Adaptive Multipersonalized Federated Learning for State of Health Estimation of Multiple Batteries

The current state-of-the-art approach for battery state-of-health (SOH) estimation typically employs a centralized computing framework, wherein data from local battery management systems (BMSs) is aggregated and trained on a cloud server, due to limited computing resources at the BMS. However, this framework presents various challenges, including frequent data communication, latency, data security, and degraded prediction accuracy. To address these issues, this study proposes a novel adaptive multipersonalized federated learning (FL) algorithm for evaluating the SOH of multiple batteries, aggregating multiple local SOH estimation models into a global model while locally preserving battery data. The algorithm utilizes the difference of importance weights between global and local models to regulate the local loss, incorporates adaptive personalization layers with loss variation, and employs clustering techniques to form multiple global models from distinct local models, leading to a more accurate and tailored prediction. Additionally, an adaptively SOH-related differential privacy protection mechanism is integrated to enhance the protection of local battery data while ensuring robust model performance. An extensive case study has demonstrated that the adaptive multipersonalized FL algorithm outperforms other methods in terms of estimation accuracy and operational risk. Specifically, it achieves a reduction in mean absolute error by 0.14% and 6.01% compared to traditional FL and local training methods, respectively, and exhibits nearly fivefold lower operational risk compared to centralized training.