Drive cycle analysis of the performance of hybrid electric vehicles

 

Compounding factors and their effect on the design of long range battery electric vehicles

 It is well known that the one of the main problems concerning battery electric vehicles (BEVs) is their short range compared to conventional petrol and diesel vehicles. In this work the technical factors that will enable long range BEVs are investigated. The concept of Compounding Factors is presented and shows that if certain parameters can be met then BEV’s can have a comparable performance to conventional petrol cars. The development and initial testing of a long range BEW prototype is presented and discussed.

Autonomous underwater vehicles in a hazardous non-deterministic environment

This research investigated the cooperation of multi underwater robots to perform a task. This combined engineering design, electronics and consensus control to create systems capable of achieving the task. Challenges such as underwater radio communications were researched and a simulation framework was created and tested on virtual and real systems.

Adaptive intelligent systems for energy management of vehicles

The thesis demonstrated the architecture of adaptive intelligent systems for energy management that is capable of interacting with complex systems including the vehicle, environment, and driver components, as well as the interrelationships between these variables, to deliver fuel consumption improvements.

Power-cycle-library-based control strategy for plug-in hybrid electric vehicles

It has been demonstrated that considering the knowledge of drive cycle as a priori in the PHEV control strategy can improve its performance. The concept of power cycle instead of drive cycle is introduced to consider the effect of noise factors in the prediction of future drivetrain power demand. To minimize the effect of noise factors, a practical solution for developing a power-cycle library is introduced. A control strategy is developed using the predicted power cycle which inherently improves the optimal operation of engine and consequently improves the vehicle performance. Since the control strategy is formed exclusively for each PHEV rather than a preset strategy which is designed by OEM, the effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered for each PHEV. Simulation results show that the control strategy based on the driver library of power cycle would improve both vehicle performance and battery health.

Tomorrow’s car – for today’s people : can tilting three wheeled vehicles be a solution for the problems of today and the future?

The current automotive industry and todays car drivers are faced with every increasing challenges, not previously experienced. Climate Change, financial issues, rising fuel prices, increased traffic congestion and reduced parking space in cities are all leading to changes in consumer preferences and the requirements of modern passenger vehicles. However, despite the shift in the industry dynamics, the principal layout of a car hasn’t changed since its invention. The design of a ’conventional’ vehicle is still principally a matchbox with four wheels, one at each corner. The concept has served its purpose well for over 100 years, but such a layout is not suited to solving today’s problems. To address the range of problems faced by the industry, a number of alternative commuting vehicles have been developed. Yet the commercialization of these ‘alternative’ vehicles has yet to be successful. This is largely due failure of these vehicles to meet the changing demands of the industry and the limited understanding of consumer behaviour, motivation and attitudes. Deakin University’s Tomorrow’s Car concept tackles all of these problems. The vehicle is a novel three-wheeler cross over concept between a car and a motorbike that combines the best of both worlds. The vehicle combines the low cost, small size and ‘fun’ factor of a motorbike together with the safety, comfort and easy to drive features of a car produce a vehicle with a fuel efficiency better than either car or scooter. Intensive market research has been conducted for various major potential markets of alternative vehicles including India, China and Australia. The research analysed consumer attitudes in relation to narrow tilting vehicles, and in particular towards Deakin’s Tomorrow’s Car (TC). The study revealed that a relatively large percentage of consumers find such a concept very appealing. For the other consumers, the overall appearance and perception of safety and not the actual safety performance were found to be the most impeding factors of such vehicles. By addressing these issues and marketing the vehicle accordingly the successful commercialization of Tomorrow’s Car can be ensured.

Real-time detection of parked vehicles from multiple image streams

We present a system to detect parked vehicles in a typical commercial parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.

