SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

• Published in: Energies (Basel) Vol. 12; no. 16; p. 3122
• Main Authors: Zeng, Miaomiao, Zhang, Peng, Yang, Yang, Xie, Changjun, Shi, Ying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.08.2019
• Subjects: Accuracy; Aging; Algorithms; Automation; Cell cycle; Electric vehicles; Energy; Experiments; fuzzy unscented Kalman filtering algorithm; improved second-order RC equivalent circuit; International conferences; joint estimation; Lithium; Methods; Neural networks; Parameter identification; power batteries; Wavelet transforms; South Korea
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12163122

In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm.