Image-based Battery Health Monitoring for Capacity Degradation Analysis

Accurate battery health prediction is crucial for prolonging battery life and ensuring safety. Traditional methods relying on raw time-series data struggle with complex temporal patterns and sensor noise. To address these limitations, we propose a novel approach that utilizes image-transformed data to perform “knee classification” and State of Health (SOH) estimation concurrently. This integrated approach detects aging events and continuously monitors SOH, enabling preemptive interventions. We employ a Convolutional Neural Network (CNN) to simultaneously perform knee classification and SOH estimation, incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance interpretability by emphasizing critical regions involved in the classification process. The proposed model achieves an 89% classification accuracy, with higher recall than the time-series-based approach, particularly in identifying the intermediate state. Additionally, the pre-trained CNN-based model attains an R 2 value of 0.977 in SOH prediction, demonstrating its effectiveness for battery condition monitoring. These findings highlight the benefits of an integrated multi-task learning approach, addressing the limitations of conventional time-series models.