An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures
The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This pap...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/8/10/140 |
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| author | Shuoyuan Mao Meilin Han Xuebing Han Languang Lu Xuning Feng Anyu Su Depeng Wang Zixuan Chen Yao Lu Minggao Ouyang |
| author_facet | Shuoyuan Mao Meilin Han Xuebing Han Languang Lu Xuning Feng Anyu Su Depeng Wang Zixuan Chen Yao Lu Minggao Ouyang |
| author_sort | Shuoyuan Mao |
| collection | DOAJ |
| description | The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system. |
| first_indexed | 2024-03-09T20:43:19Z |
| format | Article |
| id | doaj.art-fab181ce599341cc9f3d8ea0855a58cc |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-09T20:43:19Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-fab181ce599341cc9f3d8ea0855a58cc2023-11-23T22:54:46ZengMDPI AGBatteries2313-01052022-09-0181014010.3390/batteries8100140An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various TemperaturesShuoyuan Mao0Meilin Han1Xuebing Han2Languang Lu3Xuning Feng4Anyu Su5Depeng Wang6Zixuan Chen7Yao Lu8Minggao Ouyang9State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaSchool of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaThe LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system.https://www.mdpi.com/2313-0105/8/10/140LFP batterySOCRAEelectrical–thermal couplinglow temperature |
| spellingShingle | Shuoyuan Mao Meilin Han Xuebing Han Languang Lu Xuning Feng Anyu Su Depeng Wang Zixuan Chen Yao Lu Minggao Ouyang An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures Batteries LFP battery SOC RAE electrical–thermal coupling low temperature |
| title | An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures |
| title_full | An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures |
| title_fullStr | An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures |
| title_full_unstemmed | An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures |
| title_short | An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures |
| title_sort | electrical thermal coupling model with artificial intelligence for state of charge and residual available energy co estimation of lifepo4 battery system under various temperatures |
| topic | LFP battery SOC RAE electrical–thermal coupling low temperature |
| url | https://www.mdpi.com/2313-0105/8/10/140 |
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