Feature engineering for machine learning enabled early prediction of battery lifetime

Paulson, Noah H.; Kubal, Joseph; Ward, Logan; Saxena, Saurabh; Lu, Wenquan; Babinec, Susan J.

doi:10.1016/j.jpowsour.2022.231127

Cited by 82 publications

(37 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 . The state-of-the-art approach to extracting such features was implemented by Severson et al 22 and inspired the approaches to feature extraction used recently by Attia et al and Paulson et al 37 , 51 . We benchmark how our EIS-based approach performs relative to those state-of-the-art features.…”

Section: Resultsmentioning

confidence: 99%

“…Innovations in extracting features from charge/discharge curves 34 and machine learning approaches for modelling time-series data 35,36 have enabled significant improvements in the accuracy of predictions. Further studies showed that using features of the discharge curve across a small number of initial cycles, it is possible to train machine learning models that can generalise to different cell chemistries 37 . Going beyond charging and discharging curves, approaches such as electrochemical impedance spectroscopy (EIS) 21 , early cycle Coulombic efficiency 38 , current interruption 39 and acoustic time-of-flight analysis 18,40 have been used for degradation forecasting.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Impedance-based forecasting of lithium-ion battery performance amid uneven usage

2022

View full text Add to dashboard Cite

Accurate forecasting of lithium-ion battery performance is essential for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the research and development setting where cells are subjected to the same usage patterns. However, in practical operation, there is great variability in use across cells and cycles, thus making forecasting challenging. To address this challenge, here we propose a combination of electrochemical impedance spectroscopy measurements with probabilistic machine learning methods. Making use of a dataset of 88 commercial lithium-ion coin cells generated via multistage charging and discharging (with currents randomly changed between cycles), we show that future discharge capacities can be predicted with calibrated uncertainties, given the future cycling protocol and a single electrochemical impedance spectroscopy measurement made immediately before charging, and without any knowledge of usage history. The results are robust to cell manufacturer, the distribution of cycling protocols, and temperature. The research outcome also suggests that battery health is better quantified by a multidimensional vector rather than a scalar state of health.

show abstract

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Impedance-based forecasting of lithium-ion battery performance amid uneven usage

2022

View full text Add to dashboard Cite

show abstract

“…It requires domain knowledge and understanding of the requirements of the projects [49] . Researchers run exploratory data analyses to observe the relationship between different variables/covariates and extract only the best variables to make an ML model [51] .…”

Section: Methods To Improve the Accuracy Of The ML Models 121 Feature...mentioning

confidence: 99%

Machine learning application in GIS and remote sensing: An overview

Upreti¹

2022

IJMRGE

View full text Add to dashboard Cite

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing based projects.

show abstract

“…Methods to improve the accuracy of the ML models 1.2.1. Feature engineering Feature engineering is most prevalent in predictive models (Paulson et al, 2022). It is the process of filtering the most logical and influential variables/covariates in the models from the less important/influential variables, in ML terms, it is known as feature reduction(Sarith Divakar, Sudheep Elayidom, & Rajesh, 2022).It requires domain knowledge and understanding of the requirements of the projects (Paulson et al, 2022).Researchers run exploratory data analyses to observe the relationship between different variables/covariates and extract only the best variables to make an ML model (Song, Yang, Dai, Yuan, & Engineering, 2020).…”

Section: Non-linear Regressionmentioning

confidence: 99%

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Upreti¹

2022

Preprint

View full text Add to dashboard Cite

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

show abstract

Feature engineering for machine learning enabled early prediction of battery lifetime

Cited by 82 publications

References 26 publications

Impedance-based forecasting of lithium-ion battery performance amid uneven usage

Impedance-based forecasting of lithium-ion battery performance amid uneven usage

Machine learning application in GIS and remote sensing: An overview

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Contact Info

Product

Resources

About