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Keywords = Full hybrid power-train

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21 pages, 4247 KiB  
Article
Hardware-in-the-Loop Implementation of an Optimized Energy Management Strategy for Range-Extended Electric Trucks
by Ankur Shiledar, Manfredi Villani and Giorgio Rizzoni
Energies 2024, 17(21), 5294; https://doi.org/10.3390/en17215294 - 24 Oct 2024
Viewed by 743
Abstract
The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores [...] Read more.
The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores a Range-Extended Electric Vehicle (REEV) for medium-duty Class 6 pick-up and delivery trucks. This hybrid architecture combines an electric powertrain with an internal combustion engine range-extender. Maximizing the efficiency of REEVs requires an Energy Management Strategy (EMS) to optimally split the power between the two power sources. In this work, a hierarchical EMS is developed through model-based design and validated via Hardware-In-The-Loop (HIL) simulations. The proposed EMS demonstrated a 7% reduction in fuel consumption compared to a baseline control strategy, while maintaining emissions and engine start frequency comparable to a benchmark globally optimal EMS obtained with dynamic programming. Furthermore, HIL results confirmed the strategy’s real-time implementation feasibility, highlighting the practical viability of the controller. This research underscores the potential of REEVs in significantly reducing emissions and fuel consumption, as well as providing a sustainable alternative for medium-duty truck applications. Full article
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14 pages, 4612 KiB  
Article
A Simplified 4-DOF Dynamic Model of a Series-Parallel Hybrid Electric Vehicle
by Lihong Dai, Peng Hu, Tianyou Wang, Guosheng Bian and Haoye Liu
World Electr. Veh. J. 2024, 15(9), 390; https://doi.org/10.3390/wevj15090390 - 28 Aug 2024
Viewed by 870
Abstract
To research the dynamic response of a new type of dedicated transmission for a hybrid electric vehicle, a detailed dynamics model should be built. However, a model with too many degrees of freedom has a negative effect on controller design, which means the [...] Read more.
To research the dynamic response of a new type of dedicated transmission for a hybrid electric vehicle, a detailed dynamics model should be built. However, a model with too many degrees of freedom has a negative effect on controller design, which means the detailed model should be simplified. In this paper, two dynamic models are established. One is an original and detailed powertrain dynamics model (ODPDM), which can capture the transient response, and it is validated that the ODPDM can be used to accurately describe the real vehicle in some specific operating conditions. The other is a simplified torsional vibration dynamics model to study the torsional vibration characteristics of the hybrid electric vehicle. Compared with the full-order model, which is based on the ODPDM, the simplified model has a very similar vibration in low frequency. This study provides a basis for further vibration control of the hybrid powertrain during the process of a driving-mode switch. Full article
(This article belongs to the Special Issue Dynamics, Control and Simulation of Electrified Vehicles)
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21 pages, 2816 KiB  
Article
Adaptive Hybrid Beamforming Codebook Design Using Multi-Agent Reinforcement Learning for Multiuser Multiple-Input–Multiple-Output Systems
by Manasjyoti Bhuyan, Kandarpa Kumar Sarma, Debashis Dev Misra, Koushik Guha and Jacopo Iannacci
Appl. Sci. 2024, 14(16), 7109; https://doi.org/10.3390/app14167109 - 13 Aug 2024
Cited by 1 | Viewed by 1823
Abstract
This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple-input–multiple-output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within [...] Read more.
This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple-input–multiple-output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML)-based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP), consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system’s ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing the overall system performance. This work leverages MARL’s potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks. Full article
(This article belongs to the Special Issue New Challenges in MIMO Communication Systems)
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21 pages, 10921 KiB  
Article
ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC
by Mokhtar Jlidi, Oscar Barambones, Faiçal Hamidi and Mohamed Aoun
Energies 2024, 17(12), 2802; https://doi.org/10.3390/en17122802 - 7 Jun 2024
Cited by 1 | Viewed by 972
Abstract
Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction [...] Read more.
Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction tasks. This study offers a comprehensive review of predominant AI-based approaches to photovoltaic (PV) energy forecasting, with a particular emphasis on artificial neural networks (ANNs). We introduce a revolutionary methodology that amalgamates the predictive capabilities of ANN with the precision control afforded by the minimum-risk problem and sliding mode control (MRP-SMC), thereby revolutionizing the PV panel performance enhancement. Building upon this methodology, our hybrid approach utilizes the ANN as a proficient weather forecaster, accurately predicting the temperature and solar radiation levels impacting the panels. These forecasts serve as guiding principles for the MRP-SMC algorithm, enabling the proactive determination of the Maximum Power Point (MPP). Unlike conventional methods that grapple with weather unpredictability, the MRP-SMC algorithm transforms stochastic optimization challenges into controllable deterministic risk problems. Our method regulates the boost converter’s work cycle dynamically. This dynamic adaptation, guided by environmental predictions from ANNs, unlocks the full potential of PV panels, maximizing energy recovery efficiency. To train the model, we utilized a large dataset comprising 60,538 temperature and solar radiation readings from the Department of Systems Engineering and Automation at the Faculty of Engineering in Vitoria (University of the Basque Country). Our approach demonstrates a high regression coefficient (R = 0.99) and low mean square error (MSE = 0.0044), underscoring its exceptional ability to predict real energy values. In essence, this study proposes a potent fusion of artificial intelligence and control mechanisms that unleash the untapped potential of photovoltaic panels. By utilizing forecasts to guide the converter, we are paving the way for a future where solar energy shines brighter than ever. Full article
(This article belongs to the Special Issue Smart Grid and Energy Storage)
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19 pages, 9439 KiB  
Article
Combining Gasoline Compression Ignition and Powertrain Hybridization for Long-Haul Applications
by Rafael Lago Sari, Yu Zhang, Brock Merritt, Praveen Kumar and Ashish Shah
Energies 2024, 17(5), 1099; https://doi.org/10.3390/en17051099 - 25 Feb 2024
Viewed by 895
Abstract
Gasoline compression ignition (GCI) combustion was demonstrated to be an effective combustion concept to achieve high brake thermal efficiency with low-reactivity fuels while offering improved NOx–soot trade-off. Nevertheless, future greenhouse gas regulations still challenge the heavy-duty transportation sector on both engine and vehicle [...] Read more.
Gasoline compression ignition (GCI) combustion was demonstrated to be an effective combustion concept to achieve high brake thermal efficiency with low-reactivity fuels while offering improved NOx–soot trade-off. Nevertheless, future greenhouse gas regulations still challenge the heavy-duty transportation sector on both engine and vehicle basis. Hybridization is a possible solution in this scenario, allowing the avoidance of low-efficiency conditions and energy recovery during regenerative braking, improving overall vehicle efficiency. In this sense, this investigation proposes a detailed analysis to understand the optimum hybridization strategy to be used together with GCI to simultaneously harness low pollutant and CO2 emissions. For that, different hybrid architectures were defined in GT Drive (Mild hybrid 48 V P0 and P2 and full Hybrid P2 500 V) and submitted to 15 different use cases, constituted by five normative and real-driving conditions from the US, China, India, and Europe and three different payloads. Results showed that all hybridization strategies could provide fuel savings benefits to some extent. Nonetheless, usage profile is a dominant factor to be accounted for, benefiting specific hybrid powertrains. For instance, P0 and P2 48 V could provide similar savings as P2 500 V, where regenerative braking is limited. Nonetheless, P2 500 V is a superior powertrain if more demanding cycles are considered, allowing it to drive and recuperate energy without exceeding the Crate limitations of the battery. Full article
(This article belongs to the Special Issue Advances in Hybrid Electric Powertrain and Vehicle)
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20 pages, 2544 KiB  
Article
The Effectiveness of HEVs Phase-Out by 2035 in Favor of BEVs with Respect to the Production of CO2 Emissions: The Italian Case
by Francesca Maria Grimaldi and Pietro Capaldi
Energies 2024, 17(4), 961; https://doi.org/10.3390/en17040961 - 19 Feb 2024
Cited by 2 | Viewed by 1538
Abstract
The EU has planned the phase-out of new vehicles based on internal combustion engines in favor of high-efficiency battery electric vehicles (BEV) by 2035 (Fit for 55 package). However, many doubts remain about the effectiveness of this choice for each country of the [...] Read more.
