Title: Remaining energy estimation strategy for lithium-ion battery pack based on RLS-UKF algorithm

Authors: Qiuting Wang; Wei Qi; Duo Xiao

Addresses: Department of Electrical and Information Engineering, Zhejiang University City College, 51 Huzhou Road, Hangzhou, 310015, China ' Department of Electrical and Information Engineering, Zhejiang University City College, 51 Huzhou Road, Hangzhou, 310015, China ' Department of Electrical and Information Engineering, Zhejiang University City College, 51 Huzhou Road, Hangzhou, 310015, China

Abstract: The paper focuses on modelling method and online estimation strategy to estimate the remaining energy of lithium-ion battery pack. New estimation method is based on system complexity analysis and it can describe the external characteristics of different temperatures and frequencies for battery model. The recursive least squares (RLS) algorithm is introduced to update the battery parameters online. The relationship equations between the remaining energy of battery pack and the state of charge (SOC) of the single cell is established. Meanwhile, unscented Kalman filtering (UKF) algorithm is used to estimate the remaining energy online. The influence of temperature and charge/discharge ratio is considered. Finally, the inconsistent influence between different cells is analysed. The validity and reliability of our new model and estimation strategy are verified under UDDC experiments. The experimental results are compared under real-time conditions.

Keywords: vehicle system; lithium-ion battery pack; remaining energy; SOC; state of charge; recursive least squares; UKF; unscented Kalman filtering; system complexity analysis.

DOI: 10.1504/IJVSMT.2021.122826

International Journal of Vehicle Systems Modelling and Testing, 2021 Vol.15 No.4, pp.274 - 288

Received: 19 Aug 2020
Accepted: 08 Oct 2020
Published online: 13 May 2022 *

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