Multi-stage state of health estimation of lithium-ion battery with high tolerance to heavily-partial charging
Artikel i vetenskaplig tidskrift, 2022

State of health (SOH) is critical to the management of lithium-ion battery (LIB) due to its deep insight into the health diagnostic and protection. However, the absence of complete charging data, which is common in practice, risks impairing the charging-based SOH estimators. This paper proposes a multi-stage SOH estimation method with a broad scope of applications, including the unfavorable but practical scenarios of heavily-partial charging. In particular, different sets of health indicators (HIs), covering both the morphological IC features and the voltage entropy information, are extracted from the partial CC charging data with different initial charging voltage to characterize the aging status. Following this endeavor, artificial neural network (ANN)-based HI fusion is proposed to estimate the SOH of LIB precisely in real time. The proposed method is evaluated with long-term aging experiments performed on different types of LIBs. Results validate several superior merits of the proposed method, including high estimation accuracy, high tolerance to partial charging, strong robustness to cell inconsistency, and wide generality to different battery types.

partial charging

health indicators

lithium-ion battery

state of health

Författare

Zhongbao Wei

Beijing Institute of Technology

Haokai Ruan

Beijing Institute of Technology

Yang Li

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Jianwei Li

Beijing Institute of Technology

Caizhi Zhang

Chongqing University

Hongwen He

Beijing Institute of Technology

IEEE Transactions on Power Electronics

0885-8993 (ISSN)

Vol. 37 6 7432-7442

Drivkrafter

Hållbar utveckling

Styrkeområden

Transport

Energi

Ämneskategorier

Reglerteknik

Signalbehandling

Annan elektroteknik och elektronik

DOI

10.1109/TPEL.2022.3144504

Mer information

Senast uppdaterat

2022-03-14