State of Health Estimation for Lithium-Ion Batteries with Deep Learning Approach and Direct Current Internal Resistance

Zhongxian Sun, Weilin He, Junlei Wang and Xin He ()
Additional contact information
Zhongxian Sun: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Weilin He: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Junlei Wang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xin He: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2024, vol. 17, issue 11, 1-14

Abstract: Battery state of health (SOH), which is a crucial parameter of the battery management system, reflects the rate of performance degradation and the aging level of lithium-ion batteries (LIBs) during operation. However, traditional machine learning models face challenges in accurately diagnosing battery SOH in complex application scenarios. Hence, we developed a deep learning framework for battery SOH estimation without prior knowledge of the degradation in battery capacity. Our framework incorporates a series of deep neural networks (DNNs) that utilize the direct current internal resistance (DCIR) feature to estimate the SOH. The correlation of the DCIR feature with the fade in capacity is quantified as strong under various conditions using Pearson correlation coefficients. We utilize the K-fold cross-validation method to select the hyperparameters in the DNN models and the optimal hyperparameter conditions compared with machine learning models with significant advantages and reliable prediction accuracies. The proposed algorithm is subjected to robustness validation, and the experimental results demonstrate that the model achieves reliable precision, with a mean absolute error (MAE) less than 0.768% and a root mean square error (RMSE) less than 1.185%, even when LIBs are subjected to varying application scenarios. Our study highlights the superiority and reliability of combining DNNs with DCIR features for battery SOH estimation.

Keywords: state of health; lithium-ion batteries; deep neural networks; direct current internal resistance; robustness (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/11/2487/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/11/2487/ (text/html)

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:gam:jeners:v:17:y:2024:i:11:p:2487-:d:1399470

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().