Signal-Disturbance Interfacing Elimination for Unbiased Model Parameter Identification of Lithium-Ion Battery

A precisely parameterized battery model is the prerequisite of the model-based management of lithium-ion battery. However, the unexpected sensing of noises may discount the identification of model parameters in practical applications. This article focuses on the noise effect compensation and online parameter identification for the widely used equivalent circuit model. A novel degree of freedom (DOF) eliminator is proposed and combined with the Frisch scheme in a recursive fashion, for the first time, to coestimate the noise statistics and unbiased model parameters. A computationally tractable numerical solver is further proposed for the DOF eliminator to improve the real-time performance. Simulations and experiments are performed to validate the proposed method from theoretical to practical perspective. Results show that the proposed method can effectively mitigate the noise-induced identification biases and outperform the existing methods in terms of the accuracy and the robustness to noise corruption.

Automated summary

The article focuses on noise effect compensation and online parameter identification for the widely used equivalent circuit model of lithium-ion batteries. A novel degree of freedom (DOF) eliminator is proposed to coestimate noise statistics and unbiased model parameters in a recursive fashion. The proposed method effectively mitigates noise-induced biases and outperforms existing methods in terms of accuracy and robustness to noise corruption.