Battery management system for automated guided vehicle
While substantial research efforts have been devoted to optimizing various aspects of automated guided vehicles (AGVs), such as localization, path planning, and object recognition, there has been a relative lack of information concerning battery pack power management for AGVs. It is important to ack...
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| Format: | Final Year Project / Dissertation / Thesis |
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2023
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| Online Access: | http://eprints.utar.edu.my/6087/ http://eprints.utar.edu.my/6087/1/MH_1906395_LEONG_QI_YE.pdf |
| _version_ | 1848886586874265600 |
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| author | Leong, Qi Ye |
| author_facet | Leong, Qi Ye |
| author_sort | Leong, Qi Ye |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | While substantial research efforts have been devoted to optimizing various aspects of automated guided vehicles (AGVs), such as localization, path planning, and object recognition, there has been a relative lack of information concerning battery pack power management for AGVs. It is important to acknowledge that the performance and lifespan of batteries are significantly influenced by their charging and discharging patterns. Going beyond the recommended upper voltage limit during charging can trigger thermal runaway, potentially leading to battery destruction. Conversely, discharging batteries below the specified lower voltage limit can result in reduced capacity, thereby affecting overall battery performance. Therefore, a battery management system (BMS) was developed for AGVs in this Final Year Project (FYP) to provide essential functions such as charge and discharge current measurements, battery pack voltage measurement, and state of charge (SoC) estimation using the coulomb counting method. The developed BMS underwent rigorous testing on an AGV within UTAR to validate its performance. Experimental tests, including the accuracy of current sensing, battery pack voltage sensing, and SoC estimation during charging and discharging, demonstrated that the BMS effectively monitors the electrical characteristics, thus, providing insights for adequate usage and management of the AGV’s battery pack. Key features of the developed BMS include SoC estimation, crucial for accurately assessing remaining battery capacity, and integration with the Internet of Things (IoT) for real-time data collection and storage during battery charging and discharging. This data holds immense value as it can be leveraged for analysis and future research in areas such as preventive maintenance, safety operating envelopes, and assessments related to the remaining useful life of the battery. |
| first_indexed | 2025-11-15T19:40:51Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6087 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:40:51Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-60872023-11-24T17:12:58Z Battery management system for automated guided vehicle Leong, Qi Ye TJ Mechanical engineering and machinery While substantial research efforts have been devoted to optimizing various aspects of automated guided vehicles (AGVs), such as localization, path planning, and object recognition, there has been a relative lack of information concerning battery pack power management for AGVs. It is important to acknowledge that the performance and lifespan of batteries are significantly influenced by their charging and discharging patterns. Going beyond the recommended upper voltage limit during charging can trigger thermal runaway, potentially leading to battery destruction. Conversely, discharging batteries below the specified lower voltage limit can result in reduced capacity, thereby affecting overall battery performance. Therefore, a battery management system (BMS) was developed for AGVs in this Final Year Project (FYP) to provide essential functions such as charge and discharge current measurements, battery pack voltage measurement, and state of charge (SoC) estimation using the coulomb counting method. The developed BMS underwent rigorous testing on an AGV within UTAR to validate its performance. Experimental tests, including the accuracy of current sensing, battery pack voltage sensing, and SoC estimation during charging and discharging, demonstrated that the BMS effectively monitors the electrical characteristics, thus, providing insights for adequate usage and management of the AGV’s battery pack. Key features of the developed BMS include SoC estimation, crucial for accurately assessing remaining battery capacity, and integration with the Internet of Things (IoT) for real-time data collection and storage during battery charging and discharging. This data holds immense value as it can be leveraged for analysis and future research in areas such as preventive maintenance, safety operating envelopes, and assessments related to the remaining useful life of the battery. 2023 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6087/1/MH_1906395_LEONG_QI_YE.pdf Leong, Qi Ye (2023) Battery management system for automated guided vehicle. Final Year Project, UTAR. http://eprints.utar.edu.my/6087/ |
| spellingShingle | TJ Mechanical engineering and machinery Leong, Qi Ye Battery management system for automated guided vehicle |
| title | Battery management system for automated guided vehicle |
| title_full | Battery management system for automated guided vehicle |
| title_fullStr | Battery management system for automated guided vehicle |
| title_full_unstemmed | Battery management system for automated guided vehicle |
| title_short | Battery management system for automated guided vehicle |
| title_sort | battery management system for automated guided vehicle |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.utar.edu.my/6087/ http://eprints.utar.edu.my/6087/1/MH_1906395_LEONG_QI_YE.pdf |