Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813858
Yu Cai, Jiacheng Xie, Gokcin Cinar, D. Mavris
Electrified aircraft propulsion concepts are rapidly emerging due to their huge potential in fuel saving and mitigating negative environmental impact. In order to perform a linear technology progression and fairly assess the impacts of powertrain electrification, it is important to first establish parametric state-of-the-art baseline vehicle models with advanced technologies matured by 2030. For a thin haul (19-passenger) turboprop size class and a regional turboprop (50-passenger) size class, a current state-of-the-art technology reference aircraft (TRA) is identified and modeled using a multi-disciplinary analysis and optimization environment. Viable technologies for airframe and conventional propulsion system are then identified which are expected to be available by 2030. These technologies are parametrically infused in the TRA models to create advanced technology aircraft models, which will serve as the baseline models for future studies of powertrain electrification.
{"title":"Advanced 2030 Turboprop Aircraft Modeling for the Electrified Powertrain Flight Demonstration Program","authors":"Yu Cai, Jiacheng Xie, Gokcin Cinar, D. Mavris","doi":"10.1109/ITEC53557.2022.9813858","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813858","url":null,"abstract":"Electrified aircraft propulsion concepts are rapidly emerging due to their huge potential in fuel saving and mitigating negative environmental impact. In order to perform a linear technology progression and fairly assess the impacts of powertrain electrification, it is important to first establish parametric state-of-the-art baseline vehicle models with advanced technologies matured by 2030. For a thin haul (19-passenger) turboprop size class and a regional turboprop (50-passenger) size class, a current state-of-the-art technology reference aircraft (TRA) is identified and modeled using a multi-disciplinary analysis and optimization environment. Viable technologies for airframe and conventional propulsion system are then identified which are expected to be available by 2030. These technologies are parametrically infused in the TRA models to create advanced technology aircraft models, which will serve as the baseline models for future studies of powertrain electrification.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123359066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814022
Alberto Barragán-Moreno, Pere Izquierdo Gomez, T. Dragičević
The rainflow algorithm is one of the most commonly used tools for studying stress conditions of a wide variety of systems, including power electronics devices and electrochemical batteries. One of the main drawbacks of the algorithm is the trade-off between data compression and the loss of information when classifying the stress cycles into a finite amount of histogram bins. This paper proposes a novel approach for classifying the stress cycles by using fuzzy logic in order to reduce the quantization error of the traditional histogram-based analysis. The method is tested by comparing the accumulated damage estimations of two support-vector regression algorithms when trained with each type of cycle-counting procedure. NASA’s randomized battery usage data set is used as source of stress data. A 50% error reduction was observed when using the fuzzy logic-based approach compared to the traditional one. Thus, the proposed method can effectively improve the accuracy of diagnosis algorithms without penalizing their performance and memory-saving features.
{"title":"Enhancement of Stress Cycle-counting Algorithms for Li-ion Batteries by means of Fuzzy Logic","authors":"Alberto Barragán-Moreno, Pere Izquierdo Gomez, T. Dragičević","doi":"10.1109/ITEC53557.2022.9814022","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814022","url":null,"abstract":"The rainflow algorithm is one of the most commonly used tools for studying stress conditions of a wide variety of systems, including power electronics devices and electrochemical batteries. One of the main drawbacks of the algorithm is the trade-off between data compression and the loss of information when classifying the stress cycles into a finite amount of histogram bins. This paper proposes a novel approach for classifying the stress cycles by using fuzzy logic in order to reduce the quantization error of the traditional histogram-based analysis. The method is tested by comparing the accumulated damage estimations of two support-vector regression algorithms when trained with each type of cycle-counting procedure. NASA’s randomized battery usage data set is used as source of stress data. A 50% error reduction was observed when using the fuzzy logic-based approach compared to the traditional one. Thus, the proposed method can effectively improve the accuracy of diagnosis algorithms without penalizing their performance and memory-saving features.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128130459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813912
Claudio Burgos-Mellado, F. Donoso, T. Dragičević
This paper proposes a three-phase AC battery based on the modular multilevel converter (MMC) and investigates the effects of cyber attacks on it. The AC battery concept allows plug and play combinatorial integration of diverse battery cells with different characteristics such as nominal voltage, state of charge (SoC), state of health (SoH), and capacity into modular and reconfigurable battery packs that can cost-effectively cover a broad range of applications from electrified vehicles to stationary storage. To this end, in each sub-module (SM) of the MMC, battery cells (or modules) are connected to its capacitor, enabling a cell-to-cell control. In this scenario, the traditional battery management system (BMS) can be replaced by control schemes for the converter aiming to equalise critical parameters associated with battery cells. Unlike previous works, the proposed battery concept considers a local controllers (LC) in each SM of the MMC, achieving a modularisation in computing capacity for the MMC control system. Under this framework, a distributed control scheme based on the consensus theory is proposed for SoC regulation among the battery cells. Also, it is shown that cyber attacks are real threats to this electrical system. In particular, this work studies the effects of the specific cyber attack named false data injection attack (FDIA) on the proposed distributed control scheme for SoC regulation.
