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Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Author

Listed:
  • Lin Li

    (Industry 4.0 Artificial Intelligence Laboratory, School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China)

  • Alfredo Alan Flores Saldivar

    (Industry 4.0 Artificial Intelligence Laboratory, School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China)

  • Yun Bai

    (Industry 4.0 Artificial Intelligence Laboratory, School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China)

  • Yun Li

    (Industry 4.0 Artificial Intelligence Laboratory, School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China)

Abstract

Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.

Suggested Citation

  • Lin Li & Alfredo Alan Flores Saldivar & Yun Bai & Yun Li, 2019. "Battery Remaining Useful Life Prediction with Inheritance Particle Filtering," Energies, MDPI, vol. 12(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2784-:d:249978
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    References listed on IDEAS

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    Cited by:

    1. Wen, Pengfei & Zhao, Shuai & Chen, Shaowei & Li, Yong, 2021. "A generalized remaining useful life prediction method for complex systems based on composite health indicator," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    2. Ajaykumar Unagar & Yuan Tian & Manuel Arias Chao & Olga Fink, 2021. "Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning," Energies, MDPI, vol. 14(5), pages 1-12, March.
    3. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Qiangqiang Cheng & Yiqi Yan & Shichao Liu & Chunsheng Yang & Hicham Chaoui & Mohamad Alzayed, 2020. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling," Energies, MDPI, vol. 13(24), pages 1-15, December.
    5. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.

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