模型与数据双驱动的锂电池状态精准估计

Integrating model-and data-driven methods for accurate state estimation of lithium-ion batteries

针对电池荷电状态估计常用的模型驱动法与数据驱动法的缺点,本工作提出了一种模型与数据双驱动的锂电池状态精准估计算法。在建立经典二阶电池模型后,先使用扩展卡尔曼滤波器与无迹卡尔曼滤波器组成的双卡尔曼滤波器进行初步的锂电池系统状态估测,再将初步的估算结果输入LSTM神经网络实现误差纠正,得到最终估测结果。本工作利用来自NASA PCoE的电池数据集对单驱动算法和双驱动算法分别进行了性能测试,结果表明双驱动法在降低了估算系统对数据依赖性的同时提高了估算精度以及算法鲁棒性,结合了两种单驱动法的优点并弥补了各自的缺点,得到了较为优异的结果。

Addressing the inadequacies of the conventional model-and data-driven methods,an integrating strategy combining both methods, for accurate state estimation of lithium-ion batteries is proposed for estimating battery state-of-charge. After establishing the classical second-order battery model, a dual-Kalman filter, composed of an extended Kalman filter and an unscented Kalman filter, was used to estimate the status of the lithium battery system preliminarily. Then, the preliminary estimation results were input into the LSTM neural network to correct the errors and complete the data-driven part. Datasets from NASA PCoE were used to test the performance of the single-and dual-driven methods. Results show that the integrating method reduces the dependence of the estimation system on the data while improving the estimation accuracy and robustness because it combines the advantages of the model-and data-driven methods and makes up for their shortcomings. Satisfactory results were obtained.