Ultracapacitor Research Articles

Visualizing Single V-ATPase Rotation Using Janus Nanoparticles.

Understanding the function of rotary molecular motors, such as rotary ATPases, relies on our ability to visualize single-molecule rotation. Traditional imaging methods often involve tagging those motors with nanoparticles (NPs) and inferring their rotation from the translational motion of NPs. Here, we report an approach using "two-faced" Janus NPs to directly image the rotation of a single V-ATPase from Enterococcus hirae, an ATP-driven rotary ion pump. By employing a 500 nm silica/gold Janus NP, we exploit its asymmetric optical contrast, a silica core with a gold cap on one hemisphere, to achieve precise imaging of the unidirectional counterclockwise rotation of single V-ATPase motors immobilized on surfaces. Despite the added viscous load from the relatively large Janus NP probe, our approach provides accurate torque measurements of a single V-ATPase. This study underscores the advantages of Janus NPs over conventional probes, establishing them as powerful tools for the single-molecule analysis of rotary molecular motors.

Life prediction of on-board supercapacitor energy storage system based on gate recurrent unit neural network using sparse monitoring data

With the increasing use of supercapacitor in transportation and energy sectors, service life prediction becomes an important aspect to consider. As the aging process of onboard supercapacitors is closely related to practical working conditions, the actual service life may be inconsistent with the cycle life measured in the laboratory. However, the low-quality onboard monitoring data recording the historical working conditions is usually sparse and fragmented, making it difficult to extract valuable information. In our previous study, we successfully obtained the characteristic parameters from sparse and fragmented data, whereas those characteristic parameters change periodically and couldn't be used directly for life prediction. In this paper, we firstly extract the degradation trend term of supercapacitor by a composite sine and polynomial time series decomposition model from the characteristic parameters. Secondly, in order to make up for the lack of data, a GRU network is designed to generate more sample data which is in consistent with historical data evolution trends. The combination of input characteristic variables including the extracted historical characteristic capacitance C, temperature T and the time fitting sequences CtD are selected to improve the accuracy of GRU predictions. The predictive error of the characteristic capacitance C is 2.36 %. Finally, the life prediction of on-board supercapacitors based on actual working conditions is realized.

Application of an Optimal Fractional-Order Controller for a Standalone (Wind/Photovoltaic) Microgrid Utilizing Hybrid Storage (Battery/Ultracapacitor) System

Nowadays, standalone microgrids that make use of renewable energy sources have gained great interest. They provide a viable solution for rural electrification and decrease the burden on the utility grid. However, because standalone microgrids are nonlinear and time-varying, controlling and managing their energy can be difficult. A fractional-order proportional integral (FOPI) controller was proposed in this study to enhance a standalone microgrid’s energy management and performance. An ultra-capacitor (UC) and a battery, called a hybrid energy storage scheme, were employed as the microgrid’s energy storage system. The microgrid was primarily powered by solar and wind power. To achieve optimal performance, the FOPI’s parameters were ideally generated using the gorilla troop optimization (GTO) technique. The FOPI controller’s performance was contrasted with a conventional PI controller in terms of variations in load power, wind speed, and solar insolation. The microgrid was modeled and simulated using MATLAB/Simulink software R2023a 23.1. The results indicate that, in comparison to the traditional PI controller, the proposed FOPI controller significantly improved the microgrid’s transient performance. The load voltage and frequency were maintained constant against the least amount of disturbance despite variations in wind speed, photovoltaic intensity, and load power. In contrast, the storage battery precisely stores and releases energy to counteract variations in wind and photovoltaic power. The outcomes validate that in the presence of the UC, the microgrid performance is improved. However, the improvement is very close to that gained when using the proposed controller without UC. Hence, the proposed controller can reduce the cost, weight, and space of the system. Moreover, a Hardware-in-the-Loop (HIL) emulator was implemented using a C2000™ microcontroller LaunchPad™ TMS320F28379D kit (Texas Instruments, Dallas, TX, USA) to evaluate the proposed system and validate the simulation results.

