A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

By Duo Yang, Xu Zhang, Rui Pan, Yujie Wang, Zonghai Chen. 2018

679 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/048f7c7cadd4190cbee0ca3a048a9c382c20b23c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Kailong Liu
2. Xiaosong Hu
3. Xianke Lin
4. Michael G. Pecht
5. David A. Howey
6. Simona Onori
7. Widanalage Dhammika Widanage
8. Yunhong Che

In Control and Systems Engineering

1. Xiaosong Hu
2. Xianke Lin
3. Simona Onori
4. Yunhong Che
5. David A. Howey
6. Guangcai Zhao
7. Yandan Tan
8. Kailong Liu

In Safety, Risk, Reliability and Quality

1. Xiaosong Hu
2. Xianke Lin
3. Simona Onori
4. Yunhong Che
5. David A. Howey
6. Kailong Liu
7. Guangcai Zhao
8. Yandan Tan

Citations in the corpus

• Machine learning pipeline for battery state-of-health estimation (2021, methodology): “An alternative is to train an algorithm on the raw voltage-time data curve, eliminating the need for differentiation [31], [32].”
• Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020, methodology): “We use a zero mean function and a linear (LIN) covariance function Although GPR has been used in the literature in the context of Li-ion batteries 17,33,34 , we depart from those pioneering works by employing impedance spectra as input, as well employing ARD to shed light on salient frequencies.”
• Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries (2020, methodology): “Through formulating specific input features, GPR-based models have been applied successfully in both academic and industrial domains [24]–[26].”
• Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (2021, methodology): “These can range from modified linear regression to Gaussian processes, deep learning and other neural network (NN) approaches [25 – 38].”
• Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “voltage measurements mainly include: voltage increase during equal charge time (VIECT) [48], slope of charge voltage curve (SCV) [20], upper cutoff voltage point (UVP) [20], and initial charge voltage (ICHV) [22].”
• Technoeconomic model of second-life batteries for utility-scale solar considering calendar and cycle aging (2020, background): “require larger datasets [10], and other machine learning approaches using neural networks that again require large datasets [11].”
• Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries (2020, methodology): “0078), but the first 49% samples in [14] were used to train the model.”
• Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “Processed external features are normally extracted from differential charging curves under constant current rate, such as IC/DV curves [77,78] and voltage gradient curves (dV/dt) [79,80].”