Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods
Lithium-ion batteries are key elements in the development of electrical energy storage solutions. However, due to cycling, environmental, and operating conditions, battery capacity tends to degrade over time. Capacity fade is a common indicator of battery state of health (SOH) because it is an indic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/10/1/10 |
| _version_ | 1827372755658473472 |
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| author | Joaquín de la Vega Jordi-Roger Riba Juan Antonio Ortega-Redondo |
| author_facet | Joaquín de la Vega Jordi-Roger Riba Juan Antonio Ortega-Redondo |
| author_sort | Joaquín de la Vega |
| collection | DOAJ |
| description | Lithium-ion batteries are key elements in the development of electrical energy storage solutions. However, due to cycling, environmental, and operating conditions, battery capacity tends to degrade over time. Capacity fade is a common indicator of battery state of health (SOH) because it is an indication of how the capacity has been degraded. However, battery capacity cannot be measured directly, and thus, there is an urgent need to develop methods for estimating battery capacity in real time. By analyzing the historical data of a battery in detail, it is possible to predict the future state of a battery and forecast its remaining useful life. This study developed a real-time, simple, and fast method to estimate the cycle capacity of a battery during the charge cycle using only data from a short period of each charge cycle. This proposal is attractive because it does not require data from the entire charge period since batteries are rarely charged from zero to full. The proposed method allows for simultaneous and accurate real-time prediction of the health and remaining useful life of the battery over its lifetime. The accuracy of the proposed method was tested using experimental data from several lithium-ion batteries with different cathode chemistries under various test conditions. |
| first_indexed | 2024-03-08T11:05:40Z |
| format | Article |
| id | doaj.art-be303c34ab2840a09a3a3ee6bb73b27a |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-08T11:05:40Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-be303c34ab2840a09a3a3ee6bb73b27a2024-01-26T15:04:28ZengMDPI AGBatteries2313-01052023-12-011011010.3390/batteries10010010Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning MethodsJoaquín de la Vega0Jordi-Roger Riba1Juan Antonio Ortega-Redondo2Electronics Engineering Department, Universitat Politècnica de Catalunya, 08222 Terrassa, SpainElectrical Engineering Department, Universitat Politècnica de Catalunya, 08222 Terrassa, SpainElectronics Engineering Department, Universitat Politècnica de Catalunya, 08222 Terrassa, SpainLithium-ion batteries are key elements in the development of electrical energy storage solutions. However, due to cycling, environmental, and operating conditions, battery capacity tends to degrade over time. Capacity fade is a common indicator of battery state of health (SOH) because it is an indication of how the capacity has been degraded. However, battery capacity cannot be measured directly, and thus, there is an urgent need to develop methods for estimating battery capacity in real time. By analyzing the historical data of a battery in detail, it is possible to predict the future state of a battery and forecast its remaining useful life. This study developed a real-time, simple, and fast method to estimate the cycle capacity of a battery during the charge cycle using only data from a short period of each charge cycle. This proposal is attractive because it does not require data from the entire charge period since batteries are rarely charged from zero to full. The proposed method allows for simultaneous and accurate real-time prediction of the health and remaining useful life of the battery over its lifetime. The accuracy of the proposed method was tested using experimental data from several lithium-ion batteries with different cathode chemistries under various test conditions.https://www.mdpi.com/2313-0105/10/1/10batterycapacitydegradationstate of healthremaining useful lifeneural networks |
| spellingShingle | Joaquín de la Vega Jordi-Roger Riba Juan Antonio Ortega-Redondo Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods Batteries battery capacity degradation state of health remaining useful life neural networks |
| title | Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods |
| title_full | Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods |
| title_fullStr | Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods |
| title_full_unstemmed | Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods |
| title_short | Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods |
| title_sort | real time lithium battery aging prediction based on capacity estimation and deep learning methods |
| topic | battery capacity degradation state of health remaining useful life neural networks |
| url | https://www.mdpi.com/2313-0105/10/1/10 |
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