Improving energy efficiency for suburban railways: A two-stage scheduling optimization in a rail-EV smart hub

As the scale of suburban rail and electric vehicles (EVs) continues to expand with the revolution of electrification of transportation, park and ride (P&R) facilities are increasingly recognized as critical energy coupling points between suburban rail traction transformers and EV charging stations. However, flexible coordination of the energy distribution among the bidirectional power flow of multiple trains and EVs’ charging demand becomes an urgent issue. In this paper, we establish a rail-EV Smart Energy Hub (SEH) framework integrating trains, ultra-capacitors (UC), and battery-based EVs. An emendable two-stage optimization model is proposed, enabling railways to provide R2X (railway-to-anything) services. The first stage determines the optimal train trajectory and adjusts timetables to minimize the energy consumption of multiple trains. In the second stage, the charging strategy of the EV is coordinated with the charging/discharging scheme of the UC, which takes the train power flow determined in the first stage as input. Meanwhile, the voltage unbalance caused by the railway is constrained to comply with the limits set by IEC/TR 61000-3-13. Case studies based on actual suburban railway lines in China demonstrate that the proposed scheduling optimization approach can significantly reduce the energy consumption of both railways and EVs.

Green Capping Agents and Digestive Ripening for Size Control of Magnetite for Magnetic Fluid Hyperthermia

Background: Magnetite is the most recognized iron oxide candidate used for various biological applications. Objective: This work is a complete study that addresses the synthesis of magnetic nanoparticles and investigates the feasibility of using green tea and ascorbic acid as capping agents. Methods: Synthesis of magnetite by two wet chemical methods namely: coprecipitation and solvothermal methods. The samples were characterized using X-ray diffraction (XRD), transmission electron microscope (TEM), and vibrating sample magnetometer (VSM). Results: The results reveal the impact of coating on the size and morphology of the particles. The study also proves that autoclaving the samples prepared by coprecipitation results in smaller particle size and narrower size distribution due to digestive ripening. In addition, a novel and facile methodology for coating magnetite with polyethylene glycol is presented. The potential of the particles to be used for magnetic fluid hyperthermia is assessed by measuring the specific absorption rate (SAR) of the samples. Conclusión: The results show that all the prepared magnetite samples showed a promising capacity to be used as magnetic fluid hyperthermia agents.

A CNNbased energy management strategy for a Hybrid energy storage system in electric vehicles

When approaching a large-scale performance, the choice, size, and administration of an Energy Storage System (ESS) for an Electric Vehicle (EV) are crucial. As the peak-to-average power demand ratio is relatively large, particularly for an urban ride that is frequently marked by rapid deceleration as well as acceleration, the complementary characteristics of the battery and Ultra-Capacitor (UC)render this arrangement a viable Hybrid energy storage system (HESS) for EV. Enhanced dynamic responsiveness, increased miles per charge, and extended battery life provided by the HESS increase the Electric Vehicle (EV’s) effectiveness. The primary objective of the study that has been suggested is to create a smart Energy Management Strategy (EMS) for EVs. A battery package and a properly sized Ultra-Capacitor (UC) together give the required high power along with energy density. This research proposes a CNN-based power management technique to aid in efficient EMS. Additionally, by adjusting the Convolution Neural Network (CNN) classifier’s weights through Improved Honey Badger Optimization (IHBO), the adaptive approach of the Standard HBO Algorithm, the performance of the classifier is improved. In MATLAB, the suggested CNN-based model for BESS EM is simulated and experimental assessment and analysis are done in terms of converter current and battery SoC. By contrasting the suggested approach against several standardized models, the performance analysis of the proposed work is assessed to validate its performance.

Visualizing Single V-ATPase Rotation Using Janus Nanoparticles.

Understanding the function of rotary molecular motors, such as the rotary ATPases, relies on our ability to visualize the single-molecule rotation. Traditional imaging methods often involve tagging those motors with nanoparticles (NPs) and inferring their rotation from translational motion of NPs. Here, we report an approach using "two-faced" Janus NPs to directly image the rotation of single V-ATPase from Enterococcus hirae, an ATP-driven rotary ion pump. By employing a 500-nm silica/gold Janus NP, we exploit its asymmetric optical contrast - silica core with a gold cap on one hemisphere - to achieve precise imaging of the unidirectional counter-clockwise rotation of single V-ATPase motors immobilized on surfaces. Despite the added viscous load from the relatively large Janus NP probe, our approach provides accurate torque measurements of single V-ATPase. This study underscores the advantages of Janus NPs over conventional probes, establishing them as powerful tools for single-molecule analysis of rotary molecular motors.

Design of on-board energy storage system using SiC MOSFET

Design of on-board energy storage system using SiC MOSFET

Review of Hybrid Energy Storage Systems for Hybrid Electric Vehicles

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles.

