Personalized Federated Lithium-Ion Battery Capacity Prediction via Cluster and Fusion Modules

Federated learning (FL) is a promising solution for addressing information security sharing challenges in the Internet of Vehicles (IoV). It enables individual-level capacity prediction of lithium-ion batteries in electric vehicles (EVs). However, existing FL algorithms primarily focus on training a single global sharing model, neglecting the predictive capabilities of individual participants, which poses challenges for batteries with distinct capacity degradation trends. To address this limitation, we introduce battery-personalized FL (BT-PFL), a novel FL framework that provides personalized capacity prediction models based on the local data distribution of each battery. Our approach involves constructing cluster and fusion modules, creating a personalized learning space, and leveraging valuable domain knowledge. On the one hand, we proactively identify the capacity degradation distribution of different batteries, ensuring data privacy during FL within clusters. Each cluster retains domain-specific style information, enabling collaborative training and shared model parameters among participants. On the other hand, we introduce a knowledge distillation (KD) algorithm that facilitates knowledge transfer between clusters by constructing a teacher model incorporating multidomain knowledge. The experimental results on both single-domain and multidomain show that our approach not only significantly enhances individual battery prediction accuracy but also outperforms other methods across various datasets, network structures, and noise levels.