{"title":"AC Battery: Modular Layout and Cyber-secure Cell-level Control for Cost-Effective Transportation Electrification","authors":"Claudio Burgos-Mellado, F. Donoso, T. Dragičević","doi":"10.1109/ITEC53557.2022.9813912","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813912","url":null,"abstract":"This paper proposes a three-phase AC battery based on the modular multilevel converter (MMC) and investigates the effects of cyber attacks on it. The AC battery concept allows plug and play combinatorial integration of diverse battery cells with different characteristics such as nominal voltage, state of charge (SoC), state of health (SoH), and capacity into modular and reconfigurable battery packs that can cost-effectively cover a broad range of applications from electrified vehicles to stationary storage. To this end, in each sub-module (SM) of the MMC, battery cells (or modules) are connected to its capacitor, enabling a cell-to-cell control. In this scenario, the traditional battery management system (BMS) can be replaced by control schemes for the converter aiming to equalise critical parameters associated with battery cells. Unlike previous works, the proposed battery concept considers a local controllers (LC) in each SM of the MMC, achieving a modularisation in computing capacity for the MMC control system. Under this framework, a distributed control scheme based on the consensus theory is proposed for SoC regulation among the battery cells. Also, it is shown that cyber attacks are real threats to this electrical system. In particular, this work studies the effects of the specific cyber attack named false data injection attack (FDIA) on the proposed distributed control scheme for SoC regulation.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131131811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813876
Mina Naguib, Lucas Bruck, A. Emadi
Hybrid electric vehicles (HEVs) are equipped with a traditional internal combustion engine (ICE) and one or more electrical motors (EMs). HEV multi-mode power-split powertrain architecture improves fuel consumption, battery life, and vehicle emissions. However, this architecture is known for its control complexity due to the involvement of several modes of operation. Global optimal control strategies are commonly utilized as a benchmark in HEVs however they cannot be implemented on the electronic control unit (ECU) due to their extensive computational load. In this paper, a neural network (NN) -based energy management system (EMS) is proposed to control the mode and the power split of an HEV. Firstly, dynamic programming (DP), a global optimal control strategy, is utilized to achieve optimal fuel consumption using drive cycles at a wide range of conditions. Then, the proposed NN-based EMS is trained and tested using the data collected offline from the DP. The results show that the proposed NN-based EMS is able to predict the mode and power split of an HEV with only 2% higher than the optimal fuel consumption obtained by the DP.
{"title":"Neural Network-Based Online Energy Management for Multi-Mode Power Split Hybrid Vehicles","authors":"Mina Naguib, Lucas Bruck, A. Emadi","doi":"10.1109/ITEC53557.2022.9813876","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813876","url":null,"abstract":"Hybrid electric vehicles (HEVs) are equipped with a traditional internal combustion engine (ICE) and one or more electrical motors (EMs). HEV multi-mode power-split powertrain architecture improves fuel consumption, battery life, and vehicle emissions. However, this architecture is known for its control complexity due to the involvement of several modes of operation. Global optimal control strategies are commonly utilized as a benchmark in HEVs however they cannot be implemented on the electronic control unit (ECU) due to their extensive computational load. In this paper, a neural network (NN) -based energy management system (EMS) is proposed to control the mode and the power split of an HEV. Firstly, dynamic programming (DP), a global optimal control strategy, is utilized to achieve optimal fuel consumption using drive cycles at a wide range of conditions. Then, the proposed NN-based EMS is trained and tested using the data collected offline from the DP. The results show that the proposed NN-based EMS is able to predict the mode and power split of an HEV with only 2% higher than the optimal fuel consumption obtained by the DP.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128253617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814048
Mingxuan Shi, M. Ozcan, Gokcin Cinar, Jonathan C. Gladin, D. Mavris
Under the NASA University Leadership Initiative (ULI) program, a team formed by multi-disciplinary universities are collaborating on the advancement of technologies of a hybrid turbo-electric regional jet which aims to enter service in the 2030 timeframe. The major focus is to mature currently available technologies on motor drives, power electronics, batteries, and the corresponding thermal management systems. The tasks presented in paper is designing and sizing the airframe and propulsion system, integrating the subsystems developed by other institutes to the aircraft, designing the global-level thermal management systems for the integrated motor drive and the battery, as well as conducting system-level and mission-level performance analysis. In this paper, the architectures of the aircraft, propulsion system, and the thermal management systems are firstly shown. Then the corresponding modeling and analysis methodologies are discussed. Finally, the results including the fuel economy and thermal management are presented, along with a transient analysis on the propulsion system.
