The cell faults of lithium-ion batteries will lead to the atypical deterioration of battery performance and even thermal runaway. In this paper, a novel fault diagnosis method for lithium-ion batteries of electric vehicles based on real-time voltage is proposed.
In this paper, the novel method for lithium-ion battery fault diagnosis of EV based on real-time voltage is presented. The effectiveness of the method is verified based on the real-time data collected by EVs. The related conclusions are drawn as follows:
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.
To enhance the reliability and safety of lithium-ion batteries, many scholars have proposed different methods for lithium-ion battery fault diagnosis. Current fault diagnosis methods can be divided into three categories: experience-based methods, model-based methods, and data-driven methods [5, 8, 9].
The experience-based method is based on the existing prior knowledge, using logical analysis and reasoning the relationship between events to achieve battery fault diagnosis. It can be divided into the expert system , fuzzy logic , and graph theory .
In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging.