Designing a Real-time Velocity Predictor for Powertrain Optimization of Connected and Automated Vehicles PDF Download

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Languages : en
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Abstract : Connected and Automated Vehicles (CAVs) coupled with Intelligent Transportation Systems (ITS) have been able to impact significantly to the transportation and automotive sector by improving traffic mobility, increasing fuel efficiency and reducing emissions. The research intends to develop optimization algorithms by utilizing the velocity bounds provided by a traffic simulation program and generate an optimal velocity trajectory to reduce power-losses and improve drivability in vehicles. The developed optimal velocity trajectory algorithms are modified for the applications of Eco -Approach and Departure (Eco A/D) at signalized intersections and Co-operative Adaptive Cruise Control (CACC). The fuel consumption during Eco-A/D is minimized by reducing idling times at traffic intersections. The CACC algorithm allows vehicles in a platoon to maintain a closer inter-vehicular gap and improve the efficiency of the platoon. Lastly, the simulation results generated by test cases are presented and future work is discussed to translate the simulation-based results to real-world improvement.

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Languages : en
Pages : 0

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Abstract : This dissertation focuses on advancing Predictive Energy Management (PrEM) functions applied to modern connected and automated vehicles (CAV) cohorts. PrEM aims to utilize connectivity and ADAS functions to adaptively minimize vehicle energy consumption in a wide array of operations, extending the original control designed around a reduced set of test cycle procedures to adapt to real-world stochastic operating conditions. This research document is built upon three journal publications covering two PrEM schemes; the global cohort and local vehicle optimization paths. Both optimal control solutions are generated using various Neuroevolution centric processes. Chapter 1 discusses the methods and reasoning behind the need to increase the development speed of readily implementable optimal control functions for both complex and system-of-systems (SoS) applications. Neuroevolution allows for fast development time, optimal design space exploration, high-fidelity modeling usage, and seamless integration with data science processes. It additionally enables real-time implementation without modification and requires a low compute footprint. This provides a new paradigm for future automotive product development where conventional adaptive and optimal techniques deployment is still lagging due to their complexity and shortcomings. At the global level, vehicle energy consumption is minimized by optimally controlling vehicle speed in diverse environments. Chapters 2 and 3 relate to connected traffic lights and uncontrolled intersection operations respectively. In the first study, the CAV cohort optimizes its velocity based on connected traffic light information. Thanks to the Traffic Technology Services (TTS) network, this information is shared via cellular communication. Energy consumption reduction of up to 22\% is reported using simulation and during closed-loop track testing. In the second study, no such timing information exists, and the cohorts must collaborate to enable safe operation at uncontrolled intersections. Here, the cohorts share states' information to minimize deceleration and acceleration events for comfort and energy savings, primarily focusing on safety. Simulation demonstrates that effective collaboration can be achieved with cohorts' lengths of up to 100 meters in congested environments. At the local PrEM level, additional energy savings can be achieved for each specific cohort's vehicle based on its powertrain architecture. One of the more complex and relevant architectures to apply localized PrEM to are hybrid electric vehicles (HEV), where two sources of energy can be blended optimally based on a vehicle's predicted speed profile, which is directly controlled by the global PrEM optimization function. In Chapter 4, Neuroevolution and vehicle speed profile classification is applied to a P3 HEV in demonstrating significant additional energy consumption improvements.

Author: Antonio Sciarretta
Publisher: Springer
ISBN: 3030241270
Category : Technology & Engineering
Languages : en
Pages : 294

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This book elaborates the science and engineering basis for energy-efficient driving in conventional and autonomous cars. After covering the physics of energy-efficient motion in conventional, hybrid, and electric powertrains, the book chiefly focuses on the energy-saving potential of connected and automated vehicles. It reveals how being connected to other vehicles and the infrastructure enables the anticipation of upcoming driving-relevant factors, e.g. hills, curves, slow traffic, state of traffic signals, and movements of nearby vehicles. In turn, automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and to save energy. Lastly, the energy-efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. Building on classical methods of powertrain modeling, optimization, and optimal control, the book further develops the theory of energy-efficient driving. In addition, it presents numerous theoretical and applied case studies that highlight the real-world implications of the theory developed. The book is chiefly intended for undergraduate and graduate engineering students and industry practitioners with a background in mechanical, electrical, or automotive engineering, computer science or robotics.

