The Experts below are selected from a list of 5516574 Experts worldwide ranked by ideXlab platform
Fei Gao - One of the best experts on this subject based on the ideXlab platform.
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fpga based device level electro thermal Modeling of floating interleaved boost converter for fuel cell hardware in the loop applications
IEEE Transactions on Industry Applications, 2019Co-Authors: Hao Bai, Chen Liu, Shengrong Zhuo, Damien Paire, Fei GaoAbstract:Floating interleaved boost converter (FIBC) is a promising topology of the dc/dc converters to interface the fuel cell with dc bus in a hybrid powertrain. For the efficient control development of power converters, the hardware-in-the-loop (HIL) simulation plays an important role. The accuracy of the Real-Time Model determines the credibility of the HIL simulation. In order to take the power losses and thermal stress into account in the control development, the device-level Real-Time Model is becoming popular in recent years, which can effectively produce device transient voltage and current waveforms, switching power dissipations, and thermal behaviors. In this paper, a device-level electro-thermal Model of FIBC is proposed and developed using the field programmable gate array based Real-Time simulation technology. The FIBC network Model can effectively be simulated with a 500-ns time step while producing the insulated-gate bipolar transistor transient switching waveforms authentically with a 5-ns resolution. The switching power losses can thus be estimated in real time and then used for the computation of the IGBT thermal behavior. Therefore, the device junction temperature can be evaluated in the Real-Time simulation. The accuracy of the proposed FIBC device-level Model is validated by the reference Model in the Saber offline simulation tool. At last, an embedded PI controller of FIBC is employed in the Real-Time experiments to verify the effectiveness of the developed Model.
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global parameters sensitivity analysis and development of a two dimensional real time Model of proton exchange membrane fuel cells
Energy Conversion and Management, 2018Co-Authors: Daming Zhou, Thu Trang Nguyen, Elena Breaz, Dongdong Zhao, S Clenet, Fei GaoAbstract:This work is supported by European Commission H2020 grant ESPESA (H2020-TWINN-2015) EU Grant agreement No: 692224.
Bo Chen - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Model Predictive Powertrain Control for a Connected Plug-In Hybrid Electric Vehicle
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Oncken Joseph, Bo ChenAbstract:© 1967-2012 IEEE. The continued development of connected and automated vehicle technologies presents the opportunity to utilize these technologies for vehicle energy management. Leveraging this connectivity among vehicles and infrastructure allows a powertrain controller to be predictive and forward-looking. This paper presents a Real-Time predictive powertrain control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) in a connected vehicle environment. This work focuses on the optimal energy management of a multi-mode PHEV based on predicted future velocity, power demand, and road conditions. The powertrain control system in the vehicle utilizes vehicle connectivity to a cloud-based server in order to obtain future driving conditions. For predictive powertrain control, a Nonlinear Model Predictive Controller (NMPC) is developed to make torque-split decisions within each operating mode of the vehicle. The torque-split among two electric machines and one combustion engine is determined such that fuel consumption is minimized while battery SOC and vehicle velocity targets are met. The controller has been extensively tested in simulation across multiple real-world driving cycles where energy savings in the range of 1 to 4% have been demonstrated. The developed controller has also been deployed and tested in Real-Time on a test vehicle equipped with a rapid prototyping embedded controller. Real-Time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in a Real-Time environment
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Real-Time Model Predictive Powertrain Control for a Connected Plug-In Hybrid Electric Vehicle
IEEE Transactions on Vehicular Technology, 2024Co-Authors: Joseph Eugene Oncken, Bo ChenAbstract:The continued development of connected and automated vehicle technologies presents the opportunity to utilize these technologies for vehicle energy management. Leveraging this connectivity among vehicles and infrastructure allows a powertrain controller to be predictive and forward-looking. This paper presents a Real-Time predictive powertrain control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) in a connected vehicle environment. This work focuses on the optimal energy management of a multi-mode PHEV based on predicted future velocity, power demand, and road conditions. The powertrain control system in the vehicle utilizes vehicle connectivity to a cloud-based server in order to obtain future driving conditions. For predictive powertrain control, a Nonlinear Model Predictive Controller (NMPC) is developed to make torque-split decisions within each operating mode of the vehicle. The torque-split among two electric machines and one combustion engine is determined such that fuel consumption is minimized while battery SOC and vehicle velocity targets are met. The controller has been extensively tested in simulation across multiple real-world driving cycles where energy savings in the range of 1 to 4% have been demonstrated. The developed controller has also been deployed and tested in Real-Time on a test vehicle equipped with a rapid prototyping embedded controller. Real-Time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in a Real-Time environment.
G Scelba - One of the best experts on this subject based on the ideXlab platform.
