Capacity Estimation Method for Lithium-Ion Battery Based on Frequency-Domain Enhancement and Residual Connection

With the widespread adoption of lithium-ion batteries in electric vehicles, energy storage, and consumer electronics, accurate capacity estimation has become critical for battery management systems (BMS). To address the limitations of existing methods—which emphasize time-domain features, and struggle to capture periodic degradation and high-frequency disturbances in the frequency domain, and whose deep networks often under-represent long-term degradation trends due to gradient issues—this paper proposes a frequency-domain enhanced Transformer for lithium-ion battery capacity estimation. Specifically, for each cycle we apply FFT to voltage, current, and temperature signals to extract frequency-domain features and fuse them with time-domain statistics; a residual connection is introduced within the Transformer encoder to stabilize optimization and preserve long-term degradation trends, enabling high-precision capacity estimation. Evaluated on three batches of the MIT public fast-charging dataset, the method achieves Avg RMSE 0.0013, Avg MAE 0.0006, and Avg R2 0.9977 on the test set; compared with the Transformer baseline, RMSE decreases by 60.6%, MAE decreases by 73.9%, and R2 increases by 0.83%. The contribution lies in jointly embedding explicit time-frequency feature fusion and residual connections into a Transformer backbone to obtain accurate estimates with stable generalization. In terms of societal benefit, more reliable capacity/health estimates can support better BMS decision-making, improve the safety and lifetime of electric vehicles and energy-storage systems, and reduce lifecycle costs.