Výsledky vyhledávání - "Paul Takyi‐Aninakwa"

Akademický článek

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

Autor: Shunli Wang, Pu Ren, Paul Takyi-Aninakwa, Siyu Jin, Carlos Fernandez

Publikováno v: Energies, Vol 15, Iss 14, p 5053 (2022)

Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance

Externí odkaz: https://doaj.org/article/1174c01c54db43ea90e5de689199f1c3

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Multi-time scale identification of key kinetic processes for lithium-ion batteries considering variable characteristic frequency

Autor: Haotian Shi, Shunli Wang, Jianhong Liang, Paul Takyi-Aninakwa, Xiao Yang, Carlos Fernandez, Liping Wang

Publikováno v: Journal of Energy Chemistry. 82:521-536

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::eb5ef0b6f1a00b38a6e1c4707c30a09d
https://doi.org/10.1016/j.jechem.2023.02.022

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Akademický článek

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A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

Autor: Paul Takyi‐Aninakwa, Shunli Wang, Hongying Zhang, Emmanuel Appiah, Etse Dablu Bobobee, Carlos Fernandez

Publikováno v: International Journal of Energy Research. 46:16427-16444

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bc109331e30002d28bbf2fe137af8dba
https://doi.org/10.1002/er.8307

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Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Autor: Shunli Wang, Xianyi Jia, Paul Takyi-Aninakwa, Daniel-Ioan Stroe, Carlos Fernandez

Publikováno v: Wang, S, Jia, X, Takyi-Aninakwa, P, Stroe, D I & Fernandez, C 2023, ' Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs ', Journal of the Electrochemical Society, vol. 170, no. 5, 050514 . https://doi.org/10.1149/1945-7111/acd148

Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6089bab63c17da6a92ac020279010946
https://vbn.aau.dk/da/publications/3bd86c4b-acd9-4913-9489-662c801f5e4d

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Improved Compound Correction-Electrical Equivalent Circuit Modeling and Double Transform-Unscented Kalman Filtering for the High-Accuracy Closed-Circuit voltage and State-of-Charge Co-Estimation of Whole-Life-Cycle Lithium-Ion batteries

Autor: Shunli Wang, Paul Takyi-Aninakwa, Chunmei Yu, Siyu Jin, Carlos Fernandez

Publikováno v: Wang, S, Takyi-Aninakwa, P, Yu, C, Jin, S & Fernandez, C 2022, ' Improved Compound Correction-Electrical Equivalent Circuit Modeling and Double Transform-Unscented Kalman Filtering for the High-Accuracy Closed-Circuit voltage and State-of-Charge Co-Estimation of Whole-Life-Cycle Lithium-Ion batteries ', Energy Technology, vol. 10, no. 12, 2200921 . https://doi.org/10.1002/ente.202200921

For complex energy storage conditions, it is necessary to monitor the state-of-charge (SOC) and closed-circuit voltage (CCV) status accurately for the reliable power supply application of lithium-ion batteries. Herein, an improved compound correction

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e111d032ebd1b8e16bbd47a0a550e8e9
https://vbn.aau.dk/da/publications/2a6841ee-d88c-47f4-b614-3073af38eca2

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