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real time Model based estimation of soc and soh for energy storage systems
IEEE Transactions on Power Electronics, 2017Co-Authors: M Cacciato, G Nobile, G Scarcella, G ScelbaAbstract:To obtain a full exploitation of battery potential in energy storage applications, an accurate Modeling of electrochemical batteries is needed. In real terms, an accurate knowledge of state of charge (SOC) and state of health (SOH) of the battery pack is needed to allow a precise design of the control algorithms for energy storage systems (ESSs). Initially, a review of effective methods for SOC and SOH assessment has been performed with the aim to analyze pros and cons of standard methods. Then, as the tradeoff between accuracy and complexity of the Model is the major concern, a novel technique for SOC and SOH estimation has been proposed. It is based on the development of a battery circuit Model and on a procedure for setting the Model parameters. Such a procedure performs a Real-Time comparison between measured and calculated values of the battery voltage while a PI-based observer is used to provide the SOC and SOH actual values. This ensures a good accuracy in a wide range of operating conditions. Moreover, a simple start-up identification process is required based on battery data-sheet exploitation. Because of the low computational burden of the whole algorithm, it can be easily implemented in low-cost control units. An experimental comparison between SOC and SOH estimation performed by suggested and standard methods is able to confirm the consistency of the proposed approach.
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real time Model based estimation of soc and soh for energy storage systems
International Symposium on Power Electronics for Distributed Generation Systems, 2015Co-Authors: M Cacciato, G Nobile, G Scarcella, G ScelbaAbstract:Accurate Modeling of electrochemical batteries is of major concern in designing the control system of Energy Storage Systems (ESS). In particular, a precise estimation of State of Charge (SOC) and State of Health (SOH) parameters strongly affects the full exploitation of battery energy potential in real applications. In this paper a novel Real-Time estimation method is presented representing a good tradeoff between Model accuracy and algorithm complexity. In the proposed approach, SOC and SOH values are determined by a suitable algorithm that continuously performs a comparison between the ESS voltage value, calculated by an adaptive run-time circuital Model, and its real value measured at the ESS terminals. The result of such comparison is used to suitably tune two parameters of the ESS circuital Model, the no-load voltage and resistive voltage drop, in order to compensate the inaccuracy of the Model response due to parameter variations. Initially, to set the parameter of ESS electrical Model, the proposed approach requires to carry out short preliminary tests that can be easily implemented in a low cost control units. Experimental results and comparisons with other estimation methods highlight the consistency of the proposed algorithm.
George J. Cokkinides - One of the best experts on this subject based on the ideXlab platform.
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distribution system distributed quasi dynamic state estimator
IEEE Transactions on Smart Grid, 2016Co-Authors: Renke Huang, George J. Cokkinides, Clinton Hedrington, Sakis MeliopoulosAbstract:With increasing deployment of smart meters and other smart grid sensors in distribution systems, the amount of available measurements has been growing. This change enables implementation of comprehensive distribution system state estimators, a basic tool that provides the Real-Time Model and operating conditions of the distribution network. The performance of many smart grid applications is dependent upon an accurate and reliable Real-Time Model. This paper presents the development of a distributed quasi-dynamic state estimator (DS-DQSE) for distribution systems utilizing smart meter and other sensor data, as well as traditional SCADA data. The DS-DQSE implementation described in this paper works for both radial or meshed distribution networks and variable configuration systems, as distribution systems may be reconfigured following the clearing of a fault. This paper provides the problem formulation, solution methodology, Modeling issues unique to distribution systems, and test results on multiple distribution circuits.
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distributed dynamic state estimation fundamental building block for the smart grid
Power and Energy Society General Meeting, 2015Co-Authors: Sakis Meliopoulos, Renke Huang, Evangelos Polymeneas, George J. CokkinidesAbstract:The changing face of the electric power system due to new power apparatus, the proliferation of customer owned resources and smart devices creates the possibility of coordinated control of all these resources for the benefit of the power grid and its customers. Coordinated control requires Real-Time monitoring of Models and resources and subsequent Model-based analysis, optimization and control. Legacy state estimation is limited to the transmission system only and provides only the bus voltage values at transmission buses. This paper proposes a distributed dynamic state estimation (DDSE) technology that (a) extracts the dynamic Real-Time Model of all components in substations as well as transmission lines, and (b) executes each cycle thus providing the real time Model each cycle of the system. The DDSE uses all available measurements in the system, PMU data (GPS-synchronized), SCADA data, smart meter data, etc. The DDSE provides the real time Model of the system which is the fundamental building block for all smart grid applications. The representation of the Real-Time Model is object-oriented that allows interoperability among various applications. As an example, the paper discusses the seamless application of the optimal power flow using the Real-Time Model provided by the DDSE.
