锂离子电池剩余寿命（remaining useful life, RUL）预测在电池管理系统中起到十分重要作用，RUL的准确预测是保证电池系统安全可靠的前提。然而，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，本文提出一种基于动态神经网络时间序列的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。最后通过与经典的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），Closed-loop NARX以及Open-loop NARX进行对比分析，验证了所提方法的优越性。
Remaining useful life (RUL) prediction of lithium-ion batteries is a key parameter in battery management systems. The accurate prediction of RUL is a prerequisite to ensure the safety and reliability of the battery system. However, it is difficult to use in practical applications, address to this problem, nonlinear autoregressive models with exogenous inputs (NARX) dynamic neural network is proposed to predict RUL. Firstly, according to the discharge data of the lithium-ion battery, three indirect health indicators of cut-off time, constant current time and peak temperature time in discharge are proposed and grey relation analysis (GRA) is used. Then, based on NARX dynamic neural network, the lithium-ion battery RUL prediction model is established. Finally, through comparative analysis with the classic?back propagation neural network based on particle swarm optimization (BPNN-PSO), least square support vector machine (LS-SVM), Closed-loop NARX and Open-loop NARX, It is verified that the proposed method has superiority in the RUL of lithium-ion batteries.