An ensemble model for predicting the remaining useful performance of lithium-ion batteries

By Yinjiao Xing, Eden W. M. Ma, Kwok Leung Tsui, Michael G. Pecht. Microelectronics and reliability, 2013

615 citations recorded by the source corpus.

Subfields: Automotive Engineering, Safety, Risk, Reliability and Quality, Software

View Paper: https://www.semanticscholar.org/paper/f29f372c9278b921aceda2d78afe41acd4bbbaaa

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. Michael G. Pecht
2. Kailong Liu
3. Z. Ye
4. Xiaosong Hu
5. Xianke Lin
6. Guangcai Zhao
7. Yandan Tan
8. Rui Xiong

In Safety, Risk, Reliability and Quality

1. Z. Ye
2. Qingqing Zhai
3. Min Xie
4. Michael G. Pecht
5. Kailong Liu
6. Yong Guan
7. Lifeng Wu
8. Xiaohui Fu

In Software

1. Z. Ye
2. Qingqing Zhai
3. Kailong Liu
4. Yong Guan
5. Lifeng Wu
6. Xiaohui Fu
7. Min Xie
8. Michael G. Pecht

Citations in the corpus

• Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability (2013, methodology): “Prediction algorithms [34–41] could further supplement these techniques to provide accurate time-to-failure estimations.”
• A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, methodology): “More details regarding the NASA test bench and CALCE test bench can be found in [27] and [20], respectively.”
• Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error (2014, neutral): “, can be found in [19–25].”
• Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2018, methodology): “To solve this problem, the Akaike information criterion (AIC) was introduced to impose a heavier penalty on model complexity and thus avoid overfitting [10].”
• State-of-Health Estimation of Lithium-Ion Batteries by Fusing an Open Circuit Voltage Model and Incremental Capacity Analysis (2022, background): “More details about the test are provided in [37] and [38].”
• Stochastic modelling and analysis of degradation for highly reliable products (2015, methodology): “Compared with Kalman filters, the use of particle filters is more flexible for any non-linear functions [105,106].”
• A naive Bayes model for robust remaining useful life prediction of lithium-ion battery (2014, methodology): “[18] predicted RUL with a combination of exponential and polynomial regression followed by particle filtering (PF).”
• Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “CALCE data set is another widely used set for SOH estimation research [45], [46].”
• RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model (2017, methodology): “For instance, S(t;α) can take a power law form S(t;α) = t , α > 0, which has been used in many degradation modeling applications, such as the crack growth and battery degradation [27], [38].”
• Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies (2016, methodology): “[45] proposed a model fused with an empirical exponential and a polynomial regression model to describe the battery’s degradation trend.”