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

Autor:	Bhattacharjee, Arnab, Verma, Ashu, Mishra, Sukumar, Saha, Tapan K
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 13 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.00841 Zobrazit plný text záznamu View this record from Arxiv