Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Abstract

This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

Thermal Fault Detection and Localization Framework for Large Format Batteries

“…In recent years, battery thermal fault diagnostics have received considerable awareness as many model-based approaches [13][14][15][16][17][18][19][20] along with data-driven and signal processing techniques [21][22][23] have been proposed. The review papers [24][25][26] provide a comprehensive list of existing approaches.…”

“…The work in [21] utilize the LSTM-NN based approach for thermal fault detection. In [22], a big-data and Shannon entropy analysis are used to find the time and location of faults in battery pack. However, the data-driven approaches [21,22] suffer the drawbacks of large data requirement and limited ability to capture unforeseen anomalies.…”

“…In [22], a big-data and Shannon entropy analysis are used to find the time and location of faults in battery pack. However, the data-driven approaches [21,22] suffer the drawbacks of large data requirement and limited ability to capture unforeseen anomalies. The work in [27] shows that the surface temperature of pouch cells is a proper indicator for thermal runaway detection and is an appropriate replacement of internal temperature as well.…”

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

“…[125], the correlation coefficient between cell voltage s can capture the abnormal voltage drop. The entropy of battery temperature [127] and voltage [128] become the features of temperature abno rmity and voltage fault, respectively.…”

“…According to the difference between the RCCs after two adjacent charges, the leakage current and MSC resistance can be obtained. Based upon a large amount of raw temperature data derived from NSMC-EV in Beijing, Hong et al [127] applied the Shannon entropy to capture the temperature abnormity of the battery pack. Besides, the abnormity coefficient, including over-temperature and excessive temperature difference, was quantitatively evaluated to predict both the time and location of the temperature faults in battery packs.…”