Battery Voltage Estimation Using NARX Recurrent Neural Network Model

Chmielewski, A.; Możaryn, Jakub; Piórkowski, Piotr; Bogdziński, Krzysztof

doi:10.1007/978-3-030-13273-6_22

Cited by 2 publications

(4 citation statements)

References 33 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%

“…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%

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

et al. 2020

Self Cite

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“…Many methods are a mixture of different methods to compensate for each method's drawbacks or show the information to a certain extent. On the other hand, NARX offers a flexible framework and accurate prediction by its leading-edge against the analytical model and iteration approximation because it possesses high-quality generalization properties [10].…”

Section: Related Workmentioning

confidence: 99%

“…A model based on the above equations was developed to give more accurate simulation results, using SIMULINK blocks to reflect the Equation (10). The mathematical and physical models are both combined allowing the SOC estimation.…”

Section: Proposed Battery Modelmentioning

confidence: 99%

Memristive Equivalent Circuit Model for Battery

Mogadem

2021

Sustainability

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The design of mathematical models is based on conservation laws and also on the fundamental principles of modeling: structure, parameters, and physical meaning. Those kinds of modeling should have specific capabilities to deal with different working conditions and environments coping with challenges that include but are not limited to battery capacity, life-cycle, or the attempts to manipulate the current profiles during operation. Introducing memristive elements in batteries will be ideal to satisfy these fundamentals and goals of modeling, whereas addressing the recycling and sustainability concerns on the environmental impact by the placement of TiO2 memristor into this model can promote a recovery hierarchy via recycling and dispatching a slight amount to disposal as the previous focus was mainly concentrated on availability. As for battery materials, modeling, performing, and manufacturing all have proliferated to grasp the possible sustainability challenges inherited in these systems. This paper investigated electrochemical impedance spectroscopy to study this model and the dynamic behavior inside the battery. We found a solution to address the existing battery limitations that elucidate the battery degradation without affecting the performance, correspondingly by employing the dampest least-squares combination with nonlinear autoregressive exogenous for identifying such model and its associated parameters because of its embedded memory and fast convergence to diminish the influence of the vanishing gradient. Lastly, we found that this model is proven to be efficient and accurate compared to actual experimented data to validate our theory and show the value of the proposed model in real life while assuming Normal Gaussian distribution of data error with outstanding results; the auto-correlations were within the 95% confidence limit, the best validation was 2.7877, and an overall regression of 0.99993 was achieved.

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Battery Voltage Estimation Using NARX Recurrent Neural Network Model

Cited by 2 publications

References 33 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

Memristive Equivalent Circuit Model for Battery

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