239 research outputs found
Conic optimisation for electric vehicle station smart charging with battery voltage constraints
This paper proposes a new convex optimisation
strategy for coordinating electric vehicle charging, which accounts for battery voltage rise, and the associated limits on
maximum charging power. Optimisation strategies for coordinating electric vehicle charging commonly neglect the increase
in battery voltage which occurs as the battery is charged.
However, battery voltage rise is an important consideration,
since it imposes limits on the maximum charging power. This is
particularly relevant for DC fast charging, where the maximum
charging power may be severely limited, even at moderate state
of charge levels. First, a reduced order battery circuit model is
developed, which retains the nonlinear relationship between state
of charge and maximum charging power. Using this model, limits
on the battery output voltage and battery charging power are
formulated as second-order cone constraints. These constraints
are integrated with a linearised power flow model for three-phase
unbalanced distribution networks. This provides a new multiperiod optimisation strategy for electric vehicle smart charging.
The resulting optimisation is a second-order cone program, and
thus can be solved in polynomial time by standard solvers. A
receding horizon implementation allows the charging schedule
to be updated online, without requiring prior information about
when vehicles will arrive
Energy-Based Acoustic Localization by Improved Elephant Herding Optimization
UIDB/EEA/50008/2020The present work proposes a new approach to address the energy-based acoustic localization problem. The proposed approach represents an improved version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located. Secondly, rather than letting elephants to simply wander around in their search for an update of the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that, in terms of localization accuracy, the proposed approach significantly outperforms the standard EHO one for low noise settings and matches the performance of an existing enhanced version of EHO (EEHO). Nonetheless, the proposed scheme achieves this accuracy with significantly less number of function evaluations, which translates to greatly accelerated convergence in comparison with EHO and EEHO. Finally, it is also worth mentioning that the proposed methodology can be extended to any population-based metaheuristic method (it is not only restricted to EHO), which tackles the localization problem indirectly through distance measurements.publishersversionpublishe
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks
Federated learning (FL) over resource-constrained wireless networks has
recently attracted much attention. However, most existing studies consider one
FL task in single-cell wireless networks and ignore the impact of
downlink/uplink inter-cell interference on the learning performance. In this
paper, we investigate FL over a multi-cell wireless network, where each cell
performs a different FL task and over-the-air computation (AirComp) is adopted
to enable fast uplink gradient aggregation. We conduct convergence analysis of
AirComp-assisted FL systems, taking into account the inter-cell interference in
both the downlink and uplink model/gradient transmissions, which reveals that
the distorted model/gradient exchanges induce a gap to hinder the convergence
of FL. We characterize the Pareto boundary of the error-induced gap region to
quantify the learning performance trade-off among different FL tasks, based on
which we formulate an optimization problem to minimize the sum of error-induced
gaps in all cells. To tackle the coupling between the downlink and uplink
transmissions as well as the coupling among multiple cells, we propose a
cooperative multi-cell FL optimization framework to achieve efficient
interference management for downlink and uplink transmission design. Results
demonstrate that our proposed algorithm achieves much better average learning
performance over multiple cells than non-cooperative baseline schemes.Comment: This work has been accepted by IEEE Journal on Selected Areas in
Communication
Optimal speed trajectory and energy management control for connected and automated vehicles
Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle).
The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles.
In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation.
The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces
Multi-user spatial diversity techniques for wireless communication systems
Multiple antennas at the transmitter and receiver, formally known as multiple-input
multiple-output (MIMO) systems have the potential to either increase the data rates
through spatial multiplexing or enhance the quality of services through exploitation
of diversity. In this thesis, the problem of downlink spatial multiplexing, where a
base station (BS) serves multiple users simultaneously in the same frequency band is
addressed. Spatial multiplexing techniques have the potential to make huge saving
in the bandwidth utilization. We propose spatial diversity techniques with and without
the assumption of perfect channel state information (CSI) at the transmitter.
We start with proposing improvement to signal-to-leakage ratio (SLR) maximization
based spatial multiplexing techniques for both fiat fading and frequency selective
channels. [Continues.
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