A Data-Driven Comparative Analysis of Lithium-Ion Battery State of Health and Capacity Estimation

Sheikh, Shehzar Shahzad; Shah, Fawad Ali; Athar, Syed Owais; Khalid, Hassan Abdullah

doi:10.1080/15325008.2022.2145389

Cited by 10 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sheikh and other researchers proposed three data-driven methods for lithium-ion battery condition monitoring and capacity estimation, namely convolutional neural networks, feedforward neural networks, and long short-term memory networks, for comparison. The results show that machine learning techniques based on long short-term memory networks have excellent performance and high accuracy, and it is recommended for researchers to use them [18]. Shah et al proposed a review of the latest battery health assessment technology on the application and related issues of lithium-ion batteries in electric and hybrid vehicles.…”

Section: Related Workmentioning

confidence: 99%

Economic benefit analysis of lithium battery recycling based on machine learning algorithm

Zhang

2024

PLoS ONE

View full text Add to dashboard Cite

Lithium batteries, as an important energy storage device, are widely used in the fields of renewable vehicles and renewable energy. The related lithium battery recycling industry has also ushered in a golden period of development. However, the high cost of lithium battery recycling makes it difficult to accurately evaluate its recycling value, which seriously restricts the development of the industry. To address the above issues, machine learning will be applied in the field of economic benefit analysis for lithium battery recycling, and backpropagation neural networks will be combined with stepwise regression. On the basis of considering social and commercial values, a lithium battery recycling and utilization economic benefit analysis model based on stepwise regression backpropagation neural network was designed. The experimental results show that the mean square error of the model converges between 10–6 and 10–7, and the convergence speed is improved by 33%. In addition, in practical experiments, the model predicted the actual economic benefits of recycling a batch of lithium batteries. The results show that the predictions are basically in line with the true values. Therefore, the economic benefit analysis and prediction model for lithium battery recycling proposed in the study has the advantages of high accuracy and fast operation speed, providing new ideas and tools for promoting innovation in the field of economic benefit analysis. It has certain application potential in the evaluation of the benefits of lithium battery recycling.

show abstract

Section: Related Workmentioning

confidence: 99%

Economic benefit analysis of lithium battery recycling based on machine learning algorithm

Zhang

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The comparative analysis reveals that LSTM-based machine learning outperforms other techniques due to its inherent long-term memory capabilities. As a result, the study has recommended using LSTM for battery health monitoring and capacity estimation with the highest achievable accuracy [22]. Batteries consist of electrochemical cells that produce electricity to power electronic devices that continually convert chemical energy into electrical energy, requiring proper maintenance to ensure optimal efficiency.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning

Jafari,

Byun,

2023

IEEE Access

View full text Add to dashboard Cite

Lithium-ion batteries (LIBs) Remaining Useful Life (RUL) prediction is vital for Battery Management Systems (BMS). It is crucial for providing the optimum performance and longevity of batteries used in different industries. In this study, we propose an innovative approach that combines machine learning techniques and hybrid modeling strategies to enhance the accuracy and robustness of battery analysis. By leveraging the power of k-Nearest Neighbors (kNN), Random Forest (RF), and XGBoost algorithms, our proposed model effectively captures complex relationships and patterns in battery data. We meticulously curate a comprehensive dataset comprising essential battery parameters: capacity, voltage, cycle, and temperature. Through rigorous experimentation and evaluation, our proposed method outperforms existing approaches in predicting RUL and capacity fade. The insights gained from our analysis offer valuable guidance for optimizing battery performance, enabling informed maintenance planning, and improving energy storage system efficiency.

show abstract

Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

Naresh,

Ratnakara Rao,

Prabhakar

2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

This research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve this optimization. The paper covers various ML methods like supervised, unsupervised, and deep learning (DL) and ways to measure their effectiveness. Significant battery performance factors, such as state of charge (SoC), state of health (SoH), state of function (SoF), and remaining useful life (RUL), are discussed, along with methods to collect and prepare data for accurate predictions. The paper introduces an operation research model for optimizing the performance of EV Batteries. It also looks at challenges unique to battery systems and ways to overcome them. The study showcases ML models' ability to predict battery behavior for real‐time monitoring, efficient energy use, and proactive maintenance. The paper categorizes different applications and case studies, providing valuable insights and forward‐looking perspectives for researchers, practitioners, and policymakers involved in improving EV battery performance through predictive ML.This article is categorized under: Technologies > Classification Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

show abstract

A Data-Driven Comparative Analysis of Lithium-Ion Battery State of Health and Capacity Estimation

Cited by 10 publications

References 39 publications

Economic benefit analysis of lithium battery recycling based on machine learning algorithm

Economic benefit analysis of lithium battery recycling based on machine learning algorithm

A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning

Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

Contact Info

Product

Resources

About