Relevant bibliographies by topics / Battery design optimization framework

Academic literature on the topic 'Battery design optimization framework'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Battery design optimization framework"

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Vora, Ashish P., Xing Jin, Vaidehi Hoshing, Gregory Shaver, Subbarao Varigonda, and Wallace E. Tyner. "Integrating battery degradation in a cost of ownership framework for hybrid electric vehicle design optimization." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 6 (October 21, 2018): 1507–23. http://dx.doi.org/10.1177/0954407018802663.

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Prior design optimization efforts do not capture the impact of battery degradation and replacement on the total cost of ownership, even though the battery is the most expensive and least robust powertrain component. A novel, comprehensive framework is presented for model-based parametric optimization of hybrid electric vehicle powertrains, while accounting for the degradation of the electric battery and its impact on fuel consumption and battery replacement. This is achieved by integrating a powertrain simulation model, an electrochemical battery model capable of predicting degradation, and a lifecycle economic analysis (including net present value, payback period, and internal rate of return). An example design study is presented here to optimize the sizing of the electric motor and battery pack for the North American transit bus application. The results show that the optimal design parameters depend on the metric of interest (i.e. net present value, payback period, etc.). Finally, it is also observed that the fuel consumption increases by up to 10% from “day 1” to the end of battery life. These results highlight the utility of the proposed framework in enabling better design decisions as compared to methods that do not capture the evolution of vehicle performance and fuel consumption as the battery degrades.
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Hasan, Md Mahamudul, Boris Berseneff, Tim Meulenbroeks, Igor Cantero, Sajib Chakraborty, Thomas Geury, and Omar Hegazy. "A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology." Energies 15, no. 15 (July 24, 2022): 5355. http://dx.doi.org/10.3390/en15155355.

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This paper develops a multi-objective co-design optimization framework for the optimal sizing and selection of battery and power electronics in hybrid battery energy storage systems (HBESSs) connected to the grid. The co-design optimization approach is crucial for such a complex system with coupled subcomponents. To this end, a nondominated sorting genetic algorithm (NSGA-II) is used for optimal sizing and selection of technologies in the design of the HBESS, considering design parameters such as cost, efficiency, and lifetime. The interoperable framework is applied considering three first-life battery cells and one second-life battery cell for forming two independent battery packs as a hybrid battery unit and considers two power conversion architectures for interfacing the hybrid battery unit to the grid with different power stages and levels of modularity. Finally, the globally best HBESS system obtained as the output of the framework is made up of LTO first-life and LFP second-life cells and enables a total cost of ownership (TCO) reduction of 29.6% compared to the baseline.
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Xu, Huanwei, Liangwen Liu, and Miao Zhang. "Adaptive surrogate model-based optimization framework applied to battery pack design." Materials & Design 195 (October 2020): 108938. http://dx.doi.org/10.1016/j.matdes.2020.108938.

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Machchhar, R. J., and A. Bertoni. "Supporting the Transition Towards Electromobility in the Construction and Mining Sector: Optimization Framework and Demonstration on an Electrical Hauler." Proceedings of the Design Society 2 (May 2022): 1649–58. http://dx.doi.org/10.1017/pds.2022.167.

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AbstractThe paper presents a framework for the integration of the system's design variables, state variables, control strategies, and contextual variables into a design optimization problem to assist early-stage design decisions. The framework is based on a global optimizer incorporating Dynamic Programming, and its applicability is demonstrated by the conceptual design of an electrical hauler. Pareto front of optimal design solutions, in terms of time and cost, together with optimal velocity profiles and battery state-of-charge is visualized for the given mining scenario.
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Tuncel, Yigit, Sizhe An, Ganapati Bhat, Naga Raja, Hyung Gyu Lee, and Umit Ogras. "Voltage-Frequency Domain Optimization for Energy-Neutral Wearable Health Devices." Sensors 20, no. 18 (September 14, 2020): 5255. http://dx.doi.org/10.3390/s20185255.

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Wearable health and activity monitoring devices must minimize the battery charging and replacement requirements to be practical. Numerous design techniques, such as power gating and multiple voltage-frequency (VF) domains, can be used to optimize power consumption. However, circuit-level techniques alone cannot minimize energy consumption unless they exploit domain-specific knowledge. To this end, we propose a system-level framework that minimizes the energy consumption of wearable health and activity monitoring applications by combining domain-specific knowledge with low-power design techniques. The proposed technique finds the energy-optimal VF domain partitioning and the corresponding VF assignments to each partition. We evaluate this framework with experiments on two activity monitoring and one electrocardiogram applications. Our approach decreases the energy consumption by 33–58% when compared to baseline designs. It also achieves 20–46% more savings compared to a state-of-the-art approach.
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Liu, Changhong, and Lin Liu. "Optimizing Battery Design for Fast Charge through a Genetic Algorithm Based Multi-Objective Optimization Framework." ECS Transactions 77, no. 11 (July 7, 2017): 257–71. http://dx.doi.org/10.1149/07711.0257ecst.

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Zhang, Le, Ziling Zeng, and Kun Gao. "A bi-level optimization framework for charging station design problem considering heterogeneous charging modes." Journal of Intelligent and Connected Vehicles 5, no. 1 (January 24, 2022): 8–16. http://dx.doi.org/10.1108/jicv-07-2021-0009.

