Prediction of future capacity and internal resistance of Li-ion cells from one cycle of input data

There is a large demand for models able to predict the future capacity retention and internal resistance(IR)of Lithium-ion battery cells with as little testing as possible.We provide a data-centric model accurately predicting a cell’s entire capacity and IR trajectory from one single cycle of input data.This represents a significant reduction in the amount of input data needed over previous works.Our approach characterises the capacity and IR curve through a small number of key points,which,once predicted and interpolated,describe the full curve.With this approach the remaining useful life is predicted with an 8.6%mean absolute percentage error when the input-cycle is within the first 100 cycles.

Prognostic for fuel cell based on particle filter and recurrent neural network fusion structure

Fuel cells are considered as one of the most promising candidates for future power source due to its high energy density and environmentally friendly properties,whereas the short lifespan blocks its large-scale commercializa-tion.In order to enhance the reliability and durability of proton exchange membrane fuel cell,a fusion prog-nostic approach based on particle filter(model-based)and long-short term memory recurrent neural network(data-driven)is proposed in this paper.Both the remaining useful life estimation and the short-term degradation prediction can be achieved based on the prognostic method.For remaining useful life estimation,the particle filter method is used to identify the model parameters in the training phase and the long-short term memory recurrent neural network is used to update the parameters in the prediction phase.As for short-term degradation prediction,the particle filter and long-short term memory recurrent neural network are firstly trained individually in the training phase and then be fused to make predictions in the prediction phase.The proposed fusion structure is validated by the fuel cell experimental tests data,and results indicate that better prognostic performance can be obtained compared with the individual model-based or data-driven method.