Commercialization of Lithium Battery Technologies for Electric Vehicles

A review of lithium ion battery failure mechanisms and fire prevention strategies

Investigating the thermal runaway mechanisms of lithium-ion batteries based on thermal analysis database

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.

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

Recent advances of thermal safety of lithium ion battery for energy storage

State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter

Micro-short-circuit (MSC) is a latent risk in power batteries, which may give rise to thermal runaway and even catastrophic safety hazards. The motivation of this paper is to quantitatively analyze MSC in an initial stage, particularly for lithium-ion batteries. To verify the feasibility of the proposed method, an equivalent MSC experiment is carried out. Based on a cell difference model, the cell state of charge (SOC) differences with the mean SOC for a battery pack are estimated by extended Kalman filter. The evaluated SOC difference can track the actual value well. Furthermore, an MSC diagnostic method is developed by employing recursive least squares filter. The method is demonstrated to examine the short-circuit resistance accurately. The results also show that the proposed method requires low computational load for the SOC difference and short-circuit resistance diagnosis.

Micro-Short-Circuit Diagnosis for Series-Connected Lithium-Ion Battery Packs Using Mean-Difference Model

A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-ion batteries

Electrical behavior of overdischarge-induced internal short circuit in lithium-ion cells

During the usage of electric vehicles, the battery decays and the cell variations expand in the battery pack. In the discharge process, both the low-capacity cell and the micro-short-circuit (MSC) cell have the abnormal feature that the state-of-charge (SOC) differences increase continuously. Hence, a low-capacity cell is likely to be misdiagnosed as an MSC cell, and vice versa. In this article, a fault identification approach based on mutual information is proposed to detect the MSC cell and low-capacity cell. A decision tree for fault identification is established by analyzing the battery fault characteristics of the short circuit, low capacity, and the abnormality of initial SOC difference. It is pointed out that the SOC deviation of the low-capacity cell is related to the mean SOC, while that of the MSC cell is related to time. A low-pass filter is used to get internal resistance differences in order to achieve the SOC deviations based on the cell different model. Finally, the MSC cell and the low-capacity cell can be identified using the mutual information which can quantitatively calculate the correlation between the SOC deviation and the mean SOC. Experimental results prove that the proposed method is reliable to identify the MSC cell and the low-capacity cell.

Wenkai Gao

Papers

State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter

Micro-Short-Circuit Diagnosis for Series-Connected Lithium-Ion Battery Packs Using Mean-Difference Model

A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-ion batteries

Electrical behavior of overdischarge-induced internal short circuit in lithium-ion cells

Micro-Short-Circuit Cell Fault Identification Method for Lithium-Ion Battery Packs Based on Mutual Information