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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Verbruggen, Frans J. R., Emilia Silvas, and Theo Hofman. "Electric Powertrain Topology Analysis and Design for Heavy-Duty Trucks." Energies 13, no. 10 (May 12, 2020): 2434. http://dx.doi.org/10.3390/en13102434.
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Powertrain system design optimization is an unexplored territory for battery electric trucks, which only recently have been seen as a feasible solution for sustainable road transport. To investigate the potential of these vehicles, in this paper, a variety of new battery electric powertrain topologies for heavy-duty trucks is studied. Thereby, topological design considerations are analyzed related to having: (a) a central or distributed drive system (individually-driven wheels); (b) a single or a multi-speed gearbox; and finally, (c) a single or multiple electric machines. For reasons of comparison, each concurrent powertrain topology is optimized using a bilevel optimization framework, incorporating both powertrain components and control design. The results show that the combined choice of powertrain topology and number of gears in the gearbox can result in a 5.6% total-cost-of-ownership variation of the vehicle and can, significantly, influence the optimal sizing of the electric machine(s). The lowest total-cost-of-ownership is achieved by a distributed topology with two electric machines and two two-speed gearboxes. Furthermore, results show that the largest average reduction in total-cost-of-ownership is achieved by choosing a distributed drive over a central drive topology (−1.0%); followed by using a two-speed gearbox over a single speed (−0.6%); and lastly, by using two electric machines over using one for the central drive topologies (−0.3%).
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Kim, Jaemin, Naehyuck Chang, and Donghwa Shin. "Mobile GPS Application Design Based on System-Level Power and Battery Status Estimation." Energies 14, no. 17 (August 27, 2021): 5333. http://dx.doi.org/10.3390/en14175333.
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Mobile systems such as smartphones require accurate estimation of the battery-related features including the remaining energy and operating time, especially as the the power consumption of user applications is growing continuously these days. We present an energy-aware smartphone application design framework that considers the battery’s state of charge (SOC), energy depletion rate, as well as the service quality of the target application. We use a verified-accurate battery energy estimation method in an Android-OS-based mobile computing system. The battery model considers the rate-capacity effect. We apply regression-based models for the power estimation of the major subsystems in the smartphone, and then aggregate the result to yield the whole system’s power. We first determine the quality of service for the location device (GPS), the display device (LCD), and the overall system (application). Then, we control the error rate of the GPS and the brightness of the display to acquire the maximum service quality of the system for a given car trip. We show the advantage of the proposed method with a case study of a trip. In this case, the smartphone guides a user’s car trip using its GPS navigation capabilities; to do this, we propose an adaptive algorithm that exploits our improved SOC estimation and considers the car’s variable velocity. This proposed adaptive power and service quality control of the GPS application improves the quality of service in this example case and ensures there is enough remaining battery for the trip to be completed. In contrast, conventional approaches to this task provide a lower quality of service and run out of battery before the trip finishes. In conclusion, if a trip plan is provided, an application using our method delivers the maximum quality of service, such as system endurance time, location error, and display brightness.
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Xu, Shiyun, Huadong Sun, Bing Zhao, Jun Yi, Tasawar Hayat, Ahmed Alsaedi, Chunxia Dou, and Bo Zhang. "The Integrated Design of a Novel Secondary Control and Robust Optimal Energy Management for Photovoltaic-Storage System Considering Generation Uncertainty." Electronics 9, no. 1 (January 1, 2020): 69. http://dx.doi.org/10.3390/electronics9010069.
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Due to the generation uncertainty of photovoltaic (PV) power generation, it has been posing great challenges and difficulties in maintaining the stability, security, and reliability of PV-storage systems (one kind of microgrid). To overcome these challenges and difficulties, this paper is concerned with secondary control and robust energy management for PVs in a grid-connected microgrid (MG) considering uncertainty. In our designs, to maintain the stable operation of PVs in MG, a novel secondary control method combining an event-triggered finite time sliding mode controller (FTSMC) and consensus controllers is proposed. Furthermore, a robust optimization framework is established to minimize the total cost of grid-connected MG involving the operation cost of multi-battery Energy Storage Systems (BESSes) and the electricity purchased from the main grid. To eliminate the effects of PV uncertainty, the optimization problem with uncertain constraints is converted into a new optimization problem with only deterministic constraints by using the box theory to represent the PV outputs. In other words, the robust optimization strategy makes uncertain boundaries easier to be represented by setting all uncertain parameters into an uncertain domain involving all typical extreme cases. Then, a particle swarm optimization (PSO) method is employed to solve the newly converted optimization problem. Finally, the experimental results validate the effectiveness of the proposed integrated framework.
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Matos, Nuno M. B., and Andre C. Marta. "Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition." Aerospace 9, no. 7 (July 13, 2022): 378. http://dx.doi.org/10.3390/aerospace9070378.
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A coupled aerostructural aircraft design and trajectory optimization framework is developed for the Air Cargo Challenge competition to maximize the expected score based on cargo carried, altitude achieved and distance traveled. Its modular architecture makes it easily adaptable to any problem where the performance depends not only on the design of the aircraft but also on its flight trajectory. It is based on OpenAeroStruct, an aerostructural solver that uses analytic derivatives for efficient gradient-based optimization. A trajectory optimization module using a collocation method is coupled with the option of using b-splines to increase computational efficiency together with an experimentally-based power decay model that accurately determines the aircraft propulsive response to control input depending on the battery discharge level. The optimization problem totaled 206 variables and 283 constraints and was solved in less than 7 h on a standard computer with 12% reduction when using b-splines for trajectory control variables. The results revealed the need to consider the multi-objective total score to account for the different score components and highlighted the importance of the payload level and chosen trajectory. The wing area should be increased within allowable limits to maximize payload capacity, climb to maximum target height should be the focus of the first 60 s of flight and full throttle should be avoided in cruise to reduce losses and extend flight distance. The framework proved to be a valuable tool for students to easily obtain guidelines for both the model aircraft design and control to maximize the competition score.
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Tran, Manh-Kien, Carlo Cunanan, Satyam Panchal, Roydon Fraser, and Michael Fowler. "Investigation of Individual Cells Replacement Concept in Lithium-Ion Battery Packs with Analysis on Economic Feasibility and Pack Design Requirements." Processes 9, no. 12 (December 16, 2021): 2263. http://dx.doi.org/10.3390/pr9122263.
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The optimization of lithium-ion (Li-ion) battery pack usage has become essential due to the increasing demand for Li-ion batteries. Since degradation in Li-ion batteries is inevitable, there has been some effort recently on research to maximize the utilization of Li-ion battery cells in the pack. Some promising concepts include reconfigurable battery packs and cell replacement to limit the negative impact of early-degraded cells on the entire pack. This paper used a simulation framework, based on a cell voltage model and a degradation model, to study the feasibility and benefits of the cell replacement concept. The simulation conducted in MATLAB involves generating and varying Li-ion cells in the packs stochastically and simulating the life of the cells as well as the packs until they reach their end-of-life stage. It was found that the cell replacement method can increase the total number of cycles of the battery packs, effectively prolonging the lifespan of the packs. It is also determined that this approach can be more economically beneficial than the current approach of simple pack replacement. For the cell replacement concept to be practical, two main design criteria should be satisfied including individual cell monitoring and easy accessibility to cells at failure stage.
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Fleschutz, Markus, Markus Bohlayer, Marco Braun, and Michael D. Murphy. "Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems." Sustainability 14, no. 13 (June 30, 2022): 8025. http://dx.doi.org/10.3390/su14138025.
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A major barrier to investments in clean and future-proof energy technologies of local multi-energy systems (L-MESs) is the lack of knowledge about their impacts on profitability and carbon footprints due to their complex techno-economic interactions. To reduce this problem, decision support tools should integrate various forms of decarbonization measures. This paper proposes the Demand Response Analysis Framework (DRAF), a new open-source Python decision support tool that integrally optimizes the design and operation of energy technologies considering demand-side flexibility, electrification, and renewable energy sources. It quantifies decarbonization and cost reduction potential using multi-objective mixed-integer linear programming and provides decision-makers of L-MESs with optimal scenarios regarding costs, emissions, or Pareto efficiency. DRAF supports all steps of the energy system optimization process from time series analysis to interactive plotting and data export. It comes with several component templates that allow a quick start without limiting the modeling possibilities thanks to a generic model generator. Other key features are the access and preparation of time series, such as dynamic carbon emission factors or wholesale electricity prices; and the generation, handling, and parallel computing of scenarios. We demonstrate DRAF’s capabilities through three case studies on (1) the DR of industrial production processes, (2) the design optimization of battery and photovoltaic systems, and (3) the design optimization and DR of distributed thermal energy resources.
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Schick, Christoph, Nikolai Klempp, and Kai Hufendiek. "Impact of Network Charge Design in an Energy System with Large Penetration of Renewables and High Prosumer Shares." Energies 14, no. 21 (October 20, 2021): 6872. http://dx.doi.org/10.3390/en14216872.
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The transformation of our energy system toward zero net CO2 emissions correlates with a stronger use of low energy density renewable energy sources (RES), such as photovoltaic (PV) energy. As a source of flexibility, distributed PV systems, in particular, are oftentimes installed in combination with battery storage systems. These storage systems are dispatchable, i.e., controllable by the operating owners, who can thereby take over an active market role as energy prosumers. The particular battery operation modes are based on the individual prosumer decisions, which, in turn, are strongly affected by the regulatory framework in place. Regulatory frameworks differ from country to country, but almost all regulatory frameworks feature a network charge mechanism, which allocates network infrastructure and operating costs to the end customers. This raises the question of the extent to which different network charges lead to different prosumer decisions, i.e., battery operation modes, and thus different energy system configurations (system costs). In order to evaluate this question we apply (a) a fundamental linear optimization model of the energy wholesale market, which we stringently link to (b) an analysis of peak-coincident network capacity utilization as well as (c) an evaluation of the complete costs of energy for prosumers and consumers. This stringent cycle of analysis is applied to two prototypical network allocation schemes. We demonstrate that network allocation schemes that are orientated to peak-coincident network capacity utilization could both better incentivize a distribution network-oriented behaviour and better share financial burdens between prosuming and purely consuming households than would be the case for volumetric network charge designs. This paper further demonstrates that network-oriented battery operation does not, per se, result in optimal RES integration at the wholesale market level and CO2 emissions reduction. To identify effects from increasing sector integration, an analysis is both performed for a setting without and with consideration of widespread e-mobility. As a broader conclusion, our results demonstrate that future regulatory frameworks should have a stronger focus on prosumer integration by means, among other things, of an adequate network charge design reflecting the increasingly distributed nature of our future energy system.
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Bartolucci, Lorenzo, Stefano Cordiner, Vincenzo Mulone, and Stefano Pasquale. "Design of a multi-energy system under different hydrogen deployment scenarios." E3S Web of Conferences 238 (2021): 02001. http://dx.doi.org/10.1051/e3sconf/202123802001.
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Multi Energy Systems (MES) are effective means to increase Renewable Energy Sources (RES) penetration in the energy system and therefore to move toward a decentralized low-carbon system. Several energy vectors can be integrated together to exploit synergies in a MES framework, such as electricity, heat and hydrogen. The latter is one of the most promising energy carriers to promote widespread use of MES. Predictive management and well-defined sizing methodology are mandatory to achieve maximum performance out of MES. In this study a grid-connected MES consisting of a photovoltaic (PV) plant, a Battery Energy Storage System (BESS) and a Proton Exchange Membrane Fuel Cell (PEMFC) as a programmable Combined Cooling Heat and Power (CCHP) source, is modelled. Natural gas is considered as an alternative fuel to pure hydrogen. Mixed Integer Linear Programming and Genetic Algorithm are used respectively to solve operation and sizing problems. A single-objective optimization approach, including emission factors as optimization constraints, is carried out to find the optimal configuration of the MES. Several future scenarios are studied, considering different percentages of hydrogen in the gas mixture and comparing the techno-economic performance of the system with respect to a pure hydrogen fueling scenario. Results showed that the environmental objective within the design optimization, promote the use of hydrogen, especially in scenarios with high share of green hydrogen.
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Xie, Guoqi, Hao Peng, Xiongren Xiao, Yao Liu, and Renfa Li. "Design Flow and Methodology for Dynamic and Static Energy-constrained Scheduling Framework in Heterogeneous Multicore Embedded Devices." ACM Transactions on Design Automation of Electronic Systems 26, no. 5 (June 5, 2021): 1–18. http://dx.doi.org/10.1145/3450448.
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With Internet of things technologies, billions of embedded devices, including smart gateways, smart phones, and mobile robots, are connected and deeply integrated. Almost all these embedded devices are battery-constrained and energy-limited systems. In recent years, several works used energy pre-assignment techniques to study the dynamic energy-constrained scheduling of a parallel application in heterogeneous multicore embedded systems. However, the existing energy pre-assignment techniques cannot satisfy the actual energy constraint, because it is the joint constraint on dynamic energy and static energy. Further, the modeling and verification of these works are based on the simulations, which have not been verified in real embedded devices. This study aims to propose a dynamic and static energy-constrained scheduling framework in heterogeneous multicore embedded devices. Solving this problem can utilize existing energy pre-assignment techniques, but it requires a deeply integrated design flow and methodology. The design flow consists of four processes: (1) power and energy modeling; (2) power parameter measurement; (3) basic framework design including energy pre-assignment; and (4) framework optimization. Each design flow has corresponding design methodology. Both our theoretical analysis and practical verification using the low-power ODROID-XU4 device confirm the effectiveness of the proposed framework.
