Load-responsive model switching estimation for state of charge of lithium-ion batteries
Journal article, 2019

Accurately estimating state of charge (SoC) is very important to enable advanced management of lithium-ion batteries, however technical challenges mainly exist in the lack of a high-fidelity battery model whose parameters are sensitive to changes of the state and load condition. To address the problem, this paper explores and proposes a model switching estimation algorithm that online selects the most suitable model from its model library based on the relationship between load conditions for calibration and in practice. By leveraging a high-pass filter and the Coulomb counting, an event trigger procedure is developed to detect the estimation performance and then determine timely switching actions. This estimation algorithm is realized by adopting a gradient correction method for system identification and the unscented Kalman filter and H∞ observer for state estimation. Experimental results illustrate that the proposed algorithm is able to reproduce SoC trajectories under various operating profiles, with the root-mean-square errors bounded by 2.22%. The efficacy of this algorithm is further corroborated by comparing to single model-based estimators and two prevalent adaptive SoC estimators.

State of charge estimation

Battery management system

Model switching

Building energy storage system

Author

Xiaopeng Tang

Hong Kong University of Science and Technology

Furong Gao

Guangzhou HKUST Fok Ying Tung Research Institute

Hong Kong University of Science and Technology

Changfu Zou

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Ke Yao

Hong Kong University of Science and Technology

Wengui Hu

Hong Kong University of Science and Technology

Torsten Wik

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Applied Energy

0306-2619 (ISSN)

Vol. 238 423-434

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing

DOI

10.1016/j.apenergy.2019.01.057

More information

Latest update

2/13/2019