Hybrid fusion for battery degradation diagnostics using minimal real-world data: Bridging laboratory and practical applications

Unpredictability of battery lifetime has been a key stumbling block to technology advancement of safety-critical systems such as electric vehicles and stationary energy storage systems. In this work, we present a novel hybrid fusion strategy that combines physics-based and data-driven approaches to accurately predict battery capacity. This strategy, implemented via a convolutional neural network, achieves an average estimation error of only 0.63 % over the entire battery lifespan, utilizing merely 45 real-world data segments along with over 1.7 million simulated data segments derived from random partial charging cycles. By leveraging a thoroughly validated reduced-order electrochemical model, we extract typical aging patterns from laboratory aging data and extend them into a more comprehensive parameter space, encompassing diverse battery aging states in potential real-world applications while accounting for practical cell-to-cell variations. By bridging the gap between controlled laboratory experiments and real-world usage scenarios, this method highlights the significant potential of transferring underlying knowledge from high-fidelity physics-based models to data-driven models for predicting the behavior of complex dynamical systems.