{"title":"Finalized Design and Performance Analysis of a Hybrid Turbo-Electric Regional Jet for the NASA ULI Program","authors":"Mingxuan Shi, M. Ozcan, Gokcin Cinar, Jonathan C. Gladin, D. Mavris","doi":"10.1109/ITEC53557.2022.9814048","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814048","url":null,"abstract":"Under the NASA University Leadership Initiative (ULI) program, a team formed by multi-disciplinary universities are collaborating on the advancement of technologies of a hybrid turbo-electric regional jet which aims to enter service in the 2030 timeframe. The major focus is to mature currently available technologies on motor drives, power electronics, batteries, and the corresponding thermal management systems. The tasks presented in paper is designing and sizing the airframe and propulsion system, integrating the subsystems developed by other institutes to the aircraft, designing the global-level thermal management systems for the integrated motor drive and the battery, as well as conducting system-level and mission-level performance analysis. In this paper, the architectures of the aircraft, propulsion system, and the thermal management systems are firstly shown. Then the corresponding modeling and analysis methodologies are discussed. Finally, the results including the fuel economy and thermal management are presented, along with a transient analysis on the propulsion system.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128505901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/itec53557.2022.9813905
Maryam Alizadeh, Sumedh Dhale, A. Emadi
In this paper, an improved climate control system is presented for a Heating, Ventilation, and Air conditioning (HVAC) unit of a battery electric vehicle (BEV) to improve the system’s efficiency while maintaining the desired cabin temperature for the passengers. Since BEVs are entirely dependent on the battery power for HVAC usage, it is crucial to adapt the HVAC control according to the battery status to improve the battery usage. Therefore, our proposed climate control system has taken into account the dynamics of the HVAC model while considering the importance of the ambient temperature and route behavior on the power usage that is needed to provide a comfortable climate in the cabin. Since the ambient temperature has a critical role in estimating the required HVAC power, it is necessary to assess it precisely. Accordingly, a Kalman filter is designed to achieve high precision temperature estimation in real-time. Furthermore, the effect of the driving cycle on the traction motor is considered to improve the overall performance of the vehicle’s system and battery’s health by adjusting climate controller behavior in different weather conditions. A comprehensive simulation study in MATLAB/Simulink® is provided to evaluate the effectiveness of the proposed climate control technique and Kalman filter based ambient temperature estimation.
{"title":"Real-Time Ambient Temperature Estimation Using Kalman Filter and Traction Power-Aware Cabin Climate Control in Battery Electric Vehicles","authors":"Maryam Alizadeh, Sumedh Dhale, A. Emadi","doi":"10.1109/itec53557.2022.9813905","DOIUrl":"https://doi.org/10.1109/itec53557.2022.9813905","url":null,"abstract":"In this paper, an improved climate control system is presented for a Heating, Ventilation, and Air conditioning (HVAC) unit of a battery electric vehicle (BEV) to improve the system’s efficiency while maintaining the desired cabin temperature for the passengers. Since BEVs are entirely dependent on the battery power for HVAC usage, it is crucial to adapt the HVAC control according to the battery status to improve the battery usage. Therefore, our proposed climate control system has taken into account the dynamics of the HVAC model while considering the importance of the ambient temperature and route behavior on the power usage that is needed to provide a comfortable climate in the cabin. Since the ambient temperature has a critical role in estimating the required HVAC power, it is necessary to assess it precisely. Accordingly, a Kalman filter is designed to achieve high precision temperature estimation in real-time. Furthermore, the effect of the driving cycle on the traction motor is considered to improve the overall performance of the vehicle’s system and battery’s health by adjusting climate controller behavior in different weather conditions. A comprehensive simulation study in MATLAB/Simulink® is provided to evaluate the effectiveness of the proposed climate control technique and Kalman filter based ambient temperature estimation.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127146106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813980
Yixin Huangfu, Linnea Campbell, S. Habibi
Infrared cameras can be a great supplement to the environmental perception systems for autonomous driving. Compared to optical cameras, radars, or Lidars, infrared cameras exceed in detecting heat-radiating objects, such as humans and animals, potentially improving the safety of autonomous cars. The underlying detection algorithms for infrared images are generally the same deep learning models applied for optical cameras. However, as the working principles of infrared and optical cameras are different, so are the images they produce. This paper presents the visual differences in infrared images caused by ambient temperature changes and examines their effect on deep learning detectors. Specifically, this study investigates two infrared datasets, one from McMaster University CMHT group and the other from the FLIR company. They represent a northern cold climate and a tropical climate, respectively. Two YOLO-based object detection models are trained on the two datasets separately. The evaluation results show that a colder temperature results in a better performance. In the meantime, cross-evaluation shows a sharp decrease in performance when evaluating the model against the opposite dataset. Furthermore, a third model trained using both datasets outperform the previous two models in all metrics. This study highlights the importance of ambient temperature in training infrared image detectors and provides a feasible solution to performance mismatch issues.
{"title":"Temperature Effect on Thermal Imaging and Deep Learning Detection Models","authors":"Yixin Huangfu, Linnea Campbell, S. Habibi","doi":"10.1109/ITEC53557.2022.9813980","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813980","url":null,"abstract":"Infrared cameras can be a great supplement to the environmental perception systems for autonomous driving. Compared to optical cameras, radars, or Lidars, infrared cameras exceed in detecting heat-radiating objects, such as humans and animals, potentially improving the safety of autonomous cars. The underlying detection algorithms for infrared images are generally the same deep learning models applied for optical cameras. However, as the working principles of infrared and optical cameras are different, so are the images they produce. This paper presents the visual differences in infrared images caused by ambient temperature changes and examines their effect on deep learning detectors. Specifically, this study investigates two infrared datasets, one from McMaster University CMHT group and the other from the FLIR company. They represent a northern cold climate and a tropical climate, respectively. Two YOLO-based object detection models are trained on the two datasets separately. The evaluation results show that a colder temperature results in a better performance. In the meantime, cross-evaluation shows a sharp decrease in performance when evaluating the model against the opposite dataset. Furthermore, a third model trained using both datasets outperform the previous two models in all metrics. This study highlights the importance of ambient temperature in training infrared image detectors and provides a feasible solution to performance mismatch issues.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127322789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813805
Alan Gen Li, M. Preindl
Lithium-ion battery strings are important modules in battery packs. Due to cell variation, strings may have imbalanced state of charge levels, reducing pack capacity and exacerbating degradation. While much research has been devoted to individual cells, string diagnostics using pulse-injection-aided machine learning can reduce sensing requirements and simplify computations. Experimental voltage response data from pulse perturbation of battery cells is used to generate virtual cell strings and ‘design’ the state of charge imbalance within the string. A feedforward neural network is trained on thousands of unique virtual string voltages and can distinguish between the balanced and imbalanced strings with up to 95% accuracy. Verification is performed using different string configurations and state of charge levels. The proposed technique has high promise and could be used to localize or regress the degree of imbalance.
