Online model identification and state-of-charge estimate for lithium-ion battery with a recursive total least squares-based observer
Journal article, 2018

The state-of-charge (SOC) observer with onlinemodel 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 amore 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.

model identification

state of charge (SOC)

recursive total least

Bias attenuation



Zhongbao Wei

Nanyang Technological University

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

Feng Leng

Nanyang Technological University

Boon Hee Soong

Nanyang Technological University

King Jet Tseng

Singapore Institute of Technology

IEEE Transactions on Industrial Electronics

0278-0046 (ISSN) 15579948 (eISSN)

Vol. 65 2 1336-1346 2736480

Subject Categories

Computational Mathematics

Control Engineering

Signal Processing



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