Hybrid Fusion for Battery Degradation Diagnostics Using Minimal Real-World Data: Bridging Laboratory and Practical Applications

TL;DR

This paper introduces a hybrid fusion approach combining physics-based models and data-driven techniques to predict battery degradation accurately with minimal real-world data, bridging laboratory insights and practical applications.

Contribution

A novel hybrid fusion strategy that integrates physics-based and data-driven methods for precise battery capacity prediction using limited real-world data.

Findings

Achieves 0.63% average estimation error over battery lifespan

Utilizes only 45 real-world data segments and 1.7 million simulated segments

Effectively bridges laboratory models and real-world battery behavior

Abstract

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 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 physics-based battery 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.