State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction
In this FYP, a lithium-ion battery cell was monitored during its charge-and-discharge cycles in order to predict its State-of-Health (SoH) and estimate its State-of-Charge (SoC) for battery lifetime analysis. Lithium-ion battery will experience serious damage if exposed to overcharging and deep disc...
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Universiti Sains Malaysia
2018
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| Online Access: | http://eprints.usm.my/53620/1/State-Of-Health%20Prediction%20Integrated%20With%20State-Of-Charge%20Monitoring%20Of%20A%20Lithium-Ion%20Battery%20Cell%20For%20Lifetime%20Prediction_Leow%20Yoong%20Yang_E3_2018.pdf http://eprints.usm.my/53620/ |
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my.usm.eprints.53620 http://eprints.usm.my/53620/ State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction Leow, Yoong Yang T Technology (General) TK Electrical Engineering. Electronics. Nuclear Engineering In this FYP, a lithium-ion battery cell was monitored during its charge-and-discharge cycles in order to predict its State-of-Health (SoH) and estimate its State-of-Charge (SoC) for battery lifetime analysis. Lithium-ion battery will experience serious damage if exposed to overcharging and deep discharging for a long time, hence SoC estimation is crucial to help the user monitor the SoC of the battery, so the battery lifetime will not decrease due to overcharging or deep discharging. Besides, SoH prediction is used to indicate the health condition of the battery whether the battery still can operate or not since lithium-ion battery undergoes degradation as time passes. Battery lifetime analysis is performed to predict the remaining useful life of the battery, so the user can know the amount of charge-and-discharge cycles left before failure and then can prepare for the replacement in time, thus improving the system reliability. In this FYP, a simple battery management system (BMS) was developed. Then, SoC estimation was performed via Coulomb counting and OCV methods. SoH prediction was through the measurement of battery capacity. Lastly, the data obtained from SoH prediction was employed to predict the battery remaining useful life. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53620/1/State-Of-Health%20Prediction%20Integrated%20With%20State-Of-Charge%20Monitoring%20Of%20A%20Lithium-Ion%20Battery%20Cell%20For%20Lifetime%20Prediction_Leow%20Yoong%20Yang_E3_2018.pdf Leow, Yoong Yang (2018) State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted) |
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T Technology (General) TK Electrical Engineering. Electronics. Nuclear Engineering |
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T Technology (General) TK Electrical Engineering. Electronics. Nuclear Engineering Leow, Yoong Yang State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction |
| description |
In this FYP, a lithium-ion battery cell was monitored during its charge-and-discharge cycles in order to predict its State-of-Health (SoH) and estimate its State-of-Charge (SoC) for battery lifetime analysis. Lithium-ion battery will experience serious damage if exposed to overcharging and deep discharging for a long time, hence SoC estimation is crucial to help the user monitor the SoC of the battery, so the battery lifetime
will not decrease due to overcharging or deep discharging. Besides, SoH prediction is used to indicate the health condition of the battery whether the battery still can operate or not since lithium-ion battery undergoes degradation as time passes. Battery lifetime analysis is performed to predict the remaining useful life of the battery, so the user can
know the amount of charge-and-discharge cycles left before failure and then can prepare for the replacement in time, thus improving the system reliability. In this FYP, a simple
battery management system (BMS) was developed. Then, SoC estimation was performed via Coulomb counting and OCV methods. SoH prediction was through the measurement of battery capacity. Lastly, the data obtained from SoH prediction was employed to
predict the battery remaining useful life. |
| format |
Monograph |
| author |
Leow, Yoong Yang |
| author_facet |
Leow, Yoong Yang |
| author_sort |
Leow, Yoong Yang |
| title |
State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction |
| title_short |
State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction |
| title_full |
State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction |
| title_fullStr |
State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction |
| title_full_unstemmed |
State-Of-Health Prediction Integrated With State-Of-Charge Monitoring Of A Lithium-Ion Battery Cell For Lifetime Prediction |
| title_sort |
state-of-health prediction integrated with state-of-charge monitoring of a lithium-ion battery cell for lifetime prediction |
| publisher |
Universiti Sains Malaysia |
| publishDate |
2018 |
| url |
http://eprints.usm.my/53620/1/State-Of-Health%20Prediction%20Integrated%20With%20State-Of-Charge%20Monitoring%20Of%20A%20Lithium-Ion%20Battery%20Cell%20For%20Lifetime%20Prediction_Leow%20Yoong%20Yang_E3_2018.pdf http://eprints.usm.my/53620/ |
| _version_ |
1739829003936595968 |
| score |
13.211869 |