Adaptive cruise control look-ahead system for energy management of vehicles

Cruise control in motor vehicles enhances safe and efficient driving by maintaining a constant speed at a preset level. Adaptive Cruise Control (ACC) is the latest development in cruise control. It controls engine throttle position and braking to maintain a safe distance behind a vehicle in front by responding to the speed of this vehicle, thus providing a safer and more relaxing driving environment. ACC can be further developed by including the look-ahead method of predicting environmental factors such as wind speed and road slope. The conventional analytical control methods for adaptive cruise control can generate good results; however they are difficult to design and computationally expensive. In order to achieve a robust, less computationally expensive, and at the same time more natural human-like speed control, intelligent control techniques can be used. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based on ACC systems that reduces the energy consumption of the vehicle and improves its efficiency. The Adaptive Cruise Control Look-Ahead (ACC-LA) system works as follows: It calculates the energy consumption of the vehicle under combined dynamic loads like wind drag, slope, kinetic energy and rolling friction using road data, and it includes a look-ahead strategy to predict the future road slope. The cruise control system adaptively controls the vehicle speed based on the preset speed and the predicted future slope information. By using the ANFIS method, the ACC-LA is made adaptive under different road conditions (slope angle and wind direction and speed). The vehicle was tested using the adaptive cruise control look-ahead energy management system, the results compared with the vehicle running the same test but without the adaptive cruise control look-ahead energy management system. The evaluation outcome indicates that the vehicle speed was efficiently controlled through the look-ahead methodology based upon the driving cycle, and that the average fuel consumption was reduced by 3%.

Direct method for optimal power management in hybrid electric vehicles

Hybrid electric vehicles are powered by an electric system and an internal combustion engine. The components of a hybrid electric vehicle need to be coordinated in an optimal manner to deliver the desired performance. This paper presents an approach based on direct method for optimal power management in hybrid electric vehicles with inequality constraints. The approach consists of reducing the optimal control problem to a set of algebraic equations by approximating the state variable which is the energy of electric storage, and the control variable which is the power of fuel consumption. This approximation uses orthogonal functions with unknown coefficients. In addition, the inequality constraints are converted to equal constraints. The advantage of the developed method is that its computational complexity is less than that of dynamic and non-linear programming approaches. Also, to use dynamic or non-linear programming, the problem should be discretized resulting in the loss of optimization accuracy. The propsed method, on the other hand, does not require the discretization of the problem producing more accurate results. An example is solved to demonstrate the accuracy of the proposed approach. The results of Haar wavelets, and Chebyshev and Legendre polynomials are presented and discussed. © 2011 The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.

Effect of noise factors in energy management of series plug-in hybrid electric vehicles

It has been demonstrated that charge depletion (CD) energy management strategies are more efficient choices for energy management of plug-in hybrid electric vehicles (PHEVs). The knowledge of drive cycle as a priori can improve the performance of CD energy management in PHEVs. However, there are many noise factors which affect both drivetrain power demand and vehicle performance even in identical drive cycles. In this research, the effect of each noise factor is investigated by introducing the concept of power cycle instead of drive cycle for a journey. Based on the nature of the noise factors, a practical solution for developing a power-cycle library is introduced. Investigating the predicted power cycle, an energy management strategy is developed which considers the influence of temperature noise factor on engine performance. The effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered. Simulation results for a modelled series PHEV similar to GM Volt show that the suggested energy management strategy based on the driver power cycle library improves both vehicle fuel economy and battery health by reducing battery load and temperature.

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.

Glass/carbon fibre hybrid composite laminates for structural applications in automotive vehicles

Light-weight structure is one of the keys to improve the fuel efficiency and reduce the environmental buden of transport vehicles (automotive and rail). While fibreglass composites have been increasingly used to replace steel in automotive industry, the adoption rate for carbon fibre composites which are much lighter, stronger and stiffere than glass fibre composites, remains low. The main reason is the high cost of carbon fibres. To further reduce vehicle weight without excessive cost increase, one technique is to incorporate carbon fibre reinforcement into glass fibre composites and innovative design by selectively reinforcing along the main load path. Glass/carbon woven fabrics with epoxy resin matrix were utilised for preparing hybrid composite laminates. The in-plane mechanical properties such as tensile and three-point-bending flexural properties were investigated for laminates with different carbon fibre volume and lay-up scheme. It is shown that hybrid composite laminates with 50% carbon fibre reinforcement provide the best flexural properties when the carbon layers are at the exterior, while the alternating carbon/glass lay-up provides the highest compressive strength.