Optimal Motion Control for Connected and Automated Electric Vehicles at Signal-Free Intersections

Traffic congestion is one of the major issues for urban traffic networks. The connected and autonomous vehicles (CAV) is an emerging technology that has the potential to address this issue by improving safety, efficiency, and capacity of the transportation system. In this paper, the problem of optimal trajectory planning of battery-electric CAVs in the context of cooperative crossing of an unsignalized intersection is addressed. An optimization-based centralized intersection controller is proposed to find the optimal velocity trajectory of each vehicle so as to minimize electric energy consumption and traffic throughput. Solving the underlying optimization problem for a group of CAVs is not straightforward because of the nonlinear and nonconvex dynamics, especially when the powertrain model is explicitly modelled. In order to ensure a rapid solution search and a unique global optimum, the optimal control problem (OCP) is reformulated via convex modeling techniques. Several simulation case studies show the effectiveness of the proposed approach and the trade-off between energy consumption and traffic throughput is illustrated

Symbol-level Precoding in MISO Broadcast Channels for SWIPT Systems

This work investigates a problem for joint transmit beamforming and receive power splitting in multiple-input single-output downlink systems under quality of service and power transfer constraints. Rather than suppressing interference as in conventional schemes, this work takes advantage of constructive interference among users, inherent in the downlink, as a source of both useful information signal energy and electrical wireless energy. Specifically, we propose a new data-aided precoding design that minimizes the transmit power while guaranteeing the quality of service (QoS) and energy harvesting constraints for generic phase shift keying modulated signals. The QoS constraints are modified to accommodate constructive interference, based on the constructive regions in the signal constellation. Although the resulting problem is nonconvex, we propose second-order cone programming algorithms with polynomial complexity that provide upper and lower bounds to the optimal solution and establish the asymptotic optimality of these algorithms when the modulation order and signal to interference-plus-noise ratio threshold tend to infinity. Simulation results show significant power savings with the proposed data-aided precoding approach compared to the conventional precoding scheme

A convex optimal control framework for autonomous vehicle intersection crossing

Cooperative vehicle management emerges as a promising solution to improve road traffic safety and efficiency. This paper addresses the speed planning problem for connected and autonomous vehicles (CAVs) at an unsignalized intersection with consideration of turning maneuvers. The problem is approached by a hierarchical centralized coordination scheme that successively optimizes the crossing order and velocity trajectories of a group of vehicles so as to minimize their total energy consumption and travel time required to pass the intersection. For an accurate estimate of the energy consumption of each CAV, the vehicle modeling framework in this paper captures 1) friction losses that affect longitudinal vehicle dynamics, and 2) the powertrain of each CAV in line with a battery-electric architecture. It is shown that the underlying optimization problem subject to safety constraints for powertrain operation, cornering and collision avoidance, after convexification and relaxation in some aspects can be formulated as two second-order cone programs, which ensures a rapid solution search and a unique global optimum. Simulation case studies are provided showing the tightness of the convex relaxation bounds, the overall effectiveness of the proposed approach, and its advantages over a benchmark solution invoking the widely used first-in-first-out policy. The investigation of Pareto optimal solutions for the two objectives (travel time and energy consumption) highlights the importance of optimizing their trade-off, as small compromises in travel time could produce significant energy savings

Decentralized Model Predictive Control for Automated and Connected Electric Vehicles at Signal-free Intersections

The development of connected and automated vehicles (CAVs) enables improvements in the safety, smoothness, and energy efficiency of the road transportation systems. This paper addresses the problem of optimally controlling batteryelectric CAVs crossing an unsignalized intersection subject to a first-in-first-out crossing policy. The optimal velocity trajectory of each vehicle that minimizes the average energy consumption and travel time, is found by a decentralized model predictive control (DMPC) method via a convex modeling framework so as to ensure computational efficiency and the optimality of the solution. Numerical examples and comparisons with a centralized control counterpart demonstrate the effectiveness of the proposed decentralized coordination scheme and the trade-off between energy consumption and travel time. Further investigation into the size of the sampling interval is also provided in order to show the validity of the method in practice

Optimal motion control for connected and automated electric vehicles at signal-free intersections

Traffic congestion is one of the major issues for urban traffic networks. The connected and autonomous vehicles (CAV) is an emerging technology that has the potential to address this issue by improving safety, efficiency, and capacity of the transportation system. In this paper, the problem of optimal trajectory planning of battery-electric CAVs in the context of cooperative crossing of an unsignalized intersection is addressed. An optimization-based centralized intersection controller is proposed to find the optimal velocity trajectory of each vehicle so as to minimize electric energy consumption and traffic throughput. Solving the underlying optimization problem for a group of CAVs is not straightforward because of the nonlinear and nonconvex dynamics, especially when the powertrain model is explicitly modelled. In order to ensure a rapid solution search and a unique global optimum, the optimal control problem (OCP) is reformulated via convex modeling techniques. Several simulation case studies show the effectiveness of the proposed approach and the trade-off between energy consumption and traffic throughput is illustrated

Secure SWIPT by Exploiting Constructive Interference and Artificial Noise

This paper studies interference exploitation techniques for secure beamforming design in simultaneous wireless information and power transfer (SWIPT) in multiple-input single-output (MISO) systems. In particular, multiuser interference (MUI) and artificially generated noise signals are designed as constructive to the information receivers (IRs) yet kept disruptive to potential eavesdropping by the energy receivers (ERs). The objective is to improve the received signal-to-interference and noise ratio (SINR) at the IRs by exploiting the MUI and AN power in an attempt to minimize the total transmit power. We first propose second-order cone programming based solutions for the perfect channel state information (CSI) case by defining strong upper and lower bounds on the energy harvesting (EH) constraints. We then provide semidefinite programming based solutions for the problems. In addition, we also solve the worst-case harvested energy maximization problem under the proposed bounds. Finally, robust beamforming approaches based on the above are derived for the case of imperfect CSI. Our results demonstrate that the proposed constructive interference precoding schemes yield huge saving in transmit power over conventional interference management schemes. Most importantly, they show that, while the statistical constraints of conventional approaches may lead to instantaneous SINR as well as EH outages, the instantaneous constraints of our approaches guarantee both constraints at every symbol period

On the Complexity of Congestion Free Routing in Transportation Networks

Traffic congestion has been proven a difficult problem to tackle, particularly in big cities where the number of cars are steadily increasing while the infrastructure remains stagnant. Several approaches have been proposed to alleviate the effects of traffic congestion, however, so far congestion is still a big problem in most cities. In this work we investigate a new route reservation approach to address the problem which is motivated by air traffic control. This paper formulates the route reservation problem under different assumptions and examines the complexity of the resulting formulations. Two waiting strategies are investigated, (i) vehicles are allowed to wait at the source before they start their journey, and (ii) they are allowed to wait at every road junction. Strategy (i) though more practical to implement, results to an NP-complete problem while strategy (ii) results to a problem that can be solved in polynomial time but it is not easily implemented since the infrastructure does not have adequate space for vehicles to wait until congestion downstream is cleared. Finally, a heuristic algorithm (based on time-expanded networks) is derived as a solution to both proposed waiting strategies. © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works C. Menelaou, P. Kolios, S. Timotheou and C. Panayiotou, "On the Complexity of Congestion Free Routing in Transportation Networks," 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, 2015, pp. 2819-2824. doi: 10.1109/ITSC.2015.453 Document type: Conference objec

Congestion Free Vehicle Scheduling Using a Route Reservation Strategy

Traffic congestion in big cities has been proven to be a difficult problem with adverse effects in terms of driver delay and frustration, cost and impact to the environment. Motivated by the approaches used in air-traffic control, this work investigates a method for controlling traffic congestion using time-dependent route reservation. The advances in information, communication and computation technologies has made such a reservation strategy feasible. This paper illustrates that the new reservation strategy is scalable and can be applied even to large metropolitan areas. To do so, we decompose the road network spatially and temporarily and propose a vehicle scheduling and routing algorithm which completely eliminate congestion. Simulation results show that the proposed approach is very promising. © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works. C. Menelaou, P. Kolios, S. Timotheou and C. G. Panayiotou, "Congestion Free Vehicle Scheduling Using a Route Reservation Strategy," 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, 2015, pp. 2103-2108. doi: 10.1109/ITSC.2015.340 https://www.ieee.org/publications_standards/publications/rights/rights_policies.html Document type: Conference objec

A congestion-free vehicle route reservation architecture

Transportation research is mainly focused on answering the question of how to eliminate traffic congestion over large scale areas. Inasmuch as a large portion of big cities suffers from traffic congestion with severe (in many cases) consequences on personal mobility. Drawbacks of congestion include driver delay and frustration, higher fuel consumption, air pollution and financial losses (in terms of man-hours lost on working days). Congestion has, traditionally, been a difficult problem to tackle since traffic demand fluctuates dynamically. The major cause of congestion is that a portion of the network is conferred to accommodate higher number of vehicles than its actual capacity. Nonetheless, congestion usually occurs due to lack of an efficient management of transport network utilization and not because demand exceeds network's capacity [1]. Therefore, it is possible to alleviate congestion if vehicles are more effectively distributed over the entire network achieving better load balancing. • "© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works. C. Menelaou, P. Kolios, S. Timotheou and C. G. Panayiotou, "A congestion-free vehicle route reservation architecture," 2016 18th Mediterranean Electrotechnical Conference (MELECON), Lemesos, 2016, pp. 1-6. doi: 10.1109/MELCON.2016.7495458 • https://www.ieee.org/publications_standards/publications/rights/rights_policies.html Document type: Conference objec

Energy management and control of photovoltaic and storage systems in active distribution grids

The evolution of power distribution grids from passive to active systems creates reliability and efficiency challenges to the distribution system operators. In this paper, an energy management and control scheme for managing the operation of an active distribution grid with prosumers is proposed. A multi-objective optimization model to minimize (i) the prosumers electricity cost and (ii) the cost of the grid energy losses, while guaranteeing safe and reliable grid operation is formulated. This is done by determining the active and reactive power set-points of the photovoltaic and storage systems integrated in the grid buildings. The resulting optimization model is non-convex, thus a convex second-order cone program is developed by appropriately relaxing the non-convex constraints which yields optimal results in most operating conditions. The convexified model is further utilized to develop an algorithm that yields feasible solutions to the non-convex problem under any operating conditions. Moreover, a second novel algorithm to find the operating point that provides fairness between the prosumers and the grid costs is proposed. Simulation results demonstrate the effectiveness and superiority of the proposed scheme in managing an industrial distribution grid compared to a self-consumption approach