Virtual sensor module for computing spatial-temporal temperature measurements of a battery

A method for computing spatial-temporal temperature measurements of a battery using a virtual sensor module, the method comprising:

• triggering, using a Proportional-Integral-Derivative (PID) controller module communicatively coupled to a trained convolutional neural network—long-short-term memory (CNN-LSTM) module, the trained CNN-LSTM module to generate refined predicted series of real temperature sequences across the battery based on a set of optimized PID parameters (K*p, K*i, K*d), predicted error sequences, and a series of measured temperature sequences of the battery,
• whereby the CNN-LSTM module was trained based on a training set of spatial-temporal temperature measurements of the battery, and
• whereby the optimized PID parameters were generated based on a training set of measured temperature sequences and a series of known output temperature sequences.