Dynamic model of a lithium-ion cell using an artificial feedforward neural network with dynamical signal preprocessing. (October 2020)

• Record Type: Journal Article
• Authors: Dziechciaruk, Grzegorz; Michalczuk, Marek; Ufnalski, Bartlomiej; Grzesiak, Lech M.
• Is Part Of: Journal of energy storage. Volume 31(2020)
• Publication Date: 2020-10
• Subjects: Artificial neural network -- Feedforward neural network -- Dynamical signal preprocessing -- Lithium-ion -- Cell model -- Battery model -- Electrical equivalent circuit model; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2020.101503
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: The proposed model obtains accuracy comparable to electrical equivalent circuit model. Building the proposed model does not require multistep procedure of data elaboration. Only three cell tests are enough to train the neural network model successfully. Abstract: A common approach in lithium-ion cell modeling is based on an electrical equivalent circuit model. The advantage of the electrical model is its simple structure. However, estimation of its parameters usually requires several steps in post-processing of the measured data in order to achieve satisfying accuracy. In this paper, we propose a solution with an artificial feedforward neural network and dynamical signal preprocessing, which does not require complex estimation procedures and has good accuracy. In contrast to the common conviction that a neural network requires many tests in a training data set, we show that only a few tests are enough to train the neural network. In the paper, we present practical aspects of the training process including methods to overcome obstacles related to measurement inaccuracy. Finally, the results of the artificial neural network model are validated and compared with those from an electrical equivalent circuit model.