Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter
Changwen Zheng,
Yunlong Ge,
Ziqiang Chen,
Deyang Huang,
Jian Liu,
Shiyao Zhou
Affiliations
Changwen Zheng
State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Yunlong Ge
State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Ziqiang Chen
State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Deyang Huang
State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Jian Liu
State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
Shiyao Zhou
State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China
The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries.