Green Synthesis of Starch‐Capped CdS Nanoparticles Doped with Copper (II) and Manganese (II): Structural, Optical, and Photocatalytic Properties

Pure CdS and Cu²⁺/Mn²⁺‐doped CdS nanoparticles were synthesized using a green capping method via mechanochemical techniques, with starch as the capping agent. Characterization using XRD revealed a cubic phase structure of CdS nanoparticles, evidenced by peaks at 2θ values of 26.30°, 43.80°, and 51.80° corresponding to the (111), (220), and (311) planes, respectively. SEM‐EDAX analysis confirmed the presence of Cd, Cu, Mn, and S elements in the samples. Diffuse reflectance analysis indicated an average crystallite size ranging from 111 nm to 139 nm, accompanied by a band gap energy ranging from 2.25 to 1.95 eV. Raman spectroscopy identified peaks at 366 cm‐¹, 482 cm‐¹, and 556 cm‐¹, attributed to the skeletal modes of the pyranose ring in starch, thus confirming its presence in the starch‐capped CdS nanoparticles.

Simple and Rapid Monolayer Self-Assembly of Nanoparticles at the Air/Water Interface.

Colloidal crystals and their two-dimensional (2D) monolayers, which have been commonly applied in nanosphere lithography, have the potential to revolutionize many engineering disciplines; however, current production techniques are hampered by a restricted preparation area, laborious procedures, and the need for advanced equipment. We propose a self-assembly-driven, simple, and low-cost method to prepare 2D colloidal nanosphere monolayers with excellent repeatability across wide regions. The self-assembly capability of colloidal polystyrene (PS) nanospheres at the air/water interface was utilized to transfer the assembled monolayers onto a substrate. This innovative method combines the advantages of methods that permit deposition at the air/water interface, such as Langmuir and drop coating, in order to deliver defect-free, simple-to-install, and simple-to-apply deposition across vast regions. Using field emission scanning electron microscopy and atomic force microscopy, the resultant coatings were characterized. The size of the nanospheres was reduced using an oxygen plasma etch process in an inductively coupled plasma reactive ion etching system, and the reflectance properties of the substrates for various nanosphere sizes were investigated. By evaporation of a thin gold capping layer on the templates, their optical properties were compared using surface-enhanced Raman scattering spectroscopy. This work has the potential to expand the use of nanosphere lithography by offering a simple and reproducible method that eliminates the need for complicated experimental setups and reduces the amount of material required for monolayer coating, thus lowering the cost.

Economic Evaluation of Using Ultracapacitors in Electric Vehicles

The main challenge of hybridizing ultracapacitors (UCs) with batteries in electric vehicles is their uncertain economic viability, besides their complexity and weight, which should be fully addressed. Therefore, this article determines the general condition for achieving a justified economic system, which is held when the average annual cost (AAC) of a battery‐UC system over a vehicle's useful life is lower than the annual cost of a sole‐battery for a specific system design, energy management strategy, vehicle type, and driving style. As such, the energy storage system is designed in a case study vehicle, and the optimal current distribution is found by dynamic programming (DP) under UDDS, HWFET, and US06 driving cycles. Then, by economic analysis, it is indicated that although adding an UC incurs additional costs, it saves the AAC by improving the battery health and prolonging its lifespan up to a maximum of 15‐year calendar life, which proves its economic justification. Investing in UCs is more economically viable for vehicles with severe driving cycles and high current stress. Finally, the DP optimal trajectory is implemented into an experimental setup under the US06 driving cycle to verify the evaluated strategy.

Utilization and performance comparison of several HESSs with cascade optimal-FOD controller for multi-area multi-source power system under deregulated environment

