Collaborative SOC estimation of shipborne lithium batteries using ACKF-PatchTST with fuzzy adaptive weights

Objective Single physical models struggle to adapt to complex propulsion profiles and nonlinear aging characteristics of lithium-ion batteries in all-electric ships during long-endurance missions. Meanwhile, data-driven approaches are limited by the constrained computational resources available on the onboard battery management systems (BMS). To address these issues, this paper proposes a ship-shore collaborative state-of-charge (SOC) estimation framework based on fuzzy adaptive weighting.

Method A second-order RC equivalent circuit model is adopted at the ship-side edge layer, combined with particle swarm optimization (PSO) for parameter identification and an adaptive cubature Kalman filter (ACKF) for low-complexity real-time state tracking. At the shore-side (cloud), a Transformer-based PatchTST deep learning model is deployed, leveraging its patching mechanism and channel-independence strategy to capture nonlinear characteristics and aging trends from long-sequence operational data. A fuzzy logic controller is designed to dynamically adjust the fusion weights between ship-side and shore-side estimations, using voltage residuals and current variation rates as inputs.

Results Experiments based on the CALCE dataset, simulating complex ship maneuvering load profiles, demonstrate that the proposed collaborative method effectively overcomes the limitations of physical models in low-SOC regions and under highly fluctuating operating conditions, as well as issues related to aging parameter mismatch. Compared with the standalone shipborne ACKF method, the collaborative estimation approach reduces the root mean square error (RMSE) by 29.44% and the mean absolute error (MAE) by 35.71%. Furthermore, under an extreme initial error of 30%, the convergence time is reduced from 153 s to 13 s, significantly improving system robustness and convergence speed.

Conclusion This architecture provides an effective technical pathway for achieving high-precision ship-shore integrated state estimation, remote anomaly detection and fault diagnosis, and online parameter condition-based maintenance (CBM).