Evolution of parameters in the Doyle-Fuller-Newman model of cycling lithium ion batteries by multi-objective optimization

Wei-Jen Lin and Kuo-Ching Chen

Applied Energy, 2022, vol. 314, issue C, No S0306261922003476

Abstract: An electrochemical model having high-fidelity parameters enables a correct description of the performance of lithium ion batteries. Hence, developing an accurate and high-efficiency parameter identification method is crucial to precisely predicting the battery’s state of health. In the past, most studies on estimation of such parameters took the discharge voltage as the only target to be fitted. Here, the first-order derivative of the discharge curve, i.e., the dQ/dV curve, is proposed as another fitting target since this new curve is directly related to battery aging. Four different objective functions, associated with the discharge curve and its derivative curve, are used to perform the multi-objective optimization, where the two algorithms, namely the genetic algorithm and the deep neural network are employed. We show that, by using the genetic algorithms, the mean absolute errors of the discharge curve for each cycle are lower than 0.07 (V), while the errors of the ICA curve are below 0.32 (Ah/V), both of which show a good convergence. The deep neural network also leads to excellent result. We present the evolutions of 13 identified parameters and demonstrate that the initial lithium ion concentration in the negative electrode dominates the cycle age of the tested batteries.

Keywords: Lithium-ion battery; Parameter identification; Genetic algorithm; Neural network (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922003476
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003476

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.118925

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().