Supercapacitor State Of Charge Research Articles

A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors

While State-of-Charge (SOC) estimation for supercapacitors has been extensively studied, most research focuses on single units. However, the recent introduction of reconfigurable circuits significantly alters system dynamics, rendering existing SOC estimation techniques inadequate. This paper addresses this challenge by employing a switching systems approach to estimate the SOC of supercapacitors with reconfigurable circuits. We first establish an RC model for the supercapacitor integrated with the reconfigurable circuit and thoroughly analyze the state continuity and observability of the resulting switched system. Subsequently, we propose a switching observer and evaluate its convergence properties by comparing its performance against other observer techniques. Experimental validation on a hardware platform demonstrates the superiority of our proposed observer for accurate SOC estimation in this context.

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge–discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery’s SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

State-of-charge estimation for supercapacitors based on salp swarm algorithm-optimized high and low degree cubature Kalman filters considering temperature uncertainty

The accurate estimation of state-of-charge (SOC) is crucial for the supercapacitor (SC) management system of electric vehicles. In this paper, a salp swarm algorithm (SSA)-optimized high and low degree cubature Kalman filters (CKFs) for SC SOC estimation is proposed. Firstly, the key parameters are identified for the Thevenin model by SSA under SC test data. Secondly, a temperature-varied model with temperature uncertainty is constructed by polynomial fitting. Thirdly, an adaptive rule based on SSA is designed to regulate the noise covariance matrix information in real time. Combining this rule and CKFs, SSA-based generalized high-degree CKF (SSA-GHCKF) and SSA-based low-degree CKF (SSA-CKF) are developed with the minimum prediction error as the objective. Finally, the effectiveness of improved CKFs is evaluated by the urban dynamometer driving schedule in varying temperatures. The simulation results show that the root mean square errors of SOC estimation by CKF, GHCKF, and SSA-CKF are kept within 1.4553 %, 1.2880 % and 1.2152 %, while for SSA-GHCKF, this metric is merely 1.1933 %. The proposed SSA-GHCKF enables superior accuracy and robustness for SOC estimation at extreme temperatures.

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

Cost investigation of battery-supercapacitor hybrid energy storage system for grid-connected hourly dispatching wave energy converter power

This study demonstrates a successful application of a dispatching scheme for a slider-crank wave energy converter (WEC), utilizing a battery-supercapacitor hybrid energy storage system (HESS). The six sea states employed in the U.S. Department of Energy's Wave Energy Prize are incorporated to calculate the desired hourly grid reference power. The proposed architecture is responsible for fulfilling this grid demand in each dispatching period within an acceptable confidence level. A low pass filter is deployed to distribute the power between the supercapacitor and battery to exploit their benefits in the HESS configuration.Furthermore, several control techniques utilizing the supercapacitor state of charge (SOC) have been devised to predict the grid reference power accurately for each dispatching horizon. The techniques were applied to ensure that the energy storage system finishes each dispatching period with sufficient capacity for next-day operation (approximately 80 % SOC).The ESS life cycle expenditure is minimized based on three key factors: the best SOC algorithm, the best filter time constant, and the best depth of discharge (DOD) usage. The HESS was found to be the most cost-effective (2.6 ¢/kWh) for the WEC application under these conditions: a 100 ms filter time constant with a step-rules algorithm as a primary SOC controller, a 44 % DOD usage for the battery, and a 50 % DOD usage for the supercapacitor (SC). The HESS was also found to be less expensive than the battery-only (8.4 ¢/kWh) as well as the SC-only (2.8 ¢/kWh) cases. It is also noticeable that the SC calendar aging cost (61 %) and the battery cycling cost (96 %) contributed significantly to its overall HESS expenses for dispatching WEC power. Extensive MATLAB/Simulink simulations were performed to provide a realistic economic evaluation for WEC dispatching power.

