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Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer

Zhongbao Wei ; Changfu Zou (Institutionen för elektroteknik, Reglerteknik) ; Feng Leng ; Boon Hee Soong ; King-Jet Tseng
IEEE transactions on industrial electronics (0278-0046). Vol. 65 (2018), 2, p. 1336-1346.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Bias attenuation, model identification, noise, recursive total least, state of charge (SOC), electric-vehicles, health estimation, polymer battery, adaptive state, parameter



Denna post skapades 2018-01-10. Senast ändrad 2018-01-17.
CPL Pubid: 254405

 

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Institutioner (Chalmers)

Institutionen för elektroteknik, Reglerteknik

Ämnesområden

Hållbar utveckling
Sannolikhetsteori och statistik

Chalmers infrastruktur