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Purpose The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes. Design/methodology/approach The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model. Findings This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically. Originality/value The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses.
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Vora, Ashish P., Xing Jin, Vaidehi Hoshing, Xiaofan Guo, Gregory Shaver, Wallace Tyner, Eric Holloway, Subbarao Varigonda, and Joachim Kupe. "Simulation Framework for the Optimization of HEV Design Parameters: Incorporating Battery Degradation in a Lifecycle Economic Analysis." IFAC-PapersOnLine 48, no. 15 (2015): 195–202. http://dx.doi.org/10.1016/j.ifacol.2015.10.028.

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Pierri, Erika, Valentina Cirillo, Thomas Vietor, and Marco Sorrentino. "Adopting a Conversion Design Approach to Maximize the Energy Density of Battery Packs in Electric Vehicles." Energies 14, no. 7 (March 31, 2021): 1939. http://dx.doi.org/10.3390/en14071939.

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Innovative vehicle concepts have been developed in the past years in the automotive sector, including alternative drive systems such as hybrid and battery electric vehicles, so as to meet the environmental targets and cope with the increasingly stringent emissions regulations. The preferred hybridizing technology is lithium-ion battery, thanks to its high energy density. The optimal integration of battery packs in the vehicle is a challenging task when designing e-mobility concepts. Therefore, this work proposes a conceptual design procedure aimed at optimizing the sizing of hybrid and battery electric vehicles. Particularly, the influence of the cell type, physical disposition and arrangement of the electrical devices is accounted for within a conversion design framework. The optimization is focused on the trade-off between the battery pack capacity and weight. After introducing the main features of electric traction systems and their challenges compared to conventional ones, the relevant design properties of electric vehicles are analyzed. A detailed strategy, encompassing the selection of battery format and technology, battery pack design and final assessment of the proposed set-up, is presented and implemented in an exemplary application, assuming an existing commercial vehicle as the reference starting layout. Prismatic, cylindrical and pouch cells are configured aiming at achieving installed battery energy as close as possible to the reference one, while meeting the original installation space constraint. The best resulting configuration, which also guarantees similar peak power performance of the reference battery-pack, allows reducing the mass of the storage system down to 70% of its starting value.
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Takano, Hirotaka, Ryosuke Hayashi, Hiroshi Asano, and Tadahiro Goda. "Optimal Sizing of Battery Energy Storage Systems Considering Cooperative Operation with Microgrid Components." Energies 14, no. 21 (November 8, 2021): 7442. http://dx.doi.org/10.3390/en14217442.

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Battery energy storage systems (BESSs) are key components in efficiently managing the electric power supply and demand in microgrids. However, the BESSs have issues in their investment costs and operating lifetime, and thus, the optimal sizing of the BESSs is one of the crucial requirements in design and management of the microgrids. This paper presents a problem framework and its solution method that calculates the optimal size of the BESSs in a microgrid, considering their cooperative operations with the other components. The proposed framework is formulated as a bi-level optimization problem; however, based on the Karush–Kuhn–Tucker approach, it is regarded as a type of operation scheduling problem. As a result, the techniques developed for determining the operation schedule become applicable. In this paper, a combined algorithm of binary particle swarm optimization and quadratic programming is selected as the basis of the solution method. The validity of the authors’ proposal is verified through numerical simulations and discussion of their results.
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Dissertations / Theses on the topic "Battery design optimization framework"

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Bakker, Craig Kent Reddick. "A differential geometry framework for multidisciplinary design optimization." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708688.

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Liu, Qiang. "EBF3GLWingOpt: A Framework for Multidisciplinary Design Optimization of Wings Using SpaRibs." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/49665.

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A global/local framework for multidisciplinary optimization of generalized aircraft wing structure has been developed. The concept of curvilinear stiffening members (spars, ribs and stiffeners) has been applied in the optimization of a wing structure. A global wing optimization framework EBF3WingOpt, which integrates the static aeroelastic, flutter and buckling analysis, has been implemented for exploiting the optimal design at the wing level. The wing internal structure is optimized using curvilinear spars and ribs (SpaRibs). A two-step optimization approach, which consists of topology optimization with shape design variables and size optimization with thickness design variables, is implemented in EBF3WingOpt. A local panel optimization EBF3PanelOpt, which includes stress and buckling evaluation criteria, is performed to optimize the local panels bordered by spars and ribs for further structural weight saving. The local panel model is extracted from the global finite element model. The boundary conditions are defined on the edges of local panels using the displacement fields obtained from the global model analysis. The local panels are optimized to satisfy stress and buckling constraints. Stiffened panel with curvilinear stiffeners is implemented in EBF3PanelOpt to improve the buckling resistance of the local panels. The optimization of stiffened panels has been studied and integrated in the local panel optimization. EBF3WingOpt has been applied for the optimization of the wing structure of the Boeing N+2 supersonic transport wing and NASA common research model (CRM). The optimization results have shown the advantage of curvilinear spars and ribs concept. The local panel optimization EBF3PanelOpt is performed for the NASA CRM wing. The global-local optimization framework EBF3GLWingOpt, which incorporates global wing optimization module EBF3WingOpt and local panel optimization module EBF3PanelOpt, is developed using MATLAB and Python programming to integrate several commercial software: MSC.PATRAN for pre and post processing, MSC.NASTRAN for finite element analysis. An approximate optimization method is developed for the stiffened panel optimization so as to reduce the computational cost. The integrated global-local optimization approach has been applied to subsonic NASA common research model (CRM) wing which proves the methodology's application scaling with medium fidelity FEM analysis. Both the global wing design variables and local panel design variables are optimized to minimize the wing weight at an acceptable computational cost.
Ph. D.
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Zheng, Panni. "The Design and Optimization of a Lithium-ion Battery Direct Recycling Process." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93212.

