This page has only limited features, please log in for full access.

Unclaimed
Poria Fajri
Department of Electrical & Biomedical Engineering, University of Nevada, Reno, NV 89557, USA

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 07 July 2021 in Energies
Reads 0
Downloads 0

A single-phase, single-stage, differential boost inverter comprises two independently-controlled boost DC-DC converters, with the load connected between their outputs. The net voltage on the load is sinusoidal and has a controllable frequency and magnitude that is larger than that of the DC source. The present work first derives steady-state and small-signal models of the inverter with parasitic elements. The results obtained from the line-to-output transfer function, control-to-output transfer function, open-loop input impedance, and open-loop output impedance models are compared with that of the ones obtained from the experimental testbed. Using the new models, a voltage mode controller is designed in the synchronous reference frame. The regulator design is explored through the use of an example. The results are verified against the small-signal model, then PLECS simulations, and finally a laboratory experiment. The results indicate excellent agreement between the model and experiment during transients in voltage reference, input source voltage, and output load. A sensitivity analysis is performed based on the inverter model considering the parameter variation. Finally, loss and efficiency estimations are provided in this work.

ACS Style

Rasheduzzaman; Poria Fajri; Jonathan Kimball; Brad Deken. Modeling, Analysis, and Control Design of a Single-Stage Boost Inverter. Energies 2021, 14, 4098 .

AMA Style

Rasheduzzaman, Poria Fajri, Jonathan Kimball, Brad Deken. Modeling, Analysis, and Control Design of a Single-Stage Boost Inverter. Energies. 2021; 14 (14):4098.

Chicago/Turabian Style

Rasheduzzaman; Poria Fajri; Jonathan Kimball; Brad Deken. 2021. "Modeling, Analysis, and Control Design of a Single-Stage Boost Inverter." Energies 14, no. 14: 4098.

Journal article
Published: 20 May 2021 in Sustainability
Reads 0
Downloads 0

This work aims to minimize the cost of installing renewable energy resources (photovoltaic systems) as well as energy storage systems (batteries), in addition to the cost of operation over a period of 20 years, which will include the cost of operating the power grid and the charging and discharging of the batteries. To this end, we propose a long-term planning optimization and expansion framework for a smart distribution network. A second order cone programming (SOCP) algorithm is utilized in this work to model the power flow equations. The minimization is computed in accordance to the years (y), seasons (s), days of the week (d), time of the day (t), and different scenarios based on the usage of energy and its production (c). An IEEE 33-bus balanced distribution test bench is utilized to evaluate the performance, effectiveness, and reliability of the proposed optimization and forecasting model. The numerical studies are conducted on two of the highest performing batteries in the current market, i.e., Lithium-ion (Li-ion) and redox flow batteries (RFBs). In addition, the pros and cons of distributed Li-ion batteries are compared with centralized RFBs. The results are presented to showcase the economic profits of utilizing these battery technologies.

ACS Style

Reza Sabzehgar; Diba Amirhosseini; Saeed Manshadi; Poria Fajri. Stochastic Expansion Planning of Various Energy Storage Technologies in Active Power Distribution Networks. Sustainability 2021, 13, 5752 .

AMA Style

Reza Sabzehgar, Diba Amirhosseini, Saeed Manshadi, Poria Fajri. Stochastic Expansion Planning of Various Energy Storage Technologies in Active Power Distribution Networks. Sustainability. 2021; 13 (10):5752.

Chicago/Turabian Style

Reza Sabzehgar; Diba Amirhosseini; Saeed Manshadi; Poria Fajri. 2021. "Stochastic Expansion Planning of Various Energy Storage Technologies in Active Power Distribution Networks." Sustainability 13, no. 10: 5752.

Journal article
Published: 05 March 2021 in Energies
Reads 0
Downloads 0

In this study, a double-loop control strategy is proposed for power grid frequency and voltage regulation using plug-in electric vehicles (PEVs) connected to the grid through a three-level capacitor clamped inverter. The frequency and voltage regulation problem is first formulated using vector space analysis and phasor diagrams to find the boundaries and constraints in terms of the system parameters. The derived formulas are then utilized to design a double-loop controller using an exclusive phase detector control loop and a novel pulse width modulation (PWM) scheme to effectively regulate the frequency and voltage of the grid. The effectiveness and feasibility of the proposed control strategy are evaluated through simulation and experimental studies. This approach can benefit both the customers and the grid operator, as it facilitates utilizing the batteries of the connected PEVs to supply a portion or all of the active and reactive power demand, hence regulating the frequency and voltage of the grid. The extent to which active and reactive power can be supplied depends on the number of PEVs connected to the local grid.

