The state of the art approaches to estimate the state of health (SOH) and state of function (SOF) of lithium Ion batteries

Author:

Balagopal Bharat,Chow Mo-Yuen

Publisher

IEEE

Cited by 37 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. State of health estimation for lithium-ion batteries using a hybrid neural network model with Multi-scale Convolutional Attention Mechanism;Journal of Power Sources;2024-07

2. A novel multi-time scale moving average voltage quotient algorithm for the state of voltage function estimation of pure-electric-bus lithium-ion batteries;2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia);2024-05-17

3. A Review of Recent Advancements and Challenges in Battery Energy Storage System (BESS);2024 1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS);2024-04-26

4. On the State of Usability for Lithium–Ion Batteries;Batteries;2024-02-04

5. Wear Leveling-Aware Active Battery Cell Balancing;2024 37th International Conference on VLSI Design and 2024 23rd International Conference on Embedded Systems (VLSID);2024-01-06

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