End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models-Reference-Cited by-同舟云学术

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End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models

Published:2016-01-01 Issue: Volume: Page:
ISSN:
Container-title:AIAA Infotech @ Aerospace
language:
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Author:

Daigle Matthew¹,Kulkarni Chetan S.¹

Affiliation:

1. NASA Ames Research Center

Publisher

American Institute of Aeronautics and Astronautics

Link

http://arc.aiaa.org/doi/pdf/10.2514/6.2016-2132

Reference20 articles.

1. Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

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2. Reliability of AI in Predicting the State of Health of Li-Ion Batteries^*;2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS);2024-07-03

3. Thermal data-driven model reduction for enhanced battery health monitoring;Journal of Power Sources;2024-06

4. Viability of Long Short Term Memory Networks in the Health Prediction of Lithium-ion Batteries;2024 International Conference on E-mobility, Power Control and Smart Systems (ICEMPS);2024-04-18

5. Digital Twin Modeling Using High-Fidelity Battery Models for State Estimation and Control;SAE Technical Paper Series;2024-04-09

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