A review on prognostics and health monitoring of Li-ion battery

Zhang, Jingliang; Lee, Jay

doi:10.1016/j.jpowsour.2011.03.101

Cited by 633 publications

(283 citation statements)

References 17 publications

Supporting

Mentioning

280

Contrasting

Unclassified

Order By: Relevance

“…Methods such as the AutoRegressive (AR) model [12], neural network [13][14][15][16][17][18], support vector machine (SVM) [19][20][21][22], and relevance vector machine (RVM) [23][24][25][26][27][28][29][30][31][32][33] are used.…”

Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

“…The number of the association vectors of RVM is less than SVM, which has better generalization performance and can acquire point estimation and interval estimation. Zhang et al [24] introduced the advantages of RVM and regarded it as one of the main potential methods for lithium-ion battery RUL estimation. Hu et al [25] presented a sparse Bayesian learning method for Li-ion battery capacity estimation and trained an RVM regression model.…”

Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

See 1 more Smart Citation

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

146

View full text Add to dashboard Cite

Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

show abstract

Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

146

View full text Add to dashboard Cite

show abstract

“…Authors in [10] and [11] give an overview of the existing methods. The majority of methods aim at establishing a battery model and estimating or measuring important battery properties such as open circuit voltage, state of charge (SoC), state of health (SoH), impedance, etc.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Battery Lifetime Prediction and Confidence Estimation for Heavy-Duty Trucks

2018

View full text Add to dashboard Cite

Abstract-Maintenance planning is important in the automotive industry as it will allow fleet owners or regular customers to avoid unexpected failures of the components. One cause of unplanned stops of heavy-duty trucks is failure in the lead-acid starter battery. High availability of the vehicles can be achieved by changing the battery frequently, but such an approach is expensive both due to the frequent visits to a workshop and also due to the component cost. Here, a data-driven method based on Random Survival Forest (RSF) is proposed for predicting the reliability of the batteries. The data set available for the study, covering more than 50,000 trucks, has two important properties. First, it does not contain measurements related directly to the battery health, secondly there are no time series of measurements for every vehicle. In this paper, the RSF method is used to predict the reliability function for a particular vehicle using data from the fleet of vehicles given that only one set of measurements per vehicle is available. A theory for confidence bands for the RSF method is developed that is an extension of an existing technique for variance estimation in the Random Forest method. Adding confidence bands to the RSF method gives an opportunity for an engineer to evaluate the confidence of the model prediction. Some aspects of the confidence bands are considered: a) their asymptotic behavior and b) usefulness in model selection. A problem of including time related variables is addressed in the paper with arguments why it is a good choice not to add them into the model. Metrics for performance evaluation are suggested which show that the model can be used to schedule and optimize the cost of the battery replacement. The approach is illustrated extensively using the real-life truck data case study.

show abstract

“…As compared with other types of batteries, lithium-ion batteries have high energy density, a long lifetime, stable electrochemical properties, the ability to store electrical energy with low loss, and no memory effect [2]. Despite their overall advantages, their rated capacity will fade over repeated charge and discharge cycles [3].…”

Section: Introductionmentioning

confidence: 99%

“…Model-based approaches [3] need to know the battery characteristics and physical structure, but these are difficult to obtain under typical conditions. Data driven approaches [18][19][20][21] are not based on accurately modeling the physics of a system but do mine the hidden information via a variety of data analysis methods; such approaches are practical forecast methods that avoid deriving a complex model.…”

Section: Introductionmentioning

confidence: 99%

Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

Wang

Zhao

et al. 2014

Energies

View full text Add to dashboard Cite

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is important for battery management systems. Traditional empirical data-driven approaches for RUL prediction usually require multidimensional physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fading analysis of lithium-ion batteries, it is found that the energy efficiency and battery working temperature are closely related to the capacity degradation, which account for all performance metrics of lithium-ion batteries with regard to the RUL and the relationships between some performance metrics. Thus, we devise a non-iterative prediction model based on flexible support vector regression (F-SVR) and an iterative multi-step prediction model based on support vector regression (SVR) using the energy efficiency and battery working temperature as input physical characteristics. The experimental results show that the proposed prognostic models have high prediction accuracy by using fewer dimensions for the input data than the traditional empirical models.

show abstract

A review on prognostics and health monitoring of Li-ion battery

Cited by 633 publications

References 17 publications

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Data-Driven Battery Lifetime Prediction and Confidence Estimation for Heavy-Duty Trucks

Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

Contact Info

Product

Resources

About