电源学报

基于IKH-UKF-PF的电源电解电容寿命预测

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韩旭 ¹ , 张静伟 ² , 傅代印 ³ , 覃团发 ¹

电源学报 | 新能源发电与储能 2026,24(4): 215-222

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电源学报 |新能源发电与储能 2026 , 24 (4) : 215 -222

基于IKH-UKF-PF的电源电解电容寿命预测

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韩旭¹, 张静伟², 傅代印³, 覃团发¹

作者信息

^1.广西大学广西多媒体通信与网络技术重点实验室，南宁　530004

^2.国网吉林省电力有限公司白城供电公司，白城　137319

^3.国网吉林省电力有限公司吉林供电公司，吉林　132011

通讯作者:

覃团发（1966—　），男，通信作者，博士，教授。研究方向：智能电网信息技术，无线多媒体通信研究，设备寿命预测。E-mail：npic_wq@163.com。

作者简介:

韩旭（1993—　），男，本科，工程师。研究方向：电源、旋转机械等设备寿命预测。E-mail：npic_lvx@163.com。

张静伟（1984—　），男，本科，高级工程师。研究方向：设备状态监测。E-mail：84731999@qq.com。

傅代印（1988—　），男，硕士，高级工程师。研究方向：设备故障诊断和寿命预测。E-mail：fudaiyin@126.com。

Life Prediction of Power Supply Electrolytic Capacitor Based on IKH-UKF-PF

Xu HAN¹, Jingwei ZHANG², Daiyin FU³, Tuanfa QIN¹

Affiliations

^1.Guangxi Key Laboratory of Multimedia Communication and Network Technology, Guangxi University, Nanning 530004, China

^2.Baicheng Power Supply Company, State Grid Jilin Electric Power Co., Ltd., Baicheng 137319, China

^3.Jilin Power Supply Company, State Grid Jilin Electric Power Co., Ltd., Jilin 132011, China

出版时间: 2026-04-30 doi: 10.13234/j.issn.2095-2805.2026.4.215

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摘要

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开展电动汽车充电设备核心器件的可靠性评估，保障充电设备安全稳定运行变得越来越重要。提出1种先进的预测方法来估计充电设备最重要的部件之一——电源中电解电容的健康状态并预测其剩余使用寿命。根据电容损耗数据，融合Verhulst模型和指数模型作为最终的经验模型，并利用无迹卡尔曼滤波UKF（unscented Kalman filter）生成粒子滤波PF（particle filter）的建议分布来跟踪退化路径。针对粒子贫化问题，采用改进磷虾群KH（krill herd）算法优化残差重采样步长，提高预测精度。在电解电容退化数据上的实验表明，与传统粒子滤波算法相比，在预测时间点t=80 h时，预测均方根误差RMSE（root mean square error）从2.79下降至0.37、95%水平下的置信区间宽度从34 h下降至4 h；同时，与KH-UKF-PF相比，RMSE下降了27.5%，所提方法可提供更高的剩余使用寿命预测精度。

关键词

电源 / 电解电容 / 寿命预测 / 无迹卡尔曼滤波 / 磷虾群算法 / 粒子滤波

Abstract

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It is becoming increasingly important to carry out reliability evaluation on core devices in the electric vehicle charging equipment, so as to ensure the safe and stable operation of the charging equipment. An advanced prediction method is proposed to estimate the state of health of electrolytic capacitor in the power supply which is one of the most important components in the charging equipment, and its remaining useful life (RUL) is also predicted. According to the capacitance loss data, the Verhulst model and exponential model are combined as the final empirical model, and unscented Kalman filter (UKF) is used to generate a recommended distribution of particle filter (PF) to track the degradation path. Aimed at the problem of particle impoverishment, an improved krill herd (KH) algorithm is used to optimize the residual resampling step and improve the prediction accuracy. The results of experiments on the electrolytic capacitor degradation data show that compared with those of the traditional PF algorithm, the root mean square error (RMSE) of prediction result decreases from 2.79 to 0.37 at the prediction time point of 80 h, and the confidence interval width at the 95% level decreases from 34 h to 4 h. At the same time, compared with that of the KH-UKF-PF algorithm, the RMSE decreases by 27.5%, indicating that the proposed method can provide higher prediction accuracy of RUL.

Key words

Power supply / electrolytic capacitor / life prediction / unscented Kalman filter (UKF) / krill herd (KH) algorithm / particle filter (PF)

引用本文

韩旭, 张静伟, 傅代印, 覃团发. 基于IKH-UKF-PF的电源电解电容寿命预测. 电源学报, 2026 , 24 (4) : 215 -222 . DOI: 10.13234/j.issn.2095-2805.2026.4.215

Xu HAN, Jingwei ZHANG, Daiyin FU, Tuanfa QIN. Life Prediction of Power Supply Electrolytic Capacitor Based on IKH-UKF-PF[J]. Journal of Power Supply, 2026 , 24 (4) : 215 -222 . DOI: 10.13234/j.issn.2095-2805.2026.4.215

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2026年第24卷第4期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.4.215

接收时间：2023-03-16
首发时间：2026-10-29
出版时间：2026-04-30

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收稿日期：2023-03-16
修回日期：2023-12-03
录用日期：2023-12-13

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作者信息

^1.广西大学广西多媒体通信与网络技术重点实验室，南宁　530004

^2.国网吉林省电力有限公司白城供电公司，白城　137319

^3.国网吉林省电力有限公司吉林供电公司，吉林　132011

通讯作者:

覃团发（1966—　），男，通信作者，博士，教授。研究方向：智能电网信息技术，无线多媒体通信研究，设备寿命预测。E-mail：npic_wq@163.com。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.4.215

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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