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Aburukba, Raafat, A. R. Al-Ali, Ahmed H. Riaz, Ahmad Al Nabulsi, Danayal Khan, Shavaiz Khan, and Moustafa Amer. "Fog Computing Approach for Shared Mobility in Smart Cities." Energies 14, no. 23 (December 6, 2021): 8174. http://dx.doi.org/10.3390/en14238174.
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Smart transportation a smart city application where traditional individual models are transforming to shared and distributed ownership. These models are used to serve commuters for inter- and intra-city travel. However, short-range urban transportation services within campuses, residential compounds, and public parks are not explored to their full capacity compared to the distributed vehicle model. This paper aims to explore and design an adequate framework for battery-operated shared mobility within a large community for short-range travel. This work identifies the characteristics of the shared mobility for battery-operated vehicles and accordingly proposes an adequate solution that deals with real-time data collection, tracking, and automated decisions. Furthermore, given the requirement for real-time decisions with low latency for critical requests, the paper deploys the proposed framework within the 3-tier computing model, namely edge, fog, and cloud tiers. The solution design considers the power consumption requirement at the edge by offloading the computational requests to the fog tier and utilizing the LoRaWAN communication technology. A prototype implementation is presented to validate the proposed framework for a university campus using e-bikes. The results show the scalability of the proposed design and the achievement of low latency for requests that require real-time decisions.
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Barukčić, Marinko, Toni Varga, Tin Benšić, and Vedrana Jerković Štil. "Optimal Allocation of Renewable Energy Sources and Battery Storage Systems Considering Energy Management System Optimization Based on Fuzzy Inference." Energies 15, no. 19 (September 20, 2022): 6884. http://dx.doi.org/10.3390/en15196884.
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The main problem in planning the optimal operation of renewable energy sources and battery storage systems is the amount of data that must be considered to cover an entire observation period. If the observation period is one year, the characteristic days or averaged data (daily, weekly or monthly averages) are considered to reduce the number of data. Since the average values of the entered data differ from the actual values, it is better to work with hourly or 15-min data at the annual level. The study presents a framework for solving the problem of the optimal allocation and operation of renewable energy sources and battery storage systems. The proposed method simultaneously solves the optimal allocation and energy management problem considering hourly data at the annual level. The fuzzy inference-based system is proposed for scheduling optimal profiles of battery storage systems and renewable energy sources. The developed fuzzy inference system manages the power factors of the photovoltaic and wind power systems, the power factor and output of the biogas plant, and the operating status of the battery storage system. The presented method simultaneously finds the optimal parameters of the energy management system and the optimal allocation and operation of the renewable energy sources and the battery storage system. The developed method is based on the calculation of steady-state power flow. The proposed method is to be used in the design phase for the installation of various renewable energy sources and battery storage systems. In addition, the method is intended to be used to optimally control the power output of energy sources and the operation of energy storage systems during steady-state operation in order to operate the distribution network with minimum annual active energy losses. The developed method is applied to the test distribution system IEEE with 37 nodes. The reduction in annual energy losses in the tested distribution system is about 80% compared to the base case without renewable energy sources and battery storage system.
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Mamun, Abdullah-Al, Zifan Liu, Denise M. Rizzo, and Simona Onori. "An Integrated Design and Control Optimization Framework for Hybrid Military Vehicle Using Lithium-Ion Battery and Supercapacitor as Energy Storage Devices." IEEE Transactions on Transportation Electrification 5, no. 1 (March 2019): 239–51. http://dx.doi.org/10.1109/tte.2018.2869038.
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Mishra, Partha, Eric Miller, Shriram Santhanagopalan, Kevin Bennion, and Andrew Meintz. "A Framework to Analyze the Requirements of a Multiport Megawatt-Level Charging Station for Heavy-Duty Electric Vehicles." Energies 15, no. 10 (May 21, 2022): 3788. http://dx.doi.org/10.3390/en15103788.
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Widespread adoption of heavy-duty (HD) electric vehicles (EVs) will soon necessitate the use of megawatt (MW)-scale charging stations to charge high-capacity HD EV battery packs. Such a station design needs to anticipate possible station traffic, average and peak power demand, and charging/wait time targets to improve throughput and maximize revenue-generating operations. High-power direct current charging is an attractive candidate for MW-scale charging stations at the time of this study, but there are no precedents for such a station design for HD vehicles. We present a modeling and data analysis framework to elucidate the dependencies of a MW-scale station operation on vehicle traffic data and station design parameters and how that impacts vehicle electrification. This framework integrates an agent-based charging station model with vehicle schedules obtained through real-world vehicle telemetry data analysis to explore the station design and operation space. A case study applies this framework to a Class 8 vehicle telemetry dataset and uses Monte Carlo simulations to explore various design considerations for MW-scale charging stations and EV battery technologies. The results show a direct correlation between optimal charging station placement and major traffic corridors such as cities with ports, e.g., Los Angeles and Oakland. Corresponding parametric sweeps reveal that while good quality of service can be achieved with a mix of 1.2-megawatt and 100-kilowatt chargers, the resultant fast charging time of 35–40 min will need higher charging power to reach parity with refueling times.
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Li, Xiaobai, Sergei Chumakov, Jake Christensen, Xiaoxuan Zhang, and Christian Linder. "Thermal-Mechanical-Electrochemical Coupling Simulation for Electric Vehicle Batteries." ECS Meeting Abstracts MA2018-01, no. 32 (April 13, 2018): 1954. http://dx.doi.org/10.1149/ma2018-01/32/1954.
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The major trend toward vehicle electrification intensifies the need for increased battery energy densities. During the development of new electric vehicle battery technologies, it has become clear that thermal, mechanical, and electrochemical coupling effects play an important role in battery performance, degradation, and response to abusive conditions. The macro-homogeneous Dualfoil model, which solves ion transport and charge transfer dynamics, predicts the electrical response of a single small battery cell. However, when considering large cells or assembled battery modules and packs, inhomogeneity of electric potential and temperature throughout the cell volume becomes severe enough to impact battery performance. Cell expansion and contraction during charge/discharge cycles further increase such inhomogeneity, and the resulting potential and temperature imbalances cause non-uniform current distribution in the cell. Heavily cycled cell regions generate additional heat and stress, causing severe degradation in the performance and life of the battery. Given the strong coupling between thermal, mechanical, and electrochemical phenomena, it is imperative to integrate multiple physical models in the simulation of complete battery packs. In this work, a modified Dualfoil electrochemical model is coupled to a thermo-mechanical finite element solver to represent such multiphysics coupling effects. Outputs from the 3D thermo-mechanical solver, such as temperature and stress, are supplied as inputs to the Dualfoil model, which in return computes the electrochemical response and provides the local heat generation rate and Li intercalation induced volume change to the thermo-mechanical solver. Using this coupled multiphysics simulation framework, the impact of external mechanical loading under different charge/discharge profiles was investigated. The transient, non-linear behavior of internal variables, such as SOC, overpotential, and current density, during and after cycles, was obtained in the entire 3D battery pack domain. This information provides good insights for design and optimization of battery packs with high performance and long life time.
