基于WOA-LSTM的锂电池寿命预测研究

Research on life prediction of lithium battery based on WOA-LSTM

针对LSTM模型参数较难选取导致锂电池寿命预测效果较差,提出一种长短期记忆神经网络(LSTM)结合鲸鱼优化算法(WOA)的锂电池产品寿命预测方法,该方法通过WOA对参数进行优化以提高模型的准确性。在此基础上,采用NASA锂电池数据集进行对比实验分析,分别运用WOA-LSTM算法、CNN-LSTM算法和LSTM算法对锂电池的剩余使用寿命进行预测,实验结果证明,WOA-LSTM模型相较于CNN-LSTM模型和LSTM模型的精度分别提升了3.2%和4.5%,验证了WOA方法的有效性,为推动锂电池相关研究的进展提供思路和依据。

In order to alleviate the energy crisis and reduce carbon emissions,the electric aircraft industry has gained rapid development in recent years.Lithium battery is the main power source of electric aircraft,and its remaining service life RUL is directly related to the capacity,which is the focus of lithium battery predictive maintenance at present,and has important and urgently needed engineering practical significance.Aiming at the problem of poor life prediction effect of lithium battery due to difficult selection of LSTM model parameters,a long short-term memory neural network(LSTM)combined with whale optimization algorithm(WOA)was proposed to predict life of lithium battery products.The method optimizes parameters through WOA to improve the accuracy of the model.On this basis,NASA lithium battery data set is used for comparative experimental analysis,and WOA-LSTM algorithm,CNN-LSTM algorithm and LSTM algorithm are respectively used to predict the remaining service life of lithium batteries.The experimental results show that the accuracy of WOA-LSTM model is improved by 3.2%and 4.5%respectively compared with CNN-LSTM model and LSTM model,which verifies the effectiveness of WOA method and provides ideas and basis for promoting the research progress of lithium battery.