A Multi-Timescale Method for State of Charge Estimation for Lithium-Ion Batteries in Electric UAVs Based on Battery Model and Data-Driven Fusion

This study focuses on the critical problem of precise state of charge (SOC) estimation for electric unmanned aerial vehicle (UAV) battery systems, addressing a fundamental challenge in enhancing energy management reliability and flight safety. The current data-driven methods require big data and high computational complexity, and model-based methods need high-quality model parameters. To address these challenges, a multi-timescale fusion method that integrates battery model and data-driven technologies for SOC estimation in lithium-ion batteries has been developed. Firstly, under the condition of no data or insufficient data, an adaptive extended Kalman filtering with multi-innovation algorithm (MI-AEKF) is introduced to estimate SOC based on the Thévenin model in a fast timescale. Then, a hybrid bidirectional time convolutional network (BiTCN), bidirectional gated recurrent unit (BiGRU), and attention mechanism (BiTCN-BiGRU-Attention) deep learning model using battery model parameters is used to correct SOC error in a relatively slow timescale. The performance of the proposed model is validated under various dynamic profiles of battery. The results show that the the maximum error (ME), mean absolute error (MAE) and the root mean square error (RMSE) for zero data-driving, insufficient data-driving, and sufficient data-driving under various dynamic conditions are below 2.3%, 1.3% and 1.5%, 0.9%, 0.4% and 0.4%, and 0.6%, 0.3% and 0.3%, respectively, which showcases the robustness and remarkable generalization performance of the proposed method. These findings significantly advance energy management strategies for Li-ion battery systems in UAVs, thereby improving operational efficiency and extending flight endurance.