The diagnostic accuracy of the 1D-CNN was also investigated, repeating the same procedure as before. Figure indicates that the 1D-CNN converged faster and more smoothly than the DNN during training, suggesting that the optimised parameters fit the data more closely. Unlike the DNN where the neurons between each layer are fully connected, the convolutional layers connect each neuron to a region of neurons in the previous layer, reducing the number of connections in the model . The 1D-CNN possesses ~12 times fewer parameters than the DNN, as indicated by Tables and, reducing the computational demand on the optimiser. This is an important factor for on-board and real time diagnosis in operating systems. In addition, the convolutional and pooling layers within the network reduce the dimensionality of the AC voltage response input, extracting spatially correlated features. The dense layers are then used to diagnose the SoH based on this condensed feature map, rather than the entire voltage response as in the DNN, simplifying the optimisation task and reducing convergence time . The 1D-CNN reached a training accuracy and loss of 100% and 2.291 × 10 -4 respectively, indicating a slight improvement over the DNN, and achieved a diagnostic test accuracy of 100% as shown in Figure .