Modeling and state of charge estimation of lithium-ion battery

Modeling and state of charge (SOC) estimation of lithium-ion (Li-ion) battery are the key techniques of battery pack management system (BMS) and critical to its reliability and safety operation. An auto-regressive with exogenous input (ARX) model is derived from RC equivalent circuit model (ECM) due...

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Bibliographic Details
Published in:Advances in manufacturing 2015-09, Vol.3 (3), p.202-211
Main Authors: Chen, Xi-Kun, Sun, Dong
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Control
Electric batteries
Electric charge
Electric vehicles
Engineering
Equivalent circuits
Estimators
Extended Kalman filter
Ion charge
Lithium
Lithium-ion batteries
Machines
Manufacturing
Mathematical models
Mechatronics
Nanotechnology and Microengineering
Numerical stability
Parameter estimation
Parameter identification
Partial differential equations
Processes
Product safety
Rechargeable batteries
Robotics
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Summary:Modeling and state of charge (SOC) estimation of lithium-ion (Li-ion) battery are the key techniques of battery pack management system (BMS) and critical to its reliability and safety operation. An auto-regressive with exogenous input (ARX) model is derived from RC equivalent circuit model (ECM) due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square (VFFRLS) algorithm is adopted as an adaptive parameter identification method. Based on the designed model, a SOC estimator using cubature Kalman filter (CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter (EKF) algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.
ISSN:2095-3127
2195-3597
DOI:10.1007/s40436-015-0116-3