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Faraji Niri, Mona, Jimiama Mafeni Mase, and James Marco. "Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure." Energies 15, no. 12 (June 20, 2022): 4489. http://dx.doi.org/10.3390/en15124489.
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Li-ion batteries play a critical role in the transition to a net-zero future. The discovery of new materials and the design of novel microstructures for battery electrodes is necessary for the acceleration of this transition. The battery electrode microstructure can potentially reveal the cells’ electrochemical characteristics in great detail. However, revealing this relation is very challenging due to the high dimensionality of the problem and the large number of microstructure features. In fact, it cannot be achieved via the traditional trial-and-error approaches, which are associated with significant cost, time, and resource waste. In search for a systematic microstructure analysis and design method, this paper aims at quantifying the Li-ion battery electrode structural characteristics via deep learning models. Deliberately, here, a methodology and framework are developed to reveal the hidden microstructure characteristics via 2D and 3D images through dimensionality reduction. The framework is based on an auto-encoder decoder for microstructure reconstruction and feature extraction. Unlike most of the existing studies that focus on a limited number of features extracted from images, this study concentrates directly on the images and has the potential to define the number of features to be extracted. The proposed methodology and model are computationally effective and have been tested on a real open-source dataset where the results show the efficiency of reconstruction and feature extraction based on the training and validation mean squared errors between 0.068 and 0.111 and from 0.071 to 0.110, respectively. This study is believed to guide Li-ion battery scientists and manufacturers in the design and production of next generation Li-ion cells in a systematic way by correlating the extracted features at the microstructure level and the cell’s electrochemical characteristics.
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Rawa, Muhyaddin, Abdullah Abusorrah, Yusuf Al-Turki, Saad Mekhilef, Mostafa H. Mostafa, Ziad M. Ali, and Shady H. E. Abdel Aleem. "Optimal Allocation and Economic Analysis of Battery Energy Storage Systems: Self-Consumption Rate and Hosting Capacity Enhancement for Microgrids with High Renewable Penetration." Sustainability 12, no. 23 (December 4, 2020): 10144. http://dx.doi.org/10.3390/su122310144.
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Recent advances in using renewable energy resources make them more accessible and prevalent in microgrids (MGs) and nano grids (NGs) applications. Accordingly, much attention has been paid during the past few years to design and operate MGs with high renewable energy sources (RESs) penetration. Energy storage (ES) is the crucial enabler for reliable MG operation to help MGs become more resistant to disruptions, particularly with the increased penetration of RESs. In this regard, this paper formulates a two-stage optimization framework to improve a grid-connected MG performance. Firstly, the optimal allocation decisions of the battery ES systems (BESSs) are provided to enhance the self-consumption rate of the RESs and the hosting capacity (HC) of the MG. Secondly, an operation strategy with the results (number, location, and capacity) of the BESSs obtained from the first stage is handled as an objective function to minimize the MG’s total operation cost. The IEEE 33-bus radial system is modified to act as the MG with high RESs penetration. The problem is solved using a recent swarm intelligence optimization algorithm called the Harris hawks optimization (HHO) algorithm. The proposed optimal operation strategy considers numerous constraints, such as the charge-discharge balance, number and capacity limitations of the BESSs, and the different technical performance constraints of the MG. The results obtained verify the proposed optimization framework’s effectiveness for grid-connected MGs and validate the benefits gained from the appropriate allocation of BESSs. The results also indicate that oversized storage or using many unneeded storage units may adversely influence the MG’s total power losses.
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Yan, Yih-Her, Yong-Nong Chang, and Zhi-Xuan Peng. "Design of a Bidirectional CL3C Full-Bridge Resonant Converter for Battery Energy Storage Systems." Energies 15, no. 2 (January 6, 2022): 412. http://dx.doi.org/10.3390/en15020412.
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In this study, a bidirectional CL3C full-bridge resonant converter was developed using a bidirectional active bridge converter as the main framework to improve conventional LLC resonant converters. A resonant inductor and resonant capacitor were installed at the secondary side of the developed resonant converter. The bidirectional operation of this converter enables zero-voltage switching at the supply-side power switch and zero-current switching at the load side. The aforementioned phenomena enhance the overall circuit efficiency and enable the resonant tank voltage to be increased in the reverse mode, which cannot be achieved with conventional bidirectional LLC resonant converters. The electrical equipment isolation function provided by a transformer made electricity usage safer, and digital control technology was adopted to control electrical energy conversion and simulate bidirectional energy conversion. Specifically, the experiment and simulation emulated how the developed converter enables energy transmission from a DC grid to a battery energy storage system through constant current–constant voltage charging and energy transmission from a battery energy storage system to a DC grid through constant power discharging.
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Li, Limiao, Junyao Long, Wei Zhou, Alireza Jolfaei, and Mohammad Sayad Haghighi. "Joint Optimization of Energy Consumption and Data Transmission in Smart Body Area Networks." Sensors 22, no. 22 (November 21, 2022): 9023. http://dx.doi.org/10.3390/s22229023.
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In Wireless Body Area Networks (BAN), energy consumption, energy harvesting, and data communication are the three most important issues. In this paper, we develop an optimal allocation algorithm (OAA) for sensor devices, which are carried by or implanted in human body, harvest energy from their surroundings, and are powered by batteries. Based on the optimal allocation algorithm that uses a two-timescale Lyapunov optimization approach, we design a framework for joint optimization of network service cost and network utility to study energy, communication, and allocation management at the network edge. Then, we formulate the utility maximization problem of network service cost management based on the framework. Specifically, we use OAA, which does not require prior knowledge of energy harvesting to decompose the problem into three subproblems: battery management, data collection amount control and transmission energy consumption control. We solve these through OAA to achieve three main goals: (1) balancing the cost of energy consumption and the cost of data transmission on the premise of minimizing the service cost of the devices; (2) keeping the balance of energy consumption and energy collection under the condition of stable queue; and (3) maximizing network utility of the device. The simulation results show that the proposed algorithm can actually optimize the network performance.
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Ramesh Babu, Anandh, Jelena Andric, Blago Minovski, and Simone Sebben. "System-Level Modeling and Thermal Simulations of Large Battery Packs for Electric Trucks." Energies 14, no. 16 (August 6, 2021): 4796. http://dx.doi.org/10.3390/en14164796.
