A Novel Variable Forgetting Factor Recursive Least Square Algorithm to Improve the Anti-Interference Ability of Battery Model Parameters Identification

Abstract

Recursive least square (RLS) algorithms are considered as a kind of accurate parameter identification method for lithium-ion batteries. However, traditional RLS algorithms usually employ a fixed forgetting factor, which does not have adequate robustness when the algorithm has interfered. In order to solve this problem, a novel variable forgetting factor method is put forward in this paper. Comparing with traditional variable forgetting factor methods, it has higher stability and sensitivity by using some mathematic improvements. The improvements in the robustness of recursive least square with a variable forgetting factor (VFF-RLS) algorithm is verified in this paper. A Thevenin model which is frequently-used in battery management system is employed in the verification. A data loss battery working condition is designed to simulate the interference to the algorithm. A simulation platform is established in MATLAB/Simulink software, and the data used in the verification is obtained by battery experiments. The analysis indicated that the novel VFF-RLS algorithm has better robustness and convergence ability, and has an acceptable identification accuracy.

A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries

“…24 Song et al proposed a novel variable forgetting factor recursive least squares (VFF-RLS) algorithm, which improves the anti-interference ability and the robustness of the model by adjusting the forgetting factor through model voltage error. 25 Although the aforementioned online model identification methods can achieve good performance, they do not consider the impact of the unexpected sensor error and electromagnetic interference of BMS. The voltage and current measurement of the lithium-ion battery is usually corrupted by a large amount of noise in practical application.…”

“…A new decoupled weighted recursive least square (DWRLS) method which estimates separately the parameters of the battery fast and slow dynamics, was proposed to solve the defect that RLS could not identify two effective RC networks 24 . Song et al proposed a novel variable forgetting factor recursive least squares (VFF‐RLS) algorithm, which improves the anti‐interference ability and the robustness of the model by adjusting the forgetting factor through model voltage error 25 …”

Parameter identification of a lithium‐ion battery based on the improved recursive least square algorithm

“…The online parameter identification algorithms typically include the recursive least squares (RLS) type algorithms [8–16] and the filter type algorithms [17–19]. The RLS approach is widely used because it does not need complex matrix operation and requires low computing power [20, 21].…”

“…However, this algorithm does not consider the coupling problem of gain vector calculation and reversed solution from parameters. In [16], a variable forgetting factor RLS (VFFRLS) algorithm is proposed to improve the anti‐interference ability of battery model parameter identification. However, the maximum and minimum forgetting factors of the algorithm vary with the working conditions of the battery, which is not conducive to online parameter identification.…”

A Variable Regularized Recursive Subspace Model Identification Algorithm With Extended Instrumental Variable and Variable Forgetting Factor

“…However, the batch subspace model identification (SMI) algorithms are computational intensive for online implementation due to the high computational complexity of SVD. Consequently, much research on recursive subspace model identification (RSI) [6]- [9] has been devoted to update the model parameters over time with a reduced computational cost. Most RSI algorithms estimate the unknown state matrix A A A and the output matrix C C C from the ''data equation'' consisting of the input and output samples of the systems.…”