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Nowadays, Lithium-ion batteries (LIBs) have dominated the power source market in a variety of applications. Lithium cobalt oxide (LiCoO2) is one of the most common cathode materials for LIBs in consumer electronics. The recycling of LIBs is important because cobalt is an expensive element that is dependent on foreign sources for production. Lithium-ion batteries need to be recycled and disposed properly when they reach the end of life (EOL) to avoid negative environmental impact. This project focuses on recycling cathode material (LiCoO2) by direct method. Two automation stages, tape peeling stage and unrolling stage, are designed for disassembling prismatic winding cores. Different sintering conditions (e.g., temperature, sintering atmosphere, the amount of lithium addition) are investigated to recycle EOL cathode materials. The results show that the capacity of the recycled cathode materials increases with increasing temperature. The extra Li addition leads to worse cycling performance. In addition, the sintering atmosphere has little influence on small- scale sintering. Also, most of directly recycled cathode materials have better electrochemical (EC) performance than commercial LiCoO2 (LCO) from Sigma, especially when cycling with 4.45V cutoff voltage.
Master of Science
Nowadays, Lithium-ion batteries (LIBs) have dominated the power source market in a variety of applications. A LIB contains an anode, a cathode and electrolyte. The cathode material is the most valuable component in the LIB. Lithium cobalt oxide (LiCoO2) is one of the most common cathode materials for LIBs in consumer electronics. The recycling of LIBs is important because cobalt is an expensive element that is dependent on foreign sources for production. Lithium-ion batteries need to be recycled and disposed properly when they reach end of life (EOL) to avoid negative environmental impact. The direct recycling is a cost effective and energy conservative method which can be divided into two steps: retrieving the cathode materials from EOL LIBs and regenerating the cathode materials. This project focuses on recycling LiCoO2 by direct method. Two automation modules, tape peeling stage and unrolling stage, are designed for a disassembling line which is the automation line to collect the cathodes materials. The EOL cathode materials is lithium deficient (Li1-xCoO2). To regenerate the EOL cathode materials, lithium is added into structure of cathode materials which is called the re-lithiation process. The different sintering conditions (e.g., temperature, sintering atmosphere, the amount of lithium addition) are investigated for the re-lithiation process. The results show that the capacity of the recycled cathode materials increases with increasing temperature. The extra Li addition in iv Li1-xCoO2 leads to worse cycling performance. In addition, sintering atmosphere has little influence on small- scale sintering. Most of directly recycled cathode materials have better electrochemical (EC) performance than commercial LiCoO2, especially when cycling with 4.45V cutoff voltage.
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Bowman, Kelly Eric. "Optimization Constrained CAD Framework with ISO-Performing Design Generator." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2599.pdf.

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Xiong, Haoyi. "Near-optimal mobile crowdsensing : design framework and algorithms." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0005/document.

