The early prediction of battery life (EPBL) is vital for enhancing the efficiency and extending the lifespan of lithium batteries. Traditional models with fixed architectures often encounter underfitting or overfitting issues due to the diverse data distributions in different EPBL tasks. An interpretable deep learning model of flexible parallel neural network (FPNN) is proposed, which includes an InceptionBlock, a 3D convolutional neural network (CNN), a 2D CNN, and a dual-stream network. The proposed model effectively extracts electrochemical features from video-like formatted data using the 3D CNN and achieves advanced multi-scale feature abstraction through the InceptionBlock. The FPNN can adaptively adjust the number of InceptionBlocks to flexibly handle tasks of varying complexity in EPBL. The test on the MIT dataset shows that the FPNN model achieves outstanding predictive accuracy in EPBL tasks, with MAPEs of 2.47%, 1.29%, 1.08%, and 0.88% when the input cyclic data volumes are 10, 20, 30, and 40, respectively. The interpretability of the FPNN is mainly reflected in its flexible unit structure and parameter selection: its diverse branching structure enables the model to capture features at different scales, thus allowing the machine to learn informative features. The approach presented herein provides an accurate, adaptable, and comprehensible solution for early life prediction of lithium batteries, opening new possibilities in the field of battery health monitoring.

翻译：电池寿命早期预测（EPBL）对于提升锂电池效率并延长其使用寿命至关重要。传统固定架构模型因不同EPBL任务中数据分布的多样性，常面临欠拟合或过拟合问题。本文提出一种可解释的柔性并行神经网络（FPNN）深度学习模型，其包含InceptionBlock、三维卷积神经网络（3D CNN）、二维卷积神经网络（2D CNN）及双流网络。该模型利用3D CNN从类视频格式数据中有效提取电化学特征，并通过InceptionBlock实现先进的多尺度特征抽象。FPNN可自适应调整InceptionBlock数量，灵活处理EPBL中不同复杂度的任务。在MIT数据集上的测试表明，当输入循环数据量为10、20、30和40时，FPNN模型在EPBL任务中分别实现了2.47%、1.29%、1.08%和0.88%的MAPE（平均绝对百分比误差），展现出卓越的预测精度。FPNN的可解释性主要体现在其柔性单元结构与参数选择：多样化的分支结构使模型能够捕获不同尺度的特征，从而让机器学习到信息丰富的特征。本文方法为锂电池寿命早期预测提供了精确、自适应且可理解的解决方案，为电池健康监测领域开辟了新的可能性。