文章基本信息

标题：Memristive Equivalent Circuit Model for Battery
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作者：Mazin Mohammed Mogadem ; Yan Li
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2021
卷号：13
期号：20
页码：11204
DOI：10.3390/su132011204
语种：English
出版社：MDPI, Open Access Journal
摘要：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 TiO<sub>2</sub> 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.