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.

Coordinated energy management of vehicle air conditioning system

Whilst air conditioning systems increase thermal comfortableness in vehicles, they also raise the energy consumption of vehicles. Achieving thermal comfort in an energy-efficient way is a difficult task requiring good coordination between engine and the air conditioning system. This paper presents a coordinated energy management system to reduce the energy consumption of the vehicle air conditioning system while maintaining the thermal comfortableness. The system coordinates and manages the operation of evaporator, blower, and fresh air and recirculation gates to provide the desired comfort temperature and indoor air quality, under the various ambient and vehicle conditions, the energy consumption can then be optimized. Three simulations of the developed coordinated energy management system are performed to demonstrate its energy saving capacity.

The teacher educator as (re)negotiated professional : critical incidents in steering between state and market in Australia

A dominant discourse in western higher education circles is currently concerned— even obsessed—with the marketisation of knowledge as a commodity to be purchased and traded [Healy (1998); Poole (1998); Richardson (1998)]. These developments are broadly allied with managerial changes that some have called ‘steering at a distance’ [Kickert (1991); Marceav (1993)] whereby the control by the state of individual higher education workers is maintained and intensified at the same time that pressure is applied to 'wean' universities from government funding. This paper explores a different kind of 'steering', the kind that is being engaged by Australian teacher educators confronted by developing competitiveness in higher education. We argue that these changes compel teacher educators to (re)negotiate their professionalisms; to re-examine their attitudes towards, and values within, education and its practices as they (individually and collectively) steer new courses through the state and the market. We illustrate our argument by referring to three critical incidents in the professional lives of teacher educators located within a globalised, multi-campus and provincial Australian university, yet with important implications also for teacher educators outside Australia. We posit the (re)negotiated professionalisms manifested in those incidents as a few among several potential kinds of steering by Australian teacher educators.

Story as a vehicle for learning in online role play : a case study

In online role plays, students are asked to engage with a story that serves as a metaphor for real-life experience as they learn and develop skills. However, practitioners rarely examine the characteristics and management of this story as factors in the students' engagement in and learning from the activity. In this paper I present findings from a recent case study which examines these factors in an online role play that has been named as an exemplar and has been run for 19 years in Australian and international universities to teach Middle East politics and journalism. Online role plays are increasingly popular in tertiary education, in forms ranging from simple text-based role plays to virtual learning environment activities and e-simulations. The role play I studied required students to communicate in role via simulated email messages and draw on real-life resources and daily simulated online newspaper publications produced by the journalism students rather than rely on information or automated interactions built into an interface. This relatively simple format enabled me to observe clearly the impact of the technique's basic design elements. I studied both the story elements of plot, character and setting and the non-story elements of assessment, group work and online format. The data collection methods include analysis of student emails in the role play, a questionnaire, a focus group, interviews and the journal I kept as a participant-observer in the role play. In evaluating the qualities and impact of story elements I drew upon established aesthetic principles for drama and poststructuralist drama education.

Driver recognition based on dynamic handgrip pattern on steeling wheel

This paper proposes a novel biometric authentication method based on the recognition of drivers' dynamic handgrip on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect handgrip data exerted by the hands of drivers who intend to start the vehicle. Then, the likelihood-ratio-based classifier is designed to distinguish rightful driver of a car after analyzing their inherent dynamic features of grasping. The experimental results obtained in this study show that mean acceptance rates of 85.4% for the trained subjects and mean rejection rates of 82.65% for the un-trained ones are achieved by the classifier in the two batches of testing. It can be concluded that the driver verification approach based on dynamic handgrip recognition on steering wheel is a promising biometric technology and will be further explored in the near future in smart car design.

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.

Risk of falls and motor vehicle collisions in glaucoma

PURPOSE. To investigate the risk of falls and motor vehicle collisions (MVCs) in patients with glaucoma.

METHODS. The sample comprised 48 patients with glaucoma (mean visual field mean deviation [MD] in the better eye = −3.9 dB; 5.1 dB SD) and 47 age-matched normal control subjects, who were recruited from a university-based hospital eye care clinic and are enrolled in an ongoing prospective study of risk factors for falls, risk factors for MVCs, and on-road driving performance in glaucoma. Main outcome measures at baseline were previous self-reported falls and MVCs, and police-reported MVCs. Demographic and medical data were obtained. In addition, functional independence in daily living, physical activity level and balance were assessed. Clinical vision measures included visual acuity, contrast sensitivity, standard automated perimetry, useful field of view (UFOV), and stereopsis. Analyses of falls and MVCs were adjusted to account for the possible confounding effects of demographic characteristics, medications, and visual field impairment. MVC analyses were also adjusted for kilometers driven per week.

RESULTS. There were no significant differences between patients with glaucoma and control subjects with respect to number of systemic medical conditions, body mass index, functional independence, and physical activity level (P > 0.10). At baseline, 40 (83%) patients with glaucoma and 44 (94%) control subjects were driving. Compared with control subjects, patients with glaucoma were over three times more likely to have fallen in the previous year (odds ratio [OR]adjusted = 3.71; 95% CI, 1.14–12.05), over six times more likely to have been involved in one or more MVCs in the previous 5 years (ORadjusted = 6.62; 95% CI, 1.40–31.23), and more likely to have been at fault (ORadjusted = 12.44; 95% CI, 1.08–143.99). The strongest risk factor for MVCs in patients with glaucoma was impaired UFOV selective attention (ORadjusted = 10.29; 95% CI, 1.10–96.62; for selective attention >350 ms compared with ≤350 ms).

CONCLUSIONS. There is an increased risk of falls and MVCs in patients with glaucoma.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

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.