با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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31 |
Vanadium redox flow battery parameters optimization in a transportation microgrid: A case study
بهینه سازی پارامترهای باتری بادی جریان وانادیوم در یک شبکه انتقال : یک مطالعه موردی-2020 This paper addresses the concept of vanadium redox flow batteries as stationary energy storage for
achieving optimum parameters of energy and cost-effectiveness in transportation microgrids. Such energy
storage has two main purposes: to utilize the energy recovered from braking trains, and shave
power peaks. With abovementioned purposes, economic feasibility is the main driver of measures to
optimize the battery parameters, including joint energy and power capacity, as well as and energy
management strategy parameters. The optimization results obtained from the genetic algorithm and
particle swarm optimization algorithm were compared, and the comparison demonstrates that the
second method operates more sufficiently. The case study shows that the implementation of the proposed
battery system in a traction substation allows one to achieve approximately 7 year payback period
and decrease peak power and daily consumption by 581 kW and 1.77 MWh, respectively. In addition,
sensitivity analysis was conducted to determine the impact of certain factors and battery parameters on
the resulting payback period. The results show that the effect of deviation of energy management
strategy parameters from optimum values on payback period is four times more profound than deviation
of battery parameters, which demonstrates how important energy management strategy is. Keywords: Vanadium redox flow battery | Transportation system microgrid | Energy management strategy | Traction substation | Regenerative braking | Peak power sheaving |
مقاله انگلیسی |
32 |
Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship
بهینه سازی اندازه و کنترل فرکانس در سیستم ذخیره انرژی هیبریدی باتری / ابررساننده برای حامل سوخت-2020 The fuel cell is generally coupled with the hybrid energy storage system (HESS) to improve power system
dynamic performance and prolong the fuel cell lifetime. Therefore, the sizing of HESS and design of
energy management strategy (EMS) have already become key research points. Based on support vector
machine and frequency control, a novel EMS is proposed. As the sizing of HESS and the design of energy
management strategy have a strong inner link, a multi-objective optimization method for the HESS and
EMS is proposed. After that, simulations are used to compare the performance of the optimal hybrid
power system. Compared with the different hybrid power system structures, the optimal HESS can meet
power demand and reduce the cost of the energy storage device. Compared with the rule-based energy
management strategy, the energy consumption of the optimal hybrid power system reduces 5.4%, and
improves power quality and prolongs the device life. The results indicate that the proposed method can
achieve excellent performance and is easily applied. Keywords: Hybrid ship | Energy management strategy | Hybrid energy storage system | Whale optimization algorithm (WOA) | Support vector machine (SVM) |
مقاله انگلیسی |
33 |
A novel optimal energy management strategy for offshore wind/ marine current/battery/ultracapacitor hybrid renewable energy system
رویکرد استراتژی مدیریت انرژی بهینه برای بادی دریایی / دریایی فعلی / باتری / فراخازنی ترکیبی سیستم انرژی تجدید پذیر-2020 Climate change and high energy demand have significantly increased the need for renewable energy
sources (RES). Marine and ocean energy sources draw attention through their high energy potential.
Offshore wind and marine current energy is an attractive RES with great potential. The wind and current
energy in the marine produces an intermittent and unstable power by nature. Energy storage systems are
the most effective solution to minimize power fluctuations in the system and to ensure stable energy
demand. This paper presents a novel optimal energy management strategy (NOEMS) for optimal power
flow control of the offshore wind/marine current/battery/ultracapacitor hybrid power generation system
and for the most efficient harvesting of hybrid renewable energy system (HRES). The proposed NOEMS
algorithm calculates as real time the amount of power generated by the HRES and demanded by the load.
