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

Wang, Shunli; Fernandez, Carlos; Shang, Liping; Li, Zhanfeng; Li, Jianchao

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.

Citation

WANG, S., FERNANDEZ, C., SHANG, L., LI, Z. and LI, J. 2017. Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method. Journal of energy storage [online], 9, pages 69-83. Available from: https://doi.org/10.1016/j.est.2016.09.008

• Journal Article Type: Article
• Acceptance Date: Sep 20, 2016
• Online Publication Date: Dec 16, 2016
• Publication Date: Feb 28, 2017
• Deposit Date: Feb 22, 2018
• Publicly Available Date: Dec 17, 2018
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Pages: 69-83
• DOI: https://doi.org/10.1016/j.est.2016.09.008
• Keywords: Aerial lithium ion battery; Improved extended kalman filter; State of charge; Optimal prediction; Comprehensive estimation
• Public URL: http://hdl.handle.net/10059/2774
• Contract Date: Feb 22, 2018