Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures
Low temperatures induce limited charging ability and lifespan in lithium-ion batteries, and may even cause accidents. Therefore, a reliable preheating strategy is needed to address this issue. This study proposes a low-temperature preheating strategy based on neural network PID control, considering...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/14/4/83 |
| _version_ | 1797603108950376448 |
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| author | Song Pan Yuejiu Zheng Languang Lu Kai Shen Siqi Chen |
| author_facet | Song Pan Yuejiu Zheng Languang Lu Kai Shen Siqi Chen |
| author_sort | Song Pan |
| collection | DOAJ |
| description | Low temperatures induce limited charging ability and lifespan in lithium-ion batteries, and may even cause accidents. Therefore, a reliable preheating strategy is needed to address this issue. This study proposes a low-temperature preheating strategy based on neural network PID control, considering temperature increase rate and consistency. In this strategy, electrothermal films are placed between cells for preheating; battery module areas are differentiated according to the convective heat transfer rate; a controller regulates heating power to control the maximum temperature difference during the preheating process; and a co-simulation model is established to verify the proposed warm-up strategy. The numerical calculation results indicate that the battery module can be preheated to the target temperature under different ambient temperatures and control targets. The coupling relationship between the preheating time and the maximum temperature difference during the preheating process is studied and multi-objective optimization is carried out based on the temperature increase rate and thermal uniformity. The optimal preheating strategy is proven to ensure the temperature increase rate and effectively suppress temperature inconsistency of the module during the preheating process. Although preheating time is extended by 17%, the temperature difference remains within the safety threshold, and the maximum temperature difference is reduced by 49.6%. |
| first_indexed | 2024-03-11T04:24:48Z |
| format | Article |
| id | doaj.art-0866eb34def142e4997443342f007c75 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-11T04:24:48Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-0866eb34def142e4997443342f007c752023-11-17T21:49:57ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-03-011448310.3390/wevj14040083Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low TemperaturesSong Pan0Yuejiu Zheng1Languang Lu2Kai Shen3Siqi Chen4College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaCollege of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaCollege of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaClean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaLow temperatures induce limited charging ability and lifespan in lithium-ion batteries, and may even cause accidents. Therefore, a reliable preheating strategy is needed to address this issue. This study proposes a low-temperature preheating strategy based on neural network PID control, considering temperature increase rate and consistency. In this strategy, electrothermal films are placed between cells for preheating; battery module areas are differentiated according to the convective heat transfer rate; a controller regulates heating power to control the maximum temperature difference during the preheating process; and a co-simulation model is established to verify the proposed warm-up strategy. The numerical calculation results indicate that the battery module can be preheated to the target temperature under different ambient temperatures and control targets. The coupling relationship between the preheating time and the maximum temperature difference during the preheating process is studied and multi-objective optimization is carried out based on the temperature increase rate and thermal uniformity. The optimal preheating strategy is proven to ensure the temperature increase rate and effectively suppress temperature inconsistency of the module during the preheating process. Although preheating time is extended by 17%, the temperature difference remains within the safety threshold, and the maximum temperature difference is reduced by 49.6%.https://www.mdpi.com/2032-6653/14/4/83low-temperature preheatingthermal consistencyneural network PID controlmulti-objective optimization |
| spellingShingle | Song Pan Yuejiu Zheng Languang Lu Kai Shen Siqi Chen Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures World Electric Vehicle Journal low-temperature preheating thermal consistency neural network PID control multi-objective optimization |
| title | Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures |
| title_full | Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures |
| title_fullStr | Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures |
| title_full_unstemmed | Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures |
| title_short | Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures |
| title_sort | neural network pid based preheating control and optimization for a li ion battery module at low temperatures |
| topic | low-temperature preheating thermal consistency neural network PID control multi-objective optimization |
| url | https://www.mdpi.com/2032-6653/14/4/83 |
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