Yinjiao Xing

An ensemble model for predicting the remaining useful performance of lithium-ion batteries

We developed an ensemble model to characterize the capacity degradation and predict the remaining useful performance (RUP) of lithium-ion batteries. Our model fuses an empirical exponential and a polynomial regression model to track the battery’s degradation trend over its cycle life based on experimental data analysis. Model parameters are adjusted online using a particle filtering (PF) approach. Experiments were conducted to compare our ensemble model’s prediction performance with the individual results of the exponential and polynomial models. A validation set of experimental battery capacity data was used to evaluate our model. In our conclusion, we presented the limitations of our model.

Prognostics and health monitoring for lithium-ion battery

Health monitoring is used to analyze and predict the battery health status. However, no matter what health monitoring methods and parameters are, a major aim is to improve the battery reliability through surveillance and prognostics. Hence, the latest known methods of state estimation and life prediction based on battery health monitoring are discussed in this paper. Through comparing their characteristics respectively, a prognostics-based fusion technique is proposed that combines physics-of-failure (PoF) with data-driven technology. The fusion approach not only investigates battery failure mechanism caused by environmental and internal characteristics, but also assesses parameters with aid of real-time health monitoring. The specific method is presented to realize the estimation on remaining useful life (RUL) of batteries.

A comparative review of prognostics-based reliability methods for Lithium batteries

This paper discusses some concerned failure problems in battery reliability and investigates the latest known methodologies in health monitoring and life prediction of batteries. We conduct comparative studies on these different measurements and methods for Lithium batteries. Through combining with their respective performance, we introduce a fusion prognostic method based on Physics-of-Failure (PoF) approach in conjunction with data-driven technology. The fusion approach not only thoroughly analyses the battery failure mechanism as a result of the change of physical and chemical characteristics, it also estimates a number of parameters with the aid of real-time surveillance. Furthermore, we present the specific frameworks to implement the cell life prediction and battery inconsistency monitoring. The estimated State-of-Charge (SOC), State-of-Health (SOH), State-of-Life (SOL) and the level of the battery inconsistency will present a more accurate and competitive prediction according to the proposed approach.

A case study on battery life prediction using particle filtering

Batteries play a critical role for the reliability of battery-powered systems. The prognostics in batteries provide warning to the advent of failure, which requires timely maintenance and replacement of batteries. This paper reviews current research on battery degradation models and focuses on the online implementation of prognostic algorithms. The particle filtering approach is utilized to track battery performance based on two degradation models that are highly efficient for online applications. An experimental demonstration of this method is provided. Through a comparison of the prognostic results, the problems of the models and the algorithm are discussed.

Influence of parameter initialization on battery life prediction for online applications

Estimating remaining useful life (RUL) is a crucial part in a successful online monitoring system. Extrapolation of a degradation model based on particle filtering (PF) approach, which is implemented on state-space model, is a popular method to predict RUL. Taking into account the characteristics of state-space model, the initial setting of process and measurement noise has a great impact on the predicted result. This paper discusses the individual performance of two popular PFs, sequential importance resampling PF and auxiliary PF, when they come to different noise characteristics. Two groups of initial process and measurement noises were set to compare the predicted performance between these two PFs. The prediction of battery RUL was demonstrated in this paper as a case study. The comparative results were used for reference to other-related degradation components or system using PFs-based prediction.

State of Charge Estimation for Lithium-Ion Batteries Using a Temperature-Based Equivalent Circuit Model

This study investigates battery state-of-charge (SOC) estimation under different temperature conditions. A battery modeling approach is developed aiming to improve the accuracy of the SOC estimation when ambient temperature is taken into account. Firstly, a widely used equivalent circuit model with the one-order resistance-capacitor (RC) network is modified to capture battery dynamics at different temperatures. Secondly, since the open-circuit voltage verse SOC (OCV-SOC) incorporated into the battery model is also influenced by the temperature, OCV-SOC-Temperature (OCV-SOC-T) table is constructed to replace the original table based on our experimental data. The experiments with two dynamic load tests, dynamic stress test (DST) and federal urban driving schedule (FUDS) are run on the battery. The purpose of DST profile is to identify the battery model, while FUDS data is used to emulate the operation conditions and evaluate the performance of our proposed model by unscented Kalman filtering. Finally, the comparative results indicate that our temperature-based model provide more accurate SOC estimation with root mean square estimated errors than the original model without regard to temperature dependence.