Evaluation of two model predictive control schemes with different error compensation strategies for power management in fuel cell hybrid electric buses

Fuel cell hybrid electric buses (FCHEBs) employing hydrogen fuel cells as the main power source and supercapacitors as the energy buffer could be a feasible electrified transportation technology. This paper evaluates two model predictive control (MPC) schemes with different error compensation strategies for power management in FCHEBs: MPC scheme with Adaptive Compensation (denoted as AC-MPC) and MPC scheme with Gaussian Process Regression Compensation (denoted as GPRC-MPC). AC-MPC introduces adaptive weights based on the supercapacitor state of charge (SOC) and compensates for the errors associated with the linearization of the nonlinear fuel cell/supercapacitor hybrid energy storage system. GPRC-MPC utilizes Gaussian process regression to predict and compensate for the load disturbance error and the residual error. AC-MPC and GPRC-MPC are evaluated using the New York Bus (NYBus) drive cycle and a drive cycle collected along the GBG17 bus route in Sweden. Simulation results demonstrate the effectiveness of AC-MPC and GPRC-MPC in improving the hydrogen system performance by reducing the hydrogen consumption and the maximum fuel cell current change rate compared to the linear MPC scheme without any compensation while ensuring that the supercapacitor SOC is bounded.

Optimal Hybridization with Minimum Fuel Consumption of the Hybrid Fuel Cell Train

This paper describes a numerical study of the optimal distribution of energy between fuel cells and auxiliary energy storages in the hybrid train. Internal combustion engines (ICEs) are currently under pressure from environmental agencies due to their harmful gas emissions, and pure battery vehicles have a short range; a hybrid train powered by fuel cells, batteries, and supercapacitors can provide a viable propulsion solution. In this study, special energy management on the mountain railway with optimal power distribution and minimum hydrogen consumption is proposed. Considering the characteristics of the mountain railway, the vehicle uses recuperation of regenerative braking energy and thus charges additional power devices, and hybridization optimization gives favorable power to each power source device with a minimum consumption of hydrogen in the fuel cell. In this study, a simulation model was created in a Matlab/Simulink environment for the optimization of hybridized power systems on trains, and it can be easily modified for the hybridization of any type of train. Optimization was performed by using Sequential quadratic programming (SQP). The results show that this hybrid train topology has the ability to recover battery and supercapacitor state of charge (SOC) while meeting vehicle speed and propulsion power requirements. The effect of battery and supercapacitor parameters on power distribution and fuel consumption was also simulated.

Energy management in DC microgrid with an efficient voltage compensation mechanism

Direct current (DC) microgrid facilitates the integration of renewable energy sources as a form of distributed generators (DGs), DC loads, and energy storage system (ESS) devices. A new voltage compensation mechanism is presented in this study to resolve the control issues of DC microgrid in a distributed manner. In this mechanism, a fractional-order voltage compensation term is used in the outer controller loop which eliminates the voltage deviation in the steady-state condition. A detailed mathematical model is developed for the ESS along with the new voltage compensation controller to facilitate proper tuning of the control parameters. Since the proposed voltage compensation term guarantees autonomous bus voltage restoration, the supercapacitor state of charge (SoC) remains at nominal value without violation while it only buffers fluctuating power. However, the battery only compensates for the nominal power demand. The DG power control algorithm strictly maintains the battery SoC within lower and upper bounds. A DC microgrid comprising hybrid ESS, DC load, constant power load (CPL), and distributed generator is implemented with real time digital simulator (RTDS). The results show that the proposed controller is reliable, leading to excellent ESS performance and power management within the microgrid, without any DC bus voltage deviation.

Online SOC Estimation of Supercapacitor Energy Storage System Based on Fractional-Order Model

Fast and accurate estimation of state of charge (SOC) of supercapacitors is essential for the safe and reliable operation of energy storage systems. However, existing SOC estimation methods are based on the integer-order model of supercapacitors and do not take into account the fractional-order characteristics of supercapacitors. Hence, the accuracy of SOC estimation results usually ignores the impact of modeling and relies on the correction effect of the algorithm. In this paper, a supercapacitor energy storage system is firstly established for fractional-order modeling and fractional-order SOC estimation of supercapacitors. Next, a Kalman filter is devised to track the SOC of supercapacitors in real time, and it can correct the estimation results online. Finally, experiments under different working conditions are conducted to demonstrate the effectiveness of the proposed fractional-order modeling and SOC estimation scheme. Compared with the data obtained by the traditional integer-order method, the accuracy of the proposed fractional-order method is higher, and the mean relative errors are less than 5%. With the online correction of the Kalman filter, the maximum errors under different working conditions are limited to 1.3%.