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integration automation from protection to advanced energy management systems
2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization Security and Control of the Emerging Power Grid, 2013Co-Authors: Sakis Meliopoulos, George J. Cokkinides, Renke Huang, Evangelos Polymeneas, Santiago Grijalva, Paul Myrda, Evangelos Farantatos, Mel GehrsAbstract:This paper proposes an integrated and seamless infrastructure for protection, control and operation of an electric power system. At the lower level we propose a on dynamic state estimation of a protection zone for the purpose of providing protection for the zone. This scheme simplifies the protection approach for the zone by not requiring coordination with other protection zones (setting-less protection). The scheme provides the real time dynamic Model of the zone as well as the real time operating conditions. The scheme can be also implemented in present day numerical relays with GPS synchronization. Using this basic protection infrastructure, we propose that the real time Model of substation be autonomously created, send to the control center where the real time Model of the system is also autonomously created. The system wide real time Model is used to perform system optimization functions, and then send commands back through the same communication structure to specific power system components. Since protection is present in any power apparatus the proposed approach is realizable with very small investment. The availability of the real time dynamic state of the system enables the seamless integration of applications in the proposed system. Three applications are discussed in the paper: (a) setting-less protection, (b) stability monitoring, and (c) voltage/var control.
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pmu based dynamic state estimation for electric power systems
Power and Energy Society General Meeting, 2009Co-Authors: Evangelos Farantatos, George J. Cokkinides, George Stefopoulos, A.p. Sakis MeliopoulosAbstract:This paper provides a methodology to extract the dynamic real time Model of an electric power system using phasor measurement unit (PMU) data (GPS-synchronized) and other SCADA data that are available in substations. In addition to typical voltage and current measurements, PMU data include frequency and rate of change of frequency. Such data are available in raw form, as time-stamped instantaneous values, or, in the case of voltages and currents, as computed phasor data at the system fundamental frequency. A dynamic state estimator is conceptually presented that filters all available data to extract the transient swings of the system in real time. This means that the power system dynamic state estimator can be essentially used as a real time data processor, and its results can provide filtered input to many power system dynamic monitoring and control applications that are currently unavailable, such as monitoring transient stability.
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Component monitoring and dynamic loading visualization from real time power flow Model data
37th Annual Hawaii International Conference on System Sciences 2004. Proceedings of the, 2004Co-Authors: A.p. Sakis Meliopoulos, George J. Cokkinides, Thomas J. OverbyeAbstract:The technology of intelligent electronic devices in power systems has exploded and with it the available real time data. The data are typically used to extract a real time Model of the system via traditional state estimation methods. The traditional approach estimates only the system voltages and uses a small part of the available information. This paper presents a new approach for better utilization of the available information. Specifically, we propose the use of existing data for estimating detailed operational Models of major power equipment. For example, the detailed Models can be in the form of electro-thermal Models which then allow the monitoring of device temperatures, dynamic loading, etc. In general, the real time Model can be as simple or as complex depending on the type and quality of available data. For example, from typical SCADA data, the real time operational Model of a generator can be extracted. This Model can provide the operating margins, etc. Addition of other data, such as ambient temperature, etc. can also provide an electro-thermal Model in real time. The paper demonstrates the approach with the example of a power transformer. The methodology is applied to extract an electro-thermal Model of the transformer. The real time electro-thermal Model provides: (a) transformer temperatures including hot spot, (b) transformer loss of life and (c) transformer dynamic loading. The approach has been simulated in a multitasking environment using an electric power system Model with a time-function electric load. The operation of the system is simulated by solving the power flow at user selected time intervals. As the time progresses the electric load changes and a power flow solution determines by computation the operation of the system. At each time step, the real time Model of the selected power devices are extracted via statistical estimation methods. The real time Model provides vital information for these devices. Their operation conditions can be visualized and animated as desired. The paper presents these applications.
Maxime R Dubois - One of the best experts on this subject based on the ideXlab platform.
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adaptive energy management system based on a real time Model predictive control with nonuniform sampling time for multiple energy storage electric vehicle
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Oleg Gomozov, Joao P Trovao, Xavier Kestelyn, Maxime R DuboisAbstract:The performance of a dual energy storage electric vehicle system mainly depends on the quality of its power and energy managements. A Real-Time management strategy supported by a Model predictive control (MPC) using the nonuniform sampling time concept is developed and fully addressed in this paper. First, the overall multiple energy storage powertrain Model including its inner control layer is represented with the energetic macroscopic representation and used to introduce the energy strategy level. The Model of the system with its inner control layer is translated into the state-space domain in order to develop an MPC approach. The management algorithm based on mixed short- and long-term predictions is compared to rule-based and constant sampling time MPC strategies in order to assess its performance and its ability to be used in a real vehicle. The Real-Time simulation results indicate that, compared to other strategies, the proposed MPC strategy can balance the power and the energy of the dual energy storage system more effectively, and reduce the stress on batteries. Moreover, battery and supercapacitor key variables are kept within safety limits, increasing the lifetime of the overall system.
