Estimating State of Charge for xEV Batteries Using 1D Convolutional Neural Networks and Transfer Learning

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.

Automated summary

This paper proposes a one-dimensional convolutional neural network-based algorithm for estimating the state of charge of electric vehicle batteries, utilizing transfer learning to improve model generalization and accuracy, and demonstrating the model's ability to learn sufficiently with significantly less data.