A Novel Multi-model Probability Based Battery State-of-charge Fusion Estimation Approach☆

Abstract Accurate state of charge (SoC) estimation is very important for managing battery with safety and efficiency. In order to improve the reliability and redundancy of the SoC estimation, the multi-model probability fusion estimation (MMPFE) method is presented. Considering that the estimation results being dependent on models, the MMPFE method is utilized to fuse the SoC results gained by different equivalent circuit models (ECMs). LFP type battery are tested to verify the effectiveness of the method. Results indicate that the proposed approach can achieve accurate battery SoC estimation with good robust and reliability.

[1]  Delphine Riu,et al.  A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .

[2]  Hongwen He,et al.  A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique , 2016 .

[3]  Guangjun Liu,et al.  Estimation of Battery State of Charge With $H_{\infty}$ Observer: Applied to a Robot for Inspecting Power Transmission Lines , 2012, IEEE Transactions on Industrial Electronics.

[4]  Hongwen He,et al.  A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .

[5]  Hongwen He,et al.  Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach , 2011 .

[6]  Minyue Fu,et al.  A linear matrix inequality approach to robust H∞ filtering , 1997, IEEE Trans. Signal Process..

[7]  Chenbin Zhang,et al.  A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy , 2015 .

[8]  Le Yi Wang,et al.  Robust and Adaptive Estimation of State of Charge for Lithium-Ion Batteries , 2015, IEEE Transactions on Industrial Electronics.

[9]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[10]  Tahsin Koroglu,et al.  A comprehensive review on estimation strategies used in hybrid and battery electric vehicles , 2015 .