Data–Driven Fault Diagnosis and Cause Analysis of Battery Pack with Real Data

Abstract

Owing to the increasing use of electric vehicles (EVs), the demand for lithium-ion (Li-ion) batteries is rising. In this light, an essential factor governing the safety and efficiency of electric vehicles is the proper diagnosis of battery errors. In this article, we address the detection of battery problems by using the intraclass correlation coefficient (ICC) method and the order of cell voltages to enhance EV performance. Furthermore, we propose a framework for diagnosing problems with battery packs, which could be used to detect abnormal behavior. The proposed method calculates ICC values based on the terminal voltages extracted from a caravan battery pack. These ICC values are then used to determine whether the battery has a defect. In addition, the order of cell voltages is used to analyze the causes of faults. Furthermore, we conducted experiments to investigate and evaluate battery cell faults in EVs. The experimental results indicate that the proposed approach can be used to detect battery cell faults accurately.

Keywords

• battery management system (BMS)
• fault diagnosis
• LFP battery
• electric vehicle
• correlation coefficient