An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-Charge
The fluctuating nature of power produced by renewable energy sources results in a substantial supply and demand mismatch. To curb the imbalance, energy storage systems comprising batteries and supercapacitors are widely employed. However, due to the variety of operational conditions, the performance...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/5/3/50 |
| _version_ | 1819071129693716480 |
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| author | Qamar Navid Ahmed Hassan |
| author_facet | Qamar Navid Ahmed Hassan |
| author_sort | Qamar Navid |
| collection | DOAJ |
| description | The fluctuating nature of power produced by renewable energy sources results in a substantial supply and demand mismatch. To curb the imbalance, energy storage systems comprising batteries and supercapacitors are widely employed. However, due to the variety of operational conditions, the performance prediction of the energy storage systems entails a substantial complexity that leads to capacity utilization issues. The current article attempts to precisely predict the performance of a lithium-ion battery and capacitor/supercapacitor under dynamic conditions to utilize the storage capacity to a fuller extent. The grey box modeling approach involving the chemical and electrical energy transfers/interactions governed by ordinary differential equations was developed in MATLAB. The model parameters were extracted from experimental data employing regression techniques. The state-of-charge (SoC) of the battery was predicted by employing the extended Kalman (EK) estimator and the unscented Kalman (UK) estimator. The model was eventually validated via loading profile tests. As a performance indicator, the extended Kalman estimator indicated the strong competitiveness of the developed model with regard to tracking of the internal states (e.g., SoC) which have first-order nonlinearities. |
| first_indexed | 2024-12-21T17:16:56Z |
| format | Article |
| id | doaj.art-465f192aaf8d4de1a326a0eba3bfb0e7 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-12-21T17:16:56Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-465f192aaf8d4de1a326a0eba3bfb0e72022-12-21T18:56:16ZengMDPI AGBatteries2313-01052019-07-01535010.3390/batteries5030050batteries5030050An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-ChargeQamar Navid0Ahmed Hassan1Emirates Centre for Energy and Environmental Research, United Arab Emirates University, Al Ain 15551, United Arab EmirateCollege of Engineering, United Arab Emirates University, Al Ain 15551, United Arab EmiratesThe fluctuating nature of power produced by renewable energy sources results in a substantial supply and demand mismatch. To curb the imbalance, energy storage systems comprising batteries and supercapacitors are widely employed. However, due to the variety of operational conditions, the performance prediction of the energy storage systems entails a substantial complexity that leads to capacity utilization issues. The current article attempts to precisely predict the performance of a lithium-ion battery and capacitor/supercapacitor under dynamic conditions to utilize the storage capacity to a fuller extent. The grey box modeling approach involving the chemical and electrical energy transfers/interactions governed by ordinary differential equations was developed in MATLAB. The model parameters were extracted from experimental data employing regression techniques. The state-of-charge (SoC) of the battery was predicted by employing the extended Kalman (EK) estimator and the unscented Kalman (UK) estimator. The model was eventually validated via loading profile tests. As a performance indicator, the extended Kalman estimator indicated the strong competitiveness of the developed model with regard to tracking of the internal states (e.g., SoC) which have first-order nonlinearities.https://www.mdpi.com/2313-0105/5/3/50lithium-ion batterystate-of-chargestate-of-healthgrey box modelingextended Kalman estimatorunscented Kalman estimator |
| spellingShingle | Qamar Navid Ahmed Hassan An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-Charge Batteries lithium-ion battery state-of-charge state-of-health grey box modeling extended Kalman estimator unscented Kalman estimator |
| title | An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-Charge |
| title_full | An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-Charge |
| title_fullStr | An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-Charge |
| title_full_unstemmed | An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-Charge |
| title_short | An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-Charge |
| title_sort | accurate and precise grey box model of a low power lithium ion battery and capacitor supercapacitor for accurate estimation of state of charge |
| topic | lithium-ion battery state-of-charge state-of-health grey box modeling extended Kalman estimator unscented Kalman estimator |
| url | https://www.mdpi.com/2313-0105/5/3/50 |
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