The EU has planned the phase-out of new vehicles based on internal combustion engines in favor of high-efficiency battery electric vehicles (BEV) by 2035 (Fit for 55 package). However, many doubts remain about the effectiveness of this choice for each country of the Union in terms of CO2 emissions reduction, as each State is characterized by a different carbon intensity related to the production of electricity needed to manufacture and recharge vehicles. This study seeks to explore the Italian case. To this aim, carbon intensities related to electricity production were calculated considering both the Italian electricity mix production in 2022 and those envisaged in 2035, considering two energy scenarios based on different introductions of renewable energy sources (RES). Afterward, the values obtained were adopted for determining the CO2 emissions related to the whole production process of battery systems in Italy (emissions from mining and refining, scrap materials, and final assembly included) by comparing some of the most up-to-date Life-Cycle Assessment (LCA) analyses related to the manufacturing cycle of the batteries. Finally, the results were adopted to calculate the starting carbon debit for A, B, C, and M car segments for Mild Hybrid, Full Hybrid, and Full Electric powertrains. At the same time, statistical road fuel/electricity consumption data were collected and overall CO2 emissions were calculated for the same vehicles adopting a dynamic approach and plotted for a defined distance, so as to determine break-even points with respect to the cumulative (i.e., from battery and road) carbon emissions. The results showed that advantages related to electric vehicles are significant only if a low carbon intensity related to electricity production is reached by means of a very high introduction of RES, thus keeping the door open for innovative hybrid powertrain technologies, if fed with low carbon fuels. Full article
(This article belongs to the Special Issue CO2 Emissions from Vehicles (Volume II))
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31 pages, 10613 KiB  
Article
A New Generation of Hydrogen-Fueled Hybrid Propulsion Systems for the Urban Mobility of the Future
by Ivan Arsie, Michele Battistoni, Pier Paolo Brancaleoni, Roberto Cipollone, Enrico Corti, Davide Di Battista, Federico Millo, Alessio Occhicone, Benedetta Peiretti Paradisi, Luciano Rolando and Jacopo Zembi
Energies 2024, 17(1), 34; https://doi.org/10.3390/en17010034 - 20 Dec 2023
Cited by 12 | Viewed by 2130
Abstract
The H2-ICE project aims at developing, through numerical simulation, a new generation of hybrid powertrains featuring a hydrogen-fueled Internal Combustion Engine (ICE) suitable for 12 m urban buses in order to provide a reliable and cost-effective solution for the abatement of both CO [...] Read more.
