Ultracapacitor Research Articles

Incorporation of Green Capping Agents to Reduce Silver-Mediated Dentine Staining

Silver diammine fluoride (SDF) is known as a noninvasive, cost-effective, safe, and simple method of dental caries treatment. However, staining and discoloration seem inseparable with SDF and continue as a cosmetic concern. Research is ongoing to overcome these issues, for example, by using glutathione (G) or potassium iodide among others. Therefore, the study aimed to investigate the effects of incorporating different concentrations of capping agents on SDF chemistry and SDF-mediated tooth staining at different time points. Tannic acid (TA), gallic acid (GA), carboxymethyl chitosan (CM), and G at different concentrations (5, 10, and 15% w/v) were incorporated in 30% SDF. FTIR and UV-Vis spectroscopies of the prepared solutions was performed to evaluate chemical changes. Time-dependent color changes (ΔE) in bovine dentine specimens (6 × 6 × 1 ± 0.25 mm³) were measured spectrophotometrically at application/washup, 1 and 3 h, after 1, 2, 4, 7, and 14 days. Results showed suppression of FTIR peaks at 3,358 cm⁻¹ and 1,215 cm⁻¹ in capping agent-modified SDF indicative of a successful capping effect of the silver ions, which was corroborated by UV-Vis blueshift of ∼∆32 nm. The capping effect on SDF increased proportionally with the concentrations of TA, GA, CM, and G used. A more pronounced tooth staining reduction however was shown more in TA- and G- rather than in GA- and CM-modified SDF. At day 14, SDF showed the highest mean ΔE(50.14 ± 2.14), while 15% TA showed the lowest ΔE(30.14 ± 0.81). In conclusion, capping agent incorporation significantly reduced SDF-mediated tooth staining. This reduction in staining is more dependent on the respective capping agent functional groups than concentrations per se. The potential of capping agents to minimize tooth staining of SDF was TA>G>CM>GA.

Optimal coordinated design of under-frequency load shedding and energy storage systems

Under-frequency load shedding (UFLS) is considered a fundamental protection tool against power imbalances in electrical grids, particularly in small island systems. However, load shedding entails a delicate compromise between guaranteeing the stability of the system and disconnecting the lowest amount of load to achieve it. Fast-responding converter-interfaced energy storage systems (ESSs) can help improve such a compromise. In this regard, this paper proposes a methodology for the coordinated design of under-frequency load shedding (UFLS) and fast-responding converter-interfaced energy storage systems (ESSs). UFLS parameters include thresholds for frequency and its rate-of-change, and time settings of each step. ESS control parameters comprise, among others, the emulated inertia and the droop. The proposed coordinated methodology is applied to a real Spanish island power system with a 4 MW/5.6 kWh ultracapacitor (UC). Results show that the proposed methodology is able to significantly reduce the total amount of load shed with respect to the currently implemented UFLS settings and UC control parameters.

Cooperative Distributed GPU Power Capping for Deep Learning Clusters

The recent GPU-based clusters that handle deep learning (DL) tasks have the features of GPU device heterogeneity, a variety of deep neural network (DNN) models, and high computational complexity. Thus, the traditional power capping methods for CPU-based clusters or small-scale GPU devices cannot be applied to the GPU-based clusters handling DL tasks. This article develops a cooperative distributed GPU power capping (CD-GPC) system for GPU-based clusters, aiming to minimize the training completion time of invoked DL tasks without exceeding the limited power budget. Specifically, we first design the frequency scaling approach using the online model estimation based on the recursive least square method. This approach achieves the accurate tuning for DL task training time and power usage of GPU devices without needing offline profiling. Then, we formulate the proposed FS problem as a Lagrangian dual decomposition-based economic model predictive control problem for large-scale heterogeneous GPU clusters. We conduct both the NVIDIA GPU-based lab-scale real experiments and real job trace-based simulation experiments for performance evaluation. Experimental results validate that the proposed system improves the power capping accuracy to have a mean absolute error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; \!1\%$</tex-math></inline-formula> , and reduces the deadline violation ratio of invoked DL tasks by 21.5% compared with other recent counterparts.

