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Life Prediction of Supercapacitor Based on Improved Support Vector Regression
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Xiaoming LIU, Yupeng REN  Member, CPSS, Hai CHEN, Dawei FENG, Jun TIAN
Journal of Power Supply | 2025, 23(6) : 320 - 328
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Journal of Power Supply | 2025, 23(6): 320-328
• Battery and Energy Storage •
Life Prediction of Supercapacitor Based on Improved Support Vector Regression
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Xiaoming LIU, Yupeng REN  Member, CPSS, Hai CHEN, Dawei FENG, Jun TIAN
Affiliations
  • School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China
Published: 2025-10-30 doi: 10.13234/j.issn.2095-2805.2025.6.320
Outline
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The service performance and remaining life of a supercapacitor which serves as a novel energy storage device are part of the factors affecting the operation reliability of a system. A 2.7 V/10 F supercapacitor was taken as the research object, an aging experiment and test platform for energy storage devices was built, and the rules for the performance degradation of the supercapacitor and influencing factors were analyzed by collecting the variations in its characteristics. To solve the contradictory relationship between small samples and prediction accuracy of the supercapacitor lifetime prediction training set, a lifetime prediction method combining the non-dominated sorting genetic algorithm II (NSGA-II) with an elite retention strategy and support vector regression (SVR) was proposed, and the average values of charge-discharge cycles and charge-discharge duration were determined as the characterization of supercapacitor performance degradation. Moreover, a dual-objective model with the minimum sample size and highest prediction accuracy in the training set was established. Compared with the BP (back propagation), SVR and LSTM (long short-term memory) methods, the proposed NSGA-II-SVR method improved the evaluation indicators of root mean square error and mean absolute percentage error by 5 to 10 times in supercapacitor lifetime prediction under the premise of a small sample size of the training set. Finally, a generalization experiment was carried out using the open-source data set of lithium-ion batteries provided by NASA, proving that the NSGA-II-SVR method has strong generalization capability.

Supercapacitor  /  remaining life  /  non-dominated sorting genetic algorithm II (NSGA-II)  /  support vector regression (SVR)  /  training set
刘晓明, 任宇朋中国电源学会会员, 陈海, 冯大伟, 田俊. 基于改进支持向量回归的超级电容器寿命预测. 电源学报, 2025 , 23 (6) : 320 -328 . DOI: 10.13234/j.issn.2095-2805.2025.6.320
Xiaoming LIU, Yupeng REN, Hai CHEN, Dawei FENG, Jun TIAN. Life Prediction of Supercapacitor Based on Improved Support Vector Regression[J]. Journal of Power Supply, 2025 , 23 (6) : 320 -328 . DOI: 10.13234/j.issn.2095-2805.2025.6.320
  • Science & Technology Program of Hebei Province(E2021202186; E2020202086)
Year 2025 volume 23 Issue 6
56
6
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Article Info
doi: 10.13234/j.issn.2095-2805.2025.6.320
  • Receive Date:2022-11-09
  • Online Date:2026-10-26
  • Published:2025-10-30
Article Data
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History
  • Received:2022-11-09
  • Revised:2023-02-07
  • Accepted:2023-03-02
Funding
Science & Technology Program of Hebei Province(E2021202186; E2020202086)
Affiliations
    School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China
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表12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
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属
Genus
种数
Number of
species
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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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