A Simulative Approach To Predict Energy Consumption Of Future Powertrain Configurations For The Year 2040

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A Simulative Approach To Predict Energy Consumption Of Future Powertrain Configurations For The Year 2040

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Author : Tobias Stoll
language : en
Publisher: Springer Nature
Release Date : 2023-06-27

A Simulative Approach To Predict Energy Consumption Of Future Powertrain Configurations For The Year 2040 written by Tobias Stoll and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-27 with Technology & Engineering categories.

This book deals with the simulative prediction of efficiency and CO2-emissions of future powertrain systems for the year 2040. For this purpose, a suitable simulation environment is first created. This is followed by a technology extrapolation of all relevant powertrain systems, for example: combustion engines, electric drives, fuel cells as well as all relevant additional components. These components are then used to build 57 vehicle variants for the simulation. Finally, extensive simulations of the vehicle variants are carried out, evaluated and compared. Comprehensive tables of results are available for all simulated vehicle variants. The evaluations are of interest to anyone concerned with energy consumption and CO2-emissions of future vehicles.

Advanced Electric Drive Vehicles

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Author : Ali Emadi
language : en
Publisher: CRC Press
Release Date : 2014-10-24

Advanced Electric Drive Vehicles written by Ali Emadi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-24 with Technology & Engineering categories.

Electrification is an evolving paradigm shift in the transportation industry toward more efficient, higher performance, safer, smarter, and more reliable vehicles. There is in fact a clear trend to move from internal combustion engines (ICEs) to more integrated electrified powertrains. Providing a detailed overview of this growing area, Advanced Electric Drive Vehicles begins with an introduction to the automotive industry, an explanation of the need for electrification, and a presentation of the fundamentals of conventional vehicles and ICEs. It then proceeds to address the major components of electrified vehicles—i.e., power electronic converters, electric machines, electric motor controllers, and energy storage systems. This comprehensive work: Covers more electric vehicles (MEVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), range-extended electric vehicles (REEVs), and all-electric vehicles (EVs) including battery electric vehicles (BEVs) and fuel cell vehicles (FCVs) Describes the electrification technologies applied to nonpropulsion loads, such as power steering and air-conditioning systems Discusses hybrid battery/ultra-capacitor energy storage systems, as well as 48-V electrification and belt-driven starter generator systems Considers vehicle-to-grid (V2G) interface and electrical infrastructure issues, energy management, and optimization in advanced electric drive vehicles Contains numerous illustrations, practical examples, case studies, and challenging questions and problems throughout to ensure a solid understanding of key concepts and applications Advanced Electric Drive Vehicles makes an ideal textbook for senior-level undergraduate or graduate engineering courses and a user-friendly reference for researchers, engineers, managers, and other professionals interested in transportation electrification.

Itf Transport Outlook 2017

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Author : International Transport Forum
language : en
Publisher: OECD Publishing
Release Date : 2017-01-30

Itf Transport Outlook 2017 written by International Transport Forum and has been published by OECD Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-30 with categories.

The ITF Transport Outlook provides an overview of recent trends and near-term prospects for the transport sector at a global level, as well as long-term prospects for transport demand to 2050, for freight (maritime, air and surface), passenger transport (car, rail and air) and CO2 emissions.

Backpacker

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Author :
language : en
Publisher:
Release Date : 2007-09

Backpacker written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09 with categories.

Backpacker brings the outdoors straight to the reader's doorstep, inspiring and enabling them to go more places and enjoy nature more often. The authority on active adventure, Backpacker is the world's first GPS-enabled magazine, and the only magazine whose editors personally test the hiking trails, camping gear, and survival tips they publish. Backpacker's Editors' Choice Awards, an industry honor recognizing design, feature and product innovation, has become the gold standard against which all other outdoor-industry awards are measured.

Electric Hybrid And Fuel Cell Vehicles

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Author : Amgad Elgowainy
language : en
Publisher: Springer
Release Date : 2021-09-30

Electric Hybrid And Fuel Cell Vehicles written by Amgad Elgowainy and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-30 with Technology & Engineering categories.

