Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer
Artikel i vetenskaplig tidskrift, 2018

The state-of-charge (SOC) observer with online model adaption generally has high accuracy and robustness. However, the unexpected sensing of noises is shown to cause the biased identification of model parameters. To address this problem, a novel technique which integrates a recursive total least squares (RTLS) with an SOC observer is proposed to enhance the online model identification and SOC estimate. An efficient method is exploited to solve the Rayleigh quotient minimization which lays the basis of the RTLS. The number of multiplies, divides, and square roots is elaborated to show the low computational complexity of the developed RTLS. Simulation and experimental results show that the proposed RTLS-based observer attenuates the model identification bias caused by noise corruption effectively, and, thereby, provides a more reliable estimation of SOC. The proposed method is further compared with several available methods to highlight its superiority in terms of accuracy and the robustness to noise corruption.

health estimation

model identification

adaptive state

parameter

electric-vehicles

Bias attenuation

state of charge (SOC)

polymer battery

recursive total least

noise

Författare

Zhongbao Wei

Changfu Zou

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Feng Leng

Boon Hee Soong

King-Jet Tseng

IEEE Transactions on Industrial Electronics

0278-0046 (ISSN)

Vol. 65 1336-1346

Drivkrafter

Hållbar utveckling

Ämneskategorier

Sannolikhetsteori och statistik

DOI

10.1109/tie.2017.2736480