基于Wavelet降噪和支持向量机的锂离子电池容量预测研究

doi:10.19595/j.cnki.1000-6753.tces.190620

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Abstract As battery usage increases, the battery will age. By predicting the remaining capacity of the battery, reliable data support can be improved for the battery management system in the equipment system. In this study, support vector machine (SVM) is used to predict the remaining capacity of lithium-ion batteries. The improved chicken swarm algorithm (ICSO) is used to optimize the parameters of SVM. The ICSO-SVM model is established. In order to verify the feasibility of the prediction model, the following work has been done. Firstly, the capacity degradation data of B5 and B6 batteries were decomposed by db5 wavelet. And then the denoised signal was reconstructed. Secondly, the chicken swarm optimization algorithm (CSO) was improved and ICSO optimization algorithm was proposed. The convergence accuracy of ICSO is higher than that of PSO and CSO algorithms. Finally, two groups of experiments were used to verify the validity of the CSO-SVM model and the ICSO-SVM model. It is found that the average absolute deviation (AAD) value of ICSO-SVM model is less than 1.5%, RMSE value is less than 2% and average of R2 value is 0.972 6.

Key words： Lithium-ion battery support vector machine optimization algorithm wavelet denoising capacity prediction

Received: 22 May 2019

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TM572.2

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	Articles by authors
	Zhang Tingting
	Yu Ming
	Li Bin
	Liu Zhe

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Zhang Tingting,Yu Ming,Li Bin等. Capacity Prediction of Lithium-Ion Batteries Based on Wavelet Noise Reduction and Support Vector Machine[J]. Transactions of China Electrotechnical Society, 2020, 35(14): 3126-3136.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190620 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I14/3126

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