Estimating the duration of lithium-ion battery testing is crucial for devising battery testing plans and optimizing resource allocation. However, there is currently a lack of mature and practical estimation models. In this chapter, we propose a method for estimating the remaining charging time of lithium-ion batteries by integrating a Convolutional Neural Networks-Gated Recurrent Unit (CNN-GRU). This method first extracts features from the raw data of the battery testing process and then combines the advantages of convolutional neural networks in extracting spatial features and gated recurrent units in capturing temporal features. It constructs a non-linear duration estimation model considering multi-source heterogeneous features, ultimately forming a precise and efficient solution for duration estimation. Experimental results demonstrate that the CNN-GRU model constructed in this study exhibits lower estimation errors compared to other algorithms, with an average absolute percentage error of less than 2%. This model can accurately predict the remaining charging time of batteries, providing robust support for optimizing and scheduling battery testing plans.