Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors

Yanting Zhou, Yanan Wang, Kai Wang, Le Kang, Fei Peng, Licheng Wang and Jinbo Pang

Applied Energy, 2020, vol. 260, issue C, No S0306261919318562

Abstract: Supercapacitor as a clean energy storage device has been widely adopted in powering electric motors of vehicles. Precise evaluation of aging state of supercapacitors, i.e., the remaining useful life provides a feedback to replace damaged cells to sustain the comfort and safety of electric vehicle. Currently reported evaluation methods for such aim are data or model-based predications, which are either time consuming or of low precision. To achieve efficient and robust evaluation of the remaining lifetime, this work proposes a general strategy based on the combination between a recurrent neutral network method, i.e., long short-term memory, and hybrid genetic algorithm. The sequential quadratic programming as a local search operator of the genetic algorithm, enhances its global search ability, which allows quickly search for the local optimal solution in the means of the dropout probability and the number of hidden layer units. Eventually we apply this predication method to supercapacitors charging at steady state mode and succeed in estimating their remaining useful life. Such life prediction approach also gains validity in supercapacitors with dynamic operative cycle. Indeed, high accuracy has been achieved at both the online trained supercapacitors with root mean square errors ranging from 0.0161 and 0.0214, and offline historical data with 0.0264 error. Moreover, the estimation time is shortened to 3550 s, which is shortened by 60%. This evaluation model may pave the way in predicting the remaining useful lifetime of supercapacitors as well as secondary ion batteries in a precise and robust fashion.

Keywords: Supercapacitor; Remaining useful life; Device degradation; Electric vehicle; Hybrid genetic algorithm; Long short-term memory (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919318562
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:260:y:2020:i:c:s0306261919318562

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.2019.114169

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 ().