An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks

By Ji Wu, Chenbin Zhang, Zonghai Chen. 2016

472 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/24b395fc16ec96cc7348ddc9a62dae2d407782dc

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In Automotive Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “[127] used FFNN to simulate the relationship between battery charge curves at a constant current and battery RUL.”
Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (2021, background): “Wu et al. [31] use importance sampling from an extensive input feature set from the charging curves and train a DNN for capacity estimation.”
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, background): “terations of LIB would change with battery RUL and SOH [3], [4].”
Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach (2018, methodology): “Wu et al. [16] analyzed the battery terminal voltage curves at different cycles during charging, and proposed an online method using feed forward neural network (FFNN) and importance sampling (IS) to estimate lithium-ion battery RUL.”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “The algorithms currently used mainly include artificial neural networks (ANNs) [40], support vector machines (SVMs) [41], relevance vector machines (RVMs) [42], and Gaussian process regression (GPR) [20].”
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2022, unknown): “For RUL prediction, MLP is applied to learn nonlinear degradation [2], [25].”
A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, methodology): “On the basis of battery historical cycling data, various intelligent techniques such as vector machine [9], [10], neural network [11],”
State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations (2018, unknown): “[14], [15].”