Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods
This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is ca...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/8/4938 |
| _version_ | 1797606548704329728 |
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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 | This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction methods based on linear fitting and autoregressive integrated moving average algorithms. |
| first_indexed | 2024-03-11T05:16:45Z |
| format | Article |
| id | doaj.art-37de8898e904468fa4b09e96dce4cf0b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:16:45Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-37de8898e904468fa4b09e96dce4cf0b2023-11-17T18:11:23ZengMDPI AGApplied Sciences2076-34172023-04-01138493810.3390/app13084938Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing MethodsJoaquín de la Vega0Jordi-Roger Riba1Juan Antonio Ortega-Redondo2Electronics Engineering Department, Universitat Politècnica de Catalunya, 08034 Barcelona, SpainElectrical Engineering Department, Universitat Politècnica de Catalunya, 08034 Barcelona, SpainElectronics Engineering Department, Universitat Politècnica de Catalunya, 08034 Barcelona, SpainThis paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction methods based on linear fitting and autoregressive integrated moving average algorithms.https://www.mdpi.com/2076-3417/13/8/4938batterycapacitydegradationhealth indicatorpredictionstate of charge |
| spellingShingle | Joaquín de la Vega Jordi-Roger Riba Juan Antonio Ortega-Redondo Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods Applied Sciences battery capacity degradation health indicator prediction state of charge |
| title | Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods |
| title_full | Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods |
| title_fullStr | Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods |
| title_full_unstemmed | Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods |
| title_short | Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods |
| title_sort | mathematical modeling of battery degradation based on direct measurements and signal processing methods |
| topic | battery capacity degradation health indicator prediction state of charge |
| url | https://www.mdpi.com/2076-3417/13/8/4938 |
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