In this paper, the fault diagnosis of battery systems in new energy vehicles is reviewed in detail. Firstly, the common failures of lithium-ion batteries are classified, and the triggering mechanism of battery cell failure is briefly analyzed. Next, the existing fault diagnosis methods are described and classified in detail.
Due to road conditions, technology and other reasons, the storage battery, as a weak link of electric vehicles, is a frequent occurrence point of faults and the focus of fault diagnosis (Wang et al. 2017). The purpose of intelligent fault diagnosis of electric vehicles is to detect faults in the system based on actual detection data.
Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault diagnosis techniques based on three-dimensional information of voltage, current and temperature have gradually encountered bottlenecks.
Battery fault detection is limited by advanced fault detection diagnostics and methods. Capture voltage, temperature, and other safety-related diagnostic data through OBD II. Develop advanced pack fault detection diagnostics using onboard data. Standardization of CAN library would be beneficial.
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.
A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods for LIBs in advanced BMSs. This paper provides a comprehensive review on these methods.