Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols

Chunsheng Hu, Liang Ma, Shanshan Guo, Gangsheng Guo and Zhiqiang Han

Energy, 2022, vol. 246, issue C

Abstract: LiFePO4 batteries generally face a challenge of inaccurate state of charge (SoC) estimation due to the plateaus existing in the middle range of the open circuit voltage (OCV)-SoC curve. Generally, conventional SoC estimation methods are not capable of accurately estimating the SoC in this range. In this paper, a deep neural network (DNN) is constructed to estimate the SoC in the charging process. Battery data collected from five state-of-the-art charging protocols at 10 °C, 25 °C and 40 °C are used to train the DNN. The developed DNN can be used for online SoC estimation subsequently. This estimated SoC can serve as the initial SoC of the ampere-hour counting method to calculate the SoC of the discharging process. The overall maximum error and the root mean square error of the SoC estimation over charging process are within 2.5% and 0.8%, respectively. In addition, the input depth of time from 10 s to 100 s with a 10 s interval is investigated. The maximum error is less than 5% in the case of the depth time within 100 s and the error fall to 2% when the depth of time reaches 90 s.

Keywords: LiFePO4 battery; State of charge; Battery charging; Deep learning; Electric vehicles (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222003073
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:energy:v:246:y:2022:i:c:s0360544222003073

DOI: 10.1016/j.energy.2022.123404

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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