Early Warning Method for Thermal Runaway High-Risk Cells Based on Nonlinear Mapping and Multidimensional Features

In harsh marine environments, vessel energy storage systems (VESS) face elevated thermal runaway (TR) risk, yet practical early warning remains difficult because early voltage differences between TR high-risk cells and normal cells are often weak, warning thresholds vary across operating segments, and decisions relying on a single feature are prone to false or missed warnings. To overcome these difficulties, this study develops a four-part early warning strategy for TR high-risk cells in VESS. First, the original cell voltages are denoised through multiscale jump plus mode decomposition and Spearman correlation guided mode reconstruction to suppress irrelevant interference. Second, an improved Sigmoid nonlinear mapping is introduced to enhance subtle inter-cell voltage deviations and improve early separability. Third, sparse representation is used to construct a cell deviation score, and an adaptive threshold is employed to perform primary abnormal-cell screening under varying segment conditions. Finally, multidimensional mutual information value derived from voltage, temperature, and their rates of change is incorporated into a joint assessment methodology to further verify the abnormal state of flagged cells. Validation on 18 independent real operation cases comprising 2483 discharge segments shows that, across the evaluated TR high-risk cases, the shortest confirmed warning lead time achieved by the proposed strategy was 14 days. The proposed strategy also reduced false and missed warnings, outperformed the compared benchmark methods overall, and retained computational feasibility for onboard application in VESS.