The H2-ICE project aims at developing, through numerical simulation, a new generation of hybrid powertrains featuring a hydrogen-fueled Internal Combustion Engine (ICE) suitable for 12 m urban buses in order to provide a reliable and cost-effective solution for the abatement of both CO2 and criteria pollutant emissions. The full exploitation of the potential of such a traction system requires a substantial enhancement of the state of the art since several issues have to be addressed. In particular, the choice of a more suitable fuel injection system and the control of the combustion process are extremely challenging. Firstly, a high-fidelity 3D-CFD model will be exploited to analyze the in-cylinder H2 fuel injection through supersonic flows. Then, after the optimization of the injection and combustion process, a 1D model of the whole engine system will be built and calibrated, allowing the identification of a “sweet spot” in the ultra-lean combustion region, characterized by extremely low NOx emissions and, at the same time, high combustion efficiencies. Moreover, to further enhance the engine efficiency well above 40%, different Waste Heat Recovery (WHR) systems will be carefully scrutinized, including both Organic Rankine Cycle (ORC)-based recovery units as well as electric turbo-compounding. A Selective Catalytic Reduction (SCR) aftertreatment system will be developed to further reduce NOx emissions to near-zero levels. Finally, a dedicated torque-based control strategy for the ICE coupled with the Energy Management Systems (EMSs) of the hybrid powertrain, both optimized by exploiting Vehicle-To-Everything (V2X) connection, allows targeting H2 consumption of 0.1 kg/km. Technologies developed in the H2-ICE project will enhance the know-how necessary to design and build engines and aftertreatment systems for the efficient exploitation of H2 as a fuel, as well as for their integration into hybrid powertrains. Full article
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17 pages, 5365 KiB  
Article
Synergic Design and Simulation of Battery-Operated Trains on Partially Electrified Lines: A Case Study regarding the Firenze Faenza Line
by Luca Pugi
Energies 2024, 17(1), 24; https://doi.org/10.3390/en17010024 - 20 Dec 2023
Cited by 1 | Viewed by 1260
Abstract
A full electrification of many local railway lines is often not feasible or sustainable in terms of construction and maintenance costs or alternatively for the presence of additional constraints and limitations deriving from environmental or infrastructural limitations. Battery Operated or other kind of [...] Read more.
A full electrification of many local railway lines is often not feasible or sustainable in terms of construction and maintenance costs or alternatively for the presence of additional constraints and limitations deriving from environmental or infrastructural limitations. Battery Operated or other kind of hybrid solutions powertrains are currently proposed as sustainable alternatives to Internal combustion engines for the propulsion of rolling stock on not electrified lines. In this work, authors propose the adoption of a partial electrification of lines to assure higher performances and reliability of battery-operated rolling stock designed to be recharged and feed using standard technologies such as pantographs gathering power from suspended catenaries. This innovative solution is designed and sized for a vehicle inspired from an existing one and simulated for two different existing lines, also proposing an optimal distribution of electrified sections dedicated to train recharge. This Case Study is simulated considering some possible applications to some existing railway lines in Italy. Full article
(This article belongs to the Section E: Electric Vehicles)
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16 pages, 1434 KiB  
Article
Prioritization of Fluorescence In Situ Hybridization (FISH) Probes for Differentiating Primary Sites of Neuroendocrine Tumors with Machine Learning
by Lucas Pietan, Hayley Vaughn, James R. Howe, Andrew M. Bellizzi, Brian J. Smith, Benjamin Darbro, Terry Braun and Thomas Casavant
Int. J. Mol. Sci. 2023, 24(24), 17401; https://doi.org/10.3390/ijms242417401 - 12 Dec 2023
Viewed by 1294
Abstract
Determining neuroendocrine tumor (NET) primary sites is pivotal for patient care as pancreatic NETs (pNETs) and small bowel NETs (sbNETs) have distinct treatment approaches. The diagnostic power and prioritization of fluorescence in situ hybridization (FISH) assay biomarkers for establishing primary sites has not [...] Read more.
