Look-ahead Information Based Optimization Strategy for Hybrid Electric Vehicles PDF Download

Author: Mohammad Alzorgan
Publisher:
ISBN:
Category : Hybrid electric vehicles
Languages : en
Pages : 71

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Book Description
The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV energy management allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle. The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill. The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications. The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation.

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

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Book Description
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: Amir Taghavipour
Publisher: Springer
ISBN: 3030003140
Category : Technology & Engineering
Languages : en
Pages : 202

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Book Description
Intelligent Control of Connected Plug-in Hybrid Electric Vehicles presents the development of real-time intelligent control systems for plug-in hybrid electric vehicles, which involves control-oriented modelling, controller design, and performance evaluation. The controllers outlined in the book take advantage of advances in vehicle communications technologies, such as global positioning systems, intelligent transportation systems, geographic information systems, and other on-board sensors, in order to provide look-ahead trip data. The book contains simple and efficient models and fast optimization algorithms for the devised controllers to address the challenge of real-time implementation in the design of complex control systems. Using the look-ahead trip information, the authors of the book propose intelligent optimal model-based control systems to minimize the total energy cost, for both grid-derived electricity and fuel. The multilayer intelligent control system proposed consists of trip planning, an ecological cruise controller, and a route-based energy management system. An algorithm that is designed to take advantage of previewed trip information to optimize battery depletion profiles is presented in the book. Different control strategies are compared and ways in which connecting vehicles via vehicle-to-vehicle communication can improve system performance are detailed. Intelligent Control of Connected Plug-in Hybrid Electric Vehicles is a useful source of information for postgraduate students and researchers in academic institutions participating in automotive research activities. Engineers and designers working in research and development for automotive companies will also find this book of interest. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Author: Wilson Pérez
Publisher:
ISBN:
Category : Hybrid electric vehicles
Languages : en
Pages : 0

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Book Description
Most vehicles on the road today are conventional vehicles which require the use of nonrenewable fuels to operate. Coupled with this need is a large amount of emissions released into the atmosphere throughout the duration of every trip. To alleviate the burden this places on the environment, governments worldwide have pushed for strict mandates which aim to reduce and, eventually, eliminate the use of fossil fuels. To meet government requirements, hybrid and electric vehicles have been the focus of many car manufacturers. Advancements in vehicle technology have significantly increased the potential of hybrid vehicle technology to reduce levels of emissions and fuel consumption. Advanced energy management strategies have been developed to properly handle the power flow through the vehicle powertrain. These range from rule-based approaches to globally optimal techniques such as dynamic programming (DP). However, cost of high-power computational hardware and lack of a-priori knowledge of future road conditions poses difficult challenges for engineers attempting to implement globally optimal frameworks. A viable solution to the problem is to leverage on-board sensors present in most vehicles equipped with basic advanced driver assistance systems (ADAS) to obtain a prediction of the future road conditions. Known as look-ahead predictive EMS, this approach partially solves the lack of a-priori knowledge since a detailed view of the road ahead is available. However, uncertainty in sensors and the computational burden of processing large amounts of data creates more difficulties. This research aims to address the challenges mentioned above. A look-ahead predictive EMS is proposed which combines the use of a globally optimal approach (DP) with the equivalent consumption minimization strategy (ECMS) to obtain an optimal solution for a future prediction horizon. ECMS is highly sensitive to the equivalence factor, s, making it necessary to adapt during a trip to account for disturbances. A novel adaptation method is presented in this dissertation which uses a neural network to learn the nonlinear relationship between a speed and SOC trajectory prediction obtained from DP to estimate the corresponding s. Finally, an uncertainty analysis is performed to measure the distribution of fuel economy results over a broad range of traffic patterns. It is shown that the proposed EMS consistently improves fuel economy over the baseline strategy and is a viable option for a real-time EMS on production vehicles.

Author: Li Yeuching
Publisher: Springer Nature
ISBN: 3031792068
Category : Technology & Engineering
Languages : en
Pages : 123

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Book Description
The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.

