期刊
ENERGIES
卷 12, 期 16, 页码 -出版社
MDPI
DOI: 10.3390/en12163122
关键词
power batteries; improved second-order RC equivalent circuit; fuzzy unscented Kalman filtering algorithm; joint estimation
资金
- National Natural Science Foundation of China [51477125]
- Hubei Science Fund for Distinguished Young Scholars [2017CFA049]
- Hubei province technological innovation major project [2018AAA059]
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.
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