Machinery health prognostics: A systematic review from data acquisition to RUL prediction

A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique

Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Many industries have a growing awareness in utilizing new technologies to improve the reliability and availability of their systems. Prognostics, a subject concerned with the prediction of the remaining useful life (RUL), has been increasingly studied and applied to practical systems, such as electrical systems, over the past few years. Here, with the adoption of a data-driven prognostics framework, this paper proposed a health index (HI)-based prognostics method to predict the RUL of electrical machines. By assuming a linearly degrading HI over time, the proposed method predicts the RUL in two steps: 1) from input signals to HI; and then 2) mapping HI to RUL. The novelty of this method lies in the proposed dynamic HI smoothing approach where three characteristics of HI, namely monotonicity, gradualness, and consistency, are incorporated to smooth the current HI values with the previously predicted ones. Real data collected from eight electrical motors, subjected to accelerated thermal aging process, were used in the experimental studies, with the results showing the superiority of the proposed HI-based RUL prediction over the traditional direct RUL prediction (i.e., without HI).

Health Index-Based Prognostics for Remaining Useful Life Predictions in Electrical Machines

State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble

A copula-based sampling method for data-driven prognostics

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

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A Copula-based sampling method for data-driven prognostics and health management

Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery diagnostics and prognostics. As a consequence, accuracy of the battery performance estimation is dominated by the

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A Systematic Framework for Battery Performance Estimation Considering Model and Parameter Uncertainties

Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery study. As a consequence, accuracy of the SOC estimation is dominated by the model fidelity and may vary from cell-to-cell. This paper proposes a systematic framework with associated methodologies to quantify the battery model and parameter uncertainties for more effective battery SOC estimation. Such a framework is also generally applicable for estimating other battery performances of interest (e.g. capacity and power capability). There are two major benefits using the proposed framework: i) consideration of the cell-to-cell variability, and ii) accuracy improvement of the low fidelity model comparable to the high fidelity without scarifying computational efficiency. One case study is used to demonstrate the effectiveness of the proposed framework.© 2014 ASME

Rong Jing

Papers

A copula-based sampling method for data-driven prognostics

A Copula-based sampling method for data-driven prognostics and health management

A Systematic Framework for Battery Performance Estimation Considering Model and Parameter Uncertainties

State of Charge Estimation of Lithium-Ion Batteries Considering Model Bias and Parameter Uncertainties

Recent research on battery diagnostics, prognostics, and uncertainty management