Journal of Cleaner Production, Vol. 212, pp. 240-249, 2019, DOI: 10.1016/j.jclepro.2018.12.041

Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries

YongZhi Zhang 1, Rui Xiong 1, HongWen He 1, and Michael Pecht 2
1 National Engineering Laboratory for Electric Vehicles, Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China
2 CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA


Remaining useful life (RUL) prediction is an effective technique to provide the lifetime information of lithium-ion batteries for both manufacturers and users. The current methods for battery RUL prediction face two challengesdfirst, a large amount of training data is required for accurate RUL prognostics, and, second, the prediction performance of the prognostic algorithms for battery failure cannot be verified effectively. To reduce the training data, a fusion technique consisting of relevance vector machine and particle filter (PF) was developed to construct an aging model of the battery for RUL prediction. Based on the fusion technique, the training data can be reduced to 30% of the entire degradation data. Then, a validation and verification framework based on the Monte Carlo method was introduced as a baseline to calibrate the number of particles and model noise level of PF. The calibrated PF predicted the failure time 18 cycles earlier than the real value within a prediction horizon of 560 cycles.

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