BatteryCDE: A Transferable Future Capacity Estimation Method for Battery Degradation With Irregular Sampling and Missing Data

To overcome critical limitations in existing methods for battery capacity estimation, including long-term estimation, handling of irregular sampling and missing data, and transferability across different battery types, this study proposes a novel estimation method, termed batteryCDE, by integrating neural controlled differential equations (CDEs) with attention mechanisms. It first constructs a continuous data path using cubic spline interpolation, followed by neural CDEs that generate feature-wise and cycle-wise attention features to capture essential battery characteristics. These attention-weighted features are processed by another neural CDE to model differential relationships, leading to precise capacity estimations. This study also provides a theoretical analysis of the advantages of batteryCDE over conventional discrete models in terms of long-term estimation, handling of irregular sampling and missing data, and transferability. Extensive experiments evaluate batteryCDE’s performance, utilizing three scenarios: varying estimation horizons, missing data, and cross-battery transfer. Results show that batteryCDE outperforms traditional models like long short-term memory (LSTM), Transformer, and neural CDE networks. Even with 50% missing data, an estimation horizon of 100 cycles, and application to different batteries, batteryCDE maintains an estimation error below 0.05. Compared to other methods, batteryCDE reduces estimation errors by 6.59% for long-term predictions, 15.09% for handling missing data, and 17.01% for cross-battery transferability.