Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

Kaizhi Liang, Zhaosheng Zhang, Peng Liu, Zhenpo Wang and Shangfeng Jiang
Additional contact information
Kaizhi Liang: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Zhaosheng Zhang: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Peng Liu: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Zhenpo Wang: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Shangfeng Jiang: Zhengzhou Yutong Bus Co., Ltd., Henan 450016, China

Energies, 2019, vol. 12, issue 24, 1-17

Abstract: Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

Keywords: lithium-ion batteries; electric vehicles; ohmic resistance estimation; XGBoost (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/24/4772/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/24/4772/ (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:12:y:2019:i:24:p:4772-:d:297883

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 ().