Incorporating state of charge estimation methods towards more accurate monitoring of second-life lithium-ion batteries
Author:
Affiliation:
1. Technical University of Ilmenau,Department of Electrical Engineering and Information Technology,Ilmenau,Germany
2. University of Applied Sciences Fulda,Department of Electrical Engineering and Information Technology,Fulda,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10001766/10001862/10002052.pdf?arnumber=10002052
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1. Artificial Neural Networks - Industrial and Control Engineering Applications
2. Design and implementation of machine learning techniques for modeling and managing battery energy storage systems;luzi;Ph D thesis,2019
3. A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile
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5. ImageNet classification with deep convolutional neural networks
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1. Improved Approach for Online Monitoring of Second Life Lithium-Ion Batteries to Optimize the Performance in Stationary Storage Systems;2023 14th International Renewable Energy Congress (IREC);2023-12-16
2. Passive Cell Equalization & State of Charge Estimation Using Coulomb Counting Approach;2023 IEEE International Transportation Electrification Conference (ITEC-India);2023-12-12
3. Passive Cell Equalization & State of Charge Estimation Using Coulomb Counting Approach;2023 Global Conference on Information Technologies and Communications (GCITC);2023-12-01
4. Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis;World Electric Vehicle Journal;2023-11-17
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