Abstract

In this thesis, we present several context-aware and collaborative applications of electric and plug-in hybrid vehicles in the context of intelligent transportation systems, with a main focus on the design of control and optimisation algorithms to maximise the performance of such vehicles in different practical scenarios. This gives rise to four topics to be discussed in the thesis. The first topic focuses on the design of speed advisory systems for different road users. In Chapter 3, we present a framework for minimising the energy consumptions for a group of electric vehicles in a distributed manner. Using this framework, we extend the ideas of this design to the case of cyclists, where now we maximise the overall health benefits for a group of cyclists sharing a common route. In both cases, we apply a recently derived consensus mathematical result for solving both convex and quasi-convex optimisation problems with consensus constraints. The efficacy of our proposed algorithm is verified through many simulation studies. The second topic is concerned with a new design of the energy management system for plug-in hybrid electric vehicles (PHEVs) by taking into account the availability of the upcoming renewable energy generation. In Chapter 4, we introduce distributed algorithms for PHEVs to switch on/off their electric motors such that some utility functions can be maximised while achieving a demand and supply balance for power grids when vehicles travel back for recharging. This idea is then extended for the case of plug-in hybrid electric buses (PHEBs) in Chapter 5, focusing on maximising the environmental benefits of buses with some energy constraints to be satisfied. The third topic introduces a novel context-aware engine management system for PHEVs to optimally orchestrate switching between different operational modes so that the environmental benefits on pedestrians can be reached to a maximum. In Chapter 6, we present details of our design for such a system taking account of many factors in practice. We implement the proposed system in a hardware-in-the-loop platform, embedded with a real PHEV, to illustrate the efficacy of our proposed approach. The last topic investigates the ability of simple macroscopic information to identify changes in nominal urban traffic flows. In Chapter 7, we focus on using junction turning probabilities to infer the occurrence of anomalies in traffic patterns. Finally, several simulation studies are conducted in a popular mobility simulator to demonstrate the capabilities of our proposed method.

Abstract

The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network’s voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation. Document type: Article