Identification of Typical Sub-Health State of Traction Battery Based on a Data-Driven Approach

Abstract

As the core component of an electric vehicle, the health of the traction battery closely affects the safety performance of the electric vehicle. If the sub-health state cannot be identified and dealt with in time, it may cause traction battery failure, pose a safety hazard, and cause property damage to the driver and passengers. This study used data-driven methods to identify the two typical types of sub-health state. For the first type of sub-health state, the interclass correlation coefficient (ICC) method was used to determine whether there was an inconsistency between the voltage of a single battery and the overall voltage of the battery pack. In order to determine the threshold, the ICC value of each vehicle under different working conditions was analyzed using box plots, and a statistical ICC threshold of 0.805 was used as the standard to determine the first sub-health type. For the second type of sub-health state, the Z-score and the differential area method were combined to determine whether the single cell voltage deviated from the overall battery pack voltage. A battery whose voltage differential area exceeds the range of u ± 3σ is regarded as having a sub-health state. The results show that both methods can accurately judge the sub-health state type of a single battery. Furthermore, combined with the one-month operation data of the vehicle, we could calculate the sub-health state frequency of each single battery and take single batteries with a high frequency as the key object of attention in future vehicle operations.

Keywords

• data driven
• sub-health state
• ICC
• differential area
• box plot
• Pauta criterion