基于改进LSTM的脉冲大倍率工况下锂电池SOC估计

锂离子电池是电力系统中不可或缺的重要储能元件。针对脉冲大倍率放电下锂离子电池荷电状态(State of Charge,SOC)预测问题,采用改进的长短期记忆循环神经网络(Long Short-term Memory,LSTM)搭建三元锂电池SOC预测模型。所用方法在原有LSTM基础上增加两个门控单元,通过增强LSTM内部输入和输出的交互,提高模型的动态逼近能力。在脉冲大倍率放电工况下,将所用方法与BP神经网络(Back Propagation,BP)、LSTM神经网络相比较,验证了算法在脉冲放电下的预测性能。实验结果表明,改进方法可准确表征三元锂电池工作特性,满足了SOC估计的实际需求。

SOC estimation of a lithium battery under high pulse rate condition based on improved LSTM

The lithium-ion battery is an indispensable energy storage component in a power system. To predict the State of Charge (SOC) of a lithium-ion battery under high pulse rate conditions, an improved Long Short-Term Memory (LSTM) neural network is used to build the SOC prediction model of a ternary lithium-ion battery. Two gating units are added to the original LSTM to improve the dynamic approximation ability of the model by enhancing the interaction between input and output. Compared with Back Propagation (BP) and LSTM neural networks, the prediction performance of the algorithm under high pulse rate conditions is proved superior. The results show that the improved method can accurately characterize the operating characteristics of ternary lithium batteries, and meet the actual needs of SOC estimation. This work is supported by the Natural Science Foundation of Shandong Province (No. ZR201910230295) and the Key Project of Natural Science Foundation of Shandong Province (No. ZR202010290147).