ACS Style

Mohammadshayan Latifi; Reza Sabzehgar; Poria Fajri; Mohammad Rasouli. A Novel Control Strategy for the Frequency and Voltage Regulation of Distribution Grids Using Electric Vehicle Batteries. Energies 2021, 14, 1435 .

AMA Style

Mohammadshayan Latifi, Reza Sabzehgar, Poria Fajri, Mohammad Rasouli. A Novel Control Strategy for the Frequency and Voltage Regulation of Distribution Grids Using Electric Vehicle Batteries. Energies. 2021; 14 (5):1435.

Chicago/Turabian Style

Mohammadshayan Latifi; Reza Sabzehgar; Poria Fajri; Mohammad Rasouli. 2021. "A Novel Control Strategy for the Frequency and Voltage Regulation of Distribution Grids Using Electric Vehicle Batteries." Energies 14, no. 5: 1435.

Journal article
Published: 01 December 2020 in IEEE Transactions on Energy Conversion
Reads 0
Downloads 0

This paper proposes a novel approach to efficiently distribute braking force of an electric vehicle (EV) between friction and regenerative braking with an ultimate goal of maximizing harvested energy during braking. The regenerative braking performance of an EV depends on various factors influenced by the driver behavior and driving conditions, which are challenging to measure or predict in real-time. In the proposed method, the performance map of the traction motor (TM) and its controller is used to define a boundary in which blending of regenerative and friction braking is performed with the goal of maximizing recaptured energy through the regenerative braking process. The proposed method is validated on an experimental EV hardware-in-the-loop (HIL) test bench setup for a predetermined drive cycle of Urban Dynamometer Driving Schedule (UDDS). It is shown that the amount of recaptured energy through the regenerative braking process can significantly increase by taking advantage of the proposed method compared to a case, which considers a constant boundary for brake distribution.

ACS Style

Shoeib Heydari; Poria Fajri; Reza Sabzehgar; Arash Asrari. Optimal Brake Allocation in Electric Vehicles for Maximizing Energy Harvesting During Braking. IEEE Transactions on Energy Conversion 2020, 35, 1806 -1814.

AMA Style

Shoeib Heydari, Poria Fajri, Reza Sabzehgar, Arash Asrari. Optimal Brake Allocation in Electric Vehicles for Maximizing Energy Harvesting During Braking. IEEE Transactions on Energy Conversion. 2020; 35 (4):1806-1814.

Chicago/Turabian Style

Shoeib Heydari; Poria Fajri; Reza Sabzehgar; Arash Asrari. 2020. "Optimal Brake Allocation in Electric Vehicles for Maximizing Energy Harvesting During Braking." IEEE Transactions on Energy Conversion 35, no. 4: 1806-1814.

Journal article
Published: 05 August 2020 in IEEE Transactions on Sustainable Energy
Reads 0
Downloads 0

Modern distribution power systems face more complex challenges compared to the conventional systems. A powerful technique to react to such challenges is network reconfiguration, which needs to be adapted with “recent” concerns of modern power systems. Hence, it is essential to determine optimal configurations “rapidly” for better time management of hour-ahead system's decision making. This paper proposes an optimization algorithm, named parallel frog migrating algorithm (PFMA), to rapidly identify the optimal system topology for hour-ahead systems operation. The significance of the proposed technique is the development of a fuzzy-based decision making unit to realistically evaluate the necessity of implementing the identified topologies. This will noticeably decrease the computational burden of system operator in analyzing the identified optimal configurations. The effectiveness of the proposed PFMA is validated on an unbalanced 136-bus distribution network containing wind turbine and photovoltaic (PV) distributed generation units (DGs). It is demonstrated that the presented PFMA is able to identify close to optimal solutions almost “four times faster” than the nonlinear solver of General Algebraic Modeling System (GAMS) software. More importantly, it is verified that the developed decision making mechanism effectively takes advantage of renewable DGs and reduces the necessity of network reconfiguration.

