期刊
MEASUREMENT
卷 191, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2022.110817
关键词
Lithium-ion battery; Particle filter; Health monitoring; Remaining useful life prediction; Evolving fuzzy predictor
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- eMech Systems Inc.
- Bare Point Water Treatment Plant in Thunder Bay, ON, Canada
The paper proposes an enhanced PF technology to improve the accuracy of battery state estimation and RUL prediction. The limitations of PF in sample degeneracy, impoverishment, and lack of new measurement data are effectively addressed by introducing enhanced particle method and evolving fuzzy predictor.
The particle filter (PF) technique can model nonlinear degradation features of battery's system, and conduct battery state estimation based on noisy measurements. However, PF has some limitations in system state estimation related to sample degeneracy and impoverishment. In addition, its posterior probability density function cannot be updated during the prognostic period due to the absence of new battery measurements. In this work, an enhanced PF technology is proposed to deal with these problems so as to improve PF modeling accuracy for battery state-of-health monitoring and remaining useful life (RUL) prediction. Specifically, an enhanced particles method is proposed to reduce the impact of sample degeneracy and impoverishment in state estimation. An evolving fuzzy predictor is adopted and fused into the enhanced PF structure to deal with the lack of new battery measurements during the prognostic period. The effectiveness of the proposed enhanced PF technology is validated through simulation tests.
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