The voltage fault within battery pack is often caused by inconsistency in cells. By applying a certain detection threshold, the cell with abnormal voltage can be detected at the beginning of abnormity using the proposed method, which has vital significance for the future prognosis and safety management of the battery fault. 4.2.
The above analysis proves that even the slight voltage abnormities of battery system during vehicular operation can be detected and diagnosed accurately by the method proposed in this work. Moreover, this method can achieve voltage fault diagnosis in advance when the voltage of the faulty cell still within the normal range.
A novel voltage fault diagnosis method is proposed for battery systems. The proposed diagnosis method is based on the modified Shannon entropy. A large quantity of monitoring data is collected and used for validation. A safety management strategy is presented based on Z-score method.
During the operation of the battery system, the current stays the same, and the changing trend of the terminal voltage is theoretically consistent for the series-connected battery pack. If a cell has faults, its voltage trend will be different from others.
One of the common faults that occur to battery cells is the voltage abnormity including over-voltage and under-voltage. The voltage fault always implies more serious internal faults including internal short-circuit, electrode structure failure and so on.
The voltage abnormal fluctuation is a warning signal of short-circuit, over-voltage and under-voltage. This paper proposes a scheme of three-layer fault detection method for lithium-ion batteries based on statistical analysis. The first layer fault detection is based on the thresholds of over-charge and over-discharge of a battery pack.