Data quality augmentation and parallel network modeling for residual life prediction of lithium-ion batteries

Research on the data-driven health state estimation of lithium-ion batteries has gained significant attention in recent years. However, the practical implementation of obtaining one data point in one cycle has resulted in poor data quality, leading to low accuracy and prediction instability. To overcome this challenge, a two-step approach is proposed. First, available data are enriched using Akima spline curve interpolation, and the overall degradation trend of the battery is extracted as a Sigmoid function, enhancing the data quality. Second, a parallel network model that combines the strengths of the convolutional neural network (CNN) and the long short-term memory network (LSTM) is introduced. This model leverages the ability of one-dimensional convolutional neural network (1DCNN) to effectively capture local features and proficiency of the LSTM in capturing long-term dependencies. By employing this hybrid model, a better understanding and prediction of the remaining battery life is achieved. Finally, based on the NASA public battery dataset, expanded and decomposed data are trained and predicted by the parallel network model. Experimental results demonstrate that the proposed method exhibits high accuracy and strong generalization capability.