Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Accurate battery lifetime prediction is essential for improving reliability and safety in energy storage systems. However, balancing predictive accuracy, inference latency, and energy consumption remains challenging. We introduce FAST-BatPro, a Flash-Attention Sparse Transformer for Battery Prognosis. This task-aware architecture combines convolutional feature extraction with dual attention mechanisms to enable robust, efficient, and scalable prediction. The 1.869-million-parameter model is evaluated from both perspectives. Across four datasets comprising more than 240,000 cycles across chemistries, FAST-BatPro demonstrates consistent performance across fast-charging and discharging protocols, temperature variations, and chemistry-dependent degradation behaviors. With limited early-cycle data, it achieves high accuracy, with coefficient of determination values approaching or exceeding 0.90 in most test settings. It maintains an inference time of 0.103 s and requires 1.65 billion FLOPs per battery over the full lifecycle. Hidden-dimension scaling identifies a compact configuration that preserves comparable accuracy while reducing inference latency, FLOPs, and energy consumption by 12.6%, 68.1%, and 12.6%, respectively. Module-level pruning further shows that a feed-forward-network-pruned lightweight variant improves accuracy while reducing inference latency, FLOPs, and energy consumption by 11.65%, 54.64%, and 11.65%, respectively, indicating that FAST-BatPro can serve as a reference architecture for identifying task-specific redundancy and guiding efficient AI model design for battery diagnostics and predictive maintenance.