A fast estimation algorithm for lithium-ion battery state of health
Artikel i vetenskaplig tidskrift, 2018

This paper proposes a novel and computationally efficient estimation algorithm for lithium-ion battery state of health (SoH) under the hood of incremental capacity analysis. Concepts of regional capacity and regional voltage are introduced to develop an SoH model against experimental cycling data from four types of batteries. In the obtained models, SoH is a simple linear function of the regional capacity, and the R-square of linear fitting is up to 0.948 for all the considered batteries with properly selected regional voltage. The proposed method without using characteristic parameters directly from incremental capacity curves is insensitive to noise and filtering algorithms, and is effective for common current rates, where rates of up to 1C have been demonstrated. Then, a model-based SoH estimator is designed and shown to be capable of closely matching battery's aging data from NASA, with the error less than 2.5%. Furthermore, such a small scale of error is achieved in the absent of state of charge and impedance which are often used for SOH estimation in available methods.

Battery management system

State of health estimation

Incremental capacity analysis

Lithium-ion batteries

Författare

Xiaopeng Tang

Hong Kong University of Science and Technology

Changfu Zou

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Ke Yao

Hong Kong University of Science and Technology

Guangzhou HKUST Fok Ying Tung Research Institute

Guohua Chen

Hong Kong University of Science and Technology

Boyang Liu

Hong Kong University of Science and Technology

Zhenwei He

Guangzhou HKUST Fok Ying Tung Research Institute

Furong Gao

Hong Kong University of Science and Technology

Guangzhou HKUST Fok Ying Tung Research Institute

Journal of Power Sources

0378-7753 (ISSN)

Vol. 396 453-458

Ämneskategorier

Sannolikhetsteori och statistik

Reglerteknik

Signalbehandling

DOI

10.1016/j.jpowsour.2018.06.036

Mer information

Senast uppdaterat

2018-07-04