SOC Estimation of Lithium-ion Battery Based on RLS-EKF for Unmanned Aerial Vehicle. Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Zhang, Shuaishuai; Tao, Hongfei; Bi, Kaitao; Yan, Wenxu; Ni, Hongyu
• Is Part Of: Journal of physics. Volume 2216:Issue 1(2022)
• Publication Date: 2022-03-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2216/1/012002
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In recent years, in the civil and military unmanned aerial vehicle(UAV) market, the demand for UAV is increasing day by day, the iteration cycle of UAV products is significantly shortened, and UAV has developed rapidly. Among them, lithium battery has the advantages of high energy density, lightweight and bendability. At present, as the main power source of electric UAV, lithium battery affects the range and flight time of UAV, and is the key factor that directly restricts the development and application of UAV. Therefore, accurate estimation of battery state of charge (SOC) has always been the focus and difficulty in the field of UAV battery research. This paper mainly improves the accuracy of SOC estimation of power battery from the following aspects: First, the significance of researching power batteries is clarified, the advantages of lithium-ion power batteries are pointed out, the definition of SOC and its influencing factors are discussed, and the common methods of battery SOC estimation are more comprehensively introduced and analyzed. Next, a second-order RC network equivalent circuit model that is convenient for engineering realization is established, and its state-space model is deduced. The SOC-OCV relationship was measured. Then, the recursive least squares (RLS) method suitable for real-time calculation is used to identify the parameters of the equivalent circuit model of lithium-ion battery on-line, and the parameters of the equivalent circuit model are identified under UDDS conditions, which proves the correctness and rationality of the identification method. Finally, it is proposed to use the extended Kalman filter (EKF) algorithm to estimate the SOC of the lithium-ion battery, that is, use the EKF algorithm to estimate the open circuit voltage of the battery, and then estimate the battery SOC according to the open circuit voltage method. The accuracy of the joint estimation algorithm to estimate SOC is verified under simulated conditions.