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

Enhancement of Charging Resource Utilization of Electric Vehicle Fast Charging Station with Heterogeneous EV Users

This thesis presents innovative charging resource allocation and coordination strategies that maximize the limited charging resources at FCS with heterogeneous EV users. It allows opportunistic EV users (OEVs) to exploit available charging resources with dynamic event-driven charging resource allocation and coordination strategies apart from primary EV users (PEVs) (registered or scheduled EV users). Moreover, developed strategies focus on the limited charging resources that are allocated for primary/ registered EV users (PEVs) of the FCS who access the FCS with specific privileges according to prior agreements. But the available resources are not optimally utilized due to various uncertainties associated with the EV charging process such as EV mobility-related uncertainties, EVSE failures, energy price uncertainties, etc. Developed strategies consider that idle chargers and vacant space for EVs at the FCS is an opportunity for further utilizing them with OEVs using innovative charging resource coordination strategies. This thesis develops an FCS-centric performance assessment framework that evaluates the performance of developed strategies in terms of charging resource utilization, charging completion and the quality of service (QoS) aspects of EV users. To evaluate QoS of EV charging process, various parameters such as EV blockage, charging process preemptage, mean waiting time, mean charging time, availability of FCS, charging reliability, etc are derived and analyzed. In addition, the developed innovative charging resource allocation and coordination strategies with resource aggregation and demand elasticity further enhance the charging resource utilization while providing a high QoS in EV charging for both PEVs and OEVs.publishedVersio

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Sustainable microgrid and electric vehicle charging demand for a smarter grid

textA “smarter grid” is expected to be more flexible and more reliable than traditional electric power grids. Among technologies required for the “smarter grid” deployment, this dissertation presents a sustainable microgrid and a spatial and temporal model of plug-in electric vehicle charging demand for the “smarter grid”. First, this dissertation proposes the dynamic modeling technique and operational strategies for a sustainable microgrid primarily powered by wind and solar energy resources. Multiple-input dc-dc converters are used to interface the renewable energy sources to the main dc bus. The intended application for such a microgrid is an area in which there is interest in achieving a sustainable energy solution, such as a telecommunication site or a residential area. Wind energy variations and rapidly changing solar irradiance are considered in order to explore the effect of such environmental variations to the intended microgrid. The proposed microgrid can be operated in an islanded mode in which it can continue to generate power during natural disasters or grid outages, thus improving disaster resiliency of the “smarter grid”. In addition, this dissertation presents the spatial and temporal model of electric vehicle charging demand for a rapid charging station located near a highway exit. Most previous studies have assumed a fixed charging location and fixed charging time during the off-peak hours for anticipating electric vehicle charging demand. Some other studies have based on limited charging scenarios at typical locations instead of a mathematical model. Therefore, from a distribution system perspective, electric vehicle charging demand is still unidentified quantity which may vary by space and time. In this context, this study proposes a mathematical model of electric vehicle charging demand for a rapid charging station. The mathematical model is based on the fluid dynamic traffic model and the M/M/s queueing theory. Firstly, the arrival rate of discharged vehicles at a charging station is predicted by the fluid dynamic model. Then, charging demand is forecasted by the M/M/s queueing theory with the arrival rate of discharged vehicles. The first letter M of M/M/s indicates that discharged vehicles arrive at a charging station with the Poisson distribution. The second letter M denotes that the time to charge each EV is exponentially distributed, and the third letter s means that there are s identical charging pumps at a charging station. This mathematical model of charging demand may allow grid’s distribution planners to anticipate charging demand at a specific charging station.Electrical and Computer Engineerin