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Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors

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  • Zhou, Yanting
  • Wang, Yanan
  • Wang, Kai
  • Kang, Le
  • Peng, Fei
  • Wang, Licheng
  • Pang, Jinbo

Abstract

Supercapacitor as a clean energy storage device has been widely adopted in powering electric motors of vehicles. Precise evaluation of aging state of supercapacitors, i.e., the remaining useful life provides a feedback to replace damaged cells to sustain the comfort and safety of electric vehicle. Currently reported evaluation methods for such aim are data or model-based predications, which are either time consuming or of low precision. To achieve efficient and robust evaluation of the remaining lifetime, this work proposes a general strategy based on the combination between a recurrent neutral network method, i.e., long short-term memory, and hybrid genetic algorithm. The sequential quadratic programming as a local search operator of the genetic algorithm, enhances its global search ability, which allows quickly search for the local optimal solution in the means of the dropout probability and the number of hidden layer units. Eventually we apply this predication method to supercapacitors charging at steady state mode and succeed in estimating their remaining useful life. Such life prediction approach also gains validity in supercapacitors with dynamic operative cycle. Indeed, high accuracy has been achieved at both the online trained supercapacitors with root mean square errors ranging from 0.0161 and 0.0214, and offline historical data with 0.0264 error. Moreover, the estimation time is shortened to 3550 s, which is shortened by 60%. This evaluation model may pave the way in predicting the remaining useful lifetime of supercapacitors as well as secondary ion batteries in a precise and robust fashion.

Suggested Citation

  • Zhou, Yanting & Wang, Yanan & Wang, Kai & Kang, Le & Peng, Fei & Wang, Licheng & Pang, Jinbo, 2020. "Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919318562
    DOI: 10.1016/j.apenergy.2019.114169
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    as
    1. Rubino, Luigi & Capasso, Clemente & Veneri, Ottorino, 2017. "Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility," Applied Energy, Elsevier, vol. 207(C), pages 438-464.
    2. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    3. Zhu, Bangzhu & Ye, Shunxin & Jiang, Minxing & Wang, Ping & Wu, Zhanchi & Xie, Rui & Chevallier, Julien & Wei, Yi-Ming, 2019. "Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach," Applied Energy, Elsevier, vol. 233, pages 196-207.
    4. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    5. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    6. Capasso, Clemente & Lauria, Davide & Veneri, Ottorino, 2018. "Experimental evaluation of model-based control strategies of sodium-nickel chloride battery plus supercapacitor hybrid storage systems for urban electric vehicles," Applied Energy, Elsevier, vol. 228(C), pages 2478-2489.
    7. Capasso, Clemente & Veneri, Ottorino, 2015. "Experimental study of a DC charging station for full electric and plug in hybrid vehicles," Applied Energy, Elsevier, vol. 152(C), pages 131-142.
    8. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    9. Veneri, Ottorino & Capasso, Clemente & Patalano, Stanislao, 2018. "Experimental investigation into the effectiveness of a super-capacitor based hybrid energy storage system for urban commercial vehicles," Applied Energy, Elsevier, vol. 227(C), pages 312-323.
    10. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    11. Veneri, Ottorino & Capasso, Clemente & Patalano, Stanislao, 2017. "Experimental study on the performance of a ZEBRA battery based propulsion system for urban commercial vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 2005-2018.
    12. Capasso, Clemente & Veneri, Ottorino, 2014. "Experimental analysis on the performance of lithium based batteries for road full electric and hybrid vehicles," Applied Energy, Elsevier, vol. 136(C), pages 921-930.
    13. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    14. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    15. Chen, Wei-Hsin & Wu, Po-Hua & Lin, Yu-Li, 2018. "Performance optimization of thermoelectric generators designed by multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 209(C), pages 211-223.
    16. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    17. Quashie, Mike & Marnay, Chris & Bouffard, François & Joós, Géza, 2018. "Optimal planning of microgrid power and operating reserve capacity," Applied Energy, Elsevier, vol. 210(C), pages 1229-1236.
    18. Ma, Tao & Yang, Hongxing & Lu, Lin, 2015. "Development of hybrid battery–supercapacitor energy storage for remote area renewable energy systems," Applied Energy, Elsevier, vol. 153(C), pages 56-62.
    19. Veneri, Ottorino & Capasso, Clemente & Iannuzzi, Diego, 2016. "Experimental evaluation of DC charging architecture for fully-electrified low-power two-wheeler," Applied Energy, Elsevier, vol. 162(C), pages 1428-1438.
    20. Jing, Wenlong & Lai, Chean Hung & Wong, Wallace S.H. & Wong, M.L. Dennis, 2018. "A comprehensive study of battery-supercapacitor hybrid energy storage system for standalone PV power system in rural electrification," Applied Energy, Elsevier, vol. 224(C), pages 340-356.
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    8. Haris, Muhammad & Hasan, Muhammad Noman & Qin, Shiyin, 2021. "Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network," Applied Energy, Elsevier, vol. 286(C).
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