Driving Data Pattern Recognition For Intelligent Energy Management Of Plug In Hybrid Electric Vehicles

Download Driving Data Pattern Recognition For Intelligent Energy Management Of Plug In Hybrid Electric Vehicles full books in PDF, epub, and Kindle. Read online Driving Data Pattern Recognition For Intelligent Energy Management Of Plug In Hybrid Electric Vehicles ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Driving Data Pattern Recognition for Intelligent Energy Management of Plug-in Hybrid Electric Vehicles

Author : Sreejith Munthikodu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)

DOWNLOAD NOW!

Book Synopsis Driving Data Pattern Recognition for Intelligent Energy Management of Plug-in Hybrid Electric Vehicles by : Sreejith Munthikodu

Download or read book Driving Data Pattern Recognition for Intelligent Energy Management of Plug-in Hybrid Electric Vehicles written by Sreejith Munthikodu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada.

Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles

Author : Teng Liu
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681736195
Total Pages : 99 pages
Book Rating : 4.6/5 (817 download)

DOWNLOAD NOW!

Book Synopsis Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles by : Teng Liu

Download or read book Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles written by Teng Liu and published by Morgan & Claypool Publishers. This book was released on 2019-09-03 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Hybrid Electric Vehicles

Author : Simona Onori
Publisher : Springer
ISBN 13 : 1447167813
Total Pages : 121 pages
Book Rating : 4.4/5 (471 download)

DOWNLOAD NOW!

Book Synopsis Hybrid Electric Vehicles by : Simona Onori

Download or read book Hybrid Electric Vehicles written by Simona Onori and published by Springer. This book was released on 2015-12-16 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management

Author : Jili Tao
Publisher : Elsevier
ISBN 13 : 0443131902
Total Pages : 348 pages
Book Rating : 4.4/5 (431 download)

DOWNLOAD NOW!

Book Synopsis Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management by : Jili Tao

Download or read book Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management written by Jili Tao and published by Elsevier. This book was released on 2024-06-07 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modelling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering. Provides a guide to the modeling and simulation methods of hybrid electric vehicle energy systems, including fuel cell systems Describes the fundamental concepts and theory behind CNN, MPC, fuzzy control, multi objective optimization, fuzzy Q-learning and DDPG Explains how to use energy management methods such as parameter estimation, Q-learning, and pattern recognition, including battery State of Health and State of Charge prediction, and vehicle operating conditions

An Intelligent Energy Allocation Method for Hybrid Energy Storage Systems for Electrified Vehicles

Author : Xing Zhang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)

DOWNLOAD NOW!

Book Synopsis An Intelligent Energy Allocation Method for Hybrid Energy Storage Systems for Electrified Vehicles by : Xing Zhang

Download or read book An Intelligent Energy Allocation Method for Hybrid Energy Storage Systems for Electrified Vehicles written by Xing Zhang and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Electrified vehicles (EVs) with a large electric energy storage system (ESS), including Plug-in Hybrid Electric Vehicles (PHEVs) and Pure Electric Vehicles (PEVs), provide a promising solution to utilize clean grid energy that can be generated from renewable sources and to address the increasing environmental concerns. Effectively extending the operation life of the large and costly ESS, thus lowering the lifecycle cost of EVs presents a major technical challenge at present. A hybrid energy storage system (HESS) that combines batteries and ultracapacitors (UCs) presents unique energy storage capability over traditional ESS made of pure batteries or UCs. With optimal energy management system (EMS) techniques, the HESS can considerably reduce the frequent charges and discharges on the batteries, extending their life, and fully utilizing their high energy density advantage. In this work, an intelligent energy allocation (IEA) algorithm that is based on Q-learning has been introduced. The new IEA method dynamically generate sub-optimal energy allocation strategy for the HESS based on each recognized trip of the EV. In each repeated trip, the self-learning IEA algorithm generates the optimal control schemes to distribute required current between the batteries and UCs according to the learned Q values. A RBF neural networks is trained and updated to approximate the Q values during the trip. This new method provides continuously improved energy sharing solutions better suited to each trip made by the EV, outperforming the present passive HESS and fixed-cutoff-frequency method. To efficiently recognize the repeated trips, an extended Support Vector Machine (e-SVM) method has been developed to extract significant features for classification. Comparing with the standard 2-norm SVM and linear 1-norm SVM, the new e-SVM provides a better balance between quality of classification and feature numbers, and measures feature observability. The e-SVM method is thus able to replace features with bad observability with other more observable features. Moreover, a novel pattern classification algorithm, Inertial Matching Pursuit Classification (IMPC), has been introduced for recognizing vehicle driving patterns within a shorter period of time, allowing timely update of energy management strategies, leading to improved Driver Performance Record (DPR) system resolution and accuracy. Simulation results proved that the new IMPC method is able to correctly recognize driving patterns with incomplete and inaccurate vehicle signal sample data. The combination of intelligent energy allocation (IEA) with improved e-SVM feature extraction and IMPC pattern classification techniques allowed the best characteristics of batteries and UCs in the integrated HESS to be fully utilized, while overcoming their inherent drawbacks, leading to optimal EMS for EVs with improved energy efficiency, performance, battery life, and lifecycle cost.

