Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method. (February 2017)

• Record Type: Journal Article
• Authors: Wang, Shunli; Fernandez, Carlos; Shang, Liping; Li, Zhanfeng; Li, Jianchao
• Is Part Of: Journal of energy storage. Volume 9(2017)
• Page Start: 69
• Page End: 83
• Publication Date: 2017-02
• Subjects: Aerial lithium-ion battery -- Improved extended kalman filter -- State of charge -- Optimal prediction -- Comprehensive estimation; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2016.09.008
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: An effective method is proposed for the state of charge (SOC) estimation of aerial LIB packs. It is realized by using confidence calculation to evaluate the working status of LIB. The comprehensive estimation using the extended Kalman filter (EKF) together with the discharging and charging maintenance (DCM), implying the accurate SOC estimation. The method can effectively realize LIB remaining capacity check and security anticipation. Abstract: An effective method to estimate the integrated state of charge (SOC) value for the lithium-ion battery (LIB) pack is proposed, because of its capacity state estimation needs in the high-power energy supply applications, which is calculated by using the improved extended Kalman filter (EKF) method together with the one order equivalent circuit model (ECM) to evaluate its remaining available power state. It is realized by the comprehensive estimation together with the discharging and charging maintenance (DCM) process, implying an accurate remaining power estimation with low computational calculation demand. The battery maintenance and test system (BMTS) equipment for the aerial LIB pack is developed, which is based on the proposed SOC estimation method. Experimental results show that, it can estimate SOC value of the LIB pack effectively. The BMTS equipment has the advantages of high detection accuracy and stability and can guarantee its power-supply reliability. The SOC estimation method is realized on it, the results of which are compared with the conventional SOC estimation method. The estimation has been done with an accuracy rate of 95% and has an absolute root mean square error (RMSE) of 1.33% and an absolute maximum error of 4.95%. This novel method can provide reliable technical support for the LIB power supply application, which plays a core role in promoting its power supply applications.