Experimental Evaluation of Mathematical and Artificial Neural Network Modeling of Energy Storage System

Chmielewski, A.; Możaryn, Jakub; Gumiński, Robert; Bogdziński, Krzysztof; Szulim, Przemysław

doi:10.1007/978-3-319-96601-4_5

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…The previous author's research 9,45 allowed to conclude that the proposed DP model and recurrent neural network models allow for good voltage estimation and prediction for the considered SOC range. However, it was pointed out that there should be an emphasis put on the design of the current load signal and more developed RNN architecture.…”

Section: Introductionmentioning

confidence: 93%

“…It should be emphasized that battery energy storage components are characterized by many physical and chemical processes, which can be mathematically modeled 8,9 . The mathematical description of these interconnected processes is nonlinear (because of the existence of activation polarization, concentration polarization, and internal resistance) with time‐varying parameters, especially during a rapid discharging/charging phase 10‐14 .…”

Section: Introductionmentioning

confidence: 99%

“…The authors of this work used standard ANNs for the identification of the electrochemical power cell's dynamics in the form of feed‐forward models 11 and recurrent models 17 . Experimental research 9,45 showed that the recurrent models model preceded with data pre‐processing, and adequate choice of the system's parameters can be used for prediction of the electrochemical power cell performance in different operating conditions. As it is described in References 28,29, additional temperature measurements improved the data‐driven model's prediction properties.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of hybrid recurrent neural networks and dual‐polarization models of valve regulated lead acid battery

et al. 2020

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Summary Distributed energy storage using low‐cost valve regulated lead acid (VRLA) batteries becomes a promising solution, especially in prosumer microinstallations. In herein work, the model‐based research of VRLA battery in pseudo‐random and hybrid power pulse cycles has been presented. The major goal of the article was to develop and compare parametric, analytical, and data‐driven models of the battery. There were chosen the dual polarization model and the hybrid recurrent neural network model based on long short‐term memory and nonlinear autoregressive with exogenous input model for estimating voltage values and determining nonmeasurable parameters of a VRLA battery for different values of the state of charge. Satisfactory results were obtained for both methods, with the favor of the dual polarization model. Finally, the advantages and disadvantages of both methods are discussed.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of hybrid recurrent neural networks and dual‐polarization models of valve regulated lead acid battery

et al. 2020

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“…In energy storage systems usefulness of standard ANNs for identification were proposed in the form of feed-forward models [66], recurrent models [110,111] and recently Long-Short-Term-Memory (LSTM) recurrent models [112]. In particular, interesting for our research are Recurrent Artificial Neural Networks (R-ANN) where connections between units form a directed cycle.…”

Section: Introductionmentioning

confidence: 99%

“…Considered in this work, NARX model based on R-ANN plays a significant role because it can be used as a predictor, nonlinear filter and as a nonlinear model of the dynamical system which can be later used in model-based control algorithms [113,114] in the BMS systems. R-ANNs exhibit dynamic temporal behavior, a property that can be utilized for the prediction of the energy storage system performance parameters in subsequent recharging cycles [111].…”

Section: Introductionmentioning

confidence: 99%

Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle

et al. 2018

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The following work presents the model-assisted research on Valve-Regulated Lead-Acid (VRLA) Absorbent Glass Mat (AGM) battery in pulse operation cycle. The experimental research was conducted for a constant value of State of Charge (SOC) of the battery, for values ranging from 0.2 to 0.8. Based on the conducted test stand research, the parameters of the battery were identified, which were later used to model the battery using the equivalent circuit based on dual polarization (DP) model with double Resistive-Capacitive (RC) loop. Simulations were performed for the identified parameters of the battery which are described by the general form of the polynomial. The second part contains the research on utilization of Nonlinear AutoRegressive eXogenous (NARX) recurrent neural network to predict SOC and a terminal voltage of the battery. Obtained validation results with the use of the identified parameters of the double RC loop and NARX model were discussed in the following work. The article also features the advantages and disadvantages of NARX model and DP model utilization for the use of in Battery Managements Systems (BMS) and micro-installations based on renewable energy sources. Furthermore, the advantages of the addition of more RC loops to describe the dynamic states of batteries in pulse states were discussed in the article.

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Battery Voltage Estimation Using NARX Recurrent Neural Network Model

Chmielewski

Możaryn

Piórkowski

et al. 2019

Advances in Intelligent Systems and Computing

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Experimental Evaluation of Mathematical and Artificial Neural Network Modeling of Energy Storage System

Cited by 8 publications

References 25 publications

Comparison of hybrid recurrent neural networks and dual‐polarization models of valve regulated lead acid battery

Comparison of hybrid recurrent neural networks and dual‐polarization models of valve regulated lead acid battery

Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle

Battery Voltage Estimation Using NARX Recurrent Neural Network Model

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