Fuzzy Logic Based Driving Pattern Recognition for Hybrid Electric Vehicle Energy Management

Author : Sushil Kumar
Publisher :
ISBN 13 :
Total Pages : 60 pages
Book Rating : 4.:/5 (945 download)

DOWNLOAD NOW!

Book Synopsis Fuzzy Logic Based Driving Pattern Recognition for Hybrid Electric Vehicle Energy Management by : Sushil Kumar

Download or read book Fuzzy Logic Based Driving Pattern Recognition for Hybrid Electric Vehicle Energy Management written by Sushil Kumar and published by . This book was released on 2015 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: For years the automotive industry has been shifting towards hybridization and electrification of conventional powertrains due to increase in fossil fuel cost and environmental impact due heavy emission of Green House Gases (GHG) and various pollutants into atmosphere by combustion engine powered vehicles. Hybrid Electric Vehicles (HEV) have proved to achieve superior fuel economy and reduced emissions. Supervisory control strategies determining the power split among various onboard power sources are evolving with time, providing better fuel economies. With increasing complexity of control systems driving HEVs, mathematical modeling and simulation tools have become extremely advanced and have derived whole industry into adopting Model Based Design (MBD) and Hardware-in-the-loop (HIL) techniques to validate the performance of HEV systems in real world.This report will present a systematic mythology where MBD techniques are used to develop hybrid powertrain, supervisory control strategies and control systems. To validate the effectiveness of various energy management strategies for HEV energy management in a real world scenario, Conventional rule-based power split strategies are compared against advanced Equivalent Consumption Minimization Strategy (ECMS), in software and HIL environment. Since effective utilization of the fuel reduction potential of a HEV powertrain requires a careful design of the energy management control methodology, an advanced ECMS strategy involving implementation with Fuzzy Logic to reduce computational overload has been proposed. Conventional real-time implementation of ECMS based strategy is difficult due to the involvement of heavy computation. Methods like Fuzzy Logic based estimation can be used to reduce this computational overload. Real-time energy management is obtained by adding a Fuzzy Logic based on-the-fly algorithm for the estimation of driving profile and adaptive equivalent consumption minimization strategy (A-ECMS) framework. The control strategy is implemented to function without any prior knowledge of the future driving conditions. The idea is to periodically refresh the energy management strategy according to the estimated driving pattern, so that the Battery State of Charge (SOC) is maintained within the boundaries and the equivalent fuel consumption is minimized. The performance of the presented Fuzzy Logic based adaptive control strategy utilizing driving pattern recognition is benchmarked using a Dynamic Programming based global optimization approach.

Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles

Author : Li Yeuching
Publisher : Springer Nature
ISBN 13 : 3031792068
Total Pages : 123 pages
Book Rating : 4.0/5 (317 download)

DOWNLOAD NOW!

Book Synopsis Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles by : Li Yeuching

Download or read book Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles written by Li Yeuching and published by Springer Nature. This book was released on 2022-06-01 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Intelligent Control and Smart Energy Management

Author : Maude Josée Blondin
Publisher : Springer Nature
ISBN 13 : 3030844749
Total Pages : 434 pages
Book Rating : 4.0/5 (38 download)

DOWNLOAD NOW!