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Aujourd’hui, il y a une demande croissante de fournir les informations d'environnement en temps réel tels que la qualité de l'air, le niveau de bruit, état du trafic, etc. pour les citoyens dans les zones urbaines a des fins diverses. La prolifération des capteurs de smartphones et la mobilité de la population font des Mobile Crowdsensing (MCS) un moyen efficace de détecter et de recueillir des informations a un coût faible de déploiement. En MCS, au lieu de déployer capteurs statiques dans les zones urbaines, les utilisateurs avec des périphériques mobiles jouent le rôle des capteurs de mobiles à capturer les informations de leurs environnements, et le réseau de communication (3G, WiFi, etc.) pour le transfert des données pour MCS applications. En général, l'application MCS (ou tâche) non seulement exige que chaque participant de périphérique mobile de posséder la capacité de réception missions de télédétection, de télédétection et de renvoi détecte résultats vers un serveur central, il exige également de recruter des participants, attribuer de télédétection tâches aux participants, et collecter les résultats obtenues par télédétection ainsi que représente les caractéristiques de la cible zone de détection. Afin de recruter un nombre suffisant de participants, l'organisateur d'une MCS tâche devrait considérer la consommation énergétique causée par MCS applications pour chaque participant et les questions de protection dans la vie privée, l'organisateur doit donner a chaque participant un certain montant des incitations comme un encouragement. En outre, afin de recueillir les résultats obtenues par télédétection et représentant la région cible, l'organisateur doit s'assurer que les données de télédétection qualité des résultats obtenues par télédétection, p. ex., la précision et la spatio-temporelle la couverture des résultats obtenus par télédétection. Avec la consommation d'énergie, la protection de la vie privée, les mesures d'incitation, de télédétection et qualité des données à l'esprit, dans cette thèse nous avons étudié quatre problèmes d'optimisation de mobile crowdsensing et mené après quatre travaux de recherche [...]
Nowadays, there is an increasing demand to provide real-time environment information such as air quality, noise level, traffic condition, etc. to citizens in urban areas for various purposes. The proliferation of sensor-equipped smartphones and the mobility of people are making Mobile Crowdsensing (MCS) an effective way to sense and collect information at a low deployment cost. In MCS, instead of deploying static sensors in urban areas, people with mobile devices play the role of mobile sensors to sense the information of their surroundings and the communication network (3G, WiFi, etc.) is used to transfer data for MCS applications. Typically, an MCS application (or task) not only requires each participant's mobile device to possess the capability of receiving sensing tasks, performing sensing and returning sensed results to a central server, it also requires to recruit participants, assign sensing tasks to participants, and collect sensed results that well represents the characteristics of the target sensing region. In order to recruit sufficient participants, the organizer of the MCS task should consider energy consumption caused by MCS applications for each individual participant and the privacy issues, further the organizer should give each participant a certain amount of incentives as encouragement. Further, in order to collect sensed results well representing the target region, the organizer needs to ensure the sensing data quality of the sensed results, e.g., the accuracy and the spatial-temporal coverage of the sensed results. With the energy consumption, privacy, incentives, and sensing data quality in mind, in this thesis we have studied four optimization problems of mobile crowdsensing and conducted following four research works: • EEMC - In this work, the MCS task is splitted into a sequence of sensing cycles, we assume each participant is given an equal amount of incentive for joining in each sensing cycle; further, given the target region of the MCS task, the MCS task aims at collecting an expected number of sensed results from the target region in each sensing cycle.Thus, in order to minimize the total incentive payments and the total energy consumption of the MCS task while meeting the predefined data collection goal, we propose EEMC which intends to select a minimal number of anonymous participants to join in each sensing cycle of the MCS task while ensuring an minimum number of participants returning sensed results. • EMC3 - In this work, we follow the same sensing cycles and incentives assumptions/settings from EEMC; however, given a target region consisting of a set of subareas, the MCS task in this work aims at collecting sensed results covering each subarea of the target region in each sensing cycle (namely full coverage constraint).Thus, in order to minimize the total incentive payments and the total energy consumption of the MCS task under the full coverage constraint, we propose EMC3 which intends to select a minimal number of anonymous participaNts to join in each sensing cycle of the MCS task while ensuring at least one participant returning sensed results from each subarea. • CrowdRecruiter - In this work, we assume each participant is given an equal amount of incentive for joining in all sensing cycles of the MCS task; further, given a target region consisting of a set of subareas, the MCS task aims at collecting sensed results from a predefined percentage of subareas in each sensing cycle (namely probabilistic coverage constraint).Thus, in order to minimize the total incentive payments the probabilistic coverage constraint, we propose CrowdRecruiter which intends to recruit a minimal number of participants for the whole MCS task while ensuring the selected participants returning sensed results from at least a predefined percentage of subareas in each sensing cycle. • CrowdTasker - In this work, we assume each participant is given a varied amount of incentives according to [...]
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Amadori, Kristian. "On Aircraft Conceptual Design : A Framework for Knowledge Based Engineering and Design Optimization." Licentiate thesis, Linköping : Department of Management and Engineering, Linköpings universitet, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11873.

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Nezhadali, Vaheed. "Multi-objective optimization of Industrial robots." Thesis, Linköpings universitet, Maskinkonstruktion, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-113283.

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Industrial robots are the most widely manufactured and utilized type of robots in industries. Improving the design process of industrial robots would lead to further developments in robotics industries. Consequently, other dependant industries would be benefited. Therefore, there is an effort to make the design process more and more efficient and reliable. The design of industrial robots requires studies in various fields. Engineering softwares are the tools which facilitate and accelerate the robot design processes such as dynamic simulation, structural analysis, optimization, control and so forth. Therefore, designing a framework to automate the robot design process such that different tools interact automatically would be beneficial. In this thesis, the goal is to investigate the feasibility of integrating tools from different domains such as geometry modeling, dynamic simulation, finite element analysis and optimization in order to obtain an industrial robot design and optimization framework. Meanwhile, Meta modeling is used to replace the time consuming design steps. In the optimization step, various optimization algorithms are compared based on their performance and the best suited algorithm is selected. As a result, it is shown that the objectives are achievable in a sense that finite element analysis can be efficiently integrated with the other tools and the results can be optimized during the design process. A holistic framework which can be used for design of robots with several degrees of freedom is introduced at the end.
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Smaling, Rudolf M. "System architecture selection in a multi-disciplinary system design optimization framework." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/91788.

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Mahdavi, Babak. "The design of a distributed, object-oriented, component-based framework in multidisciplinary design optimization /." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=79039.

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The Multidisciplinary Design Optimization (MDO) can be defined as a methodology for the design of complex engineering systems where collaboration and abilities to mutually interacting between different disciplines are fundamental. In this thesis, Virtual Aircraft Design and Optimization fRamework (VADOR), a distributed, object-oriented, component-based framework enabling MDO practice at Bombardier Aerospace is introduced. The purpose of the VADOR framework is to enable the seamless integration of commercial and in-house analysis applications in a heterogeneous, distributed computing environment, and allow the management and sharing of the data. The VADOR distributed environment offers visibility to the process, permitting the teams to monitor progress or track changes in design projects and problems. Documentation of the MDO process is vital to ensure clear communication of the process within the team defining it and in the broader design team interacting with it. VADOR is implemented in Java, providing an object-oriented, platform-independent framework. The concepts of design pattern and component-based approach are used along with multi-tiered distributed design to deliver highly modular and flexible architecture. (Abstract shortened by UMI.)
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Lee, Bin Hong Alex. "Empty container logistics optimization : an implementation framework and methods." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90715.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 68-70).
Empty container logistics is a huge cost component in an ocean carrier's operations. Managing this cost is important to ensure profitability of the business. This thesis proposes a 3-stage framework to handle empty container logistics with cost management as the objective. The first stage studies the forecasting of laden shipment demand, which provides the empty container supply requirement. Based on the supply needs, the problem of optimizing the fleet size was then addressed by using an inventory model to establish the optimal safety stock level. Simulations were used to understand the sensitivity of safety stock to desired service level. The final stage involves using mathematical programming to optimize repositioning costs incurred by carriers to ship empty containers to places which need them due to trade imbalance. At the same time, costs that are incurred due to leasing and storage are considered. A comparison between just-in-time and pre-emptive replenishment was performed and impact due to uncertainties is investigated. The framework is then implemented in a Decision Support System for an actual ocean carrier and is used to assist the empty container logistics team to take the best course of action in daily operations. The results from the optimizations show that there are opportunities for the carrier to reduce its fleet size and cut empty container logistics related costs.
by Bin Hong Alex Lee.
S.M. in Engineering and Management
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Books on the topic "Battery design optimization framework"