Frequency support of AC microgrid with high penetration of photovoltaic using super-capacitor

Increased penetration of photovoltaic (PV) in the power system leads to a reduction in system inertia. This reduction causes a high-frequency nadir and a high rate of change of frequency (ROCOF) during disturbances, which can result in load shedding or pole slipping and cascading outages. Therefore, a lot of research has been conducted to enhance the system inertia using PV de-loading techniques or separate energy storage systems (ESSs). PV de-loading has economic drawbacks, however using separate ESSs increase the overall cost and control complexity. In this work, a super-capacitor directly integrated into the DC bus of the PV inverter is proposed. Since, the super-capacitor voltage is controlled according to the deviation in the microgrid frequency to improve the frequency response. The proposed topology and control scheme maintain the operation of PV at the maximum power point, have less cost than a separate ESS, are derivative free inertia emulation, and have less complex control. The proposed scheme is carried out on a microgrid consisting of an interconnected thermal unit and a PV system. Diverse study cases, including load disturbances and solar irradiance variations, are performed. The obtained response is compared with the performance of two other microgrids. The proposed controller succeeded to improve the frequency response in terms of less nadir, less ROCOF, and less settling time. In addition, it is capable of maintaining the system stability at a large disturbance. The mathematical modeling that describes the effect of the proposed control system on the effective PV system inertia, the overall microgrid inertia, DC voltage deviation, super-capacitor state of charge, and super-capacitor energy is deduced. Eventually, the upper and lower boundaries of the DC voltage controller are adjusted to ensure nominal power transfer and an acceptable level of injected current distortion.

An adaptive power split strategy with a load disturbance compensator for fuel cell/supercapacitor powertrains

Electric vehicles powered by fuel cell and supercapacitor hybrid power sources are of great interest. However, the power allocation between each power source is challenging and the DC bus voltage fluctuation is relatively significant in cascaded PI control schemes. This paper develops a power control strategy with an adjustable cut-off frequency, using an artificial potential field, to adaptively split the load current between the fuel cell and the supercapacitor under various load conditions. The adaptive cut-off frequency is calculated by cutting the load frequency spectrum with an allocation ratio that changes with the supercapacitor state of charge. Therefore, the relatively lower frequency portion of the load current is provided by the fuel cell and the supercapacitor handles the higher frequency portion. To enhance the control performance of the DC bus voltage regulation against the load disturbance, a load disturbance compensator is introduced to suppress the DC bus voltage fluctuation when the load variation occurs, which is implemented by a feed-forward controller that can compensate the load current variation in advance. The effectiveness of the proposed strategy is validated by extensive experiments.

Hybrid Energy Storage Management Strategy for Electric Propulsion Aircraft Based on Three-Step Power Distribution

To solve the problem of severe DC bus voltage fluctuations caused by frequent changes in the distributed electric propulsion aircraft load, and to further optimize the size and life of the hybrid energy storage system (HESS), this paper proposes a method based on three-step power distribution (TSPD). This strategy realizes the reasonable distribution of battery and supercapacitor power by using a low pass filter. Through the introduction of a supercapacitor state-of-charge (SOC) automatic recovery control and maximum power value dynamic limit strategy, the size of the HESS is optimized, and the service life of the energy storage device is extended. Finally, simulation and experiment platform are built to prove the effectiveness of the proposed strategy.

Energy management strategy for super capacitor energy storage system based on phase shifted full bridge converter

Abstract In order to improve the efficiency and extend the service life of supercapacitors, this paper proposes a supercapacitor energy management method based on phase-shifted full-bridge converter. The method uses the supercapacitor state of charge (SOC) as a reference and combines the DC bus voltage fluctuation to quickly control the energy bidirectional flow. The simulation is carried out in Matlab/Simulink. The simulation results show that the proposed method combines SOC estimation and energy conversion, which can realize the optimal management of super capacitor and has fast dynamic response capability.