{"title":"State of Charge Imbalance Classification of Lithium-ion Battery Strings using Pulse-Injection-Aided Machine Learning","authors":"Alan Gen Li, M. Preindl","doi":"10.1109/ITEC53557.2022.9813805","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813805","url":null,"abstract":"Lithium-ion battery strings are important modules in battery packs. Due to cell variation, strings may have imbalanced state of charge levels, reducing pack capacity and exacerbating degradation. While much research has been devoted to individual cells, string diagnostics using pulse-injection-aided machine learning can reduce sensing requirements and simplify computations. Experimental voltage response data from pulse perturbation of battery cells is used to generate virtual cell strings and ‘design’ the state of charge imbalance within the string. A feedforward neural network is trained on thousands of unique virtual string voltages and can distinguish between the balanced and imbalanced strings with up to 95% accuracy. Verification is performed using different string configurations and state of charge levels. The proposed technique has high promise and could be used to localize or regress the degree of imbalance.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114284717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814012
Anusha Harish, Jonathan C. Gladin, D. Mavris
Rising environmental concerns has led the aviation industry around the world to set high targets to reduce carbon emission. Revolutionary concepts with unconventional propulsion systems and energy sources are seen as a necessity to achieve carbon neutrality. These include hydrogen combustion, electrified propulsion powered by batteries or hydrogen fuel cells, sustainable aviation fuels, and distributed propulsion. With several potential alternatives still being researched and developed, the path to sustainable aviation is still unclear. This research aims to develop a methodology to quickly assess different concepts based on performance as well as environmental metrics using simple analytical equations, and provide insights about the tradespace for these concepts. At the pre-conceptual design phase, a key performance indicator is the aircraft range, which takes into account the aerodynamics, propulsion and the weight of the aircraft. The objective of this paper is to propose a unified range equation that is applicable to concepts with one or more energy sources and any powertrain architecture. The mathematical equivalence of this equation to range equations derived by other authors, specifically for electrified propulsion, is demonstrated. Finally, the overall efficiency and range equations are derived for a complex aircraft architecture with dual energy sources, multiple propellers and unconventional powertrain configurations, to demonstrate the universality and ease of use of this method.
{"title":"Universal Range Equation for Unconventional Aircraft Concepts","authors":"Anusha Harish, Jonathan C. Gladin, D. Mavris","doi":"10.1109/ITEC53557.2022.9814012","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814012","url":null,"abstract":"Rising environmental concerns has led the aviation industry around the world to set high targets to reduce carbon emission. Revolutionary concepts with unconventional propulsion systems and energy sources are seen as a necessity to achieve carbon neutrality. These include hydrogen combustion, electrified propulsion powered by batteries or hydrogen fuel cells, sustainable aviation fuels, and distributed propulsion. With several potential alternatives still being researched and developed, the path to sustainable aviation is still unclear. This research aims to develop a methodology to quickly assess different concepts based on performance as well as environmental metrics using simple analytical equations, and provide insights about the tradespace for these concepts. At the pre-conceptual design phase, a key performance indicator is the aircraft range, which takes into account the aerodynamics, propulsion and the weight of the aircraft. The objective of this paper is to propose a unified range equation that is applicable to concepts with one or more energy sources and any powertrain architecture. The mathematical equivalence of this equation to range equations derived by other authors, specifically for electrified propulsion, is demonstrated. Finally, the overall efficiency and range equations are derived for a complex aircraft architecture with dual energy sources, multiple propellers and unconventional powertrain configurations, to demonstrate the universality and ease of use of this method.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114176264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814035
Ryan Greenough, Graham McClone, M. Alvarez, Adil Khurram, J. Kleissl
A decentralized algorithm called proximal message passing (PMP) is applied to solve the AC-OPF problem for distribution networks with distributed energy resources (DERs). The second order cone relaxation of the AC-OPF is considered in the PMP algorithm which had previously been implemented only using the linearized DC power flow. In the PMP algorithm, each node shares local information regarding power and voltage (primal variables) and nodal price (dual variables) with its neighbors to minimize the local objective function at each time step. The local objective function consists of generation costs and a penalty associated with violating power flow constraints. The solution of the optimization problem provides day-ahead schedules for the economic dispatch of DERs and generators. Simulation results are presented for a modified IEEE 13 bus system and convergence of the PMP algorithm is discussed in simulations.
{"title":"Decentralized Economic Dispatch via Proximal Message Passing","authors":"Ryan Greenough, Graham McClone, M. Alvarez, Adil Khurram, J. Kleissl","doi":"10.1109/ITEC53557.2022.9814035","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814035","url":null,"abstract":"A decentralized algorithm called proximal message passing (PMP) is applied to solve the AC-OPF problem for distribution networks with distributed energy resources (DERs). The second order cone relaxation of the AC-OPF is considered in the PMP algorithm which had previously been implemented only using the linearized DC power flow. In the PMP algorithm, each node shares local information regarding power and voltage (primal variables) and nodal price (dual variables) with its neighbors to minimize the local objective function at each time step. The local objective function consists of generation costs and a penalty associated with violating power flow constraints. The solution of the optimization problem provides day-ahead schedules for the economic dispatch of DERs and generators. Simulation results are presented for a modified IEEE 13 bus system and convergence of the PMP algorithm is discussed in simulations.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"5 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120918311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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