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