Author: Shreshta Rajakumar Deshpande
Publisher:
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Category : Automated vehicles
Languages : en
Pages : 0

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Global CO2 emissions regulations, in conjunction with increasing customer demands are requiring significant improvements in vehicle energy (or fuel) efficiency. In this drive to reduce fuel consumption, improvements in the powertrain (or propulsion system) continue to be a major area of focus, particularly shifting to higher levels of electrification. A next step in the evolution of improving fuel efficiency is to have the propulsion system controller make use of vehicle-level information. In this context, Connected and Automated Vehicle (CAV) technologies offer the potential for enhancing the vehicle fuel efficiency as well as improving vehicle safety and comfort by leveraging information from advanced mapping and location, and Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. The focus of this thesis is to develop Dynamic Programming (DP) and Approximate Dynamic Programming (ADP) based approaches that combine the energy-saving potentials of powertrain electrification and CAV technologies, and further compound them. In this work, an ADP-based scheme is used to jointly optimize the vehicle velocity and energy management strategy of an electrified CAV over real-world driving routes. This predictive controls framework uses preview information from the route and environment to achieve significant fuel efficiency improvements even in the presence of variabilities (such as driver aggressiveness and varying traffic signal information). The controller was then implemented and tested in a demonstration vehicle at a proving ground facility over reconstructed route scenarios. Further, this thesis explores approaches to reducing the computational complexity of optimization methods based on Dynamic Programming, which can restrict its use in many real-time applications. To this end, two sub-optimal methodologies are proposed. One of them, the integrated DP-ECMS (Dynamic Programming-Equivalent Consumption Minimization Strategy) method embeds a heuristic strategy within the DP framework. In doing so, the resulting implementation is only marginally sub-optimal compared to the (original) DP, while mitigating the curse of dimensionality. The second method proposed to reduce computation time is the WASP (Warm Start Dynamic Programming) algorithm. Specifically, the solution to a perturbed receding horizon optimal control problem was computed in an approximately optimal manner, by making use of the value function and other properties of the original (unperturbed) DP solution. Its efficacy is demonstrated through application in simplified dynamic optimization problems.

Author: Shobhit Gupta
Publisher:
ISBN:
Category : Automated vehicles
Languages : en
Pages :

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Increasing cost of fuel and global regulatory targets are driving the automotive industry towards fuel efficient vehicles. Hybrid electric vehicles (HEVs) can significantly improve the fuel economy by the application of an efficient control strategy. Additionally, the look-ahead information available from advanced driver assistance systems and cloud applications in a connected and automated vehicle can make the powertrain more predictive in nature. This would enable the implementation of a global optimization algorithm such as Dynamic Programming (DP). In this thesis, DP is implemented to co-optimize the vehicle velocity and energy management of a 48V mild-HEV over real world driving scenarios. Velocity optimization is performed by considering the look-ahead route characteristics such as the speed limit constraints along with the position of traffic lights and stop signs. To enable close to real-time implementation of DP, efforts have been put to alleviate the well-known "Curse of Dimensionality." A variable step size strategy is adopted instead of a constant step size. Furthermore, this thesis aims at building the Rollout Algorithm using Approximate Dynamic Programming for the 48V optimal control problem. This algorithm yields a look-ahead suboptimal control policy and under certain conditions, the sub-optimality can be minimized which is shown in this thesis. To compare the benefits obtained from the rollout, an experimentally validated driver model is developed which serves as the baseline for this project.

Author: Vipin Kumar Kukkala
Publisher: Springer Nature
ISBN: 3031280164
Category : Technology & Engineering
Languages : en
Pages : 782

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This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles.

