Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics

Camargos, Murilo C. O.; Bessa, Iury; Cordovil, Luiz A. Q.; Coutinho, Pedro Henrique Silva; Leite, Daniel; Palhares, Reinaldo M.

doi:10.2991/asum.k.210827.010

Cited by 4 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where σ 2 ϵ is considered constant. The noise variance can be estimated through Monte Carlo simulations using the consequent parameters' covariance matrix estimated via RLS until time instant k (Camargos et al, 2020) or by recursively tracking the covariance of estimation errors through the online learning operation, i.e., for time instances n ∈ N ≤k (Camargos et al, 2021). In the univariate case, the mean error is recursively tracked as…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

“…While their parameters are adapted to minimize the modeling error, the structure becomes more complex to represent novel dynamics which can be related to the achievement of novel degradation stages in prognostics problems. Recently, evolving fuzzy degradation models are proposed for aiding data-stream-driven Prognostics and Health Management (PHM) systems (Camargos, Bessa, D'Angelo, Cosme, & Palhares, 2020;Camargos et al, 2021;Ahwiadi & Wang, 2022). In particular, those models are used to capture the degradation dynamics and predict the equipment RUL.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, those models are used to capture the degradation dynamics and predict the equipment RUL. In this regard, evolving prognostic approaches have been successfully applied to ball bearings (Camargos et al, 2020) and lithium-ion batteries providing (Camargos et al, 2021;Ahwiadi & Wang, 2022) competitive results with some interpretability features. This motivates the application of those methods for the challenging IGBT prognostic problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-driven Prognostics based on Evolving Fuzzy Degradation Models for Power Semiconductor Devices

et al. 2022

Self Cite

View full text Add to dashboard Cite

The increasing application of power converter systems based on semiconductor devices such as Insulated-Gate Bipolar Transistors (IGBTs) has motivated the investigation of strategies for their prognostics and health management. However, physicsbased degradation modelling for semiconductors is usually complex and depends on uncertain parameters, which motivates the use of data-driven approaches. This paper addresses the problem of data-driven prognostics of IGBTs based on evolving fuzzy models learned from degradation data streams. The model depends on two classes of degradation features: one group of features that are very sensitive to the degradation stages is used as a premise variable of the fuzzy model, and another group that provides good trendability and monotonicity is used for the auto-regressive consequent of the fuzzy model for degradation prediction. This strategy allows obtaining interpretable degradation models, which are improved when more degradation data is obtained from the Unit Under Test (UUT) in real time. Furthermore, the fuzzy-based Remaining Useful Life (RUL) prediction is equipped with an uncertainty quantification mechanism to better aid decisionmakers. The proposed approach is then used for the RUL prediction considering an accelerated aging IGBT dataset from the NASA Ames Research Center.

show abstract

Section: Uncertainty Quantificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-driven Prognostics based on Evolving Fuzzy Degradation Models for Power Semiconductor Devices

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…1. Empirical methods are built based on some historical data used to estimate parameters for a chosen structure, e.g., exponential models (Cai et al, 2022), autoregressive models (M. Camargos et al, 2021), and neural networks (Q. . 2.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the use of evolving fuzzy models has been proposed for solving the RUL prediction problems (M. O. Camargos, Bessa, D'Angelo, Cosme, & Palhares, 2020), including with applications to lithium-ion batteries (Ahwiadi & Wang, 2022;M. Camargos et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

State of Health and Lifetime Prediction of Lithium-ion Batteries Using Self-learning Incremental Models

Camargos

Angelov²

2022

PHME_CONF

Self Cite

View full text Add to dashboard Cite

Lithium-ion batteries are key energy storage elements in the context of environmental-aware energy systems representing a crucial technology to achieve the goal of zero carbon emission. Therefore, its conditions must be monitored to guarantee the safe and reliable operation of the systems that use these components. Furthermore, lithium-ion batteries’ prognostics and health management policies must cope with the nonlinear and time-varying nature of the complex electrochemical dynamics of battery degradation. This paper proposes an incremental-learning-based algorithm to estimate the State of Health (SoH) and the Remaining Useful Life (RUL) of lithium-ion batteries based on measurement data streams. For this purpose, a two-layer framework is proposed based on incremental modeling of the SoH. In the first layer, a set of representative features are extracted from voltage and current data of partial charging and discharging cycles; these features are then used to train the proposed model in a recursive procedure to estimate the battery’s SoH. The second layer uses the capacity data for incremental learning of an Autoregressive (AR) model for the SoH, which will be used to propagate the battery’s degradation through time to make the RUL prediction. The proposed method was applied to two datasets for experimental evaluation, one from CALCE and another from NASA. The proposed framework was able to estimate the SoH of 8 different lithium-ion cells with an average percentage error below 1.5% for all scenarios, while the lifetime model predicted the cell’s RUL with a maximum average error of 25%.

show abstract

A Set-Based Uncertainty Quantification of Evolving Fuzzy Models for Data-Driven Prognostics

Boutrous

Bessa

Nejjari

et al. 2022

Intelligent and Safe Computer Systems in Control and Diagnostics

View full text Add to dashboard Cite

Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics

Cited by 4 publications

References 0 publications

Data-driven Prognostics based on Evolving Fuzzy Degradation Models for Power Semiconductor Devices

Data-driven Prognostics based on Evolving Fuzzy Degradation Models for Power Semiconductor Devices

State of Health and Lifetime Prediction of Lithium-ion Batteries Using Self-learning Incremental Models

A Set-Based Uncertainty Quantification of Evolving Fuzzy Models for Data-Driven Prognostics

Contact Info

Product

Resources

About