A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency
The remaining discharge energy prediction of the battery pack is an important function of a battery management system. One of the key factors contributing to the inaccuracy of battery pack remaining discharge energy prediction is the inconsistency of the state and model parameters. For a batch of li...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/12/6/987 |
| _version_ | 1811263588188291072 |
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| author | Qiaohua Fang Xuezhe Wei Haifeng Dai |
| author_facet | Qiaohua Fang Xuezhe Wei Haifeng Dai |
| author_sort | Qiaohua Fang |
| collection | DOAJ |
| description | The remaining discharge energy prediction of the battery pack is an important function of a battery management system. One of the key factors contributing to the inaccuracy of battery pack remaining discharge energy prediction is the inconsistency of the state and model parameters. For a batch of lithium-ion batteries with nickel cobalt aluminum oxide cathode material, after analyzing the characteristics of battery model parameter inconsistency, a “specific and difference” model considering state of charge and R0 inconsistency is established. The dual time-scale dual extended Kalman filter algorithm is proposed to estimate the state of charge and R0 of each cell in the battery pack, and the remaining discharge energy prediction algorithm of the battery pack is established. The effectiveness of the state estimation and remaining discharge energy prediction algorithm is verified. The results show that the state of charge estimation error of each cell is less than 1%, and the remaining discharge energy prediction error of the battery pack is less than 1% over the entire discharge cycle. The main reason which causes the difference between the “specific and difference” and “mean and difference” models is the nonlinearity of the battery’s state of charge - open circuit voltage curve. When the nonlinearity is serious, the “specific and difference” model has higher precision. |
| first_indexed | 2024-04-12T19:48:03Z |
| format | Article |
| id | doaj.art-dd1d4216a1664acaac4932802118384b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-12T19:48:03Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-dd1d4216a1664acaac4932802118384b2022-12-22T03:18:55ZengMDPI AGEnergies1996-10732019-03-0112698710.3390/en12060987en12060987A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter InconsistencyQiaohua Fang0Xuezhe Wei1Haifeng Dai2Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaClean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaClean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaThe remaining discharge energy prediction of the battery pack is an important function of a battery management system. One of the key factors contributing to the inaccuracy of battery pack remaining discharge energy prediction is the inconsistency of the state and model parameters. For a batch of lithium-ion batteries with nickel cobalt aluminum oxide cathode material, after analyzing the characteristics of battery model parameter inconsistency, a “specific and difference” model considering state of charge and R0 inconsistency is established. The dual time-scale dual extended Kalman filter algorithm is proposed to estimate the state of charge and R0 of each cell in the battery pack, and the remaining discharge energy prediction algorithm of the battery pack is established. The effectiveness of the state estimation and remaining discharge energy prediction algorithm is verified. The results show that the state of charge estimation error of each cell is less than 1%, and the remaining discharge energy prediction error of the battery pack is less than 1% over the entire discharge cycle. The main reason which causes the difference between the “specific and difference” and “mean and difference” models is the nonlinearity of the battery’s state of charge - open circuit voltage curve. When the nonlinearity is serious, the “specific and difference” model has higher precision.http://www.mdpi.com/1996-1073/12/6/987lithium-ion battery packinconsistencySOC estimationremaining discharge energy prediction |
| spellingShingle | Qiaohua Fang Xuezhe Wei Haifeng Dai A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency Energies lithium-ion battery pack inconsistency SOC estimation remaining discharge energy prediction |
| title | A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency |
| title_full | A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency |
| title_fullStr | A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency |
| title_full_unstemmed | A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency |
| title_short | A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency |
| title_sort | remaining discharge energy prediction method for lithium ion battery pack considering soc and parameter inconsistency |
| topic | lithium-ion battery pack inconsistency SOC estimation remaining discharge energy prediction |
| url | http://www.mdpi.com/1996-1073/12/6/987 |
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