Energy Management Strategy for Fuel Cell/Battery/Ultracapacitor Hybrid Electric Vehicles Using Deep Reinforcement Learning With Action Trimming

As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

EVALUATION OF TECHNICAL PARAMETERS LOCOMOTIVE FOR RAILWAY CAREER TRANSPORT

The main technical parameters of the locomotive for quarry railway transport have been evaluated. The design parameters of the locomotive have been determined and it is established that the tangential power is 6700 kW, and the traction force for the design mode is 1300 kN. The calculations of the traction characteristics of the locomotive have been performed. The procedure for determining the parameters of the locomotive operating modes during train tasks performing has been proposed, which is based on the processing of the results of traction task solving on the section of the track. It has been established that for the test section of the track a significant part of the time in the traction mode the locomotive operates with a load of 10… 25% of the nominal, therefore it has been proposed to implement the mode of movement with disconnection of traction motors. The use of an onboard energy storage system, which allows the accumulation of energy during electrodynamics braking has been proposed. The generalized scheme of traction system which allows realizing ways of increase of energy efficiency a rolling stock has been offered.

Символы великокняжеской власти в средние века (по данным духовных грамот московских великих князей)

Symbols of power in the wills of the Grand Princes of Moscow (14th– 15th Centuries)&#x0D; After a thorough examination of the valuables listed in the will, I came to the conclusion that symbols of the later autocratic political power (gold cap, barmy, carneol box, gold chain with cross) first became part of the inheritance of the eldest son in the will of Vasily II in 1462. It was during the reign of Vasily II that they acquired a prominent role and became the regal symbols of the first-born son, the future Grand Prince.

Performance of fast responding ultracapacitor energy storage for virtual inertia emulation control

AbstractThis paper proposes a black widow optimization (BWO) algorithm‐based derivative‐type control for optimized virtual inertia (VI) emulation using fast responding ultracapacitor (UC) as energy storage system for frequency regulation in a two‐area deregulated power system composed of thermal and hydro units with high penetration of solar photovoltaic. The parameters of the system have been controlled by using newly formulated BWO and their performance have been compared for virtual inertial emulation efficacy with other algorithms under different contract obligations. The impact of the coordinated action of BWO‐based proportional‐integral (PI) controller with VI emulation has been validated on the area frequency, tie‐line power, and generated power of different units. The eigenvalue approach has been applied to verify the performance and effect of VI emulation control using fast acting UC on system stability. The proposed VI emulation control studies have been evaluated under nonlinear environment of deregulated power system. The comparative studies show that BWO optimized PI‐based VI emulation using fast responding UC provides superior dynamic responsiveness in all competitive electricity market scenarios tested under different operating conditions.

Design and Analysis of Math Function Based Controller Combined with Fuzzy Logic Applied to the Solar-Powered Electric Vehicle

The transition between the battery and ultracapacitor (UC) according to the driver requirements is the key obstacle that is related to the hybrid energy storage system (HESS) powered electric vehicles (EVs). In this effort, an innovative control scheme has been proposed, to switch the power sources corresponding to the vehicle dynamics. An MFB controller is considered with 4- math functions and programmed independently. Thereafter, a new hybrid controller has been formed by joining the designed MFB controller with an artificial neural network (ANN) to achieve the precise transition between battery and UC. The battery gets charged from the photovoltaic (PV) panel corresponding to the irradiance and temperature availability. The charging and discharging periods of battery mainly depending upon the control switches (CS) present in the circuit. From those two controllers, ANN generates required switching pulses whereas the MFB controller regulates the pulse depending upon the speed of the motor. Finally, the combination of MFB with ANN controller produces the required pulse signals to switches present in unidirectional (UDC) and bidirectional converter (BDC) related to the speed of the motor. The MATLAB/Simulink model of the system is done in four modes with different loads, and all results are discussed in the simulation and results section.

Fuzzy Logic Controller Based Modified Energy Management Strategy for Battery and UC with Improved Battery Performance

The ultracapacitor (UC) acts as a stress reducer for the battery. It is also considered as a fruitful companion for a battery in battery-UC hybrid energy storage system (HESS). Management of power between battery, UC, and drive motor, along with a suitable topology, decides the performance of the battery and drive system. Rule based energy management strategy (EMS) is simple, real-time implementable, ease in control, and based on engineer’s experience. But it lacks in controlling the state-of-charge (SOC) of battery and UC, battery current rate, and its magnitude, dc link voltage control, and UC voltage regulation. In this paper, low pass filter based conventional rule-based EMS is studied and implemented in MATLAB. Then fuzzy logic based modified rule-based EMS is proposed and simulated for the same load profile. Comparative study between conventional and modified EMS shows the proposed EMS is capable to provide constant dc link voltage, stable UC SOC operation, fast response, and reduces the large variation in speed. Proposed EMS saves the 10% of the battery energy for 20 sec load profiles as compared with battery only system. So, by use of UC, shared bus topology, and effective EMS, driving range of the vehicle and battery life can be improved and range anxiety can be reduced.