ACS Style

Arash Asrari; Meisam Ansari; Javad Khazaei; Poria Fajri; M. Hadi Amini; Benito Ramos. The Impacts of a Decision Making Framework on Distribution Network Reconfiguration. IEEE Transactions on Sustainable Energy 2020, 12, 634 -645.

AMA Style

Arash Asrari, Meisam Ansari, Javad Khazaei, Poria Fajri, M. Hadi Amini, Benito Ramos. The Impacts of a Decision Making Framework on Distribution Network Reconfiguration. IEEE Transactions on Sustainable Energy. 2020; 12 (1):634-645.

Chicago/Turabian Style

Arash Asrari; Meisam Ansari; Javad Khazaei; Poria Fajri; M. Hadi Amini; Benito Ramos. 2020. "The Impacts of a Decision Making Framework on Distribution Network Reconfiguration." IEEE Transactions on Sustainable Energy 12, no. 1: 634-645.

Journal article
Published: 05 August 2020 in IEEE Transactions on Energy Conversion
Reads 0
Downloads 0

This paper presents a model predictive self-healing control (MPSC) scheme for battery system interfaced dual active bridge (DAB) converter in navy ship power system (NSPS) with pulsed power loads (PPLs). The voltage and frequency of NSPS are vulnerable to PPLs energization. A properly controlled battery system with fast dynamic response can mitigate this vulnerability of NSPS to PPLs energization. Model predictive control (MPC) is a potential solution for the battery system interfaced DAB converter to achieve fast dynamic response and mitigate disturbances imposed to the NSPS by PPLs. However, conventional MPC framework suffers from current prediction error due to the pulsating AC-link inductor's voltage profile in DAB converter. This paper proposes a self-healing control loop that utilizes the feasible range of power transfer in conjunction with the AC-link inductor's voltage profile that can validate and autonomously correct the predicted current and phase shift in DAB converter interfaced a battery system. The proposed control scheme on DAB converter prevents voltage and frequency collapse in a NSPS during the PPL energization. The theoretical concepts are validated by several case studies implemented on HIL testbed of a NSPS.

ACS Style

Mohsen Hosseinzadehtaher; Ahmad Khan; Mitchell Easley; Mohammad B. Shadmand; Poria Fajri. Self-Healing Predictive Control of Battery System in Naval Power System With Pulsed Power Loads. IEEE Transactions on Energy Conversion 2020, 36, 1056 -1069.

AMA Style

Mohsen Hosseinzadehtaher, Ahmad Khan, Mitchell Easley, Mohammad B. Shadmand, Poria Fajri. Self-Healing Predictive Control of Battery System in Naval Power System With Pulsed Power Loads. IEEE Transactions on Energy Conversion. 2020; 36 (2):1056-1069.

Chicago/Turabian Style

Mohsen Hosseinzadehtaher; Ahmad Khan; Mitchell Easley; Mohammad B. Shadmand; Poria Fajri. 2020. "Self-Healing Predictive Control of Battery System in Naval Power System With Pulsed Power Loads." IEEE Transactions on Energy Conversion 36, no. 2: 1056-1069.

Journal article
Published: 20 May 2020 in Energies
Reads 0
Downloads 0

A nonlinear sliding-mode controller for a three-phase converter, utilized in plug-in electric vehicles (PEVs), is proposed in this paper. The proposed controller enables the utilized converter to perform multiple functions during different operating modes of the vehicle, i.e., grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The bidirectional three-phase converter and the proposed controller operate as a power factor correction circuit, bridgeless boost converter, and rectifier during G2V mode (i.e., plug-in charging), and it operates as a conventional single-stage inverter during V2G mode. The stability analysis of the proposed controller is performed by defining a proper Lyapunov function. The functionality of the proposed nonlinear controller is first evaluated through simulation studies. The feasibility and effectiveness of the proposed control strategy is then validated using an industrial control card through a hardware-in-the-loop (HIL) experimental testbed.

ACS Style

Reza Sabzehgar; Yaser M. Roshan; Poria Fajri. Modeling and Control of a Multifunctional Three-Phase Converter for Bidirectional Power Flow in Plug-In Electric Vehicles. Energies 2020, 13, 2591 .