In this study, nine different dynamic operation modes were considered. Experimental results have shown
that the battery and ultracapacitor support to the HRES. In this study, the dynamic behavior of the
NOEMS algorithm was investigated by performing a sudden load test from 18 W to 30 W. The NOEMS
algorithm shows that the system can minimize power loss, voltage fluctuation, control the charge/
discharge status of the battery and ultracapacitor. The proposed algorithm continuously shifts the
required power over the hybrid energy storage system to provide the load demand continuously. Keywords: Offshore wind | Marine current | Battery | Ultracapacitor | Hybrid energy storage | Optimal energy management |
مقاله انگلیسی |
34 |
Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method
عیب یابی باتری های لیتیوم یون برای وسایل نقلیه الکتریکی با استفاده از روش تجزیه و تحلیل آنتروپی نمونه-2020 Fault detection plays a vital role in the operation of lithium-ion batteries in electric vehicles. Typically, during
the operation of battery systems, voltage signals are susceptible to noise interference. In this paper, a novel fault
detection method based on the Empirical Mode Decomposition and Sample Entropy is proposed to identify
battery faults under various operating conditions. Firstly, effective fault features are extracted through the
proposed Empirical Mode Decomposition method by decomposing battery voltage signals and removing the
noise interference during the voltage sampling process. Experiments are conducted to quantitatively illustrate
the fault features extracted by the Empirical Mode Decomposition. Then, based on these extracted fault features,
the Sample Entropy values are calculated to help accurately detect and locate the battery faults. Moreover, an
evaluation strategy of the detected faults is designed to indicate the battery fault level. Finally, the effectiveness
of the proposed approach is verified against real-world data measured from electric vehicles in the presence of
regular and sudden faults. Keywords: Electric vehicles | Lithium-ion batteries | Fault detection | Sample entropy |
مقاله انگلیسی |
35 |
Optimal design of a university campus micro-grid operating under unreliable grid considering PV and battery storage
طراحی بهینه یک میکرو شبکه دانشگاه که تحت شبکه غیرقابل اعتماد با توجه به ذخیره سازی PV و باتری کار می کند-2020 This paper proposes a novel methodology for redesigning a micro-grid characterized by a heavy reliance
on diesel generators due to receiving power supply from an unreliable grid. The new design aims at
phasing out the diesel generators and replacing them with a hybrid energy system composed of photovoltaics
and a battery storage system. Two optimization approaches are adopted, a heuristic genetic
algorithm approach is used to achieve sub-optimal sizing of the hybrid system sources and a rules-based
dynamic programming approach to ensure optimal power flow. In order to reduce the computation time,
a novel combinational approach employing genetic algorithm, dynamic programming and rules-based
algorithm is proposed. The intervention of the dynamic programming for optimal power flow is
restricted to certain active hours within a given day, while the rules-based power flow algorithm runs
only outside those hours. The study demonstrates that the application of the hybrid system yields
minimal operational cost by almost entirely phasing out the diesel generators and significantly reducing
the energy purchased from the grid during peak hours. The micro-grid of a university campus is used as a
case study where energy and economic indicators are derived to prove the superiority of the proposed
techniques. Keywords: Microgrid optimal design | Energy management system | Genetic algorithm | Dynamic programming | Energy economics |
مقاله انگلیسی |
36 |
An optimized energy management strategy for fuel cell hybrid power system based on maximum efficiency range identification
یک استراتژی مدیریت انرژی بهینه برای سیستم قدرتمند هیبریدی سلول سوختی بر اساس شناسایی حداکثر برد بهره وری-2020 This study proposes an optimized energy management strategy (EMS) based on maximum efficiency range (MER)
identification for a fuel cell/battery hybrid sightseeing car. And the aim of this study is to optimize hydrogen
consumption of hybrid system and make sure that the power distribution between the fuel cell (FC) system and
battery is optimal. FC system has the MER and is also a strongly coupled system. The MER of FC system will move
with the change of operating conditions, and consequently, a parameter identification technique is needed to
estimate the boundary powers of MER. This goal is achieved in this paper by using a forgetting factor recursive
least square (FFRLS) online identification approach. Then the sequential quadratic programming (SQP) algorithm
is used to solve the majorization problem of equivalent consumption minimum strategy (ECMS) so that the
FC system operates as much as possible in the MER, while ensuring that the battery state of charge (SOC)
fluctuates within the limited range. This helps to improve the efficiency, performance, and durability of the FC
system and reduce the equivalent hydrogen consumption of the battery. A reduce-scale test platform is designed
to verify the feasibility of the proposed optimized ECMS (OECMS). In addition, the conventional ECMS and rulebased
state machine control (SMC) strategy are utilized in this paper to highlight the advantages of the proposed
strategy. The experiment results show that the proposed OECMS helps to improve FC performance and optimize
system hydrogen consumption. Keywords: Fuel cell (FC) | Fuel cell/battery hybrid sightseeing car | Forgetting factor recursive least square (FFRLS) | Online identification | Equivalent consumption minimum strategy | (ECMS) | Reduce-scale test platform |
مقاله انگلیسی |
37 |
Aging-aware co-optimization of battery size, depth of discharge, and energy management for plug-in hybrid electric vehicles
بهینه سازی هم افزایی اندازه باتری ، عمق تخلیه و مدیریت انرژی برای وسایل نقلیه الکتریکی هیبریدی پلاگین-2020 Plug-in hybrid electric vehicles (PHEVs) have a large battery pack, and the depth of discharge (DOD) significantly
affects the battery longevity. In this paper, the battery degradation is considered in the co-optimization of
battery size and energy management for PHEVs using convex programming. The impact of DOD on battery
degradation and energy management is also investigated. The cost function consists of fuel consumption, electrical
energy consumption, and equivalent battery life loss. A real-world speed profile collected from the urban
city bus route up to about 70 km is used as an input to evaluate the proposed method. The results suggest that, for
both cases with and without battery degradation, the total cost curve with respect to the preset final state of
charge (SOC) is an upward parabola, where the optimal DOD can be identified, and the optimal battery size and
energy management can be determined. The results also show that, with an initial SOC of 0.9, the proposed
method can reduce the total cost by 3.6 CNY compared to other existing studies with the fixed final SOC.