This volume of "Encyclopedia of Sustainability Science and Technology, Second Edition," covers the electrification of vehicles, which is key to a sustainable future of transportation in both light-duty and heavy-duty vehicle sectors to address global concerns of climate change, air pollutant emissions, energy efficiency and energy security. Vehicle electrification includes several existing and emerging technologies and powertrain architectures such as conventional hybrid electric vehicles (HEVs), plug-in hybrids with various electric driving range, short- and long-range battery electric vehicles, as well as hydrogen fuel cell electric vehicles (FCEVs). Electrification will be key to connected autonomous vehicles, which are perceived to improve mobility, increase safety, reduce energy consumption and infrastructure costs, improve productivity, decrease traffic congestion and increase customer satisfaction. While electrification of vehicle technologies is relatively mature, technology improvement and economies of scale are needed to compete against incumbent technologies and to realize their benefits in the marketplace. Significant infrastructure development is needed in the case of hydrogen fuel cell vehicles and to a lesser extent for plug-in electric vehicles. Vehicle efficiency improvement is sought through a combination of several approaches, including weight reduction, engine downsizing, increased engine compression ratio with high octane fuels, and the use of compression ignition engines with low octane fuels. Liquid hydrocarbon fuels are needed in applications where high storage energy density is required such as long-haul class-8 combination heavy-duty trucks. Shared mobility is another emerging concept that enables access to transportation services on an as-needed basis. This approach can enhance accessibility to transportation, decrease number of vehicles on the road, reduce energy use and impact on the environment, reduce cost of transportation and the need for parking, and reduce transportation time between origin and destination. In all, the reader will receive a comprehensive introduction to electric vehicles and technology trends, including energy storage, in light-, medium-, and heavy-duty sectors, as well as the infrastructure development that will be required to realize these benefits for society.

Annual Index Abstracts Of Sae Technical Papers

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Author :
language : en
Publisher:
Release Date : 2001

Annual Index Abstracts Of Sae Technical Papers written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Automobiles categories.

Real Time Predictive Control Of Connected Vehicle Powertrains For Improved Energy Efficiency

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Author :
language : en
Publisher:
Release Date : 2020

Real Time Predictive Control Of Connected Vehicle Powertrains For Improved Energy Efficiency written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

Abstract : The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption. First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity and power demand in order to optimize powersplit decisions of the vehicle. This predictive powertrain controller utilizes nonlinear model predictive control (NMPC) to perform this optimization while being cognizant of future vehicle behavior. Second, the developed NMPC powertrain controller is thoroughly evaluated both in simulation and real-time testing. The controller is assessed over a large number of standardized and real-world drive cycles in simulation in order to properly quantify the energy savings benefits of the controller. In addition, the NMPC powertrain controller is deployed onto a real-time rapid prototyping embedded controller installed in a test vehicle. Using this real-time testing setup, the developed NMPC powertrain controller is evaluated using on-road testing for both energy savings performance and real-time performance. Third, a real-time integrated predictive powertrain controller (IPPC) for a multi-mode PHEV is presented. Utilizing predictions of future vehicle behavior, an optimal mode path plan is computed in order to determine a mode command best suited to the future conditions. In addition, this optimal mode path planning controller is integrated with the NMPC powertrain controller to create a real-time integrated predictive powertrain controller that is capable of full supervisory control for a multi-mode PHEV. Fourth, the IPPC is evaluated in simulation testing across a range of standard and real-world drive cycles in order to quantify the energy savings of the controller. This analysis is comprised of the combined benefit of the NMPC powertrain controller and the optimal mode path planning controller. The IPPC is deployed onto a rapid prototyping embedded controller for real-time evaluation. Using the real-time implementation of the IPPC, on-road testing was performed to assess both energy benefits and real-time performance of the IPPC. Finally, as the controllers developed in this research were evaluated for a single vehicle platform, the applicability of these controllers to other platforms is discussed. Multiple cases are discussed on how both the NMPC powertrain controller and the optimal mode path planning controller can be applied to other vehicle platforms in order to broaden the scope of this research.