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Townsend, James C. A programming environment for distributed complex computing: An overview of the Framework for Interdisciplinary Design Optimization (FIDO) project : NASA Langley TOPS exhibit H120b. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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Bartoli, Gianni, Francesco Ricciardelli, Anna Saetta, and Vincenzo Sepe, eds. Performance of Wind Exposed Structures. Florence: Firenze University Press, 2006. http://dx.doi.org/10.36253/978-88-6453-156-4.

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PERBACCO (a free Italian acronym for Life-cycle Performance, Innovation and Design Criteria for Structures and Infrastructures Facing Æolian and Other Natural Hazards) is a research project partly funded by the Italian Ministry for University (MIUR) in the PRIN (Progetti di Ricerca di Interesse Nazionale) framework, for the years 2004-05.Within the project, a first attempt has been made to integrate different disciplines aiming at an overall optimization of the performance of a wide range of wind exposed structures and infrastructures, with consequent benefi cial impact on the society.The overall objectives were (a) to provide unifi ed concepts for "expected performance" and "risks induced by æolian and other natural hazards", to be applied to structures and infrastructures over their whole life-cycle, such to be acceptable to stakeholders in the construction process (i.e. from the owner to the end-user), (b) to provide models and methodologies for dynamic monitoring of the performance of structures and infrastructures, to be integrated in appropriately designed procedures, and (c) to collect, refi ne, fi le and disseminate the knowledge available on a European basis, concerning the performance of wind-exposed structures and facilities, in a way such to be of use to Construction Industry. This volume summarises the main results obtained during the Project, with each Section addressing a different class of problems, to which many research Units have contributed. A list of papers containing the main results of the research activities carried out within the Project is also provided in each Section.
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C, Townsend J., and Langley Research Center, eds. Integration of a CAD system into an MDO framework. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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A programming environment for distributed complex computing: An overview of the Framework for Interdisciplinary Design Optimization (FIDO) project : NASA Langley TOPS exhibit H120b. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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Haran, Kiruba, Nateri Madavan, and Tim C. O'Connell, eds. Electrified Aircraft Propulsion. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108297684.

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What are the benefits of electrified propulsion for large aircraft? What technology advancements are required to realize these benefits? How can the aerospace industry transition from today's technologies to state-of-the-art electrified systems? Learn the answers with this multidisciplinary text, combining expertise from leading researchers in electrified aircraft propulsion. The book includes broad coverage of electrification technologies – spanning power systems and power electronics, materials science, superconductivity and cryogenics, thermal management, battery chemistry, system design, and system optimization – and a clear-cut road map identifying remaining gaps between the current state-of-the-art and future performance technologies. Providing expert guidance on areas for future research and investment and an ideal introduction to cutting-edge advances and outstanding challenges in large electric aircraft design, this is a perfect resource for graduate students, researchers, electrical and aeronautical engineers, policymakers, and management professionals interested in next-generation commercial flight technologies.
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Book chapters on the topic "Battery design optimization framework"

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Roy, Anindita, and Santanu Bandyopadhyay. "Design and Optimization of Wind-Battery Systems." In Wind Power Based Isolated Energy Systems, 69–96. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00542-9_4.

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Amrit, Anand, Mohit Bahl, and Suhant Ranga. "Multi-objective Design Optimization of EV Battery Tray." In Lecture Notes in Mechanical Engineering, 821–29. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4606-6_75.

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Xie, Qingshui, and Dong-Liang Peng. "Rational Material Design and Performance Optimization of Transition Metal Oxide-Based Lithium Ion Battery Anodes." In Advanced Battery Materials, 159–208. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119407713.ch3.

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Dorne, Raphaël, and Christos Voudouris. "HSF: The iOpt’s Framework to Easily Design Metaheuristic Methods." In Applied Optimization, 237–56. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-4137-7_11.

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Gero, John S., and Udo Kannengiesser. "A Framework for Situated Design Optimization." In Innovations in Design & Decision Support Systems in Architecture and Urban Planning, 309–24. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/978-1-4020-5060-2_20.

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Garima Singh and N. C. Praveen. "Design Exploration Using Unique Multidisciplinary Design Optimization Framework." In Lecture Notes in Mechanical Engineering, 161–72. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5432-2_14.

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Roy, Anindita, and Santanu Bandyopadhyay. "Design and Optimization of Wind-PV-Battery Hybrid System." In Wind Power Based Isolated Energy Systems, 167–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00542-9_8.

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Shi, Dongcai, Jianwei Yin, Wenyu Zhang, Jinxiang Dong, and Dandan Xiong. "A Distributed Collaborative Design Framework for Multidisciplinary Design Optimization." In Lecture Notes in Computer Science, 294–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11686699_30.