AMA Style

Reza Sabzehgar, Yaser M. Roshan, Poria Fajri. Modeling and Control of a Multifunctional Three-Phase Converter for Bidirectional Power Flow in Plug-In Electric Vehicles. Energies. 2020; 13 (10):2591.

Chicago/Turabian Style

Reza Sabzehgar; Yaser M. Roshan; Poria Fajri. 2020. "Modeling and Control of a Multifunctional Three-Phase Converter for Bidirectional Power Flow in Plug-In Electric Vehicles." Energies 13, no. 10: 2591.

Journal article
Published: 27 April 2020 in IEEE Transactions on Industry Applications
Reads 0
Downloads 0

In this paper, an operational framework is proposed for peer-to-peer (P2P) energy trading between an electric vehicle (EV) charging station and a business entity equipped with solar generation that will significantly improve the benefit of both parties compared to having sole agreements with the utility. A dynamic pricing mechanism for available EVs in the charging station is developed based on the price of the stored energy that not only improves the profit of the owners, but also promotes the contribution of charging stations in P2P energy markets. To evaluate the proposed framework, the performance of the system under P2P energy sharing is compared with peer-to-grid (P2G) energy trading. The results show a 23.24% reduction in total cost of prosumers and an improvement of 10% in self-consumption of PV generation as well as 100% participation willingness of prosumers. The results also suggest that due to the availability of solar energy during the daylight hours and the possibility to trade the stored energy in EVs outside of home, P2P energy transactions under the proposed control framework will bring economic benefits for EV owners.

ACS Style

Sima Aznavi; Poria Fajri; Mohammad B. Shadmand; Arash Khoshkbar-Sadigh. Peer-to-Peer Operation Strategy of PV Equipped Office Buildings and Charging Stations Considering Electric Vehicle Energy Pricing. IEEE Transactions on Industry Applications 2020, 56, 5848 -5857.

AMA Style

Sima Aznavi, Poria Fajri, Mohammad B. Shadmand, Arash Khoshkbar-Sadigh. Peer-to-Peer Operation Strategy of PV Equipped Office Buildings and Charging Stations Considering Electric Vehicle Energy Pricing. IEEE Transactions on Industry Applications. 2020; 56 (5):5848-5857.

Chicago/Turabian Style

Sima Aznavi; Poria Fajri; Mohammad B. Shadmand; Arash Khoshkbar-Sadigh. 2020. "Peer-to-Peer Operation Strategy of PV Equipped Office Buildings and Charging Stations Considering Electric Vehicle Energy Pricing." IEEE Transactions on Industry Applications 56, no. 5: 5848-5857.

Articles
Published: 06 January 2020 in International Journal of Green Energy
Reads 0
Downloads 0

In this paper, an effective objective function is proposed to minimize the cost of operation of a microgrid with large-scale plug-in electric vehicles and renewable energy resources. The profit of consumers is taken into account by utilizing the incentives in the demand response programs, and vehicle-to-grid feature of the plug-in-electric vehicles integrated into the grid. The optimization is performed using genetic algorithms. Also, reliability indices of the economically optimized microgrid are computed for various operation configurations in both the grid-tied and islanded modes. Numerical studies are conducted on a microgrid testbed to validate the performance of the proposed strategy.

ACS Style

R. Sabzehgar; M. A. Kazemi; M. Rasouli; P. Fajri. Cost optimization and reliability assessment of a microgrid with large-scale plug-in electric vehicles participating in demand response programs. International Journal of Green Energy 2020, 17, 127 -136.

AMA Style

R. Sabzehgar, M. A. Kazemi, M. Rasouli, P. Fajri. Cost optimization and reliability assessment of a microgrid with large-scale plug-in electric vehicles participating in demand response programs. International Journal of Green Energy. 2020; 17 (2):127-136.

Chicago/Turabian Style

R. Sabzehgar; M. A. Kazemi; M. Rasouli; P. Fajri. 2020. "Cost optimization and reliability assessment of a microgrid with large-scale plug-in electric vehicles participating in demand response programs." International Journal of Green Energy 17, no. 2: 127-136.

