-made and delivered vaccines. Delivery from a root-based vehicle resulted in antigen-specific immune responses in mucosal secretions of the abomasum and small intestine and mesenteric lymph nodes. Immune responses from the corresponding leaf-based vaccine were more robust and included stimulation of antigen-specific antibodies in mucosal secretions of the abomasum. These findings suggest that oral delivery of a plant bioencapsulated antigen can survive passage through the rumen to elicit mucosal and systemic immune responses in sheep. Moreover, the plant tissue used as the vaccine delivery vehicle affects the magnitude of these responses.

Departure fuel loads in time-minimizing migrating birds can be explained by the energy costs of being heavy

Lindstrom and Alerstam presented a model that predicts optimal departure fuel loads as a function of the rate of fuel deposition in time-minimizing migrants. The basis of the model is that the coverable distance per unit of fuel deposited, diminishes with increasing fuel load. This is an effect of the increasing flight costs associated with increasing body mass. Lindstrom and Alerstam (1992) found that birds left at lower fuel loads than their model predicted for which they considered various ecological explanations. Alternatively, we hypothesize that the difference between prediction and empirical data might be a result of extra resting metabolic and transport costs associated with an increase in fuel load during stopover. We develop a new version of the Lindstrom and Alerstam (1992) model taking fuel load associated costs during stopover into account. We fit empirical data from rufous hummingbirds Selasphorus rufus and bluethroats Luscinia svecica to this new model. Estimated fuel-load costs are discussed in relation to knowledge presently available on variations in basal metabolic costs and transport costs with body mass. We show that fuel-load costs within a reasonable range can explain the observed departure fuel loads when migrating birds are time minimizers.

Rebound or resignation: developing a predictive model of return to subjective wellbeing set-point

Though subjective wellbeing (SWB) is generally stable and consistent over time, it can fall below its set-point in response to adverse life events. However, deviations from set-point levels are usually only temporary, as homeostatic processes operate to return SWB to its normal state. Given that income and close interpersonal relationships have been proposed as powerful external resources that are coincident with higher SWB, access to these resources may be an important predictor of whether or not a person is likely to recover their SWB following a departure from their set-point. Under the guiding framework of SWB Homeostasis Theory, this study considers whether access to a higher income and a committed partner can predict whether people who score lower than normal for SWB at baseline will return to normal set-point levels of SWB (rebound) or remain below the normal range (resigned) at follow-up. Participants were 733 people (53.3 % female) from the Australian Unity Longitudinal Wellbeing Study who ranged in age from 20 to 92 years (M = 59.65 years; SD = 13.15). Logistic regression analyses revealed that participants’ demographic characteristics were poor predictors of whether they rebounded or resigned. Consistent with homeostasis theory, the extent of departure from the proposed normal SWB set-point at baseline was significantly associated with rebound or resignation at time 2. These findings have implications for the way that SWB measures can be used in professional practice to identify people who are particularly vulnerable to depression and to guide the provision of appropriate and effective therapeutic interventions.