A review on prognostics and health monitoring of Li-ion battery

By Jingliang Zhang, Jay H. Lee. 2011

757 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/9a15cda867641e2e194dcb4ff2ad98b4d372b519

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. Michael G. Pecht
4. David A. Howey
5. Yong Guan
6. Lifeng Wu
7. Xiaohui Fu
8. Christoph R. Birkl

In Control and Systems Engineering

1. Xiaosong Hu
2. David A. Howey
3. Robert R. Richardson
4. Michael A. Osborne
5. Michael G. Pecht
6. Dong Wang
7. Kwok Leung Tsui
8. Xiaosheng Si

In Safety, Risk, Reliability and Quality

1. Kailong Liu
2. Michael G. Pecht
3. Yong Guan
4. Lifeng Wu
5. Xiaohui Fu
6. Xiaosong Hu
7. David A. Howey
8. Michael A. Osborne

Citations in the corpus

• Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach (2018, background): “Compared with traditional batteries, lithium-ion battery has many advantages including high output voltage, high energy density, low self-discharge, long cycle life, high reliability, etc. [1]–[3].”
• An ensemble model for predicting the remaining useful performance of lithium-ion batteries (2013, methodology): “Capacity is calculated by integrating the current over time [22].”
• A naive Bayes model for robust remaining useful life prediction of lithium-ion battery (2014, methodology): “The relevance vector machine with particle filtering (RVM–PF) framework for battery RUL prediction proposed by Saha et al. [19] falls into this category.”
• A brief review on key technologies in the battery management system of electric vehicles (2018, methodology): “Therefore, researchers have proposed using battery EIS for health diagnosis [57 – 60].”
• Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies (2016, methodology): “[24] introduced the advantages of RVM and regarded it as one of the main potential methods for lithium-ion battery RUL estimation.”
• Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error (2014, background): “Failure of lithium-ion batteries could lead to performance degradation, operational impairment, and even catastrophic failure [3–5].”