Dual-Task Learning for Joint State-of-Charge and State-of-Energy Estimation of Lithium-Ion Battery in Electric Vehicle

State-of-X (SOX) estimation of lithium-ion batteries is crucial for safe operation of electric vehicles (EVs). However, EVs have long suffered from complex and variable operation conditions. While deep learning-based state estimation demonstrates strong generalization to such operation conditions, it typically focus on estimating a single state, and is evaluated on simulated datasets such as CALCE. In this paper, we introduce a dual-task learning framework for joint state-of-charge (SOC) and state-of-energy (SOE) estimation of lithium-ion battery pack, and verify it on real vehicle data. This novel framework possesses two appealing properties: 1) It incorporates a feature attention mechanism to capture task-relevant temporal features encoded by a gated recurrent unit. 2) It leverages data preprocessing operations, including correlation analysis and sliding windows, enhancing both efficiency and accuracy of the model. Comprehensive experiments are conducted on actual operation data from six EVs with a cumulative mileage exceeding 80,000 kilometers. These data are further categorized into early, mid, and late stages based on the battery's health status. The experimental results show that our method achieves SOC and SOE errors of less than 3%, verifying the high accuracy and robustness of the proposed framework under complex and variable vehicle operating conditions.