Remaining useful life prediction of lithium-ion batteries in low-altitude vehicles based on MTL-sLSTM

A prediction technique based on multi-task learning with scalar long short-term memory (MTL-sLSTM) was presented to solve the problem of remaining useful life (RUL) prediction for lithium-ion batteries in low-altitude economy vehicles under multi-condition coupling. Firstly, a heterogeneous input layer integrates multi-dimensional time-series data from diverse flight conditions. Then, a hierarchical stacked sLSTM structure serves as a shared feature extractor, enabling deep cross-scale feature integration and the capture of common nonlinear degradation patterns. Finally, by hard-coding task IDs to dynamically modulate network weights, the multi-task learning mechanism adaptively identifies each operating condition’s unique aging behavior while simultaneously promoting knowledge transfer across domains for independent RUL estimation. According to experimental results, MTL-sLSTM achieves a 60.7%–92.3% reduction in root mean square error (RMSE) on the eVTOL dataset, outperforming six temporal approaches, including the attention mixture of experts (AttMoE) and dual-channel LSTM (Dual-LSTM). This validates the effectiveness of the multi-task learning mechanism in enhancing the generalization capability of degradation features and improving prediction accuracy under complex operating conditions.