State of Health Prediction of Lithium-ion Batteries

Barcellona, Simone; Cristaldi, L.; Faifer, Marco; Petkovski, Emil; Piegari, Luigi; Toscani, Sergio

doi:10.1109/metroind4.0iot51437.2021.9488542

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Based on experimental findings, aging curves become almost linear when the residual capacity is under 95%, and a modified irrational function is applied to account for both linear and square terms [ 44 , 45 ]:

…”

Section: Specific Degradation Modelsmentioning

confidence: 99%

A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries

Patrizi,

Martiri,

Pievatolo

et al. 2024

Sensors

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We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system’s state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods. These methods are presented as a valuable tool for designing life-testing experiments and for maintaining lithium-ion batteries.

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…”

Section: Specific Degradation Modelsmentioning

confidence: 99%

A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries

Patrizi,

Martiri,

Pievatolo

et al. 2024

Sensors

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show abstract

“…In literature, many algorithms and model have been studied and used to perform SoH estimation: in [4], SoH is estimated by using a model-based approach to model the aging behaviour. In [5] and [6], artificial neural networks have been successfully applied to estimate the SoH of li-ion batteries, while in [7], random forest algorithm is used for the same task.…”

Section: Introductionmentioning

confidence: 99%

Lithium-ion batteries soh estimation, based on support-vector regression and a feature-based approach

Marri¹,

Petkovski²,

Cristaldi³

et al. 2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

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Lithium-Ion batteries, have become enormously used in many systems and applications, and are the most widespread energy storage system. Optimizing the usage of batterie is therefore very important to increase the safety of systems like electric vehicles or portable devices, to reduce economic loss in industrial environments, and to increase their availability. An accurate State of Health (SoH) estimation is important since it allows us to know battery conditions and make an appropriate use of it, and it improves the accuracy of other diagnostic measures, like State of Charge (SoC). In this paper, an approach for SoH estimation is proposed, based on Support Vector Regression machine learning algorithm and a smart feature extraction process, finding a good trade-off between applicability, light computation effort, and accuracy of results. Features selection and parameters tuning are discussed, and performances are measured on a dataset from the Prognostics Center of Excellence at NASA, considering 3 batteries of the dataset.

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“…In the literature, many machine learning techniques have been studied and used to perform SoH estimation [20,21] apply regression to model battery aging behavior and compare the RUL-prediction capabilities of two fitting functions, while in [22], a combination of an exponential function and regression analysis is used. The authors of [23] discuss a strategy based on support vector regression (SVR) and ICA curves obtained from partial charging data.…”

Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach

et al. 2023

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Lithium-ion batteries play a vital role in many systems and applications, making them the most commonly used battery energy storage systems. Optimizing their usage requires accurate state-of-health (SoH) estimation, which provides insight into the performance level of the battery and improves the precision of other diagnostic measures, such as state of charge. In this paper, the classical machine learning (ML) strategies of multiple linear and polynomial regression, support vector regression (SVR), and random forest are compared for the task of battery SoH estimation. These ML strategies were selected because they represent a good compromise between light computational effort, applicability, and accuracy of results. The best results were produced using SVR, followed closely by multiple linear regression. This paper also discusses the feature selection process based on the partial charging time between different voltage intervals and shows the linear dependence of these features with capacity reduction. The feature selection, parameter tuning, and performance evaluation of all models were completed using a dataset from the Prognostics Center of Excellence at NASA, considering three batteries in the dataset.

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State of Health Prediction of Lithium-ion Batteries

Cited by 9 publications

References 19 publications

A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries

A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries

Lithium-ion batteries soh estimation, based on support-vector regression and a feature-based approach

Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach

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