Innovative battery thermal management system based on hydrogen storage in metal hydrides for fuel cell hybrid electric vehicles

This study proposes a new approach for dealing with the thermal management of batteries in fuel cell hybrid electric vehicles, by introducing a new concept of on-board energy storage system which integrates the battery pack with a metal hydride tank. The rationale behind this solution is to use the exothermic absorption and endothermic desorption processes of hydrogen in metal hydrides for heating and cooling the battery pack, respectively, thus ensuring an optimal thermal management control. An experimental investigation is carried out on a prototype in order to proof the concept and preliminary assess its feasibility for actual implementations. The results show that the system is capable to effectively control the temperature variations within the battery pack: for 1C and 1.5C constant-current discharge tests, a desorption of 30%–40% of hydrogen, over the total amount stored into the metal hydride tank of the system, allows the final average temperature of the battery pack to be around 15 °C lower than the one reached without thermal management. Moreover, it is found that the system is potentially capable of providing a suitable thermal management for more than four hours under realistic driving conditions. The proposed energy storage system also achieves inherently high gravimetric and volumetric energy densities, with theoretical values equal to 182 Wh/kg and 530 Wh/L, respectively. These estimates represent reference values for further design improvements.

Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach

Power and energy is the first-class design constraint for multi-core processors and is a limiting factor for future-generation supercomputers. While modern processor design provides a wide range of mechanisms for power and energy optimization, it remains unclear how software can make the best use of them. This article presents a novel approach for runtime power optimization on modern multi-core systems. Our policy combines power capping and uncore frequency scaling to match the hardware power profile to the dynamically changing program behavior at runtime. We achieve this by employing reinforcement learning (RL) to automatically explore the energy-performance optimization space from training programs, learning the subtle relationships between the hardware power profile, the program characteristics, power consumption and program running times. Our RL framework then uses the learned knowledge to adapt the chip's power budget and uncore frequency to match the changing program phases for any new, previously unseen program. We evaluate our approach on two computing clusters by applying our techniques to 11 parallel programs that were not seen by our RL framework at the training stage. Experimental results show that our approach can reduce the system-level energy consumption by 12 percent, on average, with less than 3 percent of slowdown on the application performance. By lowering the uncore frequency to leave more energy budget to allow the processor cores to run at a higher frequency, our approach can reduce the energy consumption by up to 17 percent while improving the application performance by 5 percent for specific workloads.

Renewable energy based charging station with power quality features for multi EV spots using maximum correntropy sub-band adaptive technique

ABSTRACT Here, a photovoltaic (PV)-based multi-spots electric vehicles (EVs) charging station (CS) is presented by using maximum correntropy criteria with affine projection sub-band adaptive filter (MCC-APSAF). The presented CS also includes the features of power quality at nonlinear loads connected at the point of common coupling (PCC). This CS comprises a battery bank, multi-EV batteries, and ultra-capacitor (UC) charging spots. However, a dramatic increase of EVs and charging stations in the existing grid has raised the issues related to the harmonics, voltage fluctuations, reactive power, and burden on the grid. These issues are addressed in the presented charging station. Moreover, the ultra-capacitor can provide fast charging, which minimizes the high inrush current of the CS. Therefore, the batteries during the high EVs load at the CS are protected by the ultra-capacitor spots. To stabilize the DC bus voltage, separate cascaded controllers for ultra-capacitor and battery spots are used. The CS shows the multifunctional capabilities, which include PV array to grid (PV2G), PV array to the EV battery (S2EVB), EV battery to grid (EVB2G), and PV array to home (PV2H) when PV source is present. The various EVB2G, G2EVB, battery to home (EVB2H), and grid to home (G2H) behaviors are also presented. This CS shows good dynamic efficacy under insolation variation, EVB2G, G2EVB modes, load throw, and load reconnection operations. The grid current THD using the proposed current control is 1.44%, which is in accordance with the IEEE-519 standard. Moreover, the maximum power obtained from the PV generation is 63 kW, which is successfully distributed in the battery bank and six EV batteries (30 kW), in nonlinear load (14 kW), and the rest of the power is injected to the grid (29 kW). This CS is simulated in the MATLAB/Simulink library and test results are obtained using the experimental workbench with OPAL-RT (OP5600) in the laboratory.

Sensitive Oligonucleotide Detection Using Resonant Coupling between Fano Resonance and Image Dipoles of Gold Nanoparticles.

The surface plasmon resonance (SPR)-based sensor has been widely used for biodetection. One of the attractive roles is the gold nanostructure with Fano resonance. Its sharp resonant profile takes advantage of the high figure of merit (FoM) in high-sensitivity detection. However, it is still difficult to detect small molecules at low concentrations due to the extremely low refractive index changes on the metallic surface. We propose using the coupling of image dipoles of gold nanoparticles (AuNPs) and Fano resonance of periodic capped gold nanoslits (CGNs) for sensitive small-molecule detections. The coupling mechanism was verified by three-dimensional finite-difference time-domain calculations and experiments. AuNPs on CGN form image dimer assemblies and induce image dipole with resonance wavelengths ranging from 730 to 550 nm. The surface plasmon polaritons (SPPs) interact with the image dipole of the AuNP on the CGNs and then scatter out through the periodic gold caps. The experimental results show that the peak intensity of grating resonance is decreased by the effect of image dipole and exhibits the maximum intensity change when the Fano resonance matches the resonance of image dipole. The 50 nm AuNPs can be detected with a surface density of less than one particle/μm2 by using the intensity change as the signal. With the resonant coupling between Fano resonance and image dipole extinction, the oligonucleotide with a molecular weight of 5.5 kDa can be detected at a concentration of 100 fM. The resonant coupling dramatically pushes the sensitivity boundary, and we report the limit of detection (LOD) to be 3 orders of magnitude lower than that of the prism-based SPR. This study provides a promising and efficient method for detecting low concentrations of small molecules such as aptamers, miRNA, mRNA, and peptides.

APE: Metrics for understanding application performance efficiency under power caps

As supercomputers continue to grow in size and power consumption, the ability to understand application run time performance and energy/power usage is of growing importance. In the future it may be necessary for systems to operate under power caps imposed by facilities due to external influences such as renewable power generation (e.g. solar) impacting energy availability at an affordable cost during certain times of day and more frequent natural disasters due to climate change causing power transmission disruptions. However, current energy consumption and power characterization metrics like energy-delay products do not express all of the characteristics of an application that are relevant to understanding the impact of power capping on performance.In this study, we (1) characterize the useful features of a metric that effectively captures time-to-solution and power performance dynamics; and (2) design and evaluate a novel set of application power efficiency (APE) metrics that accounts for time-to-completion, power utilization and power variability. Power utilization quantifies a system’s trapped capacity (unused power budget) and power variability quantifies an application’s variation of power consumption over time. We demonstrate how APE can be used to quantify application characteristics which, in turn, enables reasoning about the impacts of power capping on an application.

Performance Analysis of Hybrid Ultra Capacitor (HUC) based Solar Microgrid for Household Applications

Localised power generation and consumption using a solar-based hybrid micro-grid system are gaining predominance in remote locations where conventional transmission of power from far off substations work out to be uneconomical. Since the peak irradiance from the sun lasts for 4-6 hours per day, a hybrid micro-grid with batteries for storing energy is usually adopted. This study considered a typical household load of 3kW comprising resistive and inductive loads. Sudden surges will be caused by switching ON/OFF of any load, and in particular, switching ON of inductive loads (such as fans, washing machines and mixer) will further contribute to transients. These sudden changes in loads harm the cyclic life of the battery. A viable solution to overcome the above problem is to use Hybrid Ultra Capacitors (HUC) as an assistant to the Batteries. These HUC banks can take over the surge loads, whereas Batteries can focus on delivering steady power. Such a combination has the potential to enhance the battery’s life. This study discusses the performance of Hybrid Ultra Capacitor (HUC) based Hybrid Energy Storage System (HESS) in a 3kW Solar MicrogriAU: Please prod system by evaluating the electrical parameters involving experiments with different loads.

Efficient Onboard Energy Storage System Sizing for All-Electric Ship Microgrids Via Optimized Navigation Routing Under Onshore Uncertainties

Energy storage system (ESS) is a critical component in all-electric ships (AESs). However, an improper size and management of ESS will deteriorate the technical and economic performance of the shipboard microgrids. In this article, a joint optimization scheme is developed for ESS sizing and optimal power management for the whole shipboard power system. Different from traditional ESS sizing methods, which solve a minimum cost problem, a novel coordinated framework is proposed to jointly optimize the ESS size and voyage route for an AES, considering the distributionally robust ambiguity onshore electricity prices. The main goals of this method are to reduce the capital and operational cost, greenhouse gas emissions, and navigation time. Furthermore, the dynamic electricity prices are adopted to assist ship operators to select the optimal voyage routine. Several cases are compared to verify the proposed algorithm and numeral simulation results clearly demonstrate the high efficiency of the proposed method and necessity of navigation route scheduling.

Super-twisting sliding mode controller for maximum power transfer efficiency tracking in hybrid energy storage based wireless in-wheel motor

Conventionally, the in-wheel motor is powered by cables from the battery attached to electric vehicle (EV) body which has a risk of disconnection in case of high acceleration or harsh environment. To solve this issue, using wireless power transfer (WPT) technology, a wireless in-wheel motor (WIWM) based on LCC-S Compensation topology is proposed. To cater for varying power requirements of WIWM, and generate optimal power from WPT system, an ultracapacitor (UC) is connected in parallel with WIWM. The UC and onboard battery make a hybrid energy storage system for the in-wheel motor system. A super-twisting sliding mode controller-based control system has been designed to drive the WIWM at desired speed and track UC current to required reference. The stability of proposed control system is verified by Lyapunov stability criteria. The performance of the proposed controller has been compared against conventionally used PID and Lyapunov controller by driving EV according to European Extra Urban Driving Cycle (EUDC). Finally, the real-time performance of proposed control scheme has been validated by controller hardware-in-loop (C-HIL) experiments.

Power quality improvement using ultra capacitor based dynamic voltage restorer with real twisting sliding mode control

Introduction. Power quality is a major problem in today's power system, since it may have an impact on customers and utilities. Problem. Power quality is important issue of financial consequences for utilities, their consumers and load apparatus vendors. Voltage sag/swell are the most significant and usually occurring power quality issues in a secondary distribution system for sensitive loads. Goal. Dynamic voltage restorer is a fast, flexible, effective and dynamic custom power device can be used to compensate voltage sag/swell with integration of energy storage. Ultra capacitors have ideal properties of great power density and low energy density for elimination of voltage sag/swell. Their performance is mostly determined by the control strategy established for switching of voltage source converters. Originality. In this research, a strategy for the voltage source converter of dynamic voltage restorer based on the real twisting sliding mode control and ultra capacitor is developed to correct the fault that successfully eliminates the impacts of voltage sag/swell. Methodology. Ultra capacitor along with real twisting sliding mode control gives the more robustness and faster response, with also increasing the compensation time of the dynamic voltage restorer. Testing environment. To evaluate the performance of the proposed control approach, the MATLAB / Simulink SimPower System tool box is employed. Practical values. According to Simulation results clearly shows that the ultra capacitor along with real twisting sliding mode control effectively eliminate the voltage sag/swell in a very short time of 2 ms as compared to IEEE standards that is 20 ms, with less than 5 % total harmonic distortion for sensitive loads as per Information Technology Industry Council Curve and SEMI-F-47 Standards.

Short-term power demand prediction for energy management of an electric vehicle based on batteries and ultracapacitors

Model predictive control applied to energy management of hybrid energy storage system (HESS) in electric vehicles (EV) requires a proper knowledge of the power demanded by the traction system. As a key point of this work, two strategies to predict the power demand profile based on an autoregressive (AR) model and a Kalman Filter scheme are proposed. It is shown that using a Kalman filter with an AR model to predict the power demand, an error of 0.2% is achieved for the first prediction compared to 1.4% obtained for the case in which the power demand is considered constant on a standard drive cycle. These strategies are used to implement a nonlinear model predictive control (NMPC) strategy for the power split of a HESS based on batteries and Ultracapacitor (UC) in an EV. To preserve the health of the battery, a cost function is proposed to minimize large and highly variant battery currents. Regarding the cost of battery degradation, it is shown that the proposed strategies obtain results comparable to the ideal case in which the required power is fully known.

Power-sharing for dc microgrid with composite storage devices and voltage restoration without communication

In this paper, we propose a new decentralized control and power-sharing strategy to manage the power flow among energy sources (ESs), energy storage systems (ESSs) and the common dc-link. In the proposed technique, we eliminate all communication among the ESSs, to reduce the complexity and increase reliability, maintaining dc-link voltage restoration. In this context, batteries and ultracapacitors (UCs) are the ESSs, while ESs can be any power source such as photovoltaic, wind, fuel-cell and etc. This technique shares the microgrid power imbalance between batteries, proportionally to their state-of-charge (SoC) and energy capacity, achieving SoC equalization. The technique also promotes voltage restoration for the UCs, keeping their average voltage constant after supplying the power peaks during power transients. For all ESSs only local variables are measured, such as local current and dc-link voltage, with no shared data between ESSs. Small signal and stability analysis are performed, along with experimental results in a lab bench show the feasibility and performance of the technique.