Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario
The label-less characteristics of real vehicle data make engineering modeling and capacity identification of lithium-ion batteries face great challenges. Different from ideal laboratory data, the raw data collected from vehicle driving cycles have a great adverse impact on effective modeling and cap...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/9/4/216 |
| _version_ | 1827745923087728640 |
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| author | Fang Zhang Tao Sun Bowen Xu Yuejiu Zheng Xin Lai Long Zhou |
| author_facet | Fang Zhang Tao Sun Bowen Xu Yuejiu Zheng Xin Lai Long Zhou |
| author_sort | Fang Zhang |
| collection | DOAJ |
| description | The label-less characteristics of real vehicle data make engineering modeling and capacity identification of lithium-ion batteries face great challenges. Different from ideal laboratory data, the raw data collected from vehicle driving cycles have a great adverse impact on effective modeling and capacity identification of lithium-ion batteries due to the randomness and unpredictability of vehicle driving conditions, sampling frequency, sampling resolution, data loss, and other factors. Therefore, data cleaning and optimization is processed and the capacity of a battery pack is identified subsequently in combination with the improved two-point method. The current available capacity is obtained by a Fuzzy Kalman filter optimization capacity estimation curve, making use of the charging and discharging data segments. This algorithm is integrated into a new energy big data cloud platform. The results show that the identification algorithm of capacity is applied successfully from academic to engineering fields by charge and discharge mutual verification, and that life expectancy meets the engineering requirements. |
| first_indexed | 2024-03-11T05:14:19Z |
| format | Article |
| id | doaj.art-b25d4e767bab477b8b24a73ffd2df403 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T05:14:19Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-b25d4e767bab477b8b24a73ffd2df4032023-11-17T18:20:15ZengMDPI AGBatteries2313-01052023-04-019421610.3390/batteries9040216Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application ScenarioFang Zhang0Tao Sun1Bowen Xu2Yuejiu Zheng3Xin Lai4Long Zhou5School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaThe label-less characteristics of real vehicle data make engineering modeling and capacity identification of lithium-ion batteries face great challenges. Different from ideal laboratory data, the raw data collected from vehicle driving cycles have a great adverse impact on effective modeling and capacity identification of lithium-ion batteries due to the randomness and unpredictability of vehicle driving conditions, sampling frequency, sampling resolution, data loss, and other factors. Therefore, data cleaning and optimization is processed and the capacity of a battery pack is identified subsequently in combination with the improved two-point method. The current available capacity is obtained by a Fuzzy Kalman filter optimization capacity estimation curve, making use of the charging and discharging data segments. This algorithm is integrated into a new energy big data cloud platform. The results show that the identification algorithm of capacity is applied successfully from academic to engineering fields by charge and discharge mutual verification, and that life expectancy meets the engineering requirements.https://www.mdpi.com/2313-0105/9/4/216electric vehiclecloud dataerror analysiscapacity estimation |
| spellingShingle | Fang Zhang Tao Sun Bowen Xu Yuejiu Zheng Xin Lai Long Zhou Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario Batteries electric vehicle cloud data error analysis capacity estimation |
| title | Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario |
| title_full | Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario |
| title_fullStr | Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario |
| title_full_unstemmed | Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario |
| title_short | Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario |
| title_sort | identification and error analysis of lithium ion battery oriented to cloud data application scenario |
| topic | electric vehicle cloud data error analysis capacity estimation |
| url | https://www.mdpi.com/2313-0105/9/4/216 |
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