Journal article
Published: 06 December 2019 in Machines
Reads 0
Downloads 0

This article presents a new and powerful freeware software called MotorAnalysis-PM and discusses its application in electromagnetic design and analysis of permanent magnet (PM) motors for the electric vehicle (EV) industry. This new PM motor software utilizes both finite element (FE) and analytical methods to speed up the analysis and design process of PM motors significantly. The analysis and design methodology using MotorAnalysis-PM is presented and discussed for a 50 kW PM motor utilized in a commercial EV. To validate the accuracy of the software, the numerical results obtained from the PM motor design and analysis tool are compared with experimental results. The numerical and experimental results validate the flexibility of this software in achieving accurate motor design with short design times which is of great interest to EV and PM motor manufacturers.

ACS Style

Vladimir Kuptsov; Poria Fajri; Andrzej Trzynadlowski; Guoliang Zhang; Salvador Magdaleno-Adame. Electromagnetic Analysis and Design Methodology for Permanent Magnet Motors Using MotorAnalysis-PM Software. Machines 2019, 7, 75 .

AMA Style

Vladimir Kuptsov, Poria Fajri, Andrzej Trzynadlowski, Guoliang Zhang, Salvador Magdaleno-Adame. Electromagnetic Analysis and Design Methodology for Permanent Magnet Motors Using MotorAnalysis-PM Software. Machines. 2019; 7 (4):75.

Chicago/Turabian Style

Vladimir Kuptsov; Poria Fajri; Andrzej Trzynadlowski; Guoliang Zhang; Salvador Magdaleno-Adame. 2019. "Electromagnetic Analysis and Design Methodology for Permanent Magnet Motors Using MotorAnalysis-PM Software." Machines 7, no. 4: 75.

Journal article
Published: 29 November 2019 in IEEE Transactions on Industry Applications
Reads 0
Downloads 0

Coordinated energy management plays a main role in increasing the performance and economic benefits of future smart homes. This article focuses on the energy management of a smart home equipped with a Plug-in Electric Vehicle (PEV), household energy storage, and photovoltaics (PV), and it proposes an Energy Price Tag (EPT) for all energy storage devices connected to the smart home system. A hybrid approach that consists of optimization and a rule-based prioritization is presented. The proposed algorithm establishes the priority order between the PEV, household battery, and imported power from the grid based on the EPT of energy sources. The proposed energy management algorithm seeks for the minimum overall energy cost for the smart home and PEV owner, while satisfying household power demand and charging requirements of the storage devices. The performance of the energy management algorithm is examined on a typical smart home over a 24-hour period based on time-varying grid electricity price.

ACS Style

Sima Aznavi; Poria Fajri; Arash Asrari; Farshad Harirchi. Realistic and Intelligent Management of Connected Storage Devices in Future Smart Homes Considering Energy Price Tag. IEEE Transactions on Industry Applications 2019, 56, 1679 -1689.

AMA Style

Sima Aznavi, Poria Fajri, Arash Asrari, Farshad Harirchi. Realistic and Intelligent Management of Connected Storage Devices in Future Smart Homes Considering Energy Price Tag. IEEE Transactions on Industry Applications. 2019; 56 (2):1679-1689.

Chicago/Turabian Style

Sima Aznavi; Poria Fajri; Arash Asrari; Farshad Harirchi. 2019. "Realistic and Intelligent Management of Connected Storage Devices in Future Smart Homes Considering Energy Price Tag." IEEE Transactions on Industry Applications 56, no. 2: 1679-1689.

Journal article
Published: 02 August 2019 in IEEE Transactions on Smart Grid
Reads 0
Downloads 0

This paper proposes a day-ahead market framework for congestion management in smart distribution networks. The presented scheme provides a platform for collaboration between distribution-level market operator (DMO) and data traffic operator (DTO) to alleviate congested feeders such that data transmission traffic between market participants is effectively managed in a smart grid. In addition, a decentralized mechanism is developed for collaboration of electric vehicle (EV) aggregators with common clients to take advantage of EVs not only as flexible loads but also as mobile distributed storage (MDS) for congestion management. Moreover, the proposed framework outlines an administrative action for distribution system operator (DSO) to support the market when the decentralized competitions among distributed generation (DG) aggregators and EV aggregators do not fully relieve a serious congestion. The proposed day-ahead congestion management scheme is validated on an unbalanced 136-bus distribution system massively integrated with wind turbine DGs (WTDGs), photovoltaic DGs (PVDGs), diesel-engine DGs (DEDGs), and EVs.