Determining neuroendocrine tumor (NET) primary sites is pivotal for patient care as pancreatic NETs (pNETs) and small bowel NETs (sbNETs) have distinct treatment approaches. The diagnostic power and prioritization of fluorescence in situ hybridization (FISH) assay biomarkers for establishing primary sites has not been thoroughly investigated using machine learning (ML) techniques. We trained ML models on FISH assay metrics from 85 sbNET and 59 pNET samples for primary site prediction. Exploring multiple methods for imputing missing data, the impute-by-median dataset coupled with a support vector machine model achieved the highest classification accuracy of 93.1% on a held-out test set, with the top importance variables originating from the ERBB2 FISH probe. Due to the greater interpretability of decision tree (DT) models, we fit DT models to ten dataset splits, achieving optimal performance with k-nearest neighbor (KNN) imputed data and a transformation to single categorical biomarker probe variables, with a mean accuracy of 81.4%, on held-out test sets. ERBB2 and MET variables ranked as top-performing features in 9 of 10 DT models and the full dataset model. These findings offer probabilistic guidance for FISH testing, emphasizing the prioritization of the ERBB2, SMAD4, and CDKN2A FISH probes in diagnosing NET primary sites. Full article
(This article belongs to the Special Issue New Insights in Translational Bioinformatics)
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12 pages, 3996 KiB  
Article
Hybridisation Concept of Light Vehicles Utilising an Electrified Planetary Gear Set
by Michael Engels, Moritz Jakoby, Timm Fahrbach and Jakob Andert
Vehicles 2023, 5(4), 1622-1633; https://doi.org/10.3390/vehicles5040088 - 7 Nov 2023
Cited by 1 | Viewed by 1550
Abstract
Climate change and air pollution are two significant challenges facing our society and represent a major driver for new developments in the transport sector. As a consequence, automotive manufacturers have focused on the electrification of vehicle propulsion systems and offer a wide range [...] Read more.
Climate change and air pollution are two significant challenges facing our society and represent a major driver for new developments in the transport sector. As a consequence, automotive manufacturers have focused on the electrification of vehicle propulsion systems and offer a wide range of hybrid and full-electric vehicles in different classes. However, in the world’s most densely populated metropolitan areas, small and lightweight vehicles are key for the mobility of millions. Traditionally these vehicles have provided cost-effective transportation which is difficult to preserve with vehicle electrification. Many of these light vehicles, such as scooters and all-terrain vehicles, use internal combustion engines in combination with a continuously variable rubber belt transmission which provides a simple, comfortable and cost-effective transmission technology but with poor efficiency and high maintenance costs. In this contribution, a novel full hybrid powertrain concept is proposed that offers a similar driving experience to conventional continuously variable transmissions while providing significantly improved performance and fuel economy combined with low system complexity. In its basic configuration, the hybrid powertrain can operate without active actuators and even with mechanical throttle control of the internal combustion engine. This minimalist approach reduces system costs and helps to create a competitive solution for price-sensitive markets. The hybrid system is based on a planetary gear set that combines the internal combustion engine and an electric motor. It is complemented by a centrifugal clutch and one-way clutch, resulting in different operating modes for low and high speeds as well as for electric driving. This paper describes the mechanical design and control approach of the proposed hybrid powertrain layout. In order to evaluate the basic functionalities, a prototype vehicle was built and tested. This contribution shows the integration of the hybrid powertrain concept in a prototype vehicle and proves the fulfilment of all required full hybrid functionalities. Full article
(This article belongs to the Special Issue Feature Papers on Advanced Vehicle Technologies)
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22 pages, 1433 KiB  
Article
Investigation and Optimization of Energy Consumption for Hybrid Hydraulic Excavator with an Innovative Powertrain
by Van Hien Nguyen, Tri Cuong Do and Kyoung Kwan Ahn
Actuators 2023, 12(10), 382; https://doi.org/10.3390/act12100382 - 10 Oct 2023
Cited by 4 | Viewed by 2185
Abstract
This paper presents an innovative powertrain design and an energy regeneration system for hybrid hydraulic excavators to reduce energy consumption and emissions. The proposed system is designed to maximize engine efficiency and make full use of the energy gained from boom and arm [...] Read more.
