Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter

By Hancheng Dong, Xiaoning Jin, Yangbing Lou, Chang-hong Wang. 2014

288 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/2efbf33fed66280e5d562ba312348ce399b66ba8

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. Jingwen Wei
2. Zonghai Chen
3. Yong Guan
4. Lifeng Wu
5. Guangzhong Dong
6. Xiaohui Fu
7. Guangcai Zhao
8. Yandan Tan

In Electrical and Electronic Engineering

1. Jingwen Wei
2. Noureddine Zerhouni
3. Zonghai Chen
4. Guangzhong Dong
5. Kailong Liu
6. Guangcai Zhao
7. Yandan Tan
8. Widanalage Dhammika Widanage

In Safety, Risk, Reliability and Quality

1. Noureddine Zerhouni
2. Zonghai Chen
3. Yong Guan
4. Lifeng Wu
5. Xiaohui Fu

Citations in the corpus

• Direct Remaining Useful Life Estimation Based on Support Vector Regression (2017, methodology): “[25] combine SVR–PF model to estimate the capacity degradation and define the RUL by updating the probability distribution parameters when the time step changes.”
• A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation (2015, methodology): “To improve the accuracy of SOH estimation, authors in [42] use a fusion approach, where the battery model trained using SVM is combined with a particle filtering framework.”
• Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, background): “For instance, artificial neural networks (ANN) [4], support vector machine (SVM) [21], fuzzy logic [22], sparse Bayesian prediction [8], PF [23], and multiorder PF [24].”
• Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies (2016, background): “[20] mathematically modeled the relationship among the battery cycle times, the capacity, and the internal resistance.”