scholarly journals State‐of‐charge estimation technique for lithium‐ion batteries by means of second‐order extended Kalman filter and equivalent circuit model: Great temperature robustness state‐of‐charge estimation

2021 ◽  
Author(s):  
Yanyan Fang ◽  
Qiang Zhang ◽  
Hang Zhang ◽  
Weicheng Xu ◽  
Linshu Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Extended Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
State Of Charge ◽  
Estimation Technique ◽  
State Of Charge Estimation

scholarly journals Quadrature Kalman filter–based state of charge estimation for lithium-ion battery

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402094269
Author(s):  
Mengtao Huang ◽  
Chao Wang ◽  
Bao Liu ◽  
Fan Wang ◽  
Jingting Wang
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Quadrature Rule ◽  
State Of Charge ◽  
Gauss Hermite Quadrature ◽  
State Of Charge Estimation ◽  
Unscented Filter

This article presents an approach to lithium-ion battery state of charge estimation based on the quadrature Kalman filter. Among the existing state of charge estimation approaches, the extended Kalman filter–based state of charge and unscented filter–based state of charge algorithms are influenced by the linearization or the solution of sigma points. The proposed quadrature Kalman filter–based state of charge algorithm avoids these problems. Specifically, the battery system equations are built based on the second-order resistance–capacitance equivalent circuit model, and the parameters are identified according to the hybrid pulse power characterization discharging test. Then, the quadrature points and corresponding weights are defined by the Gauss–Hermite quadrature rule, and the Kronecker tensor product is adopted to solve the points of multivariate. In addition, the stability of quadrature Kalman filter–based state of charge is verified. Finally, the simulation is carried out under the discharging and urban dynamometer driving schedule condition, which demonstrates that the quadrature Kalman filter–based state of charge algorithm has a better performance compared with extended Kalman filter–based state of charge and unscented filter–based state of charge.

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