基于无迹粒子滤波改进算法的电池容量衰退预测研究

doi:10.3969/j.issn.1000-1158.2024.08.19

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Abstract In order to improve the accuracy and applicability of battery capacity prediction, an improved algorithm based on traceless particle filtering is proposed. In order to reduce the observation error of the filtering iteration process, a support vector regression algorithm is introduced to improve it. Since the kernel function and penalty factor in the support vector regression algorithm are difficult to determine, it is proposed to use the optimization ability of the genetic algorithm to solve it, forming a model improved by the genetic algorithm and support vector regression. The performance of this fusion model is evaluated and compared with UPF-SVR and UPF-RVR, and the experimental results show that the mean absolute error EMAand root mean square error SRMSE of the fusion model prediction results are lower than 2.0% and 2.5%, respectively, and the prediction accuracy is higher compared with the other models, and at the same time, the prediction level and convergence are significantly better than other models, which is more effective and feasible.

Key words： electrical measurement battery capacity prediction traceless particle filtering genetic algorithm support vector regression optimization ability

Received: 29 November 2023 Published: 05 September 2024

PACS:

TB971

Fund:Research on Architecture and Control Strategy of Energy Router in High Photovoltaic Penetration Distribution Network

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	Articles by authors
	GUO Ruijun
	XIN Yongqiang
	YANG Jianfeng

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GUO Ruijun,XIN Yongqiang,YANG Jianfeng. Research on Battery Capacity Decline Prediction Based on Improved Algorithm of Traceless Particle Filtering[J]. Acta Metrologica Sinica, 2024, 45(8): 1222-1230.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.08.19 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I8/1222

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