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| Improved Multiple Feature-electrochemical Thermal Coupling Modeling of Lithium-ion Batteries at Low-temperature with Real-time Coefficient Correction |
| Shunli Wang, Fellow, IET,Haiying Gao,Paul Takyi-Aninakwa,Josep M. Guerrero,Carlos Fernandez,Qi Huang, Fellow, IEEE |
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| Abstract: |
| Monitoring various internal parameters plays a core role in ensuring the safety of lithium-ion batteries in power supply applications. It also influences the sustainability effect and online state of charge prediction. An improved multiple feature-electrochemical thermal coupling modeling method is proposed considering low-temperature performance degradation for the complete characteristic expression of multi-dimensional information. This is to obtain the parameter influence mechanism with a multi-variable coupling relationship. An optimized decoupled deviation strategy is constructed for accurate state of charge prediction with real-time correction of time-varying current and temperature effects. The innovative decoupling method is combined with the functional relationships of state of charge and open-circuit voltage to capture energy management effectively. Then, an adaptive equivalent-prediction model is constructed using the state-space equation and iterative feedback correction, making the proposed model adaptive to fractional calculation. The maximum state of charge estimation errors of the proposed method are 4.57% and 0.223% under the Beijing bus dynamic stress test and dynamic stress test conditions, respectively. The improved multiple feature-electrochemical thermal coupling modeling realizes the effective correction of the current and temperature variations with noise influencing coefficient, and provides an efficient state of charge prediction method adaptive to complex conditions. |
| Key words: Adaptive inner state characterization, lithium-ion batteries, low-temperature automaticguided-vehicle, multiple feature-electrochemical thermal coupling modeling, real-time coefficient correction. |
| DOI：10.23919/PCMP.2023.000257 |
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| Fund:This work is supported by the National Natural Science Foundation of China (No. 62173281), the Natural Science Foundation of Sichuan Province (No. 23ZDYF0734 and No. 2023NSFSC1436), and the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province (No. 18kftk03). |
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