Estimation of state-of-power capability for lithium-ion battery considering limiting conditions recalibration

ABSTRACT

As one of the most significant deciding factors for energy management systems (EMSs) and battery management systems (BMSs) in electrified vehicles, battery state-of-power (SOP) estimation is an area of interest in battery research. Battery model parameters change obviously under the limiting working condition of SOP, and the available maximum current and state-of-charge (SOC) have to be updated for accurate SOP estimation. This paper tries to contribute to the existing literature as follows: Based on Thevenin model, recursive least-square (RLS) and H-infinity filter algorithm are adopted for parameter identification and SOC estimation for a recalibration purpose to improve the prediction of battery SOP under variable degradation. Multi-constraints are firstly adopted to calculate the limiting current, and the response function of capacity loss-temperature-discharge rate is brought back to correct the actual capacity at this current for the revised limiting current with consideration of weight distribution ratio. Finally, the experiment verifies that the final maximum charging-discharging current modifies the initial calculation value by 5% and 4%, and realizes the more accurate online SOP estimation. This method provides a more accurate and reliable SOP estimation method for electric vehicles.

KEYWORDS:

• SOC
• SOP
• limit conditions recalibration
• RLS
• H_infinite filtering algorithm

Highlights

• Fully consider the influence of temperature and current on battery state estimation.

• A highly robust method combining RLS-based parameter update and H-inf-based SOC estimation is proposed for parameter identification and SOC estimation.

• Propose to modify the SOP estimation method in the case of a limit conditions recalibration firstly.

• Apply this method on lithium-ion battery and verify the correctness of the idea.

Acknowledgments

This work was supported by the Key Laboratory of Advanced Manufacture Technology for the Automobile Parts (Chongqing University of Technology), Ministry of Education, No.2019KLMT05.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Additional information

Funding

This work was supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20181295) and Opening Foundation of Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Ministry of Education (Grant No. 2019KLMT05)..

Notes on contributors

YuFang Li

YUFANG LI was born in Shandong, China, in 1976. She received the Ph.D. degree in vehicle engineering from the Beijing Institute of Technol ogy, China, in 2007. Since 2013, she has been an Associate Professor with the Nanjing University of Aeronautics and Astronautics. From 2015 to 2016, she was a Visiting Research Scientist with University of Michigan, Ann Arbor, MI, USA. Her research interests include intelligent energy man agement, hybrid vehicles, and vehicle dynamics.

BingQin Xu

BINGQIN XU was born in Yancheng, China, in 1996. he received the B.S. degree in trans portation engineering from the Hebei University of Engineering, in 2018. he is currently pursuing the master’s degree in vehicle engineering with the Nanjing University of Aeronautics and Astro nautics. His research interest includes state estimation of power batteries.

YuMei Zhang

YUMEI ZHANG was born in Gansu, China, in 1997. She received the B.S. degree in vehicle engineering from the Jilin Agricultural University, in 2019. She is currently pursuing the master's degree in vehicle engineering with the Nanjing University of Aeronautics and Astronautics. Her research direction is the state parameter estimation of power battery management system.