Thermal management is an important factor in extending the battery's life time and ensuring the quality of the output current. A numerical evaluation of the effect of varying the configuration of the battery cells and liquid-cooled channels was conducted in this case study. For the first time, a physics-informed neural network is coupled with visual tracking and commercial software for prediction of the thermal behaviour of a battery package. Combination of physics-informed neural network and visual tracking present as pattern-based neural networks (PBANNs). This method was used to predict the surface temperature at several cooling rates (Vinlet=0.1, 0.3, and 0.5 m/s) in response to variations in the surface temperature of the battery. Compared with conventional ANN methods, PBANN can significantly reduce the computational cost of transient case studies. Furthermore, PBANN can be directly coupled with commercial software in real-time. The complexity of coding for numerical simulation could be reduced by this coupling algorithm. Based on the results of this coupling, battery configurations may affect temperature profiles. By distributing cooling tubes evenly, the average temperature of the battery and phase change material (PCM) could be reduced by 25.3%. According to the results, the combination of liquid-cooled and PCM could guarantee that the battery temperature would not exceed the limits.