This paper presents an innovative powertrain design and an energy regeneration system for hybrid hydraulic excavators to reduce energy consumption and emissions. The proposed system is designed to maximize engine efficiency and make full use of the energy gained from boom and arm retraction. The powertrain features an innovative design that incorporates a continuously variable transmission (CVT), which drives the main pump. It enables precise control of both the engine’s speed and torque, ensuring that the engine operates within the high-efficiency range. The energy regeneration system is applied to regenerate the potential energy of the boom and arm, which can be used to either charge the battery or directly supply power to the main pump. Moreover, an energy management strategy based on an equivalent consumption minimization strategy is used to distribute the power while offering maximum engine efficiency. When compared with the existing hybrid system and conventional system, the simulation results indicated that the proposed approach achieves energy-saving efficiencies of 16.9% and 77.1%, respectively, at high velocities and 22.25% and 53.5%, respectively, at medium velocities. This research signifies a promising advancement for sustainable and efficient hydraulic excavator operations. Full article
(This article belongs to the Special Issue Innovative and Intelligent Actuation for Heavy-Duty Applications)
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36 pages, 20667 KiB  
Review
A Review of Powertrain Electrification for Greener Aircraft
by Xavier Roboam
Energies 2023, 16(19), 6831; https://doi.org/10.3390/en16196831 - 26 Sep 2023
Cited by 9 | Viewed by 2542
Abstract
This review proposes an overview of hybrid electric and full electric powertrains dedicated to greener aircraft in the “sky decarbonization” context. After having situated the state of the art and context of energy hybridization in the aviation sector, we propose the visit of [...] Read more.
This review proposes an overview of hybrid electric and full electric powertrains dedicated to greener aircraft in the “sky decarbonization” context. After having situated the state of the art and context of energy hybridization in the aviation sector, we propose the visit of several architectures for powertrain electrification, situating the potential benefits but also the main challenges to be faced to takeoff these new solutions. Then, as a first example, we consider the EU project “HASTECS” (Hybrid Aircraft: reSearch on Thermal and Electric Components and Systems) in the framework of Clean Sky 2. It relates to a series hybrid chain integrated into a regional aircraft. This energy system integrates especially power electronics and electric machines with a high degree of integration, which raises the “thermal challenge” and the need to integrate cooling devices. Through the snowball effects typical of the aviation sector, this example emphasizes how important it is to “hunt for kilos”, an alternative solution consisting of eliminating the power electronics within the powertrain. This is why we propose a second example, which concerns an AC power channel without power electronics that only integrates synchronous magnet machines (generator and motor) directly coupled on an AC bus. This last architecture nevertheless raises questions in terms of stability, with one solution being to insert an auxiliary hybridization branch via battery storage. Theoretical analyses and experiments at a reduced power scale show the viability of this concept. Finally, some recommendations for future research with potential technological breakthroughs complete that review. Full article
(This article belongs to the Section E: Electric Vehicles)
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17 pages, 1144 KiB  
Article
Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow
by Zachary Kilwein, Jordan Jalving, Michael Eydenberg, Logan Blakely, Kyle Skolfield, Carl Laird and Fani Boukouvala
Energies 2023, 16(16), 5913; https://doi.org/10.3390/en16165913 - 10 Aug 2023
Cited by 1 | Viewed by 1758
Abstract
In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. [...] Read more.
In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. A prominent example that is explored in the manuscript is the security-constrained optimal power flow (SCOPF) problem, which minimizes power generation costs, while enforcing system feasibility under contingency failures in the transmission network. In its full form, this problem has been modeled as a nonlinear two-stage stochastic programming problem. In this work, we propose a hybrid structure that incorporates and takes advantage of both a high-fidelity physical model and fast machine learning surrogates. Neural network (NN) models have been shown to classify highly non-linear functions and can be trained offline but require large training sets. In this work, we present how model-guided sampling can efficiently create datasets that are highly informative to a NN classifier for non-convex functions. We show how the resultant NN surrogates can be integrated into a non-linear program as smooth, continuous functions to simultaneously optimize the objective function and enforce feasibility using existing non-linear solvers. Overall, this allows us to optimize instances of the SCOPF problem with an order of magnitude CPU improvement over existing methods. Full article
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23 pages, 2914 KiB  
Article
Cradle-to-Grave Lifecycle Environmental Assessment of Hybrid Electric Vehicles
by Shafayat Rashid and Emanuele Pagone
Sustainability 2023, 15(14), 11027; https://doi.org/10.3390/su151411027 - 14 Jul 2023
Viewed by 6495
Abstract
Demand for sustainable transportation with a reduced environmental impact has led to the widespread adoption of electrified powertrains. Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) produce lower greenhouse gas (GHG) emissions during the use phase of their lifecycle, compared to [...] Read more.