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Sane, Aamod, Ashish Singhai, and Roy H. Campbell. "Framework design for end-to-end optimization." In ECOOP’98 — Object-Oriented Programming, 135–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054090.

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Burnak, Baris, Nikolaos A. Diangelakis, and Efstratios N. Pistikopoulos. "PAROC: PARametric Optimization and Control Framework." In Integrated Process Design and Operational Optimization via Multiparametric Programming, 47–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-02089-6_3.

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Conference papers on the topic "Battery design optimization framework"

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Yaji, Kentaro, Shintaro Yamasaki, Shohji Tsushima, and Kikuo Fujita. "A Framework of Multi-Fidelity Topology Design and its Application to Optimum Design of Flow Fields in Battery Systems." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97675.

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Abstract We propose a novel framework based on multi-fidelity design optimization for indirectly solving computationally hard topology optimization problems. The primary concept of the proposed framework is to divide an original topology optimization problem into two subproblems, i.e., low- and high-fidelity design optimization problems. Hence, artificial design parameters, referred to as seeding parameters, are incorporated into the low-fidelity design optimization problem that is formulated on the basis of a pseudo-topology optimization problem. Meanwhile, the role of high-fidelity design optimization is to obtain a promising initial guess from a dataset comprising topology-optimized design candidates, and subsequently solve a surrogate optimization problem under a restricted design solution space. We apply the proposed framework to a topology optimization problem for the design of flow fields in battery systems, and confirm the efficacy through numerical investigations.
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Choi, Yonghwan, Jeong-Hun Seo, and Hae Kyu Lim. "Probabilistic design optimization of battery pack in considering the effect of external pressure with uncertainty." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2020-adm-065.

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Lithium-ion batteries have attracted wide attention as automotive applications with their advantages of high energy density and performance. The pouch type cell has been a preferred candidate based on their light weight, cost effectiveness, and design flexibility. However, due to the low mechanical stability, their characteristics are strongly influenced by environmental conditions. Especially, external pressure on cell surface directly affects the swelling phenomenon which is closely related to performance, life cycle and structural safety of the battery pack. In this paper, a novel framework for design optimization of battery pack is proposed to apply appropriate pressure on pouch cells. The effect of external pressure is investigated through cell cycling tests while thickness, pressure and capacity changes are measured. This investigation shows a recognizable correlation between pressure and cell degradation and also indicates the needs of certain pressure level which should be ensured by pack structures. The mechanical relation between cell and structural components is demonstrated in a free body diagram and utilized for deterministic analysis. To consider uncertainties in the external pressure formulation, the system is hierarchically decomposed and the uncertainty for each sub-component is analyzed. Then, the uncertainty propagation is conducted using Monte-Carlo Simulation to predict the distribution of external pressure in pack level. Based on the results, probabilistic design optimization is performed to minimize the weight of battery pack structure ensuring the external pressure range. The results of deterministic and probabilistic design optimization are compared. The deterministic analysis includes the safety factor based method and the arithmetic worst case based method. The probabilistic analysis is formulated for ensuring the minimum required pressure with the confidence level of 99.7%. After the pressure distribution analysis, the design of module and pack structure is modified to generate the appropriate pressure on the cell surface. The improved module and pack designs are verified by numerical simulations and tests. By adopting the probabilistic design optimization, it is expected that the life cycle reliability and the performance of battery pack can be improved. In addition, more than 17% of weight reduction can be achieved compared to conventional deterministic based design optimization. A novel framework for probabilistic design optimization of battery pack is proposed in considering the effect of external pressure on pouch cell. Various uncertainty factors in formulating external pressure are analyzed by using probabilistic method and compared with the deterministic method. This proposed design technique can be utilized for developing compatible automotive battery packs and improving reliability under uncertainty.
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Laird, Cary, Donald Docimo, Christopher T. Aksland, and Andrew G. Alleyne. "Graph-Based Design and Control Optimization of a Hybrid Electrical Energy Storage System." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3233.

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Abstract Hybrid energy storage systems are a popular alternative to traditional electrical energy storage mechanisms for electric vehicles. Consisting of multiple heterogeneous storage elements, these systems require thoughtful design and control techniques to ensure adequate electrical performance and minimal added weight. In this work, a graph-based design optimization framework is extended to facilitate design and control optimization of a battery-ultracapacitor hybrid energy storage system. For a given high ramp rate load profile, a hybrid electrical energy storage system consisting of battery and ultracapacitor packs with proportional-integral controllers is considered. A multi-objective optimization problem is formulated to simultaneously optimize sizing and performance of the system by minimizing mass and deviations from ideal controller performance. This optimization is achieved by adjusting the size of the energy storage system and parameters of the feedback controller. A Pareto curve is provided, which exhibits the tradeoffs between sizing and performance of the hybrid energy storage system. Dynamic simulation results demonstrate optimized designs outperform initial designs in both sizing and electrical performance objectives. The design and control optimization approach is shown to outperform a similar sizing optimization approach.
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Cui, Tonghui, Zhuoyuan Zheng, and Pingfeng Wang. "Surrogate Model Assisted Lithium-Ion Battery Co-Design for Fast Charging and Cycle Life Performances." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22433.