ACS Style

Arash Asrari; Meisam Ansari; Javad Khazaei; Poria Fajri. A Market Framework for Decentralized Congestion Management in Smart Distribution Grids Considering Collaboration Among Electric Vehicle Aggregators. IEEE Transactions on Smart Grid 2019, 11, 1147 -1158.

AMA Style

Arash Asrari, Meisam Ansari, Javad Khazaei, Poria Fajri. A Market Framework for Decentralized Congestion Management in Smart Distribution Grids Considering Collaboration Among Electric Vehicle Aggregators. IEEE Transactions on Smart Grid. 2019; 11 (2):1147-1158.

Chicago/Turabian Style

Arash Asrari; Meisam Ansari; Javad Khazaei; Poria Fajri. 2019. "A Market Framework for Decentralized Congestion Management in Smart Distribution Grids Considering Collaboration Among Electric Vehicle Aggregators." IEEE Transactions on Smart Grid 11, no. 2: 1147-1158.

Journal article
Published: 24 January 2019 in IEEE Transactions on Transportation Electrification
Reads 0
Downloads 0

This paper introduces a novel approach for dynamically detecting the lowest speed threshold at which regenerative braking is effective in Electric Vehicles (EVs). The control approach is based on real-time sensing of the motor controller DC link current and disabling regenerative braking when current changes direction while the motor is operating as a generator. Various factors influencing regenerative braking capability of EVs at low speed are discussed and simulation studies are carried out to illustrate the effect of each factor on the displacement of the low-speed threshold. Based on the results obtained from the simulation studies, a dynamic Low-Speed Cutoff Point (LSCP) detection method is proposed. This method requires no hardware modification to the vehicle braking architecture and can be implemented solely by modifying the brake controller. The proposed method is tested on an experimental EV test platform for a predetermined drive cycle. It is shown that in comparison to considering a constant low-speed threshold during braking, the amount of energy recaptured through the regenerative braking process can be improved by taking advantage of the proposed method.

ACS Style

Shoeib Heydari; Poria Fajri; Rasheduzzaman; Reza Sabzehgar. Maximizing Regenerative Braking Energy Recovery of Electric Vehicles Through Dynamic Low-Speed Cutoff Point Detection. IEEE Transactions on Transportation Electrification 2019, 5, 262 -270.

AMA Style

Shoeib Heydari, Poria Fajri, Rasheduzzaman, Reza Sabzehgar. Maximizing Regenerative Braking Energy Recovery of Electric Vehicles Through Dynamic Low-Speed Cutoff Point Detection. IEEE Transactions on Transportation Electrification. 2019; 5 (1):262-270.

Chicago/Turabian Style

Shoeib Heydari; Poria Fajri; Rasheduzzaman; Reza Sabzehgar. 2019. "Maximizing Regenerative Braking Energy Recovery of Electric Vehicles Through Dynamic Low-Speed Cutoff Point Detection." IEEE Transactions on Transportation Electrification 5, no. 1: 262-270.

Review
Published: 17 September 2018 in Energies
Reads 0
Downloads 0

This paper examines existing and future direct current (DC) distribution systems with a wide range of applications in data centers, telecommunication systems, commercial buildings, residential homes, electric vehicles, spacecraft, and aircrafts. DC distribution systems have many advantages and disadvantages over their alternating current (AC) counterparts. There are a few surviving examples of DC distribution systems; among them are the telecommunication systems and data centers that use the low-voltage 48 Vdc systems. However, recently, there has been a move towards higher DC bus voltages. In this paper, a comparative study of different DC distribution architectures and bus structures is presented and voltage level selection is discussed for maximizing system efficiency and reliability, reducing system costs, and increasing the flexibility of the system for future expansion. Furthermore, DC distribution systems are investigated from a safety standpoint and the current global market for these distribution systems is also discussed.

ACS Style

Venkata Anand Prabhala; Bhanu Prashant Baddipadiga; Poria Fajri; Mehdi Ferdowsi. An Overview of Direct Current Distribution System Architectures & Benefits. Energies 2018, 11, 2463 .

AMA Style

Venkata Anand Prabhala, Bhanu Prashant Baddipadiga, Poria Fajri, Mehdi Ferdowsi. An Overview of Direct Current Distribution System Architectures & Benefits. Energies. 2018; 11 (9):2463.