Predicting Energy Consumption For Potential Effective Use In Hybrid Vehicle Powertrain Management Using Driver Prediction

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Author : Brian Magnuson
language : en
Publisher:
Release Date : 2017

Predicting Energy Consumption For Potential Effective Use In Hybrid Vehicle Powertrain Management Using Driver Prediction written by Brian Magnuson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.

A proof-of-concept software-in-the-loop study is performed to assess the accuracy of predicted net and charge-gaining energy consumption for potential effective use in optimizing powertrain management of hybrid vehicles. With promising results of improving fuel efficiency of a thermostatic control strategy for a series, plug-ing, hybrid-electric vehicle by 8.24%, the route and speed prediction machine learning algorithms are redesigned and implemented for real- world testing in a stand-alone C++ code-base to ingest map data, learn and predict driver habits, and store driver data for fast startup and shutdown of the controller or computer used to execute the compiled algorithm. Speed prediction is performed using a multi-layer, multi-input, multi- output neural network using feed-forward prediction and gradient descent through back- propagation training. Route prediction utilizes a Hidden Markov Model with a recurrent forward algorithm for prediction and multi-dimensional hash maps to store state and state distribution constraining associations between atomic road segments and end destinations. Predicted energy is calculated using the predicted time-series speed and elevation profile over the predicted route and the road-load equation. Testing of the code-base is performed over a known road network spanning 24x35 blocks on the south hill of Spokane, Washington. A large set of training routes are traversed once to add randomness to the route prediction algorithm, and a subset of the training routes, testing routes, are traversed to assess the accuracy of the net and charge-gaining predicted energy consumption. Each test route is traveled a random number of times with varying speed conditions from traffic and pedestrians to add randomness to speed prediction. Prediction data is stored and analyzed in a post process Matlab script. The aggregated results and analysis of all traversals of all test routes reflect the performance of the Driver Prediction algorithm. The error of average energy gained through charge-gaining events is 31.3% and the error of average net energy consumed is 27.3%. The average delta and average standard deviation of the delta of predicted energy gained through charge-gaining events is 0.639 and 0.601 Wh respectively for individual time-series calculations. Similarly, the average delta and average standard deviation of the delta of the predicted net energy consumed is 0.567 and 0.580 Wh respectively for individual time-series calculations. The average delta and standard deviation of the delta of the predicted speed is 1.60 and 1.15 respectively also for the individual time-series measurements. The percentage of accuracy of route prediction is91%. Overall, test routes are traversed 151 times for a total test distance of 276.4 km.

Modeling Simulation And Optimization Of Fuel Cell Battery Hybrid Powertrains

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Author : Piyush Bubna
language : en
Publisher:
Release Date : 2010

Modeling Simulation And Optimization Of Fuel Cell Battery Hybrid Powertrains written by Piyush Bubna and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Buses categories.