Author: Stephen Boyle (Mechanical engineer)
Publisher:
ISBN:
Category : Automated vehicles
Languages : en
Pages : 0

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Connected and Autonomous vehicles (CAVs) use knowledge of road topography, traffic signal timing, and traffic conditions, obtained via vehicle-to-infrastructure communication, vehicle-to-vehicle communication, and sensors to improve fuel economy through predictive strategies, including velocity trajectory optimization and optimal traffic light arrival and departure. These powertrain control strategies operate on a slow timescale relative to the engine controller and assume that the engine torque production is instantaneous, hence the engine fast dynamics are neglected and the engine torque is saturated using a static map based on engine speed. Such inconsistencies in the timescales can result is poor drivability, constraint violation, actuator saturation, poor tracking performance, decreased powertrain efficiency, and increased emissions. Current mitigation strategies include input shapers and reference governors, but these controller are not fuel optimal and might become unstable if a delay is introduced. One possible solution is to use a Model Predictive Control (MPC) scheme to as a supplemental control layer between the supervisor and OEM controllers. Control problems dealing with multiple timescales can be challenging to solve quickly enough for real-time implementation at a sampling time consistent with the low level, OEM vehicle controllers. Therefore, the problem is decomposed based on time-scale and transformed into a hierarchical control problem.

Author: Muhammad H. Rashid
Publisher: Elsevier
ISBN: 0323950981
Category : Technology & Engineering
Languages : en
Pages : 370

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Handbook of Power Electronics in Autonomous and Electric Vehicles provides advanced knowledge on autonomous systems, electric propulsion in electric vehicles, radars and sensors for autonomous systems, and relevant aspects of energy storage and battery charging. The work is designed to provide clear technical presentation with a focus on commercial viability. It supports any and all aspects of a project requiring specialist design, analysis, installation, commissioning and maintenance services. With this book in hand, engineers will be able to execute design, analysis and evaluation of assigned projects using sound engineering principles and commercial requirements, policies, and product and program requirements. - Presents core power systems and engineering applications relevant to autonomous and electric vehicles in characteristic depth and technical presentation - Offers practical support and guidance with detailed examples and applications for laboratory vehicular test plans and automotive field experimentation - Includes modern technical coverage of emergent fields, including sensors and radars, battery charging and monitoring, and vehicle cybersecurity

Author: Xi Zhang
Publisher: Springer Science & Business Media
ISBN: 0857297368
Category : Technology & Engineering
Languages : en
Pages : 353

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Vehicle Power Management addresses the challenge of improving vehicle fuel economy and reducing emissions without sacrificing vehicle performance, reliability and durability. It opens with the definition, objectives, and current research issues of vehicle power management, before moving on to a detailed introduction to the modeling of vehicle devices and components involved in the vehicle power management system, which has been proven to be the most cost-effective and efficient method for initial-phase vehicle research and design. Specific vehicle power management algorithms and strategies, including the analytical approach, optimal control, intelligent system approaches and wavelet technology, are derived and analyzed for realistic applications. Vehicle Power Management also gives a detailed description of several key technologies in the design phases of hybrid electric vehicles containing battery management systems, component optimization, hardware-in-the-loop and software-in-the-loop. Vehicle Power Management provides graduate and upper level undergraduate students, engineers, and researchers in both academia and the automotive industry, with a clear understanding of the concepts, methodologies, and prospects of vehicle power management.

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Languages : en
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Abstract : This report presents the development of two algorithms that uses available velocity bounds and powertrain information to generate an optimal velocity trajectory over a prediction horizon for a multi-mode plug-in hybrid electric vehicle. The objective of first optimization problem is to reduce dynamic losses, and required tractive force, while completing trip distance with a given travel time. Sequential Quadratic Programming (SQP) method is employed for this nonlinearly constrained optimization problem. This development illustrates the benefits of optimal velocity trajectories. Validation is completed using 2nd generation GM Volt model in Autonomie. The objective of second optimization problem is to generate velocity trajectory within a prediction horizon to reduce tractive force while monitoring the overall travel time required for the trip. The defined optimization problem is solved incorporating distance-based traffic dynamics and road conditions as compared to time-based optimization in first method. The developed algorithm reduces energy consumption by avoiding wasteful driving maneuvers and utilizes the opportunities for regeneration. The algorithm is implemented by ACADO toolkit for real-time execution. The algorithm is validated using 2nd generation Volt powertrain model developed at MTU.