Chicago/Turabian Style

Venkata Anand Prabhala; Bhanu Prashant Baddipadiga; Poria Fajri; Mehdi Ferdowsi. 2018. "An Overview of Direct Current Distribution System Architectures & Benefits." Energies 11, no. 9: 2463.

Journal article
Published: 15 November 2016 in IEEE Transactions on Intelligent Transportation Systems
Reads 0
Downloads 0

In this paper, an optimization model is developed to find a plug-in hybrid electric vehicle (PHEV) optimum charging rate profile that dynamically varies throughout the day. From the grid point of view, the model takes into account the constraints of maximum demand and charging facilities, while from the driver's point of view, waiting and charging time restrictions are considered. The novelty of this paper lies in maximizing the energy delivered to PHEVs in a region equipped with smart grid technology by intelligently alternating charging rates during the day while incorporating both driver satisfaction constraints as well as grid limitations. Using the proposed optimization model, two cases with optimized charging rates are studied and compared with constant charging levels. Furthermore, quantitative results from the perspective of both power grid contribution and driver satisfaction are presented and discussed in detail for each case.

ACS Style

Zahra Darabi; Poria Fajri; Mehdi Ferdowsi. Intelligent Charge Rate Optimization of PHEVs Incorporating Driver Satisfaction and Grid Constraints. IEEE Transactions on Intelligent Transportation Systems 2016, 18, 1325 -1332.

AMA Style

Zahra Darabi, Poria Fajri, Mehdi Ferdowsi. Intelligent Charge Rate Optimization of PHEVs Incorporating Driver Satisfaction and Grid Constraints. IEEE Transactions on Intelligent Transportation Systems. 2016; 18 (5):1325-1332.

Chicago/Turabian Style

Zahra Darabi; Poria Fajri; Mehdi Ferdowsi. 2016. "Intelligent Charge Rate Optimization of PHEVs Incorporating Driver Satisfaction and Grid Constraints." IEEE Transactions on Intelligent Transportation Systems 18, no. 5: 1325-1332.

Journal article
Published: 15 October 2013 in Energies
Reads 0
Downloads 0

Advanced electrochemical batteries are becoming an integral part of a wide range of applications from household and commercial to smart grid, transportation, and aerospace applications. Among different battery technologies, lithium-ion (Li-ion) batteries are growing more and more popular due to their high energy density, high galvanic potential, low self-discharge, low weight, and the fact that they have almost no memory effect. However, one of the main obstacles facing the widespread commercialization of Li-ion batteries is the design of reliable battery management systems (BMSs). An efficient BMS ensures electrical safety during operation, while increasing battery lifetime, capacity and thermal stability. Despite the need for extensive research in this field, the majority of research conducted on Li-ion battery packs and BMS are proprietary works conducted by manufacturers. The available literature, however, provides either general descriptions or detailed analysis of individual components of the battery system, and ignores addressing details of the overall system development. This paper addresses the development of an experimental research testbed for studying Li-ion batteries and their BMS design. The testbed can be configured in a variety of cell and pack architectures, allowing for a wide range of BMS monitoring, diagnostics, and control technologies to be tested and analyzed. General considerations that should be taken into account while designing Li-ion battery systems are reviewed and different technologies and challenges commonly encountered in Li-ion battery systems are investigated. This testbed facilitates future development of more practical and improved BMS technologies with the aim of increasing the safety, reliability, and efficiency of existing Li-ion battery systems. Experimental results of initial tests performed on the system are used to demonstrate some of the capabilities of the developed research testbed. To the authors’ knowledge, this is the first work that addresses, at the same time, the practical battery system development issues along with the theoretical and technological challenges from cell to pack level.

ACS Style

Nima Lotfi; Poria Fajri; Samuel Novosad; Jack Savage; Robert G. Landers; Mehdi Ferdowsi. Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems. Energies 2013, 6, 5231 -5258.

AMA Style

Nima Lotfi, Poria Fajri, Samuel Novosad, Jack Savage, Robert G. Landers, Mehdi Ferdowsi. Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems. Energies. 2013; 6 (10):5231-5258.

Chicago/Turabian Style

Nima Lotfi; Poria Fajri; Samuel Novosad; Jack Savage; Robert G. Landers; Mehdi Ferdowsi. 2013. "Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems." Energies 6, no. 10: 5231-5258.