Moreover, a sensitivity analysis is conducted to explore the effect of battery price and initial SOC on the optimal
DOD and total cost. Keywords: Plug-in hybrid electric bus | Optimal depth of discharge | Convex optimization | Battery aging model | Energy management |
مقاله انگلیسی |
38 |
A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions
یک استراتژی مدیریت انرژی جدید برای باتری های لیتیوم سه قلو بر اساس مدل سازی مدار معادل پویا و فیلتر کالمن دیفرانسیل تحت شرایط متغیر زمانی-2020 The dynamic model of the ternary lithium battery is a time-varying nonlinear system due to the polarization and
diffusion effects inside the battery in its charge-discharge process. Based on the comprehensive analysis of the
energy management methods, the state of charge is estimated by introducing the differential Kalman filtering
method combined with the dynamic equivalent circuit model considering the nonlinear temperature coefficient.
The model simulates the transient response with high precision which is suitable for its high current and
complicated charging and discharging conditions. In order to better reflect the dynamic characteristics of the
power ternary lithium battery in the step-type charging and discharging conditions, the polarization circuit of the
model is differential and the improved iterate calculation model is obtained. As can be known from the experimental
verifications, the maximize state of charge estimation error is only 0.022 under the time-varying complex
working conditions and the output voltage is monitored simultaneously with the maximum error of 0.08 V and
the average error of 0.04 V. The established model can describe the dynamic battery behavior effectively, which
can estimate its state of charge value with considerably high precision, providing an effective energy management
strategy for the ternary lithium batteries. Keywords: Ternary lithium battery | Dynamic equivalent circuit modeling | Differential Kalman filtering | State of charge estimation | Parameter acquisition | Nonlinear classification |
مقاله انگلیسی |
39 |
Optimal energy management with balanced fuel economy and battery life for large hybrid electric mining truck
مدیریت بهینه انرژی با مصرف سوخت متعادل و عمر باتری برای کامیون های بزرگ برقی هیبریدی-2020 With the addition of an energy storage system (ESS) and advanced controls, a hybrid electric propulsion system
can considerably improve the fuel economy over a pure mechanical powertrain. However, the high cost and
relatively short operating life of the battery ESS constitute a significant portion of the total operation cost (TOC)
of an electrified vehicle, particularly for heavy-duty vehicles with a larger ESS. In this work, a new method for
generating the optimal energy management strategy (EMS), considering the TOC of a hybrid electric mining
truck (HEMT), is introduced. The cost associated with battery performance degradation and operation lifeshortening
under different battery use patterns is added to form the globally optimal, TOC-based EMS. The
optimal EMS under different vehicle operation profiles are identified using dynamic programming (DP) to serve
as benchmarks. An intelligent optimal ESS energy management method for achieving the minimum TOC during
real-time, open-pit HEMT operations is introduced by combining an artificial neural network (ANN) model and a
fuzzy-logic controller (FLC). The new, real-time intelligent optimal EMS led to twenty-one percent TOC reduction
of the HEMT over the traditional, pure fuel economy-oriented optimal EMS, and formed the foundation of TOCbased,
optimal EMS development for hybrid electric vehicles (HEVs). Keywords: Hybrid electric mining truck | Online energy management strategy | Battery performance degradation | Neural network | Hybrid electric vehicles |
مقاله انگلیسی |
40 |
Data-driven reinforcement-learning-based hierarchical energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles
استراتژی مدیریت انرژی سلسله مراتبی مبتنی بر تقویت یادگیری مبتنی بر داده ها برای وسایل نقلیه برقی هیبریدی سلول / باتری / ultracapacitor-2020 A reinforcement-learning-based energy management strategy is proposed in this paper for managing energy
system of Fuel Cell Hybrid Electric Vehicles (FCHEV) equipped with three power sources. A hierarchical power
splitting structure is employed to shrink large state-action space based on an adaptive fuzzy filter. Then, the
reinforcement-learning-based algorithm using Equivalent Consumption Minimization Strategy (ECMS) is proposed
for tackling high-dimensional state-action space, and finding a trade-off between global learning and realtime
implementation. The power splitting policy based on experimental data is obtained by using reinforcement
learning algorithm, which allows for many different driving cycles and traffic conditions. The proposed energy
management strategy can achieve low computation cost, optimal fuel cell efficiency and energy consumption
economy. Simulation results confirm that, compared with existing learning algorithms and optimization
methods, the proposed reinforcement-learning-based energy management strategy using ECMS can achieve high
computation efficiency, lower power fluctuation of fuel cell and optimal fuel economy of FCHEV. Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Reinforcement learning | Data driven | Hierarchical power splitting |
مقاله انگلیسی |