Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), Joao Pessoa 58051-900, Brazil.
Sensors (Basel). 2022 Feb 4;22(3):1179. doi: 10.3390/s22031179.
Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles such as greenhouse gas emissions, which have contributed to global warming as well as the depletion of non-oil renewable energy source. The lithium-ion battery is an appropriate choice for electric vehicles (EVs) due to its promising features of high voltage, high energy density, low self-discharge, and long life cycles. In this context, State of Charge (SoC) is one of the vital parameters of the battery management system (BMS). Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, it is therefore hard to determine the state of charge of the battery cell. This paper analyses the application of Artificial Neural Networks (ANNs) in the estimation of the SoC of lithium batteries using the NASA's research center dataset. Normally, the learning of these networks is performed by some method based on a gradient, having the mean squared error as a cost function. This paper evaluates the substitution of this traditional function by a measure of similarity of the Information Theory, called the Maximum Correntropy Criterion (MCC). This measure of similarity allows statistical moments of a higher order to be considered during the training process. For this reason, it becomes more appropriate for non-Gaussian error distributions and makes training less sensitive to the presence of outliers. However, this can only be achieved by properly adjusting the width of the Gaussian kernel of the correntropy. The proper tuning of this parameter is done using adaptive strategies and genetic algorithms. The proposed identification model was developed using information for training and validation, using a dataset made available in a online repository maintained by NASA's research center. The obtained results demonstrate that the use of correntropy, as a cost function in the error backpropagation algorithm, makes the identification procedure using ANN networks more robust when compared to the traditional Mean Squared Error.
如今，电动汽车因其性能和效率而广受欢迎。由于对温室气体排放等燃烧车辆对环境影响的认识不断提高，投资于这项新技术的开发是合理的，这些排放物导致了全球变暖以及非石油可再生能源的枯竭。锂离子电池是电动汽车 (EV) 的理想选择，因为它具有高电压、高能量密度、自放电低和长循环寿命等有前景的特点。在这种情况下，电池荷电状态 (SoC) 是电池管理系统 (BMS) 的重要参数之一。然而，由于电池单元的放电和充电需要复杂的化学操作，因此很难确定电池单元的荷电状态。本文分析了人工神经网络 (ANN) 在使用 NASA 研究中心数据集估计锂电池 SoC 中的应用。通常，这些网络的学习是通过基于梯度的某种方法来完成的，其代价函数为均方误差。本文评估了用信息论中的相似度度量——最大相关熵准则 (MCC) 替代这种传统函数的方法。这种相似度度量允许在训练过程中考虑更高阶的统计矩。因此，它更适合非高斯误差分布，并使训练对异常值的存在不那么敏感。然而，只有通过适当调整相关熵高斯核的宽度才能实现这一点。该相关熵参数的适当调整是使用自适应策略和遗传算法来完成的。所提出的识别模型是使用训练和验证信息开发的，使用了 NASA 研究中心维护的在线存储库中提供的数据集。所得到的结果表明，在误差反向传播算法中使用相关熵作为代价函数，使得使用 ANN 网络的识别过程比传统的均方误差更稳健。