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Electromobility has gained significance over recent years and the requirements on the performance and efficiency of electric vehicles are growing. Lithium-ion batteries are the primary source of energy in electric vehicles and their performance is highly dependent on the operating temperature. There is a compelling need to create a robust modeling framework to drive the design of vehicle batteries in the ever-competitive market. This paper presents a system-level modeling methodology for thermal simulations of large battery packs for electric trucks under real-world operating conditions. The battery pack was developed in GT-SUITE, where module-to-module discretization was performed to study the thermal behavior and temperature distribution within the pack. The heat generated from each module was estimated using Bernardi’s expression and the pack model was calibrated for thermal interface material properties under a heat-up test. The model evaluation was performed for four charging/discharging and cooling scenarios typical for truck operations. The results show that the model accurately predicts the average pack temperature, the outlet coolant temperature and the state of charge of the battery pack. The methodology developed can be integrated with the powertrain and passenger cabin cooling systems to study complete vehicle thermal management and/or analyze different battery design choices.
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AlJubayrin, Saad, Fahd N. Al-Wesabi, Hadeel Alsolai, Mesfer Al Duhayyim, Mohamed K. Nour, Wali Ullah Khan, Asad Mahmood, Khaled Rabie, and Thokozani Shongwe. "Energy Efficient Transmission Design for NOMA Backscatter-Aided UAV Networks with Imperfect CSI." Drones 6, no. 8 (July 28, 2022): 190. http://dx.doi.org/10.3390/drones6080190.
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The recent combination of ambient backscatter communication (ABC) with non-orthogonal multiple access (NOMA) has shown great potential for connecting large-scale Internet of Things (IoT) in future unmanned aerial vehicle (UAV) networks. The basic idea of ABC is to provide battery-free transmission by harvesting the energy of existing RF signals of WiFi, TV towers, and cellular base stations/UAV. ABC uses smart sensor tags to modulate and reflect data among wireless devices. On the other side, NOMA makes possible the communication of more than one IoT on the same frequency. In this work, we provide an energy efficient transmission design ABC-aided UAV network using NOMA. This work aims to optimize the power consumption of a UAV system while ensuring the minimum data rate of IoT. Specifically, the transmit power of UAVs and the reflection coefficient of the ABC system are simultaneously optimized under the assumption of imperfect channel state information (CSI). Due to co-channel interference among UAVs, imperfect CSI, and NOMA interference, the joint optimization problem is formulated as non-convex, which involves high complexity and makes it hard to obtain the optimal solution. Thus, it is first transformed and then solved by a sub-gradient method with low complexity. In addition, a conventional NOMA UAV framework is also studied for comparison without involving ABC. Numerical results demonstrate the benefits of using ABC in a NOMA UAV network compared to the conventional UAV framework.
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Donateo, Teresa, Antonio Ficarella, and Claudia Lucia De Pascalis. "Energy management-based design of a Wankel hybrid-electric UAV." Aircraft Engineering and Aerospace Technology 92, no. 5 (December 12, 2019): 701–15. http://dx.doi.org/10.1108/aeat-06-2019-0117.
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Purpose The purpose of this study is to investigate the optimization of design and energy management in a parallel hybrid-electric powertrain to replace the conventional engine of an existing tactical unmanned aerial vehicle (UAV) equipped with a Wankel engine with a pre-defined flight mission. The proposed powertrain can work in four different operating modes: electric, thermal, power-assist and charging. Design/methodology/approach The power request at propeller axis of each flight segment is used as input for an in-house model that calculates the overall fuel consumption throughout the mission (Mfuel) and the maximum payload weight (Wpay) by means of an average-point analysis. These outputs depend on the energy management strategy that is expressed by the power-split ratio between engine and electric phase (Uphase) of each mission phase, according to which the components of the hybrid system are sized. The in-house model is integrated into an optimization framework to find the optimal set of Uphase and battery size that minimizes Mfuel and maximizes Wpay. Findings It was found a 3.24% saving of the fuel mass burned throughout the mission (or, alternative an improvement of endurance by 4.3%) with about the same maximum-payload mass (+0.2%) of the original configuration, or a smaller fuel saving with +11% more payload. The fuel saving of 3.24% corresponds to −3.25% in total emissions of CO2 and a 2.34% reduction of the cost-per-mission. Practical implications This study demonstrates that environmental advantages, even if limited, can be already obtained from optimal design and management of the hybrid power system with today technologies while waiting for further benefits from the introduction of advanced technologies for batteries and electric machines. Originality/value The main novelties are the design of the powertrain on the basis of the energy management and the application of scalability and hybridization to Wankel engines.
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Fortin, Patrick. "Zero-Emission Solutions for MW-Scale Energy Systems." ECS Meeting Abstracts MA2022-01, no. 1 (July 7, 2022): 134. http://dx.doi.org/10.1149/ma2022-011134mtgabs.
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The overall aim of this project is to establish a generic modelling platform for the design and operation of zero-emission MW-scale hybrid electric energy systems, with a particular focus on maritime transport applications. The project seeks to integrate battery and fuel cell degradation models with operating strategy models to minimize the total cost of ownership (TCO) of hybrid battery/fuel cell systems. The modelling approach used here, to simulate battery and fuel cell degradation, takes advantage of empirical modelling methods already established in the scientific literature. This approach offers three distinct advantages over alternative multi-physics models, in that empirical degradation models are (i) computationally efficient, (ii) can be integrated into other modelling frameworks, and (iii) can support the quick parameterization of different systems. Real-life battery and fuel cell degradation data, collected using state-of-the-art materials, was used to quantify the model parameters and validate the proposed degradation models. A variety of advanced in-situ electrochemical characterization methods have been employed to quantify the relevant parameters at the beginning-of-life and again after various stages of ageing to determine degradation rates of the relevant parameters. The second aspect of this project is the implementation of a technoeconomic optimization model that combines the data-driven degradation models with additional inputs such as operating conditions, operational profile (i.e., drive cycle), cost of hydrogen, cost of electricity, etc. to design optimal hybrid battery/fuel cell systems and determine optimal control strategies to minimize degradation and total cost of ownership over the system lifetime. This talk will focus on the advanced in-situ electrochemical techniques that have been used to extract fuel cell degradation parameters and provide an overview of both the empirical degradation and technoeconomical models. Finally, we will present the optimization results obtained using our platform for the implementation of a hybrid energy system in a coastal Norwegian ferry.
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Echevarría Camarero, Fernando, Ana Ogando-Martínez, Pablo Durán Gómez, and Pablo Carrasco Ortega. "Profitability of Batteries in Photovoltaic Systems for Small Industrial Consumers in Spain under Current Regulatory Framework and Energy Prices." Energies 16, no. 1 (December 28, 2022): 361. http://dx.doi.org/10.3390/en16010361.
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In recent years, important regulatory changes have been introduced in Spain in the fields of self-consumption and energy tariffs. In addition, electricity prices have risen sharply, reaching record highs in the last year. This evidences the need to conduct new research studies in order to provide an accurate picture of the profitability of battery energy storage systems and photovoltaic systems. This paper proposes a complex simulation tool developed to assist in the optimal design of these kinds of facilities. The tool is used in this study to analyze the benefits of including batteries in PV systems under different self-consumption models, different consumer profiles and different locations across the country. The research results indicate that at current electricity prices, the use of batteries is less profitable than selling excess energy to the grid, unless the price of batteries drops drastically by more than 50% in all the cases analyzed. However, at current battery prices, they become a valuable resource in facilities that do not feed energy surplus into the grid.
