This paper proposes a distributed control strategy that considers several source characteristics to achieve reliable and efficient operation of a hybrid ac/dc microgrid. The proposed control strategy ...has a two-level structure. The primary control layer is based on an adaptive droop method, which allows local controllers to operate autonomously and flexibly during disturbances such as fault, load variation, and environmental changes. For efficient distribution of power, a higher control layer adjusts voltage reference points based on optimized energy scheduling decisions. The proposed hybrid ac/dc microgrid is composed of converters and distributed generation units that include renewable energy sources (RESs) and energy storage systems (ESSs). The proposed control strategy is verified in various scenarios experimentally and by simulation.
This paper proposes state-of-charge (SOC) estimation algorithms that utilize a filtered battery terminal voltage without measuring the current. These methods extract an estimated open-circuit voltage ...(OCV) or current from the battery terminal voltage through equivalent circuit model-based filters, which streamlines the estimation process. In the methods, the OCV values derived from the corresponding SOCs are used to extract the filter coefficient for ease of implementation. The relationship between the model's accuracy and estimation performance is investigated, and the variation of the SOC estimation error due to the model parameter tolerance is also derived by the Monte Carlo simulation tool to confirm the practicality of the method. To validate the performance of the proposed approach, a parameter extraction profile and an actual mobile phone profile are applied to a 2.6 Ah prismatic Li-ion battery. The experimental results show the feasibility of the proposed SOC estimation algorithm to within a 5% SOC estimation error.
For the MPC approach, the system behavior will be optimized over longer time scales, allowing the fast dynamics to be neglected. To maintain focus on islanded microgrid concepts, the microgrid does ...not connect to an auxiliary power grid or other external power system. Since the proposed MPC algorithm of the μEMS generates control signals for the diesel generators, the load switches and adjusts renewables based on the predicted load demand every 15 min, it is crucial to maintain the SOC at a certain level to compensate for uncertainty, such as prediction error, to maintain the power balance of the microgrid. 5. The negative value of the deviation means the erroneous load prediction is lower than the correct value. Since the islanded microgrid has limited diesel generation resources and renewable energy sources that cannot be controlled, load shedding can be used to maintain power balance as necessary. ...considering the reliability of the microgrid, it is not a viable option to use a single diesel generator for the islanded microgrid. ...these results indicate that the MPC can successfully manage multiple diesel generators at near-optimal operating point and provide reliable resource management strategies. 6.
Impedance measurements by EIS are used to build a physical circuit-based model that enables various fault diagnostics and lifetime predictions. These research areas are becoming increasingly crucial ...for the safety and preventive maintenance of fuel cell power systems. It is challenging to apply the impedance measurement up to commercial applications at the field level. Although EIS technology has been widely used to measure and analyze the characteristics of fuel cells, EIS is applicable mainly at the single-cell level. In the case of stacks constituting a power generation system in the field, it is difficult to apply EIS due to various limitations in the high-power condition with uncontrollable loads. In this paper, we present a technology that can measure EIS on-line by injecting the perturbation current to fuel cell systems operating in the field. The proposed EIS method is developed based on Simulink Real-Time so that it can be applied to embedded devices. Modeling and simulation of the proposed method are presented, and the procedures from the simulation in virtual space to the real-time application to physical systems are described in detail. Finally, actual usefulness is shown through experiments using two physical systems, an impedance hardware simulator and a fuel cell stack with practical considerations.
This paper proposes an offline light-emitting diode (LED) driver which is based on inverted buck topology. The proposed circuit consists of control circuit, bridge diode and inverted buck converter ...which has multiple switches connected to LED segments in parallel. While the conventional buck LED driver regulates fixed LED forward voltage, the proposed driver regulates variable LED forward voltage according to input voltage level. By the capability to adjust the LED forward voltage, it can reduce the current ripple while using same inductance. In addition, it enables to operate wide voltage range of LED lamp, simultaneously achieving high power factor. The basic operation principle and the control scheme are described. The proposed offline LED driver has been demonstrated in a 7 W, 110 V rms experimental prototype, which provides high efficiency and low LED current ripple with high power factor.
