State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network
<p>The present paper estimates for the first time the State of Charge (SoC) of a high capacity grid-scale lithium-ion battery storage system used to improve the power profile in a distribution network. The proposed long short-term memory (LSTM) neural network model can overcome the problems as...
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| مؤلفون آخرون: | , , |
| منشور في: |
2022
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| _version_ | 1864513535840616448 |
|---|---|
| author | Eyad Almaita (16855446) |
| author2 | Saleh Alshkoor (17563014) Emad Abdelsalam (14148831) Fares Almomani (12585685) |
| author2_role | author author author |
| author_facet | Eyad Almaita (16855446) Saleh Alshkoor (17563014) Emad Abdelsalam (14148831) Fares Almomani (12585685) |
| author_role | author |
| dc.creator.none.fl_str_mv | Eyad Almaita (16855446) Saleh Alshkoor (17563014) Emad Abdelsalam (14148831) Fares Almomani (12585685) |
| dc.date.none.fl_str_mv | 2022-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.est.2022.104761 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/State_of_charge_estimation_for_a_group_of_lithium-ion_batteries_using_long_short-term_memory_neural_network/24745596 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning State of charge Battery service life Lithium-ion batteries Machine learning Smart energy |
| dc.title.none.fl_str_mv | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The present paper estimates for the first time the State of Charge (SoC) of a high capacity grid-scale lithium-ion battery storage system used to improve the power profile in a distribution network. The proposed long short-term memory (LSTM) neural network model can overcome the problems associated with the nonlinear battery model and adapt to the complexity and uncertainty of the estimation process. The accuracy of the developed model was compared with results obtained from Feed-Forward Neural Network (FFNN) topology and Deep-Feed-Forward Neural Network (DFFNN) topology under three different time series. The system was trained using real data from the Al-Manara PV power plant. The LSTM with learn-and-adapt-to-train-date properties, as well as the idea of “forget gate,” shows exceptional ability to determine the SoC under various ID data. The LSTM properly calculated the SoC for all three-time models with a maximum standard error (MSE) of less than 0.62%, while the FFNN and DFFNN provided a fair estimate for the SoC with MSEs of 5.37 to 9.22% and 4.03 to 7.37%, respectively. The promising results can lead to excellent monitoring and control of battery management systems.</p><h2>Other Information</h2> <p> Published in: Journal of Energy Storage<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.est.2022.104761" target="_blank">https://dx.doi.org/10.1016/j.est.2022.104761</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bd2d1c99fbddda4aa81fd3295a357110 |
| identifier_str_mv | 10.1016/j.est.2022.104761 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24745596 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural networkEyad Almaita (16855446)Saleh Alshkoor (17563014)Emad Abdelsalam (14148831)Fares Almomani (12585685)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesMachine learningState of chargeBattery service lifeLithium-ion batteriesMachine learningSmart energy<p>The present paper estimates for the first time the State of Charge (SoC) of a high capacity grid-scale lithium-ion battery storage system used to improve the power profile in a distribution network. The proposed long short-term memory (LSTM) neural network model can overcome the problems associated with the nonlinear battery model and adapt to the complexity and uncertainty of the estimation process. The accuracy of the developed model was compared with results obtained from Feed-Forward Neural Network (FFNN) topology and Deep-Feed-Forward Neural Network (DFFNN) topology under three different time series. The system was trained using real data from the Al-Manara PV power plant. The LSTM with learn-and-adapt-to-train-date properties, as well as the idea of “forget gate,” shows exceptional ability to determine the SoC under various ID data. The LSTM properly calculated the SoC for all three-time models with a maximum standard error (MSE) of less than 0.62%, while the FFNN and DFFNN provided a fair estimate for the SoC with MSEs of 5.37 to 9.22% and 4.03 to 7.37%, respectively. The promising results can lead to excellent monitoring and control of battery management systems.</p><h2>Other Information</h2> <p> Published in: Journal of Energy Storage<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.est.2022.104761" target="_blank">https://dx.doi.org/10.1016/j.est.2022.104761</a></p>2022-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.est.2022.104761https://figshare.com/articles/journal_contribution/State_of_charge_estimation_for_a_group_of_lithium-ion_batteries_using_long_short-term_memory_neural_network/24745596CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247455962022-08-01T00:00:00Z |
| spellingShingle | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network Eyad Almaita (16855446) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning State of charge Battery service life Lithium-ion batteries Machine learning Smart energy |
| status_str | publishedVersion |
| title | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network |
| title_full | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network |
| title_fullStr | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network |
| title_full_unstemmed | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network |
| title_short | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network |
| title_sort | State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning State of charge Battery service life Lithium-ion batteries Machine learning Smart energy |