1. State-of-charge Estimation of Lithium-ion Battery Based Online Parameter Identification
- Author
-
Long Wu, Feng Juqiang, Huang Kaifeng, Jun Lu, and Xing Zhang
- Subjects
lcsh:GE1-350 ,Recursive least squares filter ,Estimation ,Computer Science::Hardware Architecture ,Identification (information) ,Schedule ,State of charge ,Control theory ,Computer science ,Kalman filter ,lcsh:Environmental sciences ,Lithium-ion battery ,Energy (signal processing) - Abstract
Accurately estimating the state of charge (SOC) of lithium-ion is very important to improving the dynamic performance and energy utilization efficiency. In order to reduce the influence of model parameters and system coloured noise on SOC estimation accuracy, this paper proposes the SOC estimation based on online identification. Based on the mixed simplified electrochemical model, the forgetting factor recursive least squares (FFRLS) method was used to identify the parameters online, and the SOC estimation was carried out in combination with Unscented Kalman Filter (UKF). Finally, the accuracy and feasibility of the method are verified by Federal Urban Driving Schedule (FUDS), the online identification and SOC estimation are carried out. The experimental results show that the SOC estimation of online parameter identification is more accurate, the system stability is faster and the error is smaller.
- Published
- 2020