Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression

Mingqiang Lin, Chenhao Yan, Jinhao Meng, Wei Wang and Ji Wu

Energy, 2022, vol. 250, issue C

Abstract: Accurate state of health (SOH) estimation is a key issue for lithium-ion batteries management and control. In this paper, a novel SOH estimation method is proposed based on the fusion of the simulated annealing algorithm and support vector regression (SVR). Firstly, considering the electrochemical and thermodynamic characteristics of the battery aging process, we extract the health factors by analyzing and sampling the differential thermal capacity (DTC) curves which are based on temperature, voltage, and current. Then, an SVR model is constructed to estimate the SOH. The mean-variance obtained from cross-validation is used as the evaluation function, and hyperparameters of the SVR are optimized using the simulated annealing algorithm. Finally, we conduct two sets of experiments on the Oxford dataset for verification. Experimental results not only show the outperformance of the DTC curves for describing the battery aging but also illustrate that our proposed prediction model exhibits higher accuracy and less error of SOH estimation under the premise of ensuring real-time performance than the other two comparative models.

Keywords: Lithium-ion batteries; State-of-health; Differential thermal capacity; Simulated annealing; Support vector regression (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222007320
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:250:y:2022:i:c:s0360544222007320

DOI: 10.1016/j.energy.2022.123829

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 ().