Estimation Of Battery State Of Health Based On Random Weight PSO-ELM-Reference-Cited by-同舟云学术

Estimation Of Battery State Of Health Based On Random Weight PSO-ELM

Published:2021-10-22 Issue: Volume: Page:
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Container-title:2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2)
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Author:

Li Jianlin¹,Wang Zhe¹,Ma Suliang¹,Li Yaxin¹,Wu Yiwen¹,Xu Dezhi²

Affiliation:

1. College of electrical and control engineering, North China University of Technology,Beijing,China

2. School of internet of things engineering, Jiangnan University,Jiangsu,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9712847/9712848/09713223.pdf?arnumber=9713223

Reference12 articles.

1. Battery thermal management and battery safety technology[J];chen;Chinese Journal of Power Sources,2020

2. Effects of Overcharge Cycling on the Aging and Safety of Lithium Ion Batteries [J];zhang;High Voltage Engineering,2020

3. State of Health Predictionfor Lithiumion Batteries Based on Health Feature Extraction and PSO-RBF Neural Network[J];chen;Journal of Kunming University of Science and Technology(Natural Sciences),2020

4. Progress of state of health evaluation methods for lithium-ion power battery[J];zhang;Chinese Journal of Power Sources,2016

5. Prediction of soil humidity based on random weight Particle Swarm Optimized Extreme Learning Machine

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