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نتیجه جستجو - باتری

تعداد مقالات یافته شده: 82
ردیف عنوان نوع
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
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