| 基于NARX动态神经网络的锂离子电池剩余寿命间接预测 |
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| 作者姓名： | 魏孟 王桥 叶敏 李嘉波 徐信芯 |
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| 作者单位： | 长安大学工程机械学院公路养护装备国家工程实验室，西安 710064 |
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| 基金项目： | 国家自然科学基金青年基金资助项目（51805041）；河南省重大科技专项资助项目（191110211500）；中央高校基金优秀博士论文基金资助项目（300203211251） |
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| 摘 要： | 锂离子电池的直接健康因子难以实现在线测量，针对此问题，提出一种基于动态神经网络时间序列的锂离子电池剩余寿命（Remaining useful life, RUL）间接预测方法。首先根据锂离子电池的放电数据，提出放电截止时间，恒流放电时间以及放电峰值温度时间三种间接健康因子并进行灰色关联分析（Grey relation analysis, GRA）。然后，基于非线性自回归（Nonlinear autoregressive models with exogenous inputs, NARX）动态神经网络建立锂离子电池RUL预测模型。最后将粒子群优化前馈神经网络（Back propagation neural network based on particle swarm optimization, BPNN-PSO），最小二乘支持向量机（Least square support vector machine, LS-SVM），极限学习机（extreme learning machine, ELM），闭环（Closed-loop）NARX和开环（Open-loop）NARX进行对比分析，验证了所提方法的优越性。
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| 关 键 词： | 锂离子电池 剩余寿命 健康因子 灰色关联分析 非线性自回归 |
| 收稿时间： | 2020-10-22 |
An indirect remaining useful life prediction of lithium-ion batteries based on a NARX dynamic neural network |
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| Authors: | WEI Meng WANG Qiao YE Min LI Jia-bo XU Xin-xin |
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| Affiliation: | National Engineering Laboratory for Highway Maintenance Equipment, School of Construction Machinery, Chang'an University, Xi'an 710064, China |
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| Abstract: | With increasingly serious energy shortages and environmental pollution, electric vehicles (EVs) have drawn widespread attention in recent years. The lithium-ion battery is widely used in the field of EVs owing to its superior energy density, life cycle, low self-discharge rate, and maintenance of memory. Prediction of the remaining useful life (RUL) of lithium-ion batteries is a key parameter in battery management systems. The accurate prediction of RUL is a prerequisite to ensuring the safety and reliability of the battery system. The gradual deterioration in the performance of lithium-ion batteries with cycling is normally predicted using capacity and resistance. However, this method is difficult to use in practical applications. To address this problem, a nonlinear autoregressive model with exogenous inputs (NARX) dynamic neural network was proposed to predict RUL. First, according to the discharge data of the lithium-ion battery, three indirect health indicators, namely, cut-off time, constant current time, and peak temperature time in discharge, were proposed, and grey relation analysis (GRA) was used to analyze their relation to capacity. The proposed three indirect health indicators have significant relationships with battery capacity. In addition, due to the influence of temperature vibration, electromagnetic interference, and external disturbance, RUL prediction of the lithium-ion battery is a typical nonlinear problem. In order to cover this weakness, the NARX dynamic neural network was established to predict the RUL of the lithium-ion battery. Finally, a closed-loop and an open-loop NARX were compared with the backpropagation neural network based on particle swarm optimization (BPNN-PSO), least-square support vector machine (LS-SVM), and extreme learning machine (ELM) of existing models under the open data of NASA. The experimental results show that the estimation performance RMSE (NO.5) of the proposed model is improved by about 33% compared with the standard ELM, verifying that the proposed model is superior to other methods in the RUL of lithium-ion batteries. |
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