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Abstract As one of the significant enablers of portable devices and electric vehicles, lithium-ion batteries are drawing much attention for their high energy density and low self-discharging rate. A major hindrance to their further development has been the “range anxiety”, that fast-charging of Li-ion battery is not attainable without sacrificing battery life. In the past, much effort has been carried out to resolve such a problem by either improve the battery design or optimize the charging/discharging protocols, while limited work has been done to address the problem simultaneously, or through a control co-design framework, for a system-level optimum. The control co-design framework is ideal for lithium-ion batteries due to the strong coupling effects between battery design and control optimization. The integration of such coupling effects can lead to improved performances as compared with traditional sequential optimization approaches. However, the challenge of implementing such a co-design framework has been updating the dynamics efficiently for design variations. In this study, we optimize the charging time and cycle life of a lithium-ion battery as a control co-design problem. Specifically, the anode volume fraction and particle size, and the corresponding charging current profile are optimized for a minimum charging time with health-management considerations. The battery is modeled as a coupled electro-thermal-aging dynamical system. The design-dependent dynamics is parameterized thru a Gaussian Processes model, that has been trained with high-fidelity multiphysics simulation samples. A nested co-design approach was implemented using direct transcription, which achieves a better performance than the sequential design approach.
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Liu, Yuanzhi, Payam Ghassemi, Souma Chowdhury, and Jie Zhang. "Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85620.

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This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.
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Chen, Li, Huachao Dong, and Zuomin Dong. "Integrated System Design and Control Optimization of Hybrid Electric Propulsion System Using a Bi-Level, Nested Approach." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97456.

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Abstract Hybrid electric powertrain systems present as effective alternatives to traditional vehicle and marine propulsion means with improved fuel efficiency, as well as reduced greenhouse gas (GHG) emissions and air pollutants. In this study, a new integrated, model-based design and optimization method for hybrid electric propulsion system of a marine vessel (harbor tugboat) has been introduced. The sizes of key hybrid powertrain components, especially the Li-ion battery energy storage system (ESS), which can greatly affect the ship’s life-cycle cost (LCC), have been optimized using the fuel efficiency, emission and lifecycle cost model of the hybrid powertrain system. Moreover, the control strategies for the hybrid system, which is essential for achieving the minimum fuel consumption and extending battery life, are optimized. For a given powertrain architecture, the optimal design of a hybrid marine propulsion system involves two critical aspects: the optimal sizing of key powertrain components, and the optimal power control and energy management. In this work, a bi-level, nested optimization framework was proposed to address these two intricate problems jointly. The upper level optimization aims at component size optimization, while the lower level optimization carries out optimal operation control through dynamic programming (DP) to achieve the globally minimum fuel consumption and battery degradation for a given vessel load profile. The optimized Latin hypercube sampling (OLHS), Kriging and the widely used Expected Improvement (EI) online sampling criterion are used to carry out “small data” driven global optimization to solve this nested optimization problem. The obtained results showed significant reduction of the vessel LCC with the optimized hybrid electric powertrain system design and controls. Reduced engine size and operation time, as well as improved operation efficiency of the hybrid system also greatly decreased the GHG emissions compared to traditional mechanical propulsion.
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Lee, Ungki, Sunghyun Jeon, and Ikjin Lee. "Shared Autonomous Vehicle System Design for Battery Electric Vehicle (BEV) and Fuel Cell Electric Vehicle (FCEV)." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-67734.

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Abstract Shared autonomous vehicles (SAVs) encompassing autonomous driving technology and car-sharing service are expected to become an essential part of transportation system in the near future. Although many studies related to SAV system design and optimization have been conducted, most of them are focused on shared autonomous battery electric vehicle (SABEV) systems, which employ battery electric vehicles (BEVs) as SAVs. As fuel cell electric vehicles (FCEVs) emerge as alternative fuel vehicles along with BEVs, the need for research on shared autonomous fuel cell electric vehicle (SAFCEV) systems employing FCEVs as SAVs is increasing. Therefore, this study newly presents a design framework of SAFCEV system by developing an SAFCEV design model based on a proton-exchange membrane fuel cell (PEMFC) model. The test bed for SAV system design is Seoul, and optimization is conducted for SABEV and SAFCEV systems to minimize the total cost while satisfying the customer wait time constraint, and the optimization results of both systems are compared. From the results, it is verified that the SAFCEV system is feasible and the total cost of the SAFCEV system is even lower compared to the SABEV system. In addition, several observations on various operating environments of SABEV and SAFCEV systems are obtained from parametric studies.
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Luciani, Sara, Stefano Feraco, Angelo Bonfitto, Andrea Tonoli, Nicola Amati, and Maurizio Quaggiotto. "A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-68469.

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Abstract In the automotive framework, an accurate assessment of the State of Charge (SOC) in lead-acid batteries of heavy-duty vehicles is of major importance. SOC is a crucial battery state that is non-observable. Furthermore, an accurate estimation of the battery SOC can prevent system failures and battery damage due to a wrong usage of the battery itself. In this context, a technique based on machine learning for SOC estimation is presented in this study. Thus, this method could be used for safety and performance monitoring purposes in electric subsystem of heavy-duty vehicles. The proposed approach exploits a Genetic Algorithm (GA) in combination with Artificial Neural Networks (ANNs) for SOC estimation. Specifically, the training parameters of a Nonlinear Auto-Regressive with Exogenous inputs (NARX) ANN are chosen by the GA-based optimization. As a consequence of the GA-based optimization, the ANN-based SOC estimator architecture is defined. Then, the proposed SOC estimation algorithm is trained and validated with experimental datasets recorded during real driving missions performed by a heavy-duty vehicle. An equivalent circuit model representing the retained lead-acid battery is used to collect the training, validation and testing datasets that replicates the recorded experimental data related to electrical consumers and the cabin systems or during overnight stops in heavy-duty vehicles. This article illustrates the architecture of the proposed SOC estimation algorithm along with the identification procedure of the ANN parameters with GA. The method is able to estimate SOC with a low estimation error, being suitable for deployment on common on-board Battery Management Systems (BMS).
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Kang, Namwoo, Fred M. Feinberg, and Panos Y. Papalambros. "Autonomous Electric Vehicle Sharing System Design." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46491.

