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

TL;DR

This paper introduces a CNN-based method for estimating the state of charge in xEV batteries, utilizing transfer learning to improve generalization across different battery types with less data.

Contribution

The paper presents a novel CNN-based algorithm combined with transfer learning to enhance accuracy and generalization in battery state of charge estimation.

Findings

Transfer learning improves estimation accuracy with less data.

The CNN model is robust against different noise types.

The method outperforms traditional approaches in speed and accuracy.

Abstract

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.