Electric power system network is evolving rapidly towards a more flexible, robust and secured interconnected network which can provide better power quality within a competitive environment commonly known as deregulated power system (DPS). For safeguarding DPS, energy storage systems (ESSs) and an efficient control method are required to maintain equilibrium between load demand and generated power known as automatic generation control (AGC). This study highlights an attempt of performance comparison of several hybrid ESSs (HESSs) for AGC of multi-area multi-source power system under deregulated scenario. The considered system comprises of thermal, hydro and gas (THG) generating units in each area of a two-area DPS. A cascade optimal controller-fractional order derivative (COC-FOD) is suggested for AGC performance advancement and its performance is compared with optimal controller (OC). A nature inspired salp swarm algorithm (SSA) is utilized to optimise secondary FOD controller gains while primary OC is designed via full state feedback control approach. The time domain analysis and bode plot reveal the eminence of COC-FOD over OC by returning 66 % lower value of cost function, 24 % lower undershoot, 98 % lower overshoot, at least 24 % lesser rise time for area frequency deviation, and 30 % greater stability margins with comparable settling time and lesser number of oscillations. The performances of various HESSs like ultra-capacitor (UC) + redox flow battery (RFB), superconducting magnetic energy storage (SMES) + RFB and flywheel energy storage (FES) + RFB in the attendance of OC and COC-FOD controller are compared and performance of COC-FOD in the presence of SMES+RFB is found superior to other HESS combinations by giving 75 % lesser value of cost function and at least 43 % lesser undershoot for area frequency deviation compared to the scenario where COC-FOD is simulated without ESSs. To present a more realistic scenario, system nonlinearities are considered. Finally, COC-FOD controller proved its resilience and efficacy by providing satisfactory stabilized performance under wide variations in the system parameters.

Modelling a DC Electric Railway System and Determining the Optimal Location of Wayside Energy Storage Systems for Enhancing Energy Efficiency and Energy Management

Global demand for fossil fuels is highly increasing, necessitating energy efficiency to be enhanced in transitioning to low-carbon energy systems. Electric railways are highly efficient in reducing the transportation demand for fossil fuels as they are lightweight and their energy demand can be fed by renewable energy resources. Further, the regenerative braking energy of decelerating trains can be fed to accelerating trains and stored in onboard energy storage systems (ESSs) and stationary ESSs. It is fundamental to model electric railways accurately before investigating approaches to enhancing their energy efficiency. However, electric railways are challenging to model as they are nonlinear, resulting from the rectifier substations, overvoltage protection circuits, and the unpredictability and uncertainty of the load according to the train position. There have been few studies that have examined the ESS location’s impact on improving the energy efficiency of electric railways while using specialised simulation tools in electric railways. However, no single study exists that has studied the location impact of stationary ESSs on the energy efficiency of electric railways while the trains are supported by onboard ESSs. Given these goals and challenges, the main objective of this work is to develop a model using commercial software used by industry practitioners. Further, the energy saving is aimed to be maximised using stationary ESSs installed in optimal locations while trains are supported by onboard ESSs. The model includes trains, onboard ESSs, rail tracks, passenger stations, stationary ESSs, and traction power systems involving power lines, connectors, switches, sectioning, and isolators. In this article, a test scenario is presented comprising two trains running on a 20 km with three passenger stations and two substations. The trains and track are modelled in OpenTrack simulation software (Version 1.9) while the power system is modelled in OpenPowerNet simulation software (Version 1.11). The two simulation tools are used in the railway industry and can produce realistic results by taking into account the entire electrical network structure. A stationary ESS is added on the wayside and moved in steps of 1 km to obtain the optimal location before investigating the impact of stationary ESSs on the performance and energy management of onboard ESSs. It is found that the energy saving when installing a stationary ESS at the optimal location is 56.05%, the peak-power reduction of Substation 1 is 4.37%, and the peak-power reduction of Substation 2 is 18.67%.

A regulatory power split strategy for energy management with battery and ultracapacitor

Electric vehicle batteries face fast degradation due to the high frequency of charging/discharging cycles and great peak power demands. Lifetime, continuity of supply and power density of these batteries affect the performance of electric vehicles (EVs). Hybrid energy storage systems (HESS) offers a feasible solution by incorporating other energy storage elements like ultra-capacitor (UC) along with battery. Their combination provides higher efficiency and better performance in terms energy/power density. UC can behave like a power buffer when the EV is accelerating and regenerating. The HESS needs a controller that can split the available power between different sub systems as per demand. This paper presents a regulatory control strategy useful in HESS with battery and UC for the speed regulation of a brushless DC (BLDC) motor using a 3-port bidirectional DC-DC converter. The regulatory control strategy monitors the state of charge (SOC) of UC and a fuzzy logic controller regulates the power flow between HESS and the motor. Simulation in MATLAB validates the efficacy of the strategy. Simulation results and hardware evaluation confirm that the regulatory control scheme is effective in splitting the available power according to the load demand and achieves better energy efficiency.