State-of-Charge Estimation of Supercapacitors in Transiently-Powered Sensor Nodes

Transiently-powered devices rely solely on energy harvesters. Such devices typically use capacitors to store the harvested energy, but recent systems employ supercapacitors to store energy for extended operations. In a supercapacitor-based transiently-powered system, the energy-efficient estimation of the state-of-charge (SoC) of a supercapacitor is critical for practical use of the system, due to its tight energy budget. Conventional voltage-based schemes for capacitors do not provide accuracy in SoC estimation for supercapacitors. Also, the supercapacitor-specific SoC estimation which exploits its charge redistribution characteristics provides an accuracy, but the scheme demands significant overhead, and thus, is not applicable to transiently-powered systems, such as wireless sensor nodes. In this article, we specify three requirements for estimating supercapacitor SoC that should be met to function in transiently-powered systems. We then propose a scheme that meets those requirements. The proposed scheme does not require additional hardware, has a low computation cost, and operates when the system is intermittently powered. Thus, the scheme fits the energy-efficient operation of transiently-powered systems. We implemented the proposed scheme in real hardware and evaluated its functionality and accuracy. The proposed scheme estimated the SoC of supercapacitors with high accuracy, in various configurations and use scenarios, while guaranteeing operations in a typical energy harvesting environment.

Fuzzy control of dual storage system of an electric drive vehicle considering battery degradation

In this manuscript, fuzzy logic energy management strategy for dual storage system inclu\-ding supercapacitors and battery is proposed in order to prolong battery lifespan and enhance the range of electric drive vehicle (EDV). First an EDV model and three drive cycles (NEDC, UDDS, and NREL) are established in Matlab/Simulink. Then a fuzzy inference system is designed considering three inputs: power demand, state of charge (SOC) of battery and SOC of supercapacitors. An output, which refers to split ratio between supercapacitors and battery power, is determined. Fuzzy rules are constituted in order to decrease not only high level battery current but also number of charge/discharge cycle of battery which are the main factors of battery deterioration. For a performance verification of the proposed method, three drive cycles with different characteristics are considered. Obtained results are compared to two other strategies; one of them is battery only system and the other one is dual storage system managed by logic threshold method. It is shown that the proposed method delivers better and robust performance to prolong battery lifespan.

Dynamic power allocation of the hybrid energy storage system in islanded AC microgrid based on virtual impedance

To realise the distributed control of the hybrid energy storage system (HESS) in an islanded AC microgrid, a dynamic HESS power allocation strategy based on the virtual impedance (VI) for supercapacitor (SC) and the battery is proposed. Dynamic power-sharing of two kinds of energy storage devices can be achieved without real-time measuring of load power. The state of charge (SOC) recovery of SC is achieved with a SOC loop integrated into the VI loop. The principle of dynamic power-sharing of HESS is derived based on the establishment of HESS equivalent circuit model. By analysing the influence of the voltage and current double loop controller parameters on the output impedance and the VI of the DC/AC converter, the method of setting the controller parameters for power distribution is presented. A simulation model is established with the designed equivalent power fluctuation conditions. The simulation results show that under various working conditions, the SC loop can compensate for the high-frequency part of the equivalent power fluctuation and the battery loop absorbs the low-frequency part of the power fluctuation. The dynamic power-sharing of HESS with the SC SOC recovery is realised effectively.

Secondary voltage controls of virtual‐droop‐controlled bidirectional DC/DC converters in hybrid energy storage system

For a hybrid energy storage system consisting of battery and super-capacitor (SC) in More Electric Aircraft, a decentralised control strategy, which is based on the virtual impedance droop control, can implement the frequency domain allocation of load power avoiding communication delay and a single point of failure. Although the output voltage deviation and state-of-charge (SoC) limitation are solved by existing methods, it still suffers from settling time extending. Secondary voltage controls (SVCs) of virtual-droop-controlled for battery and SC are proposed to solve the issue. SVC for the output voltage is designed to compensate for the output voltage deviation in the battery converter based on virtual resistance droop control. SC converter, which is controlled by the SVC for input voltage in virtual capacitance droop control, enforces SC SoC back to the initial value and maintains consistent settling time. In this study, a set of detailed design methods for the controller parameters of SVC are developed not only to regulate the output voltage and SoC recovery but also to keep the same settling time. The results of the simulation and experiment validate the effectiveness of the proposed SVC and design method.