For taking the advantages of battery in the energy storage, advanced methods are required to accurately monitor and control the battery via the battery management system (BMS). This study ...investigates a more efficient method to increase accuracy and robustness without the high magnitude of the transmitted data. An alternative approach to overcome those problems, the dual extended Kalman filter (DEKF) can be selected because it can archive the good accuracy and robustness using less historical data. A major focus in DEKF is how to reflect state-of-charge (SOC) - open-circuit voltage (OCV) relation, which is the crucial characteristics of the battery. Most of the prior research has applied the SOC-OCV relation using a non-linear function such as a polynomial equation. However, since the nonlinear function is defined by the experimental data in the conventional method, the BMS needs larger storage for estimating the SOC and state-of-health (SOH). To overcome the limitation of the conventional DEKF, this study proposes improved DEKF combined with a discrete derivative method based on parameter identification. Thus, the main objective of this study is to construct an efficient and simple SOC-OCV function using the discretization method and online parameter identification method. Experimental studies using two different types of batteries sets illustrate the high accuracy and adaptability of the proposed framework in lithium-ion battery SOC and SOH estimation.
•Complementary approach to reflect the aging factor in SOC and capacity estimator is proposed.•Local linearization method is proposed for dual extended Kalman filter.•SOC-OCV relationship is updated to dual extended Kalman filter in real-time.•The storage space of battery management system can be reduced using proposed method.•Performance of the proposed method is verified by two type batteries and comparison results.
•The denoising autoencoder is trained is follows: Gaussian noise, missing value, and the combination of two noise types.•The RUL prediction models are learned by multi-step and increment methods ...properly used in subsequent SOH.•The proposed framework is an ensemble of DAE and prediction models.•The DAE reconstructs the distorted data into the original data and provides it to the RUL prediction model.•The RUL prediction accuracy achieved RMSE 5% in a combination of Gaussian noise and missing values.
Accurate prediction of the lithium-ion battery lifetime is important to maintain the performance of the battery system. Because data-driven methods are extensively used in the analysis of nonlinear dynamical systems in research, the existing literature is largely focused on the application of these methods for the prediction of battery state. Data-driven methods that are highly dependent on data are sensitive to the noise of the measurement data. Distorted data diminish the performance of data-driven models for lifetime prediction. In this study, we propose an artificial neural network-based framework, which is robust to noise, to increase the prediction accuracy of the remaining-useful-life (RUL) of lithium-ion batteries. A denoising autoencoder trained using a distorted dataset with Gaussian noise and dropout is presented to improve the robustness of the model to noise. Artificial neural network models predict RUL based on the state-of-health estimated using measurement data in the initial step. The proposed prediction model is compared with the base model and the training-noise model using distorted data to validate the robustness to noise. The results demonstrate that the proposed framework is robust to noise and has lower cycle errors compared to other methods.
Photovoltaic (PV) systems are required to function at their maximum power point (MPP) to fully utilize solar energy. Solar PV panels are configured with maximum power point tracking (MPPT) systems to ...enhance the generation and supply of the maximum available power. However, owing to external environmental factors and partial shading conditions (PSCs), the tracking process becomes complex. Conventional MPPT methods cannot always achieve the global MPP. Thus, an advanced metaheuristic MPPT scheme for PV systems, the flower pollination algorithm (FPA), is implemented in this study to find the best global duty cycle to obtain the maximum power output, and a Levy flight is utilized to enhance FPA convergence. The FPA MPPT model was established, and its performance was compared with the conventional, and some meta-heuristic-based MPPT techniques; perturb and observe (PO), incremental conductance (INC), particle swarm optimization (PSO), cuckoo search (CS), grey wolf optimization (GWO), genetic algorithm (GA), double integral sliding mode control (DISMC), and hill-climbing (HC). Different scenarios were considered for the simulation: partial shading, complex partial shading, long-width, and long-length partial shading, and different temperature conditions employing series and series-parallel PV array configurations. In addition, real-world field atmospheric irradiance data for Daejeon city is used for the study. In terms of efficiency, the FPA attains at most 83%–96% of the expected GMP from the PV array as compared with the other MPPT techniques in all scenarios considered. The adopted FPA obtained the following root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) values respectively for each Scenario as follows: Scenario I: RMSE: 0.0061, MRE: 0.0463, MAE: 6.0745e-06, Scenario II: RMSE: 0.0116, MRE: 0.0430, MAE: 1.0738e-05, Scenario III: RMSE: 0.0640, MRE: 0.206, MAE: 4.8239e-05, Scenario IV (varying temperature conditions): RMSE: 0.0062–0.0074, MRE: 0.0460–0.0548, MAE: 6.1333e-06 – 7.4922e-06. A unique sensitivity performance comparison analysis is done employing the power spectral analysis to validate the effectiveness of the adopted FPA to the other MPPT techniques. The FPA performs better than all the techniques in all scenarios in comparing their maximum power tracked, convergence speed and accuracy, system efficiency, and optimum statistical results attained.
•Solar panels are configured with maximum power point tracking (MPPT) systems.•Photovoltaic (PV) systems are required to function at their maximum power point.•Flower pollination algorithm (FPA)-based MPPT scheme is implemented in this work.•Two types PV configurations such as 3S and 4S3P are employed for the system analysis.•Presented FPA-MPPT method has high convergence accuracy and fast-tracking speed.
With the use of batteries increases, the complexity of battery management systems (BMSs) also rises. Thus, assessing the functionality of BMSs and performance of the BMS hardware is of utmost ...importance. Testing with embedded boards at an early stage of BMS development is a pragmatic approach for developing a BMS because it is cost- and time-efficient and considers hardware performance. In this study, we tested and analyzed the real-time state-of-charge (SOC) estimation using a test platform with limited CPU performance as well as memory resources of the embedded board. We collected battery data on a single-cell basis using a first-order RC equivalent circuit and achieved an accuracy of 95% compared to the measured data obtained using actual battery tests. The SOC estimation method applies the extended Kalman filter (EKF) and unscented Kalman filter (UKF). The experiment was performed on the real-time test platform, with 1%, 2%, and 5% noise in the measurement data. The algorithm complexity and hardware implementation were evaluated in terms of the resources used and processing speed. Although the EKF is cost-effective, its error rate increases by 5% with noise interference. The UKF exhibits high accuracy and noise robustness; however, it has a high resource occupancy.
State-of-charge (SOC) estimation plays a crucial role in battery management systems to ensure safe and reliable operation. However, SOC estimation remains challenging due to the dynamic nature of ...battery systems and varying ambient conditions. Data-driven methods have emerged as effective tools for analyzing nonlinear dynamical systems, but their performance heavily relies on data quality. In actual applications, data susceptible to distortions caused by external factors such as sensor failure, circuitry, and temperature variations, leading to degraded model performance. To address the performance degradation resulting from data quality deterioration, this paper introduces a denoising autoencoder is implemented as a stacked multi-layer perceptron, which learns to reconstruct distorted data. Furthermore, we propose the ensemble method that combines the autoencoder with an estimation model for SOC estimation in lithium-ion batteries. The effectiveness of the proposed model is demonstrated through tests conducted on a dataset comprising drive cycle profile of Panasonic 18650PF cells. The model validated under two ambient temperatures scenarios: identical and different, using a distorted dataset with added randomly added noise and dropout. The experimental results reveal that the proposed model achieved a 3 % error in training the drive profile relative to the actual values at different ambient temperatures. When compared to the plain model, the proposed ensemble model showed an increased RMSE of 4 %. Additionally, the performance of different estimation models was compared, with the LSTM model achieving an RMSE 0.67 at different ambient temperatures, outperforming the Support Vector Regression (SVR) with an RMSE 1.35 and the Extended Kalman Filter (EKF) with an RMSE of 0.87.
•A noise immune state-of-charge method using stacked autoencoder is proposed.•Combinations of the denoising autoencoder and deep learning models•Performance comparisons between model-based methods and data-driven•The framework increases the estimation accuracy of the SOC in different temperatures.
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