State-of-the-art battery packs exhibit system voltages of up to 800V with almost 200 cell blocks in serial configuration , whereby the number of cells in parallel is determined by the capacity of the selected cell and power/energy demand of the application.
Similarly in the same case when the battery pack is being discharged, the weaker cells will discharge faster than the healthy cell and they will reach the minimum voltage faster than other cells. As we learnt in our BMS article the pack will be disconnected from load even if one cell reaches the minimum voltage.
The systematic faults of battery pack and possible abnormal state can be diagnosed by one coefficient. For the voltage abnormality, an accurate detection and location algorithm of the abnormal cell voltage are attained by combining the data analysis method and the visualization technique.
Common electrical faults of battery packs can be divided into three categories: abuse , sensor faults and connection faults . Battery abuse faults mainly refer to external short circuit (ESC), internal short circuit (ISC), overcharge and over-discharge.
One of the most significant factors is cell imbalance which varies each cell voltage in the battery pack overtime and hence decreases battery capacity rapidly. To increase the lifetime of the battery pack, the battery cells should be frequently equalized to keeps up the difference between the cells as small as possible.
By applying the designed coefficient, the systematic faults of battery pack and possible abnormal state can be timely diagnosed. 2) The t-SNE technique, The K-means clustering and Z-score methods are exploited to detect and accurately locate the abnormal cell voltage.