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 (RQ) minimization which lays the basis of the RTLS. The number of multiplies, divides, and square roots (MADs) 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.
recursive total least squares
state of charge