Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search

Minho Kim, Huiyong Chun, Jungsoo Kim, Kwangrae Kim, Jungwook Yu, Taegyun Kim and Soohee Han

Applied Energy, 2019, vol. 254, issue C

Abstract: Lithium-ion batteries have been used in many applications owing to their high energy density and rechargeability. It is very important to monitor the internal physical parameters of the lithium-ion battery for safe and efficient usage, because this can help estimate the state of the battery, develop battery aging models, and schedule optimal operation of batteries. Parameter optimization methods using an accurate electrochemical battery model are much less expensive than direct parameter measurement methods, such as post-mortem methods. Thus, many model-based parameter optimization methods have been developed so far. However, most of these methods are random search methods that are based on heuristic rules, which leads to data-inefficient parameter identification. This means that they require many time-consuming battery model simulation runs to identify optimal parameters. Herein, a novel learning-based method is proposed for data-efficient parameter identification of lithium-ion batteries. A deep Bayesian neural network is used to efficiently identify optimal parameters. The simulations and experimental data validation show that the proposed method requires much fewer battery model simulation runs to identify optimal parameters than existing methods such as genetic algorithms, particle swarm optimization, and the Levenberg-Marquardt algorithm. The parameter estimation error of the proposed method is about 10 times lower than that of the second-best algorithm.

Keywords: Data-efficient parameter identification; Deep Bayesian neural network; Electrochemical battery model; Lithium-ion battery (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919313315
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:254:y:2019:i:c:s0306261919313315

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.2019.113644

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 ().