Micro short circuit diagnosis for lithium-ion batteries based on Hellinger distance and inverse Markov method

Micro short circuits (MSCs) are minor faults within lithium-ion batteries and represent an early stage in the progression toward safety failures and thermal runaway. Early detection of MSCs is crucial for enhancing battery safety. However, this remains technically challenging, as the voltage anomalies caused by MSCs can easily be obscured by complex and frequent charging/discharging cycles. In this study, a two-step diagnostic approach is proposed for detecting MSC faults. Firstly, internal resistances are estimated using the forgetting factor recursive least squares (FFRLS) method, and suspected MSC cells are identified based on the Hellinger distance calculated between each cell and a reference value. Secondly, the inverse Markov method is applied to analyze voltage transfer anomalies in the suspected cells, thereby confirming the faults. To support algorithm validation, MSCs are first induced through slight extrusion and the faulty cells are then operated in series with normal cells under the UDDS cycle, thereby establishing a labeled database containing both normal and MSC data segments. The effectiveness of the proposed approach is validated using this dataset, and experimental results demonstrate that the method can effectively identify MSC faults, achieving a precision of over 98.0%. • A two-step mechanical stress-induced MSC diagnosis method is proposed • Hellinger distance and inverse Markov method are integrated for reliable diagnosis • A labeled dataset is constructed using extruded and normal batteries • The proposed method is validated and achieves high diagnostic accuracy