Lithium-ion battery capacity estimation from random charging data using depthwise and pointwise convolutions

Accurate and reliable capacity estimation is a critical factor in ensuring the safe and stable operation of lithium-ion batteries. However, most data-driven battery capacity estimation methods rely on data collected either during a complete charging process or within specific voltage ranges, which limits their applicability in real-world scenarios. Hence, this paper proposes a novel framework that utilizes only randomly sampled continuous fragments of charging data as input, without relying on specific data ranges. The framework comprises a multi-convolutional neural network (CNN) block, an attention-enhanced long short-term memory network, and a fully connected layer. The multi-CNN block consists of multiple depthwise and pointwise convolution layers, combined via gating mechanisms. By separately processing information extraction and feature learning for different variable types, the model can comprehensively capture both inter-variable temporal relationships and intra-variable temporal dependencies. In addition, the framework processes voltage data to obtain the rate of voltage change over time. The derived rate is then used as an input parameter, thereby enriching the representation of the input features. Experiments on the CALCE dataset demonstrate that the proposed framework achieves the greatest improvement on the best-performing battery cells, with the mean absolute error reduced by 6.3% and the root mean square error reduced by 4.8%. Overall, MCLA can effectively adapt to charging data in practical scenarios that are incomplete or irregular, thereby achieving accurate and reliable capacity estimation.