Miniature Real-Time Compact Deep Neural Network With Zero-Shot Neural Architecture Search for Lithium-Ion Battery Fault Diagnosis

Battery energy storage systems (BESS) are essential for modern energy management, supporting renewable integration and grid stability. However, fault diagnosis for BESS requires extensive manual network tuning. To overcome this, we introduce a zero-shot neural architecture search approach for BESS fault diagnosis. First, the neural network is broken down into piecewise linear functions, and the Rademacher complexity is calculated for this class of functions. To prevent batch normalization (BN) layers from repeatedly scaling the Rademacher complexity and invalidating network comparisons, the Rademacher complexity is approximated using the variance of the BN layers. Finally, the selected models are then compressed via 8-bit quantization to facilitate deployment on mobile devices. This approach achieves 99.42% accuracy in just 0.51 GPU h, significantly reducing model search time without needing pretrained models. We validate this method on a self-developed BESS platform featuring a battery management system and custom mobile app, accessible online.