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.

Electric field analysis of spinneret design for needleless electrospinning of nanofibers

Concentrated electric field is crucial in generation of needleless electrospinning; the electric field profile together with electric field intensity of the spinneret directly affect the needleless electrospinning performance. Understanding the electric field of different spinnerets would definitely benefit the design and optimization of needleless electrospinning. Three-dimensional (3D) finite element analysis has been used to analyze the electric field profile and electric field intensity of different spinnerets for needleless electrospinning by using the simulation software COMSOL Multiphysics 3.5a. It has been found that evolution of the spinneret of needleless electrospinning from cylinder to multiple disks and then to multiple rings results in stronger and more concentrated electric field. The analysis based on 3D simulation of the electric field could benefit further development of needleless electrospinning in which the production rate and quality of as-spun nanofibers are of great importance.

Comparative study on MoO3 and HxMoO3 nanobelts : structure and electric transport

In this study, the suspension of MoO3 nanobelts was first prepared in a hydrothermal way from Mo powders and H2O2 solution, which could be transformed into the suspension of HxMoO3 nanobelts under an acidic condition using N2H4 ·H2O as the reducing agent. Three paper-form samples made from MoO3 and HxMoO3 nanobelts (low or high hydrogen content) were then fabricated via a vacuum filtration method, followed by their structural comparative analysis such as FESEM, XRD, Raman spectra, and XPS, etc. The measurement of electric resistances at room temperature shows that the conductance of HxMoO3 nanobelts is greatly improved because of hydrogen doping. The temperature-dependent resistances of HxMoO3 nanobelts agree with the exponential correlation, supporting that the conducting carriers are the quasi-free electrons released from Mo5+. In addition, the formation process of HxMoO3 nanobelts from MoO3 nanobelts is also discussed.

Intelligent control and look-ahead energy management of hybrid electric vehicles

A review of the state of knowledge in the field of control and energy management in HEVs is carried out. The key innovation of the project is the development of a model of a PHEV using the real road data with an intelligent look-ahead online controller. Another novelty of this work is the method of route planning. It combines the information of vehicle sensors such as accelerometer and speedometer with the data of a GPS to create a road grade map for use within the look-ahead energy management strategy in the vehicle. For the PHEV, an adaptive cruise controller is modelled and an optimisation method is applied to obtain the best speed profile during a trajectory. Finally, the nonlinear model of the vehicle is applied with the sliding mode controller. The effect of using this controller is compared with the universal cruise controller. The stability of the system is studied and proved.

Effects of mechanical deformation on electric performance of rechargeable batteries embedded in load carrying composite structures

Integrating rechargeable battery cells with fibre reinforced polymer matrix composites is a promising technology to enable composite structures to concurrently carry load and store electric energy, thus significantly reducing weight at the system level. To develop a design criterion for structural battery composites, rechargeable lithium polymer battery cells were embedded into carbon fibre/epoxy matrix composite laminates, which were then subjected to tensile, flexural and compressive loading. The electric charging/discharging properties were measured at varying levels of applied loads. The results showed that degradation in battery performance, such as voltagea and energy storage capacity, correlated well with the applied strain under three different loading conditions. Under compressive loading, battery cells, due to their multilayer construction, were unable to prevent buckling of composite face sheets due to the low lateral stiffness, leading to lower compressive strength that sandwich panels with foam core.

Integrated thermal and energy management of plug-in hybrid electric vehicles

In this research, the significant influence of engine and cabin thermal management on the fuel efficiency and emissions of plug-in hybrid electric vehicles is investigated. A practical solution to implement an optimal energy management strategy of plug-in hybrid electric vehicles which considers the temperature noise factor is introduced.

Modern kitchens : and what their owners say of the speed, comfort, economy and convenience of electric cooking

"In the pages of this booklet we have given you actual quotations from a few of the many letters which we have recived from delighted users of Electric Ranges." -- blurb.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

Electric contributions to magnetic force microscopy response from graphene and MoS2 nanosheets

Magnetic force microscopy (MFM) signals have recently been detected from whole pieces of mechanically exfoliated graphene and molybdenum disulfide (MoS2) nanosheets, and magnetism of the two nanomaterials was claimed based on these observations. However, non-magnetic interactions or artefacts are commonly associated with MFM signals, which make the interpretation of MFM signals not straightforward. A systematic investigation has been done to examine possible sources of the MFM signals from graphene and MoS2 nanosheets and whether the MFM signals can be correlated with magnetism. It is found that the MFM signals have significant non-magnetic contributions due to capacitive and electrostatic interactions between the nanosheets and conductive cantilever tip, as demonstrated by electric force microscopy and scanning Kevin probe microscopy analyses. In addition, the MFM signals of graphene and MoS2 nanosheets are not responsive to reversed magnetic field of the magnetic cantilever tip. Therefore, the observed MFM response is mainly from electric artefacts and not compelling enough to correlate with magnetism of graphene and MoS2 nanosheets.