Demand for sustainable transportation with a reduced environmental impact has led to the widespread adoption of electrified powertrains. Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) produce lower greenhouse gas (GHG) emissions during the use phase of their lifecycle, compared to conventional internal combustion engine vehicles (ICEVs). However, a full understanding of their total environmental impact, from resource extraction to end-of-life (EOL), of a contemporary, real-world HEV and PHEV remains broadly elusive in the scientific literature. In this work, for the first time, a systematic cradle-to-grave lifecycle analysis (LCA) of a Toyota Prius XW50, as a HEV and PHEV, was used to comprehensively assess its environmental impact throughout its entire lifecycle using established lifecycle inventory databases. The LCA revealed that the gasoline fuel cycle (extraction, refinement, and transportation) is a major environmental impact “hotspot”. The more electrified PHEV model consumes 3.2% more energy and emits 5.6% more GHG emissions within the vehicle’s lifecycle, primarily owed to the manufacturing and recycling of larger traction batteries. However, when factoring in the fuel cycle, the PHEV model exhibits a 29.6% reduction in overall cradle-to-grave life energy consumption, and a 17.5% reduction in GHG emissions, in comparison to the less-electrified HEV. This suggests that the higher-electrified PHEV has a lower environmental impact than the HEV throughout the whole lifecycle. The presented cradle-to-grave LCA study can be a valuable benchmark for future research in comparing other HEVs and PHEVs or different powertrains for similarly sized passenger vehicles. Full article
(This article belongs to the Special Issue Operations Research: Optimization, Resilience and Sustainability)
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17 pages, 6954 KiB  
Article
Comparison of the Energy Consumption and Exhaust Emissions between Hybrid and Conventional Vehicles, as Well as Electric Vehicles Fitted with a Range Extender
by Andrzej Ziółkowski, Paweł Fuć, Aleks Jagielski, Maciej Bednarek and Szymon Konieczka
Energies 2023, 16(12), 4669; https://doi.org/10.3390/en16124669 - 12 Jun 2023
Cited by 10 | Viewed by 1767
Abstract
The introduction of new Euro exhaust emission standards and CO2 limits has forced carmakers to implement alternative hybrid and electric powertrains. We are observing a dynamic advancement of this sector. The authors’ primary motivation was to perform a series of measurements of [...] Read more.
The introduction of new Euro exhaust emission standards and CO2 limits has forced carmakers to implement alternative hybrid and electric powertrains. We are observing a dynamic advancement of this sector. The authors’ primary motivation was to perform a series of measurements of the exhaust emissions and fuel mileages from vehicles fitted with hybrid, conventional and electric (range extender) powertrains. Three vehicles were used in the research project. The first one was a passenger car with a full hybrid powertrain. The vehicle was fitted with a 1.6 dm3 spark ignition engine. The second one was fitted with a 2.2 dm3 diesel engine. The third one was fitted with a 125 kW electric motor and a 28 kW combustion engine used as a range extender. The investigations were carried out according to the RDE (Real Driving Emission) methodology on a test route composed of urban, rural and highway portions. The test route was set in the Poznan agglomeration, and its distance was approx. 80 km. For the measurements, the authors used SEMTECH-DS from the PEMS (Portable Emissions Measurement System) equipment group. Based on the obtained results, the authors validated the test route in terms of the RDE compliance and determined the exhaust emissions and fuel mileages. The authors also analyzed the influence of the conditions of the measurements on the powertrain characteristics of each of the tested vehicles. Full article
(This article belongs to the Section I2: Energy and Combustion Science)
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