健康状况 (SOH) 和剩余使用寿命 (RUL) 预测对于电池管理系统 (BMS) 至关重要。然而，由于复杂的电池老化机制，准确的 SOH 和 RUL 预测仍然需要改进。这项工作将基于二阶 RC 模型的增量容量分析 (ICA) 和差分电压分析 (DVA) 与改进的双向门控循环单元 (BiGRU) 相结合，以开发 SOH 和 RUL 预测框架。首先通过二阶RC模型对电压进行重构，得到增量电容（IC）和差分电压（DV）曲线，避免测量噪声的影响以及滤波方式中复杂的参数调整过程对IC和DV的影响曲线。然后，从重塑的 IC 和 DV 曲线中提取一组新的电池老化特征，以提高 SOH 和 RUL 预测的准确性和鲁棒性。接下来，使用具有注意力机制的 BiGRU 方法（BiGRU-AM）构建电池老化特征、SOH 和 RUL 的预测模型。为了降低容量再生现象的影响，采用自适应噪声的完全集成经验模态分解（CEEMDAN）方法对SOH预测结果进行分解，并将分解后的残差作为输入，提高RUL的预测精度。RUL预测结果的不确定性通过蒙特卡洛（MC）模拟进行分析。最后，所提出的方法通过高级生命周期工程中心（CALCE）和桑迪亚国家实验室的实验电池数据进行了验证。实验结果表明，将基于二阶RC模型的电压重构结果应用于ICA和DVA分析，有效避免了噪声的影响。电压重构的RMSE在0.0006以内，重构的IC/DV曲线提取的4个老化特征与SOH的Pearson相关系数在0.9以上。此外，该方法对电池不一致性、温度不确定性具有良好的鲁棒性，对不同电池化学成分具有令人满意的泛化能力，CALCE 和 Sandia 电池的最大 RUL 预测 AE 分别在 10 和 5 以内。从重建的IC/DV曲线中提取的4个老化特征与SOH的皮尔逊相关系数均在0.9以上。此外，该方法对电池不一致性、温度不确定性具有良好的鲁棒性，对不同电池化学成分具有令人满意的泛化能力，CALCE 和 Sandia 电池的最大 RUL 预测 AE 分别在 10 和 5 以内。从重建的IC/DV曲线中提取的4个老化特征与SOH的皮尔逊相关系数均在0.9以上。此外，该方法对电池不一致性、温度不确定性具有良好的鲁棒性，对不同电池化学成分具有令人满意的泛化能力，CALCE 和 Sandia 电池的最大 RUL 预测 AE 分别在 10 和 5 以内。
State of health (SOH) and remaining useful life (RUL) prediction are crucial for battery management systems (BMS). However, accurate SOH and RUL prediction still need to be improved due to the complicated battery aging mechanism. This work combines incremental capacity analysis (ICA) and differential voltage analysis (DVA) based on the second-order RC model with an improved Bidirectional Gated Recurrent Unit (BiGRU) to develop SOH and RUL prediction framework. Firstly, the voltage is reconstructed through the second-order RC model to obtain the incremental capacity (IC) and differential voltage (DV) curves to avoid the influence of measurement noise and the complex parameter adjustment process in the filtering method on the IC and DV curves. Then, a new set of battery aging features are extracted from the reshaped IC and DV curves to improve SOH and RUL prediction accuracy and robustness. Next, the BiGRU method with attention mechanism (BiGRU-AM) is used to build the prediction models for battery aging features, SOH, and RUL. To reduce the impact of the capacity regeneration phenomenon, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is used to decompose the SOH prediction results, and the decomposed residual is used as the input to improve the prediction accuracy of RUL. The uncertainty of RUL prediction results is analyzed by Monte Carlo (MC) simulation. Finally, the proposed method is verified by experimental battery data from Center for Advanced Life Cycle Engineering (CALCE) and Sandia National Laboratory. Experimental results show that the voltage reconstruction results based on the second-order RC model are applied to ICA and DVA analysis, effectively avoiding the influence of noise. The RMSE of voltage reconstruction is within 0.0006, and the Pearson correlation coefficient between the four aging features extracted from the reconstructed IC/DV curve and SOH is above 0.9. Moreover, this method has good robustness to the cell inconsistency, temperature uncertainty, and a satisfied generalization ability to different battery chemistries, which the maximum RUL predicted AE of CALCE and Sandia battery is within 10 and 5, respectively.
