Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Lin Li, Alfredo Alan Flores Saldivar, Yun Bai and Yun Li
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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

Energies, 2019, vol. 12, issue 14, 1-18

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.

Keywords: lithium-ion battery; battery remaining useful life; particle filter; evolutionary computation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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