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

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

Real-time Autonomous Cruise Control of Connected Plug-in Hybrid Electric Vehicles Under Uncertainty

Advances in embedded digital computing and communication networks have enabled the development of automated driving systems. Autonomous cruise control (ACC) and cooperative ACC (CACC) systems are two popular types of these technologies, which can be implemented to enhance safety, traffic flow, driving comfort and energy economy. This PhD thesis develops robust and adaptive controllers for plug-in hybrid electric vehicles (PHEVs), with the Toyota Plug-in Prius as the baseline vehicle, in order to enable them to perform safe and robust car-following and platooning with improved vehicle performance. Three controllers are designed here to achieve three main goals. The first goal of this thesis is the development of a real-time Ecological ACC (Eco-ACC) system for PHEVs, that is robust to uncertainties. A novel adaptive tube-based nonlinear model predictive control (AT-NMPC) approach to the design of Eco-ACC systems is proposed. Through utilizing two separate models to define the constrained optimal control problem, this method takes into account uncertainties, modeling errors and delayed data in the design of the controller and guaranties robust constraint handling for the assumed uncertainty bounds. {In addition, it adapts to changes in order to improve the control performance when possible.} Furthermore, a Newton/GMRES fast solver is employed to implement the designed AT-NMPC in real-time. The second goal is the development of a real-time Ecological CACC (Eco-CACC) system that can simultaneously satisfy the frequency-domain and time-domain platooning criteria. A novel distributed reference governor (RG) approach to the constraint handling of vehicle platoons equipped with CACC is presented. RG sits behind the controlled string stable system and keeps the output inside the defined constraints. Furthermore, to improve the platoon's energy economy, a controller is presented for the leader's control using NMPC method, assuming it is a PHEV. The third objective of this thesis is the control of heterogeneous platoons using an adaptive control approach. A direct model reference adaptive controller (MRAC) is designed that enforces a string stable behavior on the vehicle platoon despite different dynamical models of the platoon members and the external disturbances acting on the systems. The proposed method estimates the controller coefficients on-line to adapt to the disturbances such as wind, changing road grade and also to different vehicle dynamic behaviors. The main purpose of all three controllers is to maintain the driving safety of connected vehicles in car-following and platooning while being real-time implementable. In addition, when there is a possibility for performance enhancement without sacrificing safety, ecological improvement is also considered. For each designed controller, Model-in-the-Loop (MIL) simulations and Hardware-in-the-Loop (HIL) experiments are performed using high-fidelity vehicle models in order to validate controllers' performance and ensure their real-time implementation capability

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Ecological Control and Coordination of Connected and Automated PHEVs at Roundabouts under Uncertainty

During the last decade, comprehensive research efforts were concentrated on autonomous driving. Annually, many car accidents happen as a result of human faults. Extreme traffic congestion prolongs commute time, increase air pollution and cause other transportation inefficiencies. Consequently, using advanced technologies to make vehicles less dependent on human drivers enable more efficient use of time for passengers and decrease car accidents. Connectivity between vehicles and automation provides a spectacular opportunity to improve traffic flow, safety, and efficiency. There are different main active research subjects under the broad domain of autonomous driving, one of them is intersection control for connected and automated vehicles (CAVs) which can be categorized into centralized and decentralized approaches. The environmental and strict regulatory demands require automotive companies to reduce Carbon Dioxide emissions by investing more in Electric Vehicles (EVs) and Plug-in Hybrid Electric vehicles (PHEVs). A PHEV equipped with connectivity and automation looks more interesting to automobile consumers since they can have advantages of both fewer emissions and enhanced abilities. Since the powertrain of PHEVs consists of different sources of power, advanced control techniques such as Model Predictive Control (MPC) is needed. Coordination of vehicles at roundabouts is a demanding problem especially by knowing that the chance of both lateral and longitudinal collision exists. To this end, first, we proposed a centralized nonlinear MPC-based controller to adhere to calculated priorities for connected and automated PHEVs (CA-PHEVs). We further continued this research by proposing an approach for solving nonlinear multi-objective optimal control problem of decentralized coordination of CA-PHEVs at roundabouts with consideration of fuel economy. It was found that the proposed controller can calculate priority based on a navigation function and provide a safe gap between vehicles. A novel priority calculation logic based on optimal control is proposed as well and its performance is compared with the navigation function approach. In addition to the decentralized control approach, we considered a more realistic robust tube-based nonlinear MPC decentralized approach to solve this problem in the presence of uncertainties. We used simulations to test the controller and a Toyota Prius PHEV high-fidelity model is used in this thesis for simulations. Simulation results show that the addition of robustness, and energy economy to performance index can improve the fuel consumption of the vehicle. One of the major concerns in designing a controller for automotive applications is real-time implementation. The results of hardware-in-the-loop experiments show the real-time implementation of the controllers

Validation of trajectory planning strategies for automated driving under cooperative, urban, and interurban scenarios.

