A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering framework
By Chao Lyu, Qingzhi Lai, Tengfei Ge, Honghai Yu, Lixin Wang, Na Ma. 2017
166 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/48d66772d4202c1acb44b06072fd9ea1728e63f7
How researchers see this paper
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In Automotive Engineering
- Rui Xiong
- Hongwen He
- Kailong Liu
- Yongzhi Zhang
- Widanalage Dhammika Widanage
- Michael G. Pecht
- Ouyang Quan
- Yunlong Shang
In Electrical and Electronic Engineering
- Hongwen He
- Rui Xiong
- Yongzhi Zhang
- Widanalage Dhammika Widanage
- Michael G. Pecht
- Ouyang Quan
- Kailong Liu
- Yunlong Shang
In Safety, Risk, Reliability and Quality
- Kailong Liu
- Rui Xiong
- Hongwen He
- Yongzhi Zhang
- Widanalage Dhammika Widanage
- Michael G. Pecht
- Ouyang Quan
- Yunlong Shang
Citations in the corpus
- A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, methodology): “For the specific model-based approach, a suitable model with the priori knowledge of battery, such as the electrochemical model [3], Brownian motion model [4], along with observers”
- Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2018, methodology): “[11] introduced an electrochemical model-based PF framework to describe the capacity degradation of a battery and thus predict battery RUL.”