Abstract
Battery system diagnosis and prognosis are essential for ensuring the safe operation of electric vehicles (EVs). This paper proposes a diagnosis method of thermal runaway for ternary lithium-ion battery systems based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. Two-dimensional fault characteristics are first extracted according to battery voltage, and DBSCAN clustering is used to diagnose the potential thermal runaway cells (PTRC). The periodic risk assessing strategy is put forward to evaluate the fault risk of battery cells. The feasibility, reliability, stability, necessity, and robustness of the proposed algorithm are analyzed, and its effectiveness is verified based on datasets collected from real-world operating electric vehicles. The results show that the proposed method can accurately predict the locations of PTRC in the battery pack a few days before the thermal runaway occurrence.
Highlights
In order to cope with the issues of fossil oil depletion and environmental pollution, electric vehicles (EVs) are being actively developed and incrementally deployed worldwide [1]
This paper proposes an online thermal runaway diagnosis method for lithium-ion battery systems based on real-world data
The results show that the diagnosis method established by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering can effectively predict the potential thermal runaway cells (PTRC)
Summary
In order to cope with the issues of fossil oil depletion and environmental pollution, electric vehicles (EVs) are being actively developed and incrementally deployed worldwide [1]. Seo et al [14] proposed a model-based switching model method (SMM) to detect the short circuit in lithium-ion batteries These models could reveal the details of the thermal runaway process, but were insufficient to predict the thermal runaway occurrence. In order to predict thermal runaway occurrence, Feng et al [15] explored the correlation between the measured voltage, current, temperature and internal short-circuit (ISC) status using a 3D electrochemical-thermal-ISC coupled model, and proposed a scheme for on-line detection of internal short-circuit. During the real-world operation of EVs, the characteristics of battery systems are affected by various factors such as driving conditions, driver’s behaviors, and battery aging levels These may significantly curtail the performance of laboratory-synthesized approaches for thermal runaway prediction [16].
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