Author: Simona Onori
Publisher: Springer
ISBN: 1447167813
Category : Technology & Engineering
Languages : en
Pages : 121

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Book Description
This SpringerBrief deals with the control and optimization problem in hybrid electric vehicles. Given that there are two (or more) energy sources (i.e., battery and fuel) in hybrid vehicles, it shows the reader how to implement an energy-management strategy that decides how much of the vehicle’s power is provided by each source instant by instant. Hybrid Electric Vehicles: •introduces methods for modeling energy flow in hybrid electric vehicles; •presents a standard mathematical formulation of the optimal control problem; •discusses different optimization and control strategies for energy management, integrating the most recent research results; and •carries out an overall comparison of the different control strategies presented. Chapter by chapter, a case study is thoroughly developed, providing illustrative numerical examples that show the basic principles applied to real-world situations. The brief is intended as a straightforward tool for learning quickly about state-of-the-art energy-management strategies. It is particularly well-suited to the needs of graduate students and engineers already familiar with the basics of hybrid vehicles but who wish to learn more about their control strategies.

Author: Bharatkumar Hegde
Publisher:
ISBN:
Category : Hybrid electric vehicles
Languages : en
Pages :

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Book Description
Hybrid electric vehicles are a result of a global push towards cleaner and fuel-efficient vehicles. They use both electrical and traditional fossil-fuel based energy sources, which makes them ideal for the transition towards much cleaner electric vehicles. A key part of the hybridization effort is designing effective energy management algorithms because they are crucial in reducing fuel consumption and emission of the hybrid vehicle. In the automotive industry, energy management systems are designed, prototyped, and validated in a software simulation environment before implementation on the hybrid vehicle. The software simulation uses model-based design techniques which reduce development time and cost. Traditionally, the design of energy management systems is based on statutory drive-cycles. Drive-cycle based solutions to energy management systems improve fuel economy of the vehicle and are well suited for statutory certification of fuel economy and emissions. In recent times however, the fuel economy and emissions over real-world driving is being considered increasingly for statutory certification. In light of these developments, methodologies to simulate and design new energy management strategies for real-world driving are needed. The work presented in this dissertation systematically addresses the challenges faced in the development of such a methodology. This work identifies and solves three sub-problems which together form the methodology for model-based real-world look-ahead energy management system development. First, a simulation framework to simulate real-world driving and look-ahead sensor emulation is developed. The simulation framework includes traffic simulation and powertrain simulation capabilities. It is termed traffic integrated powertrain co-simulation.Second, a comprehensive algorithm is developed to utilize look-ahead sensor data to accurately predict the vehicle's future velocity trajectories. Finally, through the use of optimal control algorithms, a look-ahead energy management system is developed to understand the utility of different look-ahead technologies in the improvement of fuel economy.

Author: Teng Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681736195
Category : Technology & Engineering
Languages : en
Pages : 99

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Book Description
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.

Author: Adithya Jayakumar
Publisher:
ISBN:
Category : Calibration
Languages : en
Pages :

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Book Description
The particular simulator and application addressed here is the optimization of fuel economy in hybrid electric vehicles (HEVs). Accurately estimating the energy consumption of hybrid electric vehicles is complicated by the fact that these vehicles have multiple power sources and complex control strategies. As a starting point of this research, to ensure that available vehicle simulators can be validated, a thorough literature review of energy consumption in HEVs was done both on a component and an overall level. This then allowed model validation to be performed. New methods of model validation for the case of vehicle simulators were also developed and are discussed in this dissertation. Also in this document, the optimization framework developed to robustly minimize fuel economy in a hybrid electric vehicle simulator is discussed. Since the vehicle simulator is a hybrid system using LUTs, the methodology developed here will be applicable in many simulation optimization environments.

Author: Emanuele Crisostomi
Publisher: CRC Press
ISBN: 1498745008
Category : Technology & Engineering
Languages : en
Pages : 261

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Book Description
This book explores the behavior of networks of electric and hybrid vehicles. The topics that are covered include: energy management issues for aggregates of plug-in vehicles; the design of sharing systems to support electro-mobility; context awareness in the operation of electric and hybrid vehicles, and the role that this plays in a Smart City context; and tools to test and design massively large-scale networks of such vehicles. The book also introduces new and interesting control problems that are becoming prevalent in the EV-PHEV's context, as well as identifying some open questions. A particular focus of the book is on the opportunities afforded by networked actuation possibilities in electric and hybrid vehicles, and the role that such actuation may play in air-quality and emissions management.