149 p.En esta Tesis se estudia, diseña e implementa una arquitectura de control para vehículos automatizados de forma dual, que permite realizar pruebas en simulación y en vehículos reales con los mínimos cambios posibles. La arquitectura descansa sobre seis módulos: adquisición de información de sensores, percepción del entorno, comunicaciones e interacción con otros agentes, decisión de maniobras, control y actuación, además de la generación de mapas en el módulo de decisión, que utiliza puntos simples para la descripción de las estructuras de la ruta (rotondas, intersecciones, tramos rectos y cambios de carril)Tecnali

Design, Modeling and Control of a Thermal Management System for Hybrid Electric Vehicles

Hybrid electric vehicle (HEV) technology has evolved in the last two decades to become economically feasible for mass produced automobiles. With the integration of a lithium battery pack and electric motors, HEVs offer a significantly higher fuel efficiency than traditional vehicles that are driven solely by an internal combustion engine. However, the additional HEV components also introduce new challenges for the powertrain thermal management system design. In addition to the common internal combustion engine, the battery pack, the generator(s), as well as the electric motor(s) are now widely applied in the HEVs and have become new heat sources and they also require proper thermal management. Conventional cooling systems have been typically equipped with a belt driven water pump and radiator fan, as well as other mechanical actuators such as the thermostat valve. The operation of these components is generally determined by the engine speed. This open-loop cooling strategy has a low efficiency and suffers the risk of over-cooling the coolant and components within the system. In advanced thermal management systems, the mechanical elements are upgraded by computer controlled actuators including a servo-motor driven pump, variable speed fans, a smart thermostat, and an electric motor driven compressor. These electrified actuators offer the opportunity to improve temperature tracking and reduce parasitic losses. This dissertation investigates a HEV powertrain thermal management system featuring computer controlled cooling system actuators. A suite of mathematical models have been created to describe the thermal behaviour of the HEV powertrain components. Model based controllers were developed for the vehicle\u27s cooling systems including the battery pack, electric motors, and internal combustion engine. Optimal control theory has been applied to determine the ideal battery cooling air temperature and the desired heat removal rate on e-motor cooling surface. A model predictive controller(MPC) was developed to regulate the refrigerant compressor and track the battery cooling air temperature. A series of Lyapunov-based nonlinear controllers have been implemented to regulate the coolant pumps and radiator fans in the cooling systems for the engine and e-motors. Representative numerical results are presented and discussed. Overall, the proposed control strategies have demonstrated the effectiveness in improving both the temperature tracking performance and the cooling system power consumption reduction. The peak temperature error in the selected A123 battery core can be tracked within 0.25 C of the target; a 50% reduction of the vapor compression system energy consumption can be obtained by properly designing the cooling air flow structure. Similarly, the cooling system of HEV electric motors shows that the machine internal peak temperature can be tracked to the target value with a maximum error of 3.9 C and an average error of 0.13 C. A 70% to 81% cooling system energy consumption reduction can be achieved under different driving cycle comparing to classical controller applied to maintain a similar level of hotspot temperature stabilization. The proposed optimal nonlinear controller tracks the engine coolant temperature with an average error of 0.35 C and at least 13% reduction in engine cooling power. Further, a close analysis on the cooling system energy consumption reduction has been conducted with a heat exchanger simulation tool established for cooling system design optimization. This research has developed the basis for the holistic control of HEV powertrain thermal management systems by including a suite of model based nonlinear controllers to simultaneously regulate the cooling actuators for the battery pack, e-motors, and conventional internal combustion engine. Numerical studies has been conducted with a high fidelity HEV model under real driving cycles to demonstrate the advantages of introducing advanced control theory into multi-mode vehicle drive systems