锂离子电池充电状态智能深度学习估计器设计与MATLAB实现——以锂离子电池为例

N. Tudoroiu, M. Zaheeruddin, Roxana-Elena Tudoroiu, M. Radu, Hana Chammas
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引用次数: 2

摘要

本研究论文的主要目的是从一大类深度学习模型中使用浅神经网络(SNN)和NARX架构开发两个智能状态估计器。本研究开发了一种新的建模设计方法,即改进的混合自适应神经模糊推理系统(ANFIS)电池模型,该模型简单、准确、实用,非常适合在HEV/EV应用中实时实现,是本研究的主要贡献之一。在此模型的基础上,我们建立了四个高精度的荷电状态(SOC)估计器,在稳定状态下的评估百分比误差小于0.5%,而该领域文献报道的误差为2%。此外,这些估计器对模型参数值的变化和SOC的初始“猜测值”的鲁棒性在80-90%至30-40%之间表现出色,并通过三个著名的驾驶循环程序测试进行了模拟,即两个欧洲标准WLTP和NEDC,以及EPA美国标准FTP-75。此外,NARX SNN SOC估计器的SOC估计均方误差(MSE)为7.97 × 10−11,电压预测的均方误差(MSE)为5.43 × 10−6,优于传统的SOC估计器。通过与传统的扩展卡尔曼滤波器(EKF)和自适应非线性观测器(ANOE)状态估计器的性能比较,通过广泛的MATLAB仿真证明了它们的有效性,从而揭示了监督学习算法在精度,在线实时实现能力方面的优势,从而解决了广泛的HEV/EV应用。
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Intelligent Deep Learning Estimators of a Lithium-Ion Battery State of Charge Design and MATLAB Implementation—A Case Study
The main objective of this research paper was to develop two intelligent state estimators using shallow neural network (SNN) and NARX architectures from a large class of deep learning models. This research developed a new modelling design approach, namely, an improved hybrid adaptive neural fuzzy inference system (ANFIS) battery model, which is simple, accurate, practical, and well suited for real-time implementations in HEV/EV applications, with this being one of the main contributions of this research. On the basis of this model, we built four state of charge (SOC) estimators of high accuracy, assessed by a percentage error of less than 0.5% in a steady state compared to the 2% reported in the literature in the field. Moreover, these estimators excelled by their robustness to changes in the model parameters values and the initial “guess value” of SOC from 80–90% to 30–40%, performing in the harsh and aggressive realistic conditions of the real world, simulated by three famous driving cycle procedure tests, namely, two European standards, WLTP and NEDC, and an EPA American standard, FTP-75. Furthermore, a mean square error (MSE) of 7.97 × 10−11 for the SOC estimation of the NARX SNN SOC estimator and 5.43 × 10−6 for voltage prediction outperformed the traditional SOC estimators. Their effectiveness was proven by the performance comparison with a traditional extended Kalman filter (EKF) and adaptive nonlinear observer (ANOE) state estimators through extensive MATLAB simulations that reveal a slight superiority of the supervised learning algorithms by accuracy, online real-time implementation capability, in order to solve an extensive palette of HEV/EV applications.
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