Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries
Abstract This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This resea...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80719-1 |
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| author | Gopal Krishna Rajesh Singh Anita Gehlot Ahmad Almogren Ayman Altameem Ateeq Ur Rehman Seada Hussen |
| author_facet | Gopal Krishna Rajesh Singh Anita Gehlot Ahmad Almogren Ayman Altameem Ateeq Ur Rehman Seada Hussen |
| author_sort | Gopal Krishna |
| collection | DOAJ |
| description | Abstract This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN. |
| format | Article |
| id | doaj-art-a2ae0894d4584dc6a55ba1101d2b681c |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-a2ae0894d4584dc6a55ba1101d2b681c2025-08-20T02:30:58ZengNature PortfolioScientific Reports2045-23222024-12-0114111810.1038/s41598-024-80719-1Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteriesGopal Krishna0Rajesh Singh1Anita Gehlot2Ahmad Almogren3Ayman Altameem4Ateeq Ur Rehman5Seada Hussen6Uttaranchal Institute of Technology, Uttaranchal UniversityUttaranchal Institute of Technology, Uttaranchal UniversityUttaranchal Institute of Technology, Uttaranchal UniversityDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud UniversitySchool of Computing, Gachon UniversityDepartment of Electrical Power, Adama Science and Technology UniversityAbstract This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN.https://doi.org/10.1038/s41598-024-80719-1Lithium-ionBatteriesBattery management systemsInternet of thingsLSTMSoH |
| spellingShingle | Gopal Krishna Rajesh Singh Anita Gehlot Ahmad Almogren Ayman Altameem Ateeq Ur Rehman Seada Hussen Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries Scientific Reports Lithium-ion Batteries Battery management systems Internet of things LSTM SoH |
| title | Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries |
| title_full | Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries |
| title_fullStr | Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries |
| title_full_unstemmed | Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries |
| title_short | Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries |
| title_sort | advanced battery management system enhancement using iot and ml for predicting remaining useful life in li ion batteries |
| topic | Lithium-ion Batteries Battery management systems Internet of things LSTM SoH |
| url | https://doi.org/10.1038/s41598-024-80719-1 |
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