Battery defect detection for real world vehicles based on Gaussian distribution parameterization developed LCSS

Accurate detection of early faults in lithium-ion (Li-ion) battery packs plays an important role in preventing safety accidents and reducing property damage. At present, fault diagnosis research based on actual vehicle single-cell voltage consistency has become a hot topic, but the consistency evolution law is not uniform due to complex environmental conditions and operating conditions, so the generalization of current methods is poor. To this end, this paper proposes a multi-layer fault diagnosis framework, which first considers the evolution law of single-unit voltage difference to determine whether there is a potential risk, and proposes a flexible screening method for state of charge (SOC) interval segments, which improves the accuracy and efficiency of inspection. Next, the thresholds required by the developed longest common subsequence (DLCSS) algorithm are parameterized based on Gaussian distribution and combined with z-score to construct fault diagnosis indicators. The parameters of the framework are calculated from different segment data statistics, enhancing the objectivity and interpretability of the parameters. The results are validated using actual vehicle data and show that the proposed framework can greatly reduce the required time and identify faulty batteries more accurately with a 96.2 % precision and a 72.4 % detection rate, which is better than other algorithms and has better robustness.