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Reddy, Sohail R., Matthias K. Scharrer, Franz Pichler, Daniel Watzenig, and George S. Dulikravich. "Accelerating parameter estimation in Doyle–Fuller–Newman model for lithium-ion batteries." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 5 (September 2, 2019): 1533–44. http://dx.doi.org/10.1108/compel-12-2018-0533.
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Purpose This paper aims to solve the parameter identification problem to estimate the parameters in electrochemical models of the lithium-ion battery. Design/methodology/approach The parameter estimation framework is applied to the Doyle-Fuller-Newman (DFN) model containing a total of 44 parameters. The DFN model is fit to experimental data obtained through the cycling of Li-ion cells. The parameter estimation is performed by minimizing the least-squares difference between the experimentally measured and numerically computed voltage curves. The minimization is performed using a state-of-the-art hybrid minimization algorithm. Findings The DFN model parameter estimation is performed within 14 h, which is a significant improvement over previous works. The mean absolute error for the converged parameters is less than 7 mV. Originality/value To the best of the authors’ knowledge, application of a hybrid optimization framework is new in the field of electrical modelling of lithium-ion cells. This approach saves much time in parameterization of models with a high number of parameters while achieving a high-quality fit.
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Carvalho, Maria Leonor, Andrea Temporelli, and Pierpaolo Girardi. "Life Cycle Assessment of Stationary Storage Systems within the Italian Electric Network." Energies 14, no. 8 (April 7, 2021): 2047. http://dx.doi.org/10.3390/en14082047.
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The introduction of stationary storage systems into the Italian electric network is necessary to accommodate the increasing share of energy from non-programmable renewable sources and to reach progressive decarbonization targets. In this framework, a life cycle assessment is a suitable tool to assess environmental impacts during the entire life cycle of stationary storage systems, i.e., their sustainability. A Li-ion battery (lithium–iron–phosphate (LFP), nickel–manganese–cobalt (NMC) 532, and NMC 622) entire life cycle assessment (LCA) based on primary and literature data was performed. The LCA results showed that energy consumption (predominantly during cell production), battery design (particularly binder choice), inventory accuracy, and data quality are key aspects that can strongly affect results. Regarding the battery construction phase, LFP batteries showed better performance than the NMC ones, but when the end-of-life (EoL) stage was included, NMC cell performance became very close to those of LFPs. Sensitivity and uncertainty analyses, done using the Monte Carlo methodology, confirmed that the results (except for the freshwater eutrophication indicator) were characterized by a low dispersion and that the energy mix choice, during the different battery life phases, was able to greatly influence the overall impact. The use of primary and updated data related to battery cell production, like those used in the present paper, was necessary to obtain reliable results, and the application to a European production line is an item of novelty of this paper.
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Jately, Vibhu, Balaji Venkateswaran V., Stefan Azzopardi, and Brian Azzopardi. "Design and Performance Investigation of a Pilot Micro-Grid in the Mediterranean: MCAST Case Study." Energies 14, no. 20 (October 19, 2021): 6846. http://dx.doi.org/10.3390/en14206846.
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This paper discusses the simulation framework developed for an in-campus pilot micro-grid at MCAST, Malta, to enhance its efficiency and reliability. One year of real-time metered data were used to arrive at the load curves, categorize the loads as essential and non-essential ones, and decide the micro-grid domain within MCAST. The potential scenarios were modeled to observe the behavior of the present status of the micro-grid, with an increased photovoltaic (PV) generation capacity, by using an optimum battery storage system with a diesel generator of suitable capacity and finally integrating electric vehicles (EVs) to discuss the potential of vehicle to grid (V2G) operation modes. The existing building management system (BMS) of MCAST was interfaced within the micro-grid to introduce the geographic information system (GIS) and Building Information Modeling (BIM) for developing an intelligent 3D model of the micro-grid. The results of the simulation framework for various potential case scenarios were obtained in a MATLAB/Simulink environment to assess the performance of the micro-grid. Previously formulated key performance indices (KPIs) that describe the financial aspects of micro-grid operation and ecological benefits of the investigated micro-grid were evaluated. A sensitivity analysis of these KPIs shows encouraging results with the potential of cost-competitiveness.
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Liu, Xiaodong, Hongqiang Guo, Xingqun Cheng, Juan Du, and Jian Ma. "A Robust Design of the Model-Free-Adaptive-Control-Based Energy Management for Plug-in Hybrid Electric Vehicle." Energies 15, no. 20 (October 11, 2022): 7467. http://dx.doi.org/10.3390/en15207467.
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This paper proposes a robust design approach based on the Design for Six Sigma (DFSS), to promote the robustness of our previous model-free-adaptive-control-based (MFAC-based) energy management strategy (EMS) for the plug-in hybrid electric vehicles (PHEVs) in real-time application. First, the multi-island genetic algorithm (MIGA) is employed for a deterministic design of the MFAC-based EMS, and the Monte Carlo simulation (MCS) is utilized to evaluate the sigma level of the strategy with the deterministic design results. Second, a DFSS framework is formulated to reinforce the robustness of the MFAC-based EMS, in which the velocity and the vehicle mass are considered external disturbances whilst the terminal state of charge (SOC) of the battery and the fuel consumption (FC) are conducted as responses. In addition, real-time SOC constraints are incorporated into Pontryagin’s minimum principle (PMP) to confine the fluctuation of battery SOC in MFAC-based EMS to make it closer to the solution of the dynamic programming (DP). Finally, the effectiveness of the robust design results is assessed by contrasting with other strategies for various combined driving cycles (including velocity, vehicle mass, and road slope). The comparisons demonstrate the remarkable promotion of the robust design in terms of the energy-saving potential and the performance against external disturbance. The average improvement of the FCs can reach up to a considerable 19.66% and 9.79% in contrast to the charge-depleting and charge-sustaining (CD-CS) strategy as well as the deterministic design of MFAC-based EMS. In particular, the energy-saving performance is comparable to DP, where there is only a gap of −1.68%.
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Bellinaso, Lucas V., Edivan L. Carvalho, Rafael Cardoso, and Leandro Michels. "Price-Response Matrices Design Methodology for Electrical Energy Management Systems Based on DC Bus Signalling." Energies 14, no. 6 (March 23, 2021): 1787. http://dx.doi.org/10.3390/en14061787.
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Prosumers’ electrical installations (PEIs), as nanogrids and low-voltage microgrids, have gained importance in recent years following the development of standards such as the IEC 60364-8 series. In these systems, all distributed energy resources (DERs) are usually integrated using dc bus coupling. The IEC 60364-8-3 predicts an electrical energy management system (EEMS) for power-sharing. The overall research framework of this paper is the nanogrid power management, where complex algorithms are required, as well as the conventional state machines and hierarchical controls. However, the addition of new DERs in such systems is not straightforward due to the complicated parameter settings for energy usage optimization. A different control strategy, named price-based power management, has been conceived to make the EEMS scalable to include new sources and simplify parameterization. Since it is analogous to economic markets, most users understand the concepts and feel comfortable tuning parameters according to their own cost/benefits goals. This paper proposes a price-based power management algorithm for EEMS to automatically design the price-response matrices (PRMs). The PRMs are a way to organize power management, considering new DERs and variable price of energy. The main contribution is the methodology to design the PRMs. Experimental results are carried out to demonstrate the effectiveness of the proposed strategy. The results were obtained with a 1.5 kW prototype composed of a PV generator, battery energy storage, loads, and grid connection.
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Kafetzis, Alexandros, Chrysovalantou Ziogou, Simira Papadopoulou, Spyridon Voutetakis, and Panos Seferlis. "Nonlinear Model Predictive Control of an Autonomous Power System Based on Hydrocarbon Reforming and High Temperature Fuel Cell." Energies 14, no. 5 (March 3, 2021): 1371. http://dx.doi.org/10.3390/en14051371.
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The integration and control of energy systems for power generation consists of multiple heterogeneous subsystems, such as chemical, electrochemical, and thermal, and contains challenges that arise from the multi-way interactions due to complex dynamic responses among the involved subsystems. The main motivation of this work is to design the control system for an autonomous automated and sustainable system that meets a certain power demand profile. A systematic methodology for the integration and control of a hybrid system that converts liquefied petroleum gas (LPG) to hydrogen, which is subsequently used to generate electrical power in a high-temperature fuel cell that charges a Li-Ion battery unit, is presented. An advanced nonlinear model predictive control (NMPC) framework is implemented to achieve this goal. The operational objective is the satisfaction of power demand while maintaining operation within a safe region and ensuring thermal and chemical balance. The proposed NMPC framework based on experimentally validated models is evaluated through simulation for realistic operation scenarios that involve static and dynamic variations of the power load.
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Shezan, Sk A., Kazi Nazmul Hasan, Akhlaqur Rahman, Manoj Datta, and Ujjwal Datta. "Selection of Appropriate Dispatch Strategies for Effective Planning and Operation of a Microgrid." Energies 14, no. 21 (November 2, 2021): 7217. http://dx.doi.org/10.3390/en14217217.
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The power system responsiveness may be improved by determining the ideal size of each component and performing a reliability analysis. This study evaluated the design and optimization of an islanded hybrid microgrid system with multiple dispatch algorithms. As the penetration of renewable power increases in microgrids, the importance and influence of efficient design and operation of islanded hybrid microgrids grow. The Kangaroo Island in South Australia served as the study’s test microgrid. The sizing of the Kangaroo Island hybrid microgrid system, which includes solar PV, wind, a diesel engine, and battery storage, was adjusted for four dispatch schemes. In this study, the following dispatch strategies were used: (i) load following, (ii) cycle charging, (iii) generator order, and (iv) combination dispatch. The CO2 emissions, net present cost (NPC), and energy cost of the islanded microgrid were all optimized (COE). The HOMER microgrid software platform was used to build all four dispatch algorithms, and DIgSILENT PowerFactory was used to analyze the power system’s responsiveness and dependability. The findings give a framework for estimating the generation mix and required resources for an islanded microgrid’s optimal functioning under various dispatch scenarios. According to the simulation results, load following is the optimum dispatch technique for an islanded hybrid microgrid that achieves the lowest cost of energy (COE) and net present cost (NPC).
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Uwineza, Laetitia, Hyun-Goo Kim, Jan Kleissl, and Chang Ki Kim. "Technical Control and Optimal Dispatch Strategy for a Hybrid Energy System." Energies 15, no. 8 (April 8, 2022): 2744. http://dx.doi.org/10.3390/en15082744.
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Optimal dispatch is a major concern in the optimization of hybrid energy systems (HESs). Efficient and effective dispatch models that satisfy the load demand at the minimum net present cost (NPC) are crucial because of the high capital costs of renewable energy technologies. The dispatch algorithms native to hybrid optimization of multiple energy resources (HOMER) software, cycle-charging (CC) and load-following (LF), are powerful for modeling and optimizing HESs. In these control strategies, the decision to use fuel cell systems (FCs) or battery energy storage systems (BESs) at each time step is made based on the lowest cost choice. In addition, the simultaneous operation of a FC with a BES reduces the operating efficiency of the FC. These deficiencies can affect the optimal design of HESs. This study introduces a dispatch algorithm specifically designed to minimize the NPC by maximizing the usage of FCs over other components of HESs. The framework resolves the dispatch deficiencies of native HOMER dispatch algorithms. The MATLAB Version 2021a, Mathworks Inc., Natick, MA, USA Link feature in HOMER software was used to implement the proposed dispatch (PD) algorithm. The results show that the PD achieved cost savings of 4% compared to the CC and LF control dispatch strategies. Furthermore, FCs contributed approximately 23.7% of the total electricity production in the HES, which is more than that of CC (18.2%) and LF (18.6%). The developed model can be beneficial to engineers and stakeholders when optimizing HESs to achieve the minimum NPC and efficient energy management.
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Khan, Zafar A., Muhammad Imran, Abdullah Altamimi, Ogheneruona E. Diemuodeke, and Amged Osman Abdelatif. "Assessment of Wind and Solar Hybrid Energy for Agricultural Applications in Sudan." Energies 15, no. 1 (December 21, 2021): 5. http://dx.doi.org/10.3390/en15010005.
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In addition to zero-carbon generation, the plummeting cost of renewable energy sources (RES) is enabling the increased use of distributed-generation sources. Although the RES appear to be a cheaper source of energy, without the appropriate design of the RES with a true understanding of the nature of the load, they can be an unreliable and expensive source of energy. Limited research has been aimed at designing small-scale hybrid energy systems for irrigation pumping systems, and these studies did not quantify the water requirement, or in turn the energy required to supply the irrigation water. This paper provides a comprehensive feasibility analysis of an off-grid hybrid renewable energy system for the design of a water-pumping system for irrigation applications in Sudan. A systematic and holistic framework combined with a techno-economic optimization analysis for the planning and design of hybrid renewable energy systems for small-scale irrigation water-pumping systems is presented. Different hybridization cases of solar photovoltaic, wind turbine and battery storage at 12 different sites in Sudan are simulated, evaluated, and compared, considering the crop water requirement for different crops, the borehole depth, and the stochasticity of renewable energy resources. Soil, weather, and climatic data from 12 different sites in Sudan were used for the case studies, with the key aim to find the most robust and reliable solution with the lowest system cost. The results of the case studies suggest that the selection of the system is highly dependent on the cost, the volatility of the wind speed, solar radiation, and the size of the system; at present, hybridization is not the primary option at most of sites, with the exception of two. However, with the reduction in price of wind technology, the possibility of hybrid generation will rise.
