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.

Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey.

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

Optimal coordination of G2V and V2G to support power grids with high penetration of renewable energy

Electric vehicles (EVs) have recently gained much popularity as a green alternative to fossil-fuel cars and a feasible solution to reduce air pollution in big cities. The use of EVs can also be extended as a demand response tool to support high penetration of renewable energy (RE) sources in future smart grid. Based on the certainty equivalent adaptive control (CECA) principle and a customer participation program, this paper presents a novel control strategy using optimization technique to coordinate not only the charging but also the discharging of EV batteries to deal with the intermittency in RE production. In addition, customer charging requirements and schedules are incorporated into the optimization algorithm to ensure customer satisfaction, and further improve the control performance. The merits of this scheme are its simplicity, efficiency, robustness and readiness for practical applications. The effectiveness of the proposed control algorithm is demonstrated by computer simulations of a power system with high level of wind energy integration.

Distributed generation capacity planning for distribution networks to minimize energy loss: an unbalanced multi-phase optimal power flow based approach

This chapter presents an unbalanced multi-phase optimal power flow (UMOPF) based planning approach to determine the optimum capacities of multiple distributed generation units in a distribution network. An adaptive weight particle swarm optimization algorithm is used to find the global optimum solution. To increase the efficiency of the proposed scheme, a co-simulation platform is developed. Since the proposed method is mainly based on the cost optimization, variations in loads and uncertainties within DG units are also taken into account to perform the analysis. An IEEE 123 node distribution system is used as a test distribution network which is unbalanced and multi-phase in nature, for the validation of the proposed scheme. The superiority of the proposed method is investigated through the comparisons of the results obtained that of a Genetic Algorithm based OPF method. This analysis also shows that the DG capacity planning considering annual load and generation uncertainties outperform the traditional well practised peak-load planning.

Using national input-output data for embodied energy analysis of individual residential buildings

Embodied energy (EE) analysis has become an important area of energy research, in attempting to trace the direct and indirect energy requirements of products and services throughout their supply chain. Typically, input-output (I-O) models have been used to calculate EE because they are considered to be comprehensive in their analysis. However, a major deficiency of using I-O models is that they have inherent errors and therefore cannot be reliably applied to individual cases. Thus, there is a need for the ability to disaggregate an I-O model into its most important 'energy paths', for the purpose of integrating case-specific data. This paper presents a new hybrid method for conducting EE analyses for individual buildings, which retains the completeness of the I-O model. This new method is demonstrated by application to an Australian residential building. Only 52% of the energy paths derived from the I-O model were substituted using case-specific data. This indicates that previous system boundaries for EE studies of individual residential buildings are less than optimal. It is envisaged that the proposed method will provide construction professionals with more accurate and reliable data for conducting life cycle energy analysis of buildings. Furthermore, by analysing the unmodified energy paths, further data collection can be prioritized effectively.

Learning to minimize energy costs and maximize mechanical work in a bimanual coordination task

The authors addressed the hypothesis that economy in motor coordination is a learning phenomenon realized by both reduced energy cost for a given workload and more external work at the same prepractice metabolic and attentional energy expenditure. "Self-optimization" of movement parameters has been proposed to reflect learned motor adaptations that minimize energy costs. Twelve men aged 22.3 [+ or -] 3.9 years practiced a 90[degrees] relative phase, upper limb, independent ergometer cycling task at 60 rpm, followed by a transfer test of unpracticed (45 and 75 rpm) and self-paced cadences. Performance in all conditions was initially unstable, inaccurate, and relatively high in both metabolic and attentional energy costs. With practice, coordinative stability increased, more work was performed for the same metabolic and attentional costs, and the same work was done at a reduced energy cost. Self-paced cycling was initially below the metabolically optimal, but following practice at 60 rpm was closer to optimal cadence. Given the many behavioral options of the motor system in meeting a variety of everyday movement task goals, optimal metabolic and attentional energy criteria may provide a solution to the problem of selecting the most adaptive coordination and control parameters.

Optimal provisioning theory and regulatory distortions

Firms must often invest in large-scale capacity expansions in order to service their expectation of future demand. This is known as provisioning. This paper introduces and examines issues and contributions in optimal provisioning theory. It discusses some regulatory distortions and suggests how an awareness of optimal provisioning can improve regulatory outcomes.

On the effect of path loss rates on the optimal receiver position in sensor networks

In this paper we examine the problem of finding an optimal position for a receiver node in a single-hop sensor network. The basic idea is to minimize the network energy consumption according to a particular network cost function. Our contribution is to simply show the effect of signal path loss rates and sensor weighting on the optimal receiver position.

Maintaining optimal co-channel interference for power efficient wireless communication

Maintaining QoS (quality of service) guaranteed communication links, and improving the energy consumption; are two aspects that received a significant consideration in the modern wireless sensor network research. This paper formulates a transmission power control problem which satisfies both considerations mentioned above. Moreover, a class of functions for an iterative controller was introduced and analyzed for its convergence. The experimental evaluation of the controller justifies the theoretical assertions as well as the applicability of the control scheme in wireless nodes with minimum measurement capabilities.

Performance optimization for energy-aware adaptive checkpointing in embedded real-time systems

Using additional store-checkpoinsts (SCPs) and compare-checkpoints (CCPs), we present an adaptive checkpointing for double modular redundancy (DMR) in this paper. The proposed approach can dynamically adjust the checkpoint intervals. We also design methods to calculate the optimal numbers of checkpoints, which can minimize the average execution time of tasks. Further, the adaptive checkpointing is combined with the DVS (dynamic voltage scaling) scheme to achieve energy reduction. Simulation results show that, compared with the previous methods, the proposed approach significantly increases the likelihood of timely task completion and reduces energy consumption in the presence of faults.

Longer guts and higher food quality increase energy intake in migratory swans

1.  Within the broad field of optimal foraging, it is increasingly acknowledged that animals often face digestive constraints rather than constraints on rates of food collection. This therefore calls for a formalization of how animals could optimize food absorption rates.

2.  Here we generate predictions from a simple graphical optimal digestion model for foragers that aim to maximize their (true) metabolizable food intake over total time (i.e. including nonforaging bouts) under a digestive constraint.

3.  The model predicts that such foragers should maintain a constant food retention time, even if gut length or food quality changes. For phenotypically flexible foragers, which are able to change the size of their digestive machinery, this means that an increase in gut length should go hand in hand with an increase in gross intake rate. It also means that better quality food should be digested more efficiently.

4.  These latter two predictions are tested in a large avian long-distance migrant, the Bewick's swan (Cygnus columbianus bewickii), feeding on grasslands in its Dutch wintering quarters.

5.  Throughout winter, free-ranging Bewick's swans, growing a longer gut and experiencing improved food quality, increased their gross intake rate (i.e. bite rate) and showed a higher digestive efficiency. These responses were in accordance with the model and suggest maintenance of a constant food retention time.

6.  These changes doubled the birds’ absorption rate. Had only food quality changed (and not gut length), then absorption rate would have increased by only 67%; absorption rate would have increased by only 17% had only gut length changed (and not food quality).

7.  The prediction that gross intake rate should go up with gut length parallels the mechanism included in some proximate models of foraging that feeding motivation scales inversely to gut fullness. We plea for a tighter integration between ultimate and proximate foraging models.

Local energy and pre-envelope

We observe that the local energy is the pre-envelope for analytic function. The maxima and phase of this function can be used to compute and classify visual features such as motion and stereo disparity, texture, etc. We examine the construction of new filters for computing Local Energy, and compare these filters with the Gabor filters and the three-point-filter of Venkatesh and Owens.

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.

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.