Title: Lithium battery model online parameter identification method based on multi-innovation least squares

Authors: Jie Wu; Huigang Xu; Peiyi Zhu

Addresses: School of Electric and Automatic Engineering, Changshu Institute of Technology, Suzhou, China; China University of Mining and Technology, Xuzhou, China ' School of Electric and Automatic Engineering, Changshu Institute of Technology, Suzhou, China ' School of Electric and Automatic Engineering, Changshu Institute of Technology, Suzhou, China

Abstract: In this paper, the second-order RC equivalent circuit model of lithium-ion battery is studied, and the online identification of model parameters by multi-innovation least squares method is presented, which uses multi-innovation to correct the difference between the observed value output at the previous time and the estimated value of the model identified at the previous time, which extends the single information of the original least squares method to multiple innovations. Data are collected through HPPC cycle conditions and NEDC conditions experiments. The accuracy and convergence speed of the conventional recursive least squares estimation algorithm is described, to compare the absolute error between the estimated battery port voltage and the real value of the battery with different new interest lengths of the multi-innovation least squares algorithm. The experimental results show that the multi-innovation least squares algorithm with longer new interest length has higher accuracy and convergence speed, which verifies the effectiveness and feasibility of the proposed method.

Keywords: lithium-ion battery; estimation of battery states; RLS; battery model parameters; multi-innovation least squares; HPPC cycle conditions; NEDC conditions experiments; absolute error.

DOI: 10.1504/IJMIC.2024.135547

International Journal of Modelling, Identification and Control, 2024 Vol.44 No.1, pp.1 - 13

Received: 24 Aug 2022
Received in revised form: 31 Oct 2022
Accepted: 14 Nov 2022

Published online: 18 Dec 2023 *

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