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In this paper we propose a one-dimensional convolutional neural network(CNN)-based state of charge estimation algorithm for electric vehicles. The CNNis trained using two publicly available battery datasets. The influence ofdifferent types of noises on the estimation capabilities of the CNN model hasbeen studied. Moreover, a transfer learning mechanism is proposed in order tomake the developed algorithm generalize better and estimate with an acceptableaccuracy when a battery with different chemical characteristics than the oneused for training the model, is used. It has been observed that using transferlearning, the model can learn sufficiently well with significantly less amountof battery data. The proposed method fares well in terms of estimationaccuracy, learning speed and generalization capability.

Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning