Mobile app for battery prognostic
Energy storage is vital for advancing sustainable energy solutions, with lithium batteries serving as a cornerstone in electric vehicles, renewable energy systems, and portable electronics. However, safety risks and aging challenge their reliability and durability, often leaving end-users lacking ex...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2025
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| Online Access: | https://hdl.handle.net/10356/182441 |
| _version_ | 1824455326735269888 |
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| author | Li, Zhengfan |
| author2 | Hung Dinh Nguyen |
| author_facet | Hung Dinh Nguyen Li, Zhengfan |
| author_sort | Li, Zhengfan |
| collection | NTU |
| description | Energy storage is vital for advancing sustainable energy solutions, with lithium batteries serving as a cornerstone in electric vehicles, renewable energy systems, and portable electronics. However, safety risks and aging challenge their reliability and durability, often leaving end-users lacking expertise to bear these risks. This study introduces an intelligent battery management system (BMS) integrated into a mobile application to provide intuitive decision-making support.
The work makes uses of recent developments on the modeling and characterization of feasible operating conditions of Li-ion batteries to reliably predict the expected remaining useful life of lithium ion batteries. Such feasible operating conditions are embedded into the app and varies along with the health condition estimated. The app also monitors the basic states of the batteries, including temperature, current, voltage, and power, and SOC and also estimates RUL. Due to the limitation of computational power in mobile app, only simple RUL estimation is used. The app will generate warning signals if the battery condition doesn’t meet the specified standards, and also recommend the suitable operating conditions regarding the range of charging/discharging currents, cut off voltages, and the range of SOC (minimum and maximum SOC).
Finally, in the case analysis, Simulink simulation data was used as battery input for the app to validate the effectiveness of its functionality. |
| first_indexed | 2025-02-19T03:36:26Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/182441 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:36:26Z |
| publishDate | 2025 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1824412025-02-07T15:48:09Z Mobile app for battery prognostic Li, Zhengfan Hung Dinh Nguyen School of Electrical and Electronic Engineering hunghtd@ntu.edu.sg Engineering Battery management system (BMS) Remaining useful life (RUL) Real-time monitoring Vue.js framework Battery safety Energy storage is vital for advancing sustainable energy solutions, with lithium batteries serving as a cornerstone in electric vehicles, renewable energy systems, and portable electronics. However, safety risks and aging challenge their reliability and durability, often leaving end-users lacking expertise to bear these risks. This study introduces an intelligent battery management system (BMS) integrated into a mobile application to provide intuitive decision-making support. The work makes uses of recent developments on the modeling and characterization of feasible operating conditions of Li-ion batteries to reliably predict the expected remaining useful life of lithium ion batteries. Such feasible operating conditions are embedded into the app and varies along with the health condition estimated. The app also monitors the basic states of the batteries, including temperature, current, voltage, and power, and SOC and also estimates RUL. Due to the limitation of computational power in mobile app, only simple RUL estimation is used. The app will generate warning signals if the battery condition doesn’t meet the specified standards, and also recommend the suitable operating conditions regarding the range of charging/discharging currents, cut off voltages, and the range of SOC (minimum and maximum SOC). Finally, in the case analysis, Simulink simulation data was used as battery input for the app to validate the effectiveness of its functionality. Master's degree 2025-02-03T11:49:39Z 2025-02-03T11:49:39Z 2024 Thesis-Master by Coursework Li, Z. (2024). Mobile app for battery prognostic. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/182441 https://hdl.handle.net/10356/182441 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Battery management system (BMS) Remaining useful life (RUL) Real-time monitoring Vue.js framework Battery safety Li, Zhengfan Mobile app for battery prognostic |
| title | Mobile app for battery prognostic |
| title_full | Mobile app for battery prognostic |
| title_fullStr | Mobile app for battery prognostic |
| title_full_unstemmed | Mobile app for battery prognostic |
| title_short | Mobile app for battery prognostic |
| title_sort | mobile app for battery prognostic |
| topic | Engineering Battery management system (BMS) Remaining useful life (RUL) Real-time monitoring Vue.js framework Battery safety |
| url | https://hdl.handle.net/10356/182441 |
| work_keys_str_mv | AT lizhengfan mobileappforbatteryprognostic |