Comprehensive Review of Fault Diagnosis Methods: An extensive review of data-driven approaches for diagnosing faults in lithium-ion battery management systems is provided. 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.
The problems of this method aim to solve involve fault diagnosis in LIB packs, which involves identifying issues in the batteries, such as voltage sensor faults, incorrect data, and predicting the SOH and RUL of LIBs to ensure safe and efficient operation. The effectiveness of ANNs in fault diagnosis for LIBs has been well-established.
Hazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during operation, aging, or charging. Robust early fault diagnosis algorithms are essential for enhancing safety, efficiency, and reliability.
Accurate evaluation of Li-ion battery safety conditions can reduce unexpected cell failures. Here, authors present a large-scale electric vehicle charging dataset for benchmarking existing algorithms, and develop a deep learning algorithm for detecting Li-ion battery faults.
Identification and Categorization of Fault Types: The review categorizes various fault types within lithium-ion battery packs, e.g. internal battery issues, sensor faults. Analysis of Current Challenges and Future Trends: It addresses the current challenges in implementation of data-driven fault diagnosis methods and presents emerging trends.
Given the intricate multi-layer internal structure of a LIB and the electrothermal coupling effect caused by faults, establishing a well-balanced battery model between fidelity and complexity poses a critical challenge to battery fault diagnosis.