Book Synopsis Intelligent Control and Smart Energy Management by : Maude Josée Blondin

Download or read book Intelligent Control and Smart Energy Management written by Maude Josée Blondin and published by Springer Nature. This book was released on 2022-05-28 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume aims to provide a state-of-the-art and the latest advancements in the field of intelligent control and smart energy management. Techniques, combined with technological advances, have enabled the deployment of new operating systems in many engineering applications, especially in the domain of transport and renewable resources. The control and energy management of transportation and renewable resources are shifting towards autonomous reasoning, learning, planning and operating. As a result, these techniques, also referred to as autonomous control and energy management, will become practically ubiquitous soon. The discussions include methods, based on neural control (and others) as well as distributed and intelligent optimization. While the theoretical concepts are detailed and explained, the techniques presented are tailored to transport and renewable resources applications, such as smart grids and automated vehicles. The reader will grasp the most important theoretical concepts as well as to fathom the challenges and needs related to timely practical applications. Additional content includes research perspectives and future direction as well as insight into the devising of techniques that will meet tomorrow’s scientific needs. This contributed volume is for researchers, graduate students, engineers and practitioners in the domains of control, energy, and transportation.

DNA Computing Based Genetic Algorithm

Author : Jili Tao
Publisher : Springer Nature
ISBN 13 : 981155403X
Total Pages : 280 pages
Book Rating : 4.8/5 (115 download)

DOWNLOAD NOW!

Book Synopsis DNA Computing Based Genetic Algorithm by : Jili Tao

Download or read book DNA Computing Based Genetic Algorithm written by Jili Tao and published by Springer Nature. This book was released on 2020-07-01 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities.

Intelligent Control of Connected Plug-in Hybrid Electric Vehicles

Author : Amir Taghavipour
Publisher : Springer
ISBN 13 : 3030003140
Total Pages : 202 pages
Book Rating : 4.0/5 (3 download)

DOWNLOAD NOW!

Book Synopsis Intelligent Control of Connected Plug-in Hybrid Electric Vehicles by : Amir Taghavipour

Download or read book Intelligent Control of Connected Plug-in Hybrid Electric Vehicles written by Amir Taghavipour and published by Springer. This book was released on 2018-09-26 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles

Author : Teng Liu
Publisher : Synthesis Lectures on Advances
ISBN 13 : 9781681736204
Total Pages : 99 pages
Book Rating : 4.7/5 (362 download)

DOWNLOAD NOW!

Book Synopsis Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles by : Teng Liu

Download or read book Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles written by Teng Liu and published by Synthesis Lectures on Advances. This book was released on 2019-09-03 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

iHorizon-Enabled Energy Management for Electrified Vehicles

Author : Clara Marina Martinez
Publisher : Butterworth-Heinemann
ISBN 13 : 0128150114
Total Pages : 434 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!

Book Synopsis iHorizon-Enabled Energy Management for Electrified Vehicles by : Clara Marina Martinez

Download or read book iHorizon-Enabled Energy Management for Electrified Vehicles written by Clara Marina Martinez and published by Butterworth-Heinemann. This book was released on 2018-09-11 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: iHorizon-Enabled Energy Management for Electrified Vehicles proposes a realistic solution that assumes only scarce information is available prior to the start of a journey and that limited computational capability can be allocated for energy management. This type of framework exploits the available resources and closely emulates optimal results that are generated with an offline global optimal algorithm. In addition, the authors consider the present and future of the automotive industry and the move towards increasing levels of automation. Driver vehicle-infrastructure is integrated to address the high level of interdependence of hybrid powertrains and to comply with connected vehicle infrastructure. This book targets upper-division undergraduate students and graduate students interested in control applied to the automotive sector, including electrified powertrains, ADAS features, and vehicle automation. Addresses the level of integration of electrified powertrains Presents the state-of-the-art of electrified vehicle energy control Offers a novel concept able to perform dynamic speed profile and energy demand prediction

Modeling, Dynamics, and Control of Electrified Vehicles

Author : Haiping Du
Publisher : Woodhead Publishing
ISBN 13 : 0128131098
Total Pages : 521 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!

Book Synopsis Modeling, Dynamics, and Control of Electrified Vehicles by : Haiping Du

Download or read book Modeling, Dynamics, and Control of Electrified Vehicles written by Haiping Du and published by Woodhead Publishing. This book was released on 2017-10-19 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modelling, Dynamics and Control of Electrified Vehicles provides a systematic overview of EV-related key components, including batteries, electric motors, ultracapacitors and system-level approaches, such as energy management systems, multi-source energy optimization, transmission design and control, braking system control and vehicle dynamics control. In addition, the book covers selected advanced topics, including Smart Grid and connected vehicles. This book shows how EV work, how to design them, how to save energy with them, and how to maintain their safety. The book aims to be an all-in-one reference for readers who are interested in EVs, or those trying to understand its state-of-the-art technologies and future trends. Offers a comprehensive knowledge of the multidisciplinary research related to EVs and a system-level understanding of technologies Provides the state-of-the-art technologies and future trends Covers the fundamentals of EVs and their methodologies Written by successful researchers that show the deep understanding of EVs

ICT: Innovation and Computing

Author : Amit Joshi
Publisher : Springer Nature
ISBN 13 : 9819994861
Total Pages : 500 pages
Book Rating : 4.8/5 (199 download)

DOWNLOAD NOW!

Book Synopsis ICT: Innovation and Computing by : Amit Joshi

Download or read book ICT: Innovation and Computing written by Amit Joshi and published by Springer Nature. This book was released on with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Big Data Analytics for Smart Transport and Healthcare Systems

Author : Saeid Pourroostaei Ardakani
Publisher : Springer Nature
ISBN 13 : 9819966205
Total Pages : 197 pages
Book Rating : 4.8/5 (199 download)

DOWNLOAD NOW!

Book Synopsis Big Data Analytics for Smart Transport and Healthcare Systems by : Saeid Pourroostaei Ardakani

Download or read book Big Data Analytics for Smart Transport and Healthcare Systems written by Saeid Pourroostaei Ardakani and published by Springer Nature. This book was released on 2024-01-04 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to introduce big data solutions in urban sustainability applications—mainly smart transportation and healthcare systems. It focuses on machine learning techniques and data processing approaches which have the capacity to handle/process huge, live, and complex datasets in real-time transportation and healthcare applications. For this, several state-of-the-art data processing approaches including data pre-processing, classification, regression, and clustering are introduced, tested, and evaluated to highlight their benefits and constraints where data is sensitive, real-time, and/or semi-structured.

Future Powertrain Technologies

Author : Stephan Rinderknecht
Publisher : MDPI
ISBN 13 : 3039437534
Total Pages : 264 pages
Book Rating : 4.0/5 (394 download)

DOWNLOAD NOW!

Book Synopsis Future Powertrain Technologies by : Stephan Rinderknecht

Download or read book Future Powertrain Technologies written by Stephan Rinderknecht and published by MDPI. This book was released on 2020-12-17 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the various factors greatly influencing the development process of future powertrain technologies, the trends in climate change and digitalization are of huge public interest. To handle these trends, new disruptive technologies are integrated into the development process. They open up space for diverse research which is distributed over the entire vehicle design process. This book contains recent research articles which incorporate results for selecting and designing powertrain topology in consideration of the vehicle operating strategy as well as results for handling the reliability of new powertrain components. The field of investigation spans from the identification of ecologically optimal transformation of the existent vehicle fleet to the development of machine learning-based operating strategies and the comparison of complex hybrid electric vehicle topologies to reduce CO2 emissions.

Electric Systems for Transportation

Author : Maria Carmen Falvo
Publisher : MDPI
ISBN 13 : 3036504885
Total Pages : 690 pages
Book Rating : 4.0/5 (365 download)

DOWNLOAD NOW!

Book Synopsis Electric Systems for Transportation by : Maria Carmen Falvo

Download or read book Electric Systems for Transportation written by Maria Carmen Falvo and published by MDPI. This book was released on 2021-09-02 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation systems play a major role in the reduction of energy consumptions and environmental impact all over the world. The significant amount of energy of transport systems forces the adoption of new solutions to ensure their performance with energy-saving and reduced environmental impact. In this context, technologies and materials, devices and systems, design methods, and management techniques, related to the electrical power systems for transportation are continuously improving thanks to research activities. The main common challenge in all the applications concerns the adoption of innovative solutions that can improve existing transportation systems in terms of efficiency and sustainability.