Zhentong Liu

Bio: Zhentong Liu is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Fault detection and isolation & Residual. The author has an hindex of 3, co-authored 3 publications receiving 83 citations. Previous affiliations of Zhentong Liu include Ohio State University & Center for Automotive Research.

Fault Detection and Isolation for Lithium-Ion Battery System Using Structural Analysis and Sequential Residual Generation

Abstract: This paper presents a systematic methodology based on structural analysis and sequential residual generators to design a Fault Detection and Isolation (FDI) scheme for nonlinear battery systems. The faults to be diagnosed are highlighted using a detailed hazard analysis conducted for battery systems. The developed methodology includes four steps: candidate residual generators generation, residual generators selection, diagnostic test construction and fault isolation. State transformation is employed to make the residuals realizable. The simulation results show that the proposed FDI scheme successfully detects and isolates the faults injected in the battery cell with cooling system at different times. In addition, there are no false or missed detections of the faults.Copyright © 2014 by ASME

Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

Abstract: Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles

Abstract: In electric vehicles, a battery management system highly relies on the measured current, voltage, and temperature to accurately estimate state of charge (SOC) and state of health. Thus, the normal operation of current, voltage, and temperature sensors is of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two nonadjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the predefined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme.