scholarly journals Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

Energies ◽  
2012 ◽  
Vol 5 (4) ◽  
pp. 1098-1115 ◽  
Author(s):  
Caiping Zhang ◽  
Jiuchun Jiang ◽  
Weige Zhang ◽  
Suleiman M. Sharkh
Keyword(s):  
Lithium Ion Batteries ◽  
Kalman Filtering ◽  
Lithium Ion ◽  
State Of Charge ◽  
Extended Kalman Filtering

State of Charge Estimation of Lithium-Ion Batteries by Extended Kalman Filtering

2012 ◽  
Vol 8 (1) ◽  
pp. 332-336
Author(s):  
Kuo-Chang Han ◽  
Van-Tsai Liu ◽  
Edward Chiu ◽  
Yi Huis Lin
Keyword(s):  
Lithium Ion Batteries ◽  
Kalman Filtering ◽  
Lithium Ion ◽  
State Of Charge ◽  
Extended Kalman Filtering ◽  
State Of Charge Estimation

A novel voltage matching-adaptive extended Kalman filtering construction method for the state of charge prediction of lithium-ion batteries

2021 ◽  
pp. 014233122110294
Author(s):  
Weihao Shi ◽  
Shunli Wang ◽  
Lili Xia ◽  
Peng Yu ◽  
Bowen Li
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Kalman Filtering ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Extended Kalman Filtering

Accurately estimating the state of charge of lithium-ion batteries is of great significance to the development of the new energy industry. This research proposes a method for estimating the state of charge of lithium-ion batteries based on a voltage matching-adaptive extended Kalman filtering algorithm. The voltage matching part and the first-order resistance-capacitance (RC) part is combined into a new equivalent circuit model. This model improves the accuracy of voltage simulation at different charging and discharging stages through segment matching. Model-based adaptive extended Kalman filter algorithm adds a noise correction factor to adaptively correct the influence of noise on the estimation process and improve the estimation accuracy. The forgetting factor is introduced to improve the real-time performance of the algorithm. To verify the reliability of the model and algorithm, a multi-condition experiment is carried out on the lithium-ion battery. The verification results show that the simulation error of the circuit model to the working state of the lithium-ion battery is less than 0.0487V. The improved algorithm can accurately estimate the state of charge of lithium-ion batteries, the estimation accuracy of the discharge stage is 98.34%, and the estimation accuracy of the charging stage is 97.75%.

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