Battery Swapping Station management supported by Intelligent Transport Systems: an MPC approach.

Para lograr la completa integración del vehículo eléctrico (EV), es necesario solucionar varios problemas relacionados con el proceso de carga. Recientemente las “Battery Swapping Stations” o estaciones de intercambio de batería han surgido como una alternativa prometedora al planteamiento tradicional de las estaciones de carga de baterías (Battery Charging Stations). No obstante, las BSSs no están exentas de problemas que han de ser resueltos para que dicha estructura sea operacionalmente factible. Este proyecto propone un MPC como método de control de una BSS. En paralelo, este trabajo analiza la influencia de la precisión de la predicción de la demanda en el desempeño del control en términos de calidad de serivio (QoS) y beneficios; presenta un “Sistema de Transporte Inteligente” (ITS de sus siglas en inglés) para que las estaciones de carga tengan información en tiempo real de la congestión de tráfico y de su estado en los alrededores; y propone una estrategia de predicción basada en dicha información que posee un alto grado de robustez. Además, se desarrolla un entorno de simulación configurable que se utilizará como herramienta para testear las estrategias de control y de predicción de demanda presentados. Los resultados muestran el propio funcionamiento del algoritmo de control propuesto, la importancia de la exactitud de la predicción y detalla las ventajas que acarrea el uso de información en tiempo real en esos términos.For a successful Electric Vehicle (EV) integration, it is necessary to cover several problems related to the charging process. Recently, Battery Swapping Station (BSS) strategies are arising as a promising alternative to the traditional Battery Charging Station (BCS) approach since that provides a wider set of business opportunities. However, BSS approach are not exempt from new challenging issues that must be solved for achieving its operationally feasibility. This thesis proposes an MPC to control completely a BSS . In parallel, this work analyses the influence of the demand prediction accuracy in the control performance in terms of Quality of Service (QoS) and revenues; presents an Intelligent Transport System (ITS) for the BSSs to get real time information about the traffic in the surrounding area; and proposes a robust demand prediction strategy built on the aforementioned ITS approach. Furthermore, a configurable simulation environment is developed as a tool to test the strategies and demand prediction methods presented. The results show the proper working of the BSS managing algorithm proposed; the importance of the prediction accuracy, mainly in little congested BSSs ; as well as the advantages provided by using real time information in those terms

Torque distribution strategy for a four In-wheel fully electric car

Jornadas de Automática, 2 - 4 de septiembre de 2015. BilbaoElectromobility promises to have a strong impact in several aspects of our life: introducing new means of transport concepts, proposing new business models and allowing to create new vehicle configurations impossible with traditional combustion engines. Regarding the latter, this paper presents a novel torque distribution strategy for a 4 in-wheel electric vehicle which aims to reduce the total longitudinal slip. The control strategy is designed off-line supported by a simulator and tested both in simulation (with a different model from the used for designing) as well as on a real sized prototype. The results show that the total longitudinal slip is successfully reduced after applying the control strategy and additionally, the radius described by the vehicle while cornering is slightly closer to the theoretical Ackerman radius.Ministerio de Economía y Competitividad DPI2013-46912-C2-

Motion planning for CAVs in mixed traffic, a study on roundabouts

Driving is a very challenging task to automatize despite how naturally and efficiently it may come to experienced human drivers. The complexity stems from the need to (i) understand the surrounding context and forecast how it is likely to evolve, (ii) plan motions so that maneuvers can be performed with a certain level of anticipation despite the uncertainty of the future traffic state, and (iii) act on the throttle and the steering wheel to execute the planned motions accurately. These tasks match the major research topics concerning autonomous driving, namely perception and prediction, motion planning, and control. In this thesis, we study challenges related to motion planning and decision-making for connected automated vehicles (CAVs) in mixed traffic. That is for CAVs that coexist with human drivers, other CAVs, and unconnected automated vehicles (AVs). Even though we intend to formulate the proposed methods so that they are context agnostic, their assessment is carried out in roundabout scenarios. Roundabouts are ideal testing scenarios due to the complexity of the traffic interaction and overall traffic dynamics, the impact that uncertainty has on the coordination performance, as well as the strong influence that dynamic occlusions of the surroundings caused by nearby vehicles have on the decision-making process. We propose a novel approach concerning how an AV's surrounding space is represented and described, which brings benefits to the motion planning module. Unlike the classical planning approach based on object detection and avoidance, we study an alternative strategy based on free space identification and exploitation, which is shown to be a suitable mechanism to account for occlusions and other perception uncertainties. Our planning solutions are model-based and--inspired on the way human drivers seem to make decisions--aim to make safe yet efficient decisions without the need to explicitly explore all possible trajectories that can be followed. Instead, we propose a low-dimensional driving maneuver representation that enables us to characterize the solution-space of the decision-making problem at a high-level. In particular, a novel planning framework is presented in this thesis to address four significant planning aspects. Firstly, a reactive gap-acceptance behavior is formulated, which represents an appropriate baseline behavior despite its simplicity. Afterward, we investigate a decision-making approach for CAVs in fully connected scenarios, whereby CAVs would consider the impact of their decisions on the overall traffic before executing them. Then, we address the challenge of making AVs cooperate with other unconnected vehicles through a so-called implicitly cooperative mechanism. Furthermore, we present a predictive-reactive planning strategy where the challenge of planning motions taking into account longer traffic predictions, and the possibility of them being wrong is tackled. Finally, the suitability of some of the proposed theoretical results is assessed in a more realistic setup, where the methods are applied to real data provided by our industrial partners. This dissertation provides new ideas and methods to address the complexity of motion planning in mixed traffic. Specifically, we tackle the problem through a versatile motion planning framework and a set of pragmatic model-based decision-making strategies, paving the way towards feasible, efficient, and more reliable solutions

Merging into Single-Lane Roundabouts in the Presence of Uncertainty

Merging efficiently into roundabouts represents a challenge for autonomous vehicles due to the speed difference between merging traffic flows and the lack of certainty regarding drivers' intent, specially when the road is shared with human drivers and/or inter-vehicle communication is not available. We propose herein a strategy to merge into roundabouts, which is based on characterizing the set of merging trajectories that are safe w.r.t. the traffic on the circulatory lane and reachable by the ego vehicle. Our solution leverages the belief that some vehicles in the roundabout will exit the intersection following a non-conflicting path, and generates efficient merging trajectories without compromising safety. Moreover, our decision-making policy is formulated at a high level and does not involve explicitly generating any trajectory, whereby the required computational time remains sufficiently low. In simulation, our strategy brings benefits not only to the smoothness of the merging trajectories themselves but also to the overall traffic performance, which improves 25% w.r.t. a simpler reactive merging approach