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Car-sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: Autonomous vehicles eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero-emissions. Designing an Autonomous Electric Vehicle (AEV) fleet must account for the relationships among fleet operations, charging station operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four sub-system problems: Fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. A case study for an autonomous fleet operating in Ann Arbor, Michigan, is used to examine AEV sharing system profitability and feasibility for a variety of market scenarios.
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Malikopoulos, Andreas A., and David E. Smith. "An Optimization Model for Plug-In Hybrid Electric Vehicles." In ASME 2011 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/icef2011-60028.

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The necessity for environmentally conscious vehicle designs in conjunction with increasing concerns regarding U.S. dependency on foreign oil and climate change have induced significant investment towards enhancing the propulsion portfolio with new technologies. More recently, plug-in hybrid electric vehicles (PHEVs) have held great intuitive appeal and have attracted considerable attention. PHEVs have the potential to reduce petroleum consumption and greenhouse gas (GHG) emissions in the commercial transportation sector. They are especially appealing in situations where daily commuting is within a small amount of miles with excessive stop-and-go driving. The research effort outlined in this paper aims to investigate the implications of motor/generator and battery size on fuel economy and GHG emissions in a medium-duty PHEV. An optimization framework is developed and applied to two different parallel powertrain configurations, e.g., pre-transmission and post-transmission, to derive the optimal design with respect to motor/generator and battery size. A comparison between the conventional and PHEV configurations with equivalent size and performance under the same driving conditions is conducted thus allowing an assessment of the fuel economy and GHG emissions potential improvement. The post-transmission parallel configuration yields higher fuel economy and less GHG emissions compared to pre-transmission configuration partly attributable to the enhanced regenerative braking efficiency.
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Reports on the topic "Battery design optimization framework"

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Jacobson, Sheldon H. A Heuristic Design Information Sharing Framework for Hard Discrete Optimization Problems. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada467897.

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Allen, Luke, Joon Lim, Robert Haehnel, and Ian Detwiller. Rotor blade design framework for airfoil shape optimization with performance considerations. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41037.

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A framework for optimizing rotor blade airfoil shape is presented. The framework uses two digital workflows created within the Galaxy Simulation Builder (GSB) software package. The first is a workflow enabling the automated creation of a surrogate model for predicting airfoil performance coefficients. An accurate surrogate model for the rapid generation of airfoil coefficient tables has been developed using linear interpolation techniques that is based on C81Gen and ARC2D CFD codes. The second workflow defines the rotor blade optimization problem using GSB and the Dakota numerical optimization library. The presented example uses a quasi-Newton optimization algorithm to optimize the tip region of the UH-60A main rotor blade with respect to vehicle performance. This is accomplished by morphing the blade tip airfoil shape for optimum power, subject to a constraint on the maximum pitch link load.
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Adams, Brian M., Mohamed Salah Ebeida, Michael S. Eldred, John Davis Jakeman, Laura Painton Swiler, John Adam Stephens, Dena M. Vigil, et al. Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :. Office of Scientific and Technical Information (OSTI), May 2014. http://dx.doi.org/10.2172/1177077.

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Eldred, Michael Scott, Dena M. Vigil, Keith R. Dalbey, William J. Bohnhoff, Brian M. Adams, Laura Painton Swiler, Sophia Lefantzi, Patricia Diane Hough, and John P. Eddy. DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Office of Scientific and Technical Information (OSTI), December 2011. http://dx.doi.org/10.2172/1031910.

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ELDRED, MICHAEL S., ANTHONY A. GIUNTA, BART G. VAN BLOEMEN WAANDERS, STEVEN F. WOJTKIEWICZ, JR, WILLIAM E. HART, and MARIO ALLEVA. DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 3.0. Office of Scientific and Technical Information (OSTI), April 2002. http://dx.doi.org/10.2172/800774.

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Griffin, Joshua D., Michael Scott Eldred, Monica L. Martinez-Canales, Jean-Paul Watson, Tamara Gibson Kolda, Brian M. Adams, Laura Painton Swiler, et al. DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 reference manual. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/895073.

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Griffin, Joshua D., Michael Scott Eldred, Monica L. Martinez-Canales, Jean-Paul Watson, Tamara Gibson Kolda, Anthony Andrew Giunta, Brian M. Adams, et al. DAKOTA, a multilevel parellel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 uers's manual. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/895703.

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Griffin, Joshua D., Michael Scott Eldred, Monica L. Martinez-Canales, Jean-Paul Watson, Tamara Gibson Kolda, Anthony Andrew Giunta, Brian M. Adams, et al. Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 developers manual. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/896280.

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Eldred, Michael Scott, Keith R. Dalbey, William J. Bohnhoff, Brian M. Adams, Laura Painton Swiler, Patricia Diane Hough, David M. Gay, John P. Eddy, and Karen H. Haskell. DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, developers manual. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/991840.

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Eldred, Michael Scott, Keith R. Dalbey, William J. Bohnhoff, Brian M. Adams, Laura Painton Swiler, Patricia Diane Hough, David M. Gay, John P. Eddy, and Karen H. Haskell. DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/991842.

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