Fuel cells have emerged as one of the most promising candidates for fuel-efficient and emission-free vehicle power generation. Fuel cells are typically paired with reversible energy storage devices such as batteries or ultracapacitors to create hybrid electric powertrains. The electrification of the propulsion system and the presence of multiple onboard power sources require optimization of the hybrid system design in order to achieve good performance, high fuel economy, and enhanced component life at low cost. The overall goal of this research is to develop accurate vehicle models and conduct simulations to explore and demonstrate improvements in a fuel cell/battery hybrid bus. The first part of this thesis presents the features incorporated to improve a hybrid powertrain simulation package called Light, Fast and Modifiable (LFM). The improved LFM simulator was validated against test data acquired from various sensors onboard UD's Phase 1 fuel cell bus, and shown to be a reliable tool to simulate hybrid powertrain performance which could be used to perform design and optimization studies of future fuel cell hybrid systems. This attribute of LFM was then demonstrated by optimizing the fuel cell/battery hybrid power management by introducing a new prediction-based power management strategy. Simulation results for this strategy showed significant improvements in fuel cell system efficiency and reduction in hydrogen consumption compared to a conventional, baseline strategy of charge sustenance. A stable power request which promotes fuel cell durability was also realized with the help of this novel strategy. Finally, the benefits predicted from simulation studies were confirmed through implementation of the proposed strategy in the Phase 1 fuel cell/battery hybrid bus. It was concluded that the prediction-based strategy will lead to energy savings for transit applications. The validated LFM tool was next used to evaluate one approach to reducing battery stress by adding an ultracapacitor module, and thereby enhancing battery lifetime. Simulation of the energy storage performance showed a substantial reduction in battery current-load and energy throughput for the blended storage system, which are two of the contributing factors towards battery degradation. These results have opened up new research directions in which powertrain simulations can help in further evaluation of the blended storage concept and assess its feasibility and usefulness in electric-drive vehicles. Finally, the thermal behavior of the Altairnano LiTi battery, the future battery of UD fuel cell buses, was investigated. Preliminary experiments were conducted to understand the thermal behavior of batteries under typical operating conditions. A model was developed to predict the temperature during charging and discharging of the battery. The findings of this work should prove useful in designing effective and efficient battery thermal management systems.

Simulation Tools For Predicting Energy Consumption And Range Of Electric Two Wheelers

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Author : Nathan Lord
language : en
Publisher:
Release Date : 2016

Simulation Tools For Predicting Energy Consumption And Range Of Electric Two Wheelers written by Nathan Lord and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.

This research investigates the design and implementation of simulation tools used to predict the energy consumption and range of electric two-wheelers. The simulation tools developed can be used as design tools or as a real-time prediction of available range. These simulation tools can be used to design electric motorcycles which could decrease traffic congestion and pollution in urban areas. The first simulator was developed in collaboration with GenZe to develop a simulation tool to help design their next electric scooter. The equations for each component were developed to accurately estimate the energy consumption of the first GenZe scooter and developed to be modular in order to be used as a design tool in the future. Each component of the simulator was calibrated to the current GenZe scooter by conducting multiple experiments on the individual components and full vehicle testing. The motor and inverter models were calibrated using data collected at the Center for Automotive Research (CAR) using a dynamometer and a power analyzer. The data collected was used to find the motor constants and efficiency of the motor and inverter. The battery model was calibrated using a battery tester and a environmental chamber at CAR. The data collected was used to find the internal resistance and first-order equivalent circuit RC parameters of a single cell at multiple states of charge and temperatures. The electric powertrain model and the full GenZe model were validated using a chassis dynamometer and riding the scooter at the Transportation Research Center. The electric powertrain model was validated with low error between the predicted results and data collected on a chassis dynamometer which met the requirements of the GenZe project. Errors were found between the full GenZe model and collected data from riding the scooter on the road which suggested the chassis model does not accurately predict energy consumption during turning. In light of the limitations seen in the GenZe model an investigation into two-dimensional vehicle dynamics modeling was conducted. Two additional chassis models were developed to model a turning two-wheeler. The first model predicts the two-dimensional location of the vehicle by estimating the lateral tire forces on the motorcycle. The second model extends the first model by estimating the lean of the motorcycle given the speed and corner radius. The predicted energy consumption of the two models and the GenZe chassis model were compared to BikeSim through multiple turning profiles. It was found the models underestimate the energy consumption compared to BikeSim, which suggested the models do not predict all the forces that slow a two-wheeler during turning. Further comparison between the models and BikeSim shows a difference between the predicted normal forces on the tires and the rear tire radius suggesting future work should investigate the pitching and tire dynamics of a two-wheeler during turning. This work uncovers the complexity of estimating the road forces on a two-wheeler. Ultimately future work should focus on the road forces of two-wheelers in order to increase the accuracy of energy consumption prediction of electric motorcycle simulation tools.