RUL Prediction for Lithium Batteries Using a Novel Ensemble Learning Method

Wu, Jiaju; Kong, Linggang; Cheng, Zheng; Yang, Yongheng; Zuo, Hongfu

doi:10.1016/j.egyr.2022.10.298

Cited by 18 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When training the model, the capacity curve was used with the number of discharge cycles of the Li-ion batteries. From the results obtained by the authors, it is found that the method proposed in this paper has a RMSE lower than the five individual methods [19].…”

Section: Literature Reviewmentioning

confidence: 79%

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Andrioaia,

Gaitan,

Culea

et al. 2024

Computers

View full text Add to dashboard Cite

Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems.

show abstract

Section: Literature Reviewmentioning

confidence: 79%

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Andrioaia,

Gaitan,

Culea

et al. 2024

Computers

View full text Add to dashboard Cite

show abstract

“…The prediction method based on ensemble learning is a method of combining various student bases to obtain optimal results. Usually, ensemble learning has better learning abilities compared to just relying on a single algorithm [34]. The algorithms are then combined into ensemble Figure 2, a bagging ensemble shown in Figure 2…”

Section: Build Ensemble Modelmentioning

confidence: 99%

Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification

Barid,

Hadiyanto,

Wibowo

2024

IJEECS

View full text Add to dashboard Cite

<p>Rapid economic development, industrialization, and urbanization in Indonesia have caused a large increase in air pollution with negative impacts on the environment and public health. The aim of this research is to use machine learning techniques to categorize air quality and generate an air quality index (AQI) using a dataset that includes six prevalent air pollutants. Next steps are preprocessing and data extraction, K-nearest neighbors (KNN) classification, support vector machine (SVM), and random forest (RF) models are implemented. Furthermore, synthetic minority oversampling technique (SMOTE) is incorporated into the ensemble learning process to improve the results. This research uses K-fold cross validation for improve classification accuracy and reduce overfitting. Research findings show that the application of SMOTE brings a significant increase in model accuracy, effectively solving the problem of imbalanced data sets. These insights provide direction for effective air quality monitoring systems and informed decision making in air pollution management.</p>

show abstract

“…A drawback of this approach is it may not be appropriate for complex systems, as representing the degradation behavior based on physical and chemical aspects may not be suitable (J. Wu, Kong, Cheng, Yang, & Zuo, 2022). In addition, implementing parameters based on the electrochemical models is time consuming due to need of elaborate experimental setups, as stated in (Y.…”

Section: Background Of Battery Aging Lifetime Mod-els and Current Dev...mentioning

confidence: 99%

State Machine-Based Approach for Estimating the Capacity Loss of Lithium-Ion Batteries

David,

Söffker

2023

PHM_CONF

View full text Add to dashboard Cite

The use of Lithium-Ion Batteries (LIBs) have increased in recent years in many applications such as hybrid electrical vehicles (HEV), consumer electronic equipment, and electricity grid. The batteries undergo degradation during usage due to material aging and electrochemical processes, leading to efficiency reduction of battery-powered systems as well as catastrophic events. Several stress factors such as battery temperature, ambient temperature, and C-rate in the loading profiles influence the degradation. Therefore, predicting the health of the battery has gained attention. The service life can be extended or a system failure can be avoided by maintenance measures precisely matched to the function loss or by changing usage strategies. The State-of Health (SoH) condition of the battery can be determined by the application of lifetime models. Various health indicators such as remaining useful lifetime (RuL) and capacity fade are determined by the models based on the stress factors (utilization variables). For optimal use of the battery, it is helpful to develop an accurate lifetime model to represent the dynamic properties. However, models developed are less computationally efficient and unable to represent the non-linear degradation behavior well. The development of a precise model with correct parameterization is also costly. This is particularly true for models developed based on physical and chemical properties of the battery. In this contribution, an artificial neural network (ANN)-based state machine approach is introduced for capacity fade estimation. The degradation process is represented using three states modeling three different levels and the progression from the first state to the last. Capacity associated with each state is described using the non-linear auto regressive neural network with external input (NARX). The NARX is selected due to its ability to accurately model non linear behavior and time series data. Unlike known models, which are developed using analytical mathematical equations related to the battery properties, a combined machine learning approach is used here instead to learn the capacity behavior from historical data. Battery data sets from NASA are used for experimental verification. Based on the results, the estimated capacity fade show close proximity to actual capacity fade, with a low mean square error for different data sets. In addition, the estimated state progression follows the actual state progression.

show abstract

RUL Prediction for Lithium Batteries Using a Novel Ensemble Learning Method

Cited by 18 publications

References 12 publications

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification

State Machine-Based Approach for Estimating the Capacity Loss of Lithium-Ion Batteries

Contact Info

Product

Resources

About