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Kwon, Laeun, Dae-Seung Cho, and Changsun Ahn. "Degradation-Conscious Equivalent Consumption Minimization Strategy for a Fuel Cell Hybrid System." Energies 14, no. 13 (June 24, 2021): 3810. http://dx.doi.org/10.3390/en14133810.
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The design of an energy management strategy is critical to improving the fuel efficiency of a vehicle system with an alternative powertrain system, such as hybrid electric vehicles or fuel cell electric vehicles. In particular, in fuel cell electric vehicles, the energy management strategy should consider system degradation and fuel savings because the hardware cost of the fuel cell system is much higher than that of a conventional powertrain system. In this paper, an easily implantable near-optimal energy management controller is proposed. The proposed controller distributes power generation between the fuel cell and the battery to simultaneously minimize system degradation and fuel usage. The controller is designed to consider the degradation cost and fuel cost in the framework of the equivalent consumption minimization strategy concept. The proposed controller was validated with a fuel cell electric vehicle model in MATLAB/Simulink (MathWorks, Natick, MA, USA). The proposed control strategy showed significant overall cost reduction compared to a thermostat control strategy and a conventional Equivalent Consumption Minimization Strategy (ECMS) strategy.
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Tu, Chia-Heng, Qihui Sun, and Hsiao-Hsuan Chang. "RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study." ACM Transactions on Cyber-Physical Systems 5, no. 4 (October 31, 2021): 1–28. http://dx.doi.org/10.1145/3472612.
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Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.
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Povolato, Margherita, Alessandro Prada, Sara Verones, and Paolo Baggio. "On the Effect of the Time Interval Base and Home Appliance on the Renewable Quota of a Building in an Alpine Location." Energies 16, no. 1 (December 29, 2022): 384. http://dx.doi.org/10.3390/en16010384.
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The European goal of decarbonization drives design toward high-performance buildings that maximize the use of renewable sources. Therefore, the European RED II Directive and Italian law raise the minimum renewable share required for new buildings and major renovations. Currently, the renewable energy ratio (RER) is used for the mandatory verification, obtained with a quasi-steady state calculation on a monthly basis, while much of the scientific literature uses self-consumption factor (SCF) and load coverage factor (LCF) often calculated through dynamic simulation. However, the use of a monthly balance implies the use of the national grid as a virtual battery through the net metering mechanism. The actual share of renewable coverage in the absence of expensive electric storage will necessarily be lower. The link between the different indices, the effect of the time base used in the calculation as well as the actual renewable share achieved by buildings, considering also plug loads not in the regulatory verification framework, are still open issues. This work analyzes the actual renewable share achievable for a new building in a heating-dominated climate, i.e., the mountainous area of the municipality of Trento. The renewable share is evaluated through a coupled dynamic simulation of the building and the energy systems. The results show that the RER decreases by 13% and 15% when switching from monthly to instantaneous balance in the case without and with additional home appliance loads, respectively. Similarly, simulations show how the time interval base affects the difference between the RER index and the LCF of PV energy.
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Umoren, Ifiok Anthony, and Muhammad Zeeshan Shakir. "Electric Vehicle as a Service (EVaaS): Applications, Challenges and Enablers." Energies 15, no. 19 (September 30, 2022): 7207. http://dx.doi.org/10.3390/en15197207.
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Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicles as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system.
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Alberto Alvarez, Ernesto, Mika Korkeakoski, Ariel Santos Fuentefría, Miriam Lourdes Filgueiras Sainz de Rozas, Ramsés Arcila Padura, and Jyrki Luukkanen. "Long-Range Integrated Development Analysis: The Cuban Isla de la Juventud Study Case." Energies 14, no. 10 (May 15, 2021): 2865. http://dx.doi.org/10.3390/en14102865.
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The use of renewable energy sources (RES) has increased exponentially worldwide, as an alternative to the indiscriminate use of fossil fuels and to mitigate their effects on the environment. Cuba is not lagging behind in this development since the government’s plan until 2030 includes the contribution of renewable sources as a fundamental component in the national energy mix. This paper models possible scenarios based on 2019 statistics for achieving a 25% and 100% penetration of renewable sources by 2030 in the Isla de la Juventud’s (an island south of the main island of Cuba) electrical power system (EPS). This modeling is carried out utilizing and open source Excel-based accounting framework Long-range Integrated Development Analysis (LINDA). For this purpose, international and national trends in the use and development of renewable energy sources and the influence of the characteristics of each renewable source (wind, solar, biodiesel, battery storage) were analyzed. The analysis of Isla de la Juventud’s electrical power system was based on the characteristics of its energy mix, the possibilities of renewable energy penetration and the current and future energy demand by sector. Based on the analysis, two probable scenarios were modeled with LINDA model: a 25% renewable energy-based scenario (RENES) and a 100% renewables-based scenario (MAXRES). Results from RENES and MAXRES scenarios show high penetration of renewable energy sources in electricity generation is theoretically possible with the abundance of renewable energy resources, and thus it is possible for Cuba to move towards 100% renewable energy mix. However, the choices regarding the best fit energy mix need to be carefully analyzed in order to design a least cost system that answers the needs of the future demand.
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Cotoc, George Gabriel, Liliana Rusu, Florin Pacuraru, and Antti Pösö. "Ship design optimization framework." Analele Universităţii "Dunărea de Jos" din Galaţi Fascicula XI Construcţii navale/ Annals of "Dunărea de Jos" of Galati Fascicle XI Shipbuilding 45 (December 3, 2022): 119–24. http://dx.doi.org/10.35219/annugalshipbuilding/2022.45.14.
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This study intends to develop a Computational Fluid Dynamics space exploration frame-work, which creates a bridge between NAPA software and OpenFOAM 9 simulation soft-ware. With the use of Free Form Deformation function implemented in NAPA, iterations of KCS hull were automatically generated by changing the length of the bulb. The newly created versions of the model were further analyzed with numerical investigations to de-termine the ship resistance simulation using RANS equations. The optimization process and the data transfer between the two software packages is monitored by the Dakota op-timization software.
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Lin, Xianke, and Wei Lu. "A Framework for Optimization on Battery Cycle Life." Journal of The Electrochemical Society 165, no. 14 (2018): A3380—A3388. http://dx.doi.org/10.1149/2.0741814jes.
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Lin, Ni, Song Ci, Dalei Wu, and Haifeng Guo. "An Optimization Framework for Dynamically Reconfigurable Battery Systems." IEEE Transactions on Energy Conversion 33, no. 4 (December 2018): 1669–76. http://dx.doi.org/10.1109/tec.2018.2850853.
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