The objective of this paper is development of a remaining useful life (RUL) prediction algorithm based on estimation of parameters of an enhanced single particle model (eSPM) that could be implemented using vehicle charging data. First, we use data from an aging study conducted on LMO-NMC-cathode graphite-anode battery cells to develop an eSPM that can predict the evolution of parameters associated with the aging of the battery. In particular, the parameters we estimate in this work as indicators of state of health (SOH) 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 SOH measured from the experimental aging study. Finally, a composite SOH metric derived from the estimated eSPM parameters is used to design a RUL predictor based on a particle filter (PF) that can predict the RUL utilizing the evolution of the SOH metric. The RUL estimation algorithm is validated using experimental data collected on several LMO-NMC battery cells, showing that it is possible to infer battery SOH and RUL from charging data readily available in plug-in battery-electric or hybrid vehicles.

本文的目的是基于可使用车辆充电数据实现的增强型单粒子模型（eSPM）参数的估计，开发剩余使用寿命（RUL）预测算法。首先，我们使用来自对LMO-NMC-阴极石墨-阳极电池单元进行的老化研究的数据来开发eSPM，该eSPM可以预测与电池老化相关的参数的演变。特别是，我们在这项工作中估计的作为健康状态（SOH）指标的参数是可循环锂的摩尔数和耐欧姆性。演示了一种从实验数据估计这些参数的方法，并表明它们与根据实验老化研究测得的电池SOH相关。最后，基于估计的eSPM参数得出的复合SOH度量用于基于粒子滤波器（PF）设计RUL预测器，该粒子滤波器可以利用SOH度量的演变来预测RUL。使用在几个LMO-NMC电池上收集的实验数据对RUL估计算法进行了验证，表明可以从插电式电动汽车或混合动力汽车中容易获得的充电数据推断电池SOH和RUL。