Focus on Battery Management Systems (BMS) and Sensors: The critical roles of BMS and sensors in fault diagnosis are studied, operations, fault management, sensor types. Identification and Categorization of Fault Types: The review categorizes various fault types within lithium-ion battery packs, e.g. internal battery issues, sensor faults.
The integration of battery management systems (BMSs) with fault diagnosis algorithms has found extensive applications in EVs and energy storage systems [12, 13]. Currently, the standard fault diagnosis systems include data collection, fault diagnosis and fault handling , and reliable data acquisition [, , ] is the foundation.
Battery system faults can be categorized into three main types: battery faults, sensor faults, and connection faults. 3. Battery faults Battery faults are typically classified into three categories: overcharge, over-discharge, and internal or external short circuits.
So far, many data-driven methods, including machine-learning tools, have been used in the case of battery state estimation. Nevertheless, for fault detection, just a few methods based on neural networks, SVM and deep learning are investigated. The conventional methods commonly used for fault detection of EVs are mainly model-based and signal-based.
In addition, a battery system failure index is proposed to evaluate battery fault conditions. The results indicate that the proposed long-term feature analysis method can effectively detect and diagnose faults. Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems.
There is a lack of research on the coupled evolution of multidimensional states in the battery fault process. Although numerous new sensors are believed to hold potential for early fault diagnosis, they are often applied to monitor different signals of a battery independently.