论文标题
使用1D卷积神经网络估算XEV电池的充电状态并转移学习
Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning
论文作者
论文摘要
在本文中，我们提出了一种基于一维卷积神经网络（CNN）的电荷状态估计算法。使用两个公共电池数据集对CNN进行了培训。已经研究了不同类型的噪声对CNN模型估计功能的影响。此外，提出了一种转移学习机制，以使开发的算法在使用与用于训练模型的电池不同的电池时，以可接受的准确性来更好地概括和估计。已经观察到，使用转移学习，模型可以通过少量的电池数据来学习足够好的学习。提出的方法在估计准确性，学习速度和概括能力方面很好地票房。
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.