Power management system for a hybrid energy storage electric vehicle using fuzzy logic controller

This research work introduces a power management system for a hybrid energy storage system (PMHESS) configuration for urban electric vehicles utilizing a fuzzy logic controller (FLC). Consequently, the configuration includes two DC/DC interleaved converters that establish a connection between the battery and ultra-capacitors (UCs), thereby ensuring a substantial power capacity. The FLC is adaptable and sturdy, considering information from sources other than the vehicle, such as other vehicles or road infrastructure. The study explores incorporating road topography into the control structure to improve hybrid storage performance. Simulation results show that combining lithium batteries with UCs improves the energy source’s performance and reliability. The power management algorithm reduces sudden demands on the battery by considering the slope of the ground. The work proposes energy storage integration for electric vehicles, exploring its benefits through simulations. Overall, the proposed hybrid energy storage system (HESS) demonstrates high efficiency and power for urban electric vehicles. The results are validated using MATLAB/Simulink.

Hybrid energy storage system for electric motorcycles: Technical and economic analysis

This paper presents the multiple energy storage system usability for electric motorcycle focused on hybrid topology. This study focuses on evaluating the cost-effectiveness of a hybrid energy storage system (HESS) for e-motorcycles over a 10-year period by considering the number of battery replacements instead of solely the initial system price. The motorcycle studied in this manuscript, is a recreational electric motorcycle utilized as an experimental test bench in EV-SCHS laboratory at the OJEN company. The 10-year equivalent price of the energy storage system includes both the initial purchase cost and the cost of battery replacements over the system's lifespan. A battery life model is essential for estimating the frequency of replacements over the 10-year period. Incorporating ultracapacitors (UC) in the HESS enhances power specifications, leading to improved acceleration and regenerative braking capabilities. This reduces battery current and extends battery life, resulting in fewer required replacements and lower overall system costs over the 10-year period. The study findings demonstrate that optimally-sized HESS configurations offer favorable power specifications, with the 10-year equivalent price of HESS being $3466 lower than that of conventional energy storage systems, despite the initial price of HESS being $833 higher.

Coordinated Control of the Onboard and Wayside Energy Storage System of an Urban Rail Train Based on Rule Mining

There are three major challenges to the broad implementation of energy storage systems (ESSs) in urban rail transit: maximizing the absorption of regenerative braking power, enabling online global optimal control, and ensuring algorithm portability. To address these problems, a coordinated control framework between onboard and wayside ESSs is proposed in this study, and the related control strategy is obtained by transforming the global optimization problem into a combination of rule-based and local optimization problems. The design process of the strategy is divided into three parts: offline optimization, rule extraction, and local optimization. First, a genetic algorithm is used to optimize the charge/discharge threshold curves under typical operational conditions. Then, the rules of offline optimization results are obtained from an expert system, and the nonlinear rules are mined with local optimization methods. Finally, a coordinated strategy is presented based on the outcomes of the three parts. A power hardware-in-the-loop (PHIL) experimental platform based on RTLAB is built, and the above coordination strategy is verified based on data for the Beijing Metro Batong Line. The experimental results show that the strategy can effectively improve the energy-saving rate and reduce the regeneration failure rate substantially, with the effect being close to the offline global optimal solution. The algorithm proposed in this paper achieves near global optimal energy-saving optimization results with lower computational costs, and has strong portability, providing a good solution for the large-scale application of onboard and wayside energy storage coordination control.

OptimML: Joint Control of Inference Latency and Server Power Consumption for ML Performance Optimization

Power capping is an important technique for high-density servers to safely oversubscribe the power infrastructure in a data center. However, power capping is commonly accomplished by dynamically lowering the server processors’ frequency levels, which can result in degraded application performance. For servers that run important machine learning (ML) applications with Service-Level Objective (SLO) requirements, inference performance such as recognition accuracy must be optimized within a certain latency constraint, which demands high server performance. In order to achieve the best inference accuracy under the desired latency and server power constraints, this paper proposes OptimML, a multi-input-multi-output (MIMO) control framework that jointly controls both inference latency and server power consumption, by flexibly adjusting the machine learning model size (and so its required computing resources) when server frequency needs to be lowered for power capping. Our results on a hardware testbed with widely adopted ML framework (including PyTorch, TensorFlow, and MXNet) show that OptimML achieves higher inference accuracy compared with several well-designed baselines, while respecting both latency and power constraints. Furthermore, an adaptive control scheme with online model switching and estimation is designed to achieve analytic assurance of control accuracy and system stability, even in the face of significant workload/hardware variations.

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy.