新型复合锂离子电池健康状况估计方法

Newapproach of remaining useful life prediction for composite lithium-ion battery

设计一种基于增强型单粒子模型(eSPM)参数估计的健康状况(SOH)预测算法,利用LMO-NMC-石墨锂离子电池老化实验数据建立增强型单粒子模型,预测电池老化参数的变化趋势。参数估计值为锂离子电池健康状况(S_(OH))的指标参数,包括可循环锂摩尔数和欧姆电阻。提出基于实时实验数据估算模型参数的方法,结果表明模型参数值与老化实验所用的电池S_(OH)有关。利用eSPM估计参数推导出联合SOH模型,设计基于粒子滤波器(PF)的锂离子电池健康状况预测算法。文中利用LMO-NMC-石墨锂离子电池组的老化实验数据对SOH估计算法进行验证,结果表明,新模型和新算法适用于纯电式电动汽车或插电式混合动力汽车的充电数据推断锂离子电池S_(OH)。

Developing a state of charge(SOH)prediction algorithm based on estimation of parameters of an enhanced single particle model(eSPM).First,we use data from an aging study conducted on LMO-NMC-cathode graphite-anode battery cells to develop an eSPM.It can predict the evolution of parameters associated with the aging of the battery.In particular,the parameters estimated in this work as indicators of state of health(S_(OH))are number of moles of cyclable lithium and ohmic resistance.A method is demonstrated for estimating these parameters from experimental data,and it is shown that they are correlated with battery S_(OH) measured from the experimental aging study.Finally,a composite SOH metric derived from the estimated eSPM parameters is used to design a SOH predictor based on a particle filter(PF).The SOH estimation algorithm is validated using experimental data collected on several LMO-NMC battery cells,showing that it is possible to infer battery S_(OH) from charging data readily available in plug-in battery-electric or hybrid vehicles.