An Improved Control Method for Power Conversion System under a Weak Grid by the Adoption of Virtual Resistors

Gao, Ning; Sang, Shun; Li, Rui; Cai, Xu

doi:10.6113/jpe.2017.17.3.756

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…[55] Also, a multikernel support vector machine (MSVM) model based on polynomial and radial kernel functions was proposed to predict the battery remaining useful life (RUL). [56] In order to obtain more [36] Copyright 2017, Elsevier. b) Weight diagram of descriptors obtained from the top 25 combinations of descriptors, for the melting point prediction.…”

Section: Theoretical Modeling Of Lithium-ion Batteriesmentioning

confidence: 99%

“…[ 55 ] Also, a multikernel support vector machine (MSVM) model based on polynomial and radial kernel functions was proposed to predict the battery remaining useful life (RUL). [ 56 ] In order to obtain more accurate diagnoses of the state of the batteries, ML methods have been applied to obtain accurate predictions and monitoring of the SOC, SOH, and RUL parameters of lithium‐ion batteries. [ 57 ]…”

Section: Theoretical Modeling and Simulations Of Lithium‐ion Batteriesmentioning

confidence: 99%

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Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators

Miranda

Gonçalves

Wuttke

et al. 2023

Advanced Energy Materials

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For the proper design and evaluation of next‐generation lithium‐ion batteries, different physical‐chemical scales have to be considered. Taking into account the electrochemical principles and methods that govern the different processes occurring in the battery, the present review describes the main theoretical electrochemical and thermal models that allow simulation of the performance of lithium‐ion batteries, including different materials and components (electrodes and separators) and battery geometries. As the separator plays an essential role in the performance and safety of lithium‐ion batteries, the recent theoretical simulation work for this battery component are shown, with particular emphasis on morphology, dendrite growth, ionic transport, and mechanical properties. Further theoretical simulations and modeling of this battery component are still required for improving performance, taking into consideration varying geometric parameters such as pore size, porosity, and tortuosity as well as the optimization of the lithium diffusion process and ionic conductivity value. Theoretical simulations of battery separators will play an essential role in the new generation of lithium‐ion batteries, allowing the improvement of their performance while reducing experimental probes and time.

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Section: Theoretical Modeling Of Lithium-ion Batteriesmentioning

confidence: 99%

“…[ 55 ] Also, a multikernel support vector machine (MSVM) model based on polynomial and radial kernel functions was proposed to predict the battery remaining useful life (RUL). [ 56 ] In order to obtain more accurate diagnoses of the state of the batteries, ML methods have been applied to obtain accurate predictions and monitoring of the SOC, SOH, and RUL parameters of lithium‐ion batteries. [ 57 ]…”

Section: Theoretical Modeling and Simulations Of Lithium‐ion Batteriesmentioning

confidence: 99%

Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators

Miranda

Gonçalves

Wuttke

et al. 2023

Advanced Energy Materials

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“…After comparison, it was found that the grid‐side current feedback active damping was selected as a superior scheme to realize the virtual resistor function. By adding virtual resistors in the control loop, it is possible to improve the stability of PCS by changing its equivalent impedance . Literature approaches the integration of active damping based on virtual resistor and FCS‐MPC in grid‐connected power converters.…”

Section: Introductionmentioning

confidence: 99%

Finite Control Set Model Predictive Control with Model Parameter Correction for Power Conversion System in Battery Energy Storage Applications

Gao

Chen

et al. 2020

IEEJ Transactions Elec Engng

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Adding a Battery Energy Storage System (BESS) in the vicinity of renewable energy sources is a feasible solution to overcome the inherent negative impact caused by their random power fluctuating problems. Power conversion system performs as an interface between the battery packs and grid. The control method of power conversion system is essential to BESS. Finite Control Set Model Predictive Control (FCS‐MPC) is favorable to be chosen as the core control method for grid‐injected current regulation in such a system due to its outstanding benefactions, including fast dynamics, multiobjective optimization and simple implementations. Hence, this paper presents a FCS‐MPC with active damping feature for an Inductor(L) Capacitor(C) Inductor(L) (LCL)‐filter‐based power conversion system first. However, it is also well known that the practical effect of model predictive control significantly relies on the accuracy of mathematical model embedded within the controller. Parametric mismatch of the model tends to generate a prediction error, leading to a deterioration of the power quality of power conversion system or even instability issues furthermore. Therefore, this paper proposes an additional model correction strategy based on a comprehensive on‐line analysis on the difference between predictive and real value of respective voltage or current signals during the ever‐lasting several grid cycles to eliminate the influence of parametric mismatch. Simulation of an LCL‐filter‐based power conversion system is carried out to verify the validity of the theoretical analysis and control method in this paper. Finally, experimental results obtained from a down‐scaled prototype are provided to confirm the feasibility of the overall control strategy. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Prediction of Lithium‐Ion Battery Remaining Useful Life via Empirical Mode Decomposition‐ Autoregressive Integrated Moving Average and Regularized Particle Filter Algorithm

Cai

Zheng

et al. 2023

Energy Tech

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This article focuses on improving the prediction accuracy of lithium‐ion battery's remaining useful life (RUL) by combining empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA), and regularized particle filter (RPF). First, to obtain detailed information about the battery capacity degradation, the monitored capacity data are decoupled by EMD. Second, the long‐term predicted model is constructed by ARIMA for the decoupled components. Finally, the long‐term prediction results are utilized as the measurement equation of the RPF prediction framework. In the proposed novel hybrid framework combining EMD‐ARIMA and RPF, the capacity prediction values are corrected and updated during the iteration of the regularized particle filter. With the estimated capacity data of every cycle, it can be detected whether the batteries reach their service life threshold. To validate the performance of the aforementioned method, the comparative experiments which are based on the NASA Prognostic Center of Excellence battery data sets are performed. According to the analysis results, higher prediction accuracy has been obtained with the proposed method in the lithium‐ion battery RUL, compared with the other three methods.

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An Improved Control Method for Power Conversion System under a Weak Grid by the Adoption of Virtual Resistors

Cited by 9 publications

References 26 publications

Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators

Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators

Finite Control Set Model Predictive Control with Model Parameter Correction for Power Conversion System in Battery Energy Storage Applications

Prediction of Lithium‐Ion Battery Remaining Useful Life via Empirical Mode Decomposition‐ Autoregressive Integrated Moving Average and Regularized Particle Filter Algorithm

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