An innovative approach of optimizing size and cost of hybrid energy storage system with state of charge regulation for stand-alone direct current microgrids

• This paper proposed a mathematical model for multi-objective optimization of hybrid energy storage system (HESS) in detail. • A novel penalty cost function for the HESS and a new SOC regulation concept for supercapacitor storage system have been introduced. • The proposed model was analysed under a case study. • An optimal HESS sizing was achieved to obtain the minimum total investment cost of HESS including the future replacement costs. • The best value for the energy ratio between supercapacitor storage and battery storage for the considered load profile was found as 25%. This study proposed a novel approach to optimize size and cost of hybrid energy storage systems (HESS) based on a solar photovoltaic (PV) fed stand-alone DC microgrid, while considering the state of charge (SOC) of both batteries and supercapacitors to assure the long life of batteries and well-being during the operation. The sizing strategy is combined with an optimization model and a HESS Assessment Algorithm. The strategy differs from traditional strategies since it comprises of not only the system cost, but also SOC regulation of both batteries and supercapacitors. A unique penalty cost function depending on the unutilized solar power generation and unserved demand power after HESS is fully charged or discharged has been introduced. By this penalty cost function, SOC of HESS can be kept within a safer margin to guarantee the system stability. Also, system availability can be assured by penalizing the unserved demand power. A new SOC regulation concept for supercapacitors has been introduced. SOC of supercapacitors is maintained in between a predefined focused band to ensure the supercapacitors availability to absorb or deliver sudden power fluctuations. The lifetime of HESS and the solar PV system are considered in the objective function to find the optimum sizing of HESS. The simulation was performed under Hambantota Solar Park weather conditions using MATLAB. Multi-objective genetic algorithm is used to optimize the objective functions. Comparing with a traditional sizing method, it shows that the novel optimal sizing strategy can reduce the BS system capacity by 18% by the hybridization of supercapacitors. The best value for the energy ratio between supercapacitors and batteries, for the considered load profile was found to be 25%. Results prove that the number of penalties, surplus solar power generations and unserved demand power were made minimum while obtaining an optimal sizing of HESS with a minimum system investment cost.

Study on Hybrid Energy Storage Configuration and Control Strategy of Grid-connected Wind Hydrogen System

The randomness and volatility of wind energy bring great challenges to wind power grid-connected. The hybrid energy storage technology based on electrolysis cell hydrogen production and super capacitor has become an effective way to stabilize wind power fluctuations. Based on the establishment of the grid-type wind-hydrogen coupling system working characteristic constraint and the minimum initial investment cost of the hybrid energy storage system, an alkaline electrolytic cell-super capacitor hybrid storage based on low-pass filtering-fluctuation observation is proposed. The method can be configured, and a hybrid energy storage coordinated control strategy based on the super capacitor SOC (state of charge) is developed. The case study results show that the hybrid energy storage system configuration method and control strategy proposed in this paper are effective, which can reduce the fluctuation of wind power grid-connected power to meet the grid connection standard.

An Efficient Energy Management Scheme for a Hybrid FC-SC-Battery Electric Vehicle using Model Predictive Control and Multi-Objective Particle Swarm Optimization

This paper presents a new Design for an efficient Energy Management System (EMS) for a Plug-in Hybrid Electric Vehicle (PHEV) which is supplied by Fuel Cell/Super-capacitor/Battery sources to attain maximum energy savings and minimize the amount of the fossil fuel utilized by the transportation sector. Model Predictive Control (MPC) combined with Multi-Objective Particle Swarm Optimization (MOPSO) control strategy was proposed to manage the battery and the SC State of Charge (SOC) and decide the optimal power distribution between the Fuel Cell (FC), the Battery and the Super Capacitor (SC). The main target of the proposed EMS calculation technique is to achieve maximum energy efficiency, minimum battery current variation, minimum variation of the state of charge of the SC and minimum cumulative hydrogen consumption during the driving cycle. The proposed MOPSO based EMS methodology has been checked by the High Speed (US06) Driving Cycle utilizing the MATLAB/Simulink. Five diverse driving patterns are utilized to assess the speculation capacity of the developed technique. Simulation results and the real-time experiment demonstrate that the proposed MOPSO-Model Predictive Control based EMS technique can accomplish better energy effectiveness compared with Fuzzy Logic Rule-based EMS and GA-based EMS.