Lithium battery two-dimensional region of support transductive learning and state of health prediction oriented to few charge-discharge cycles
The state of health(SOH)of lithium-ion batteries is a key indicator reflecting the degree of battery aging.However,due to the nonlinearity and uncertainty of battery aging,it is challenging to estimate SOH accurately.Besides,affected by the high time cost of battery data collection and the phenomenon of capacity regeneration,the traditional data driving method is less effective when the number of historical charge-discharge cycles is small.To solve the above problems,this paper innovatively proposes a two-dimensional region of support transductive learning(2D-RoSTL)method,and establishes a precise data division method from coarse to fine,which is used for SOH prediction oriented to few charge-discharge cycles.On the one hand,considering the batch characteristics of the same model block battery,using historical data and batch data to construct a two-dimensional region of support to expand the information source of the model,providing an extensive range of samples for selection.On the other hand,we first attempt to solve the SOH prediction task by transductive learning method.Using offline and online information in the sample feature space,our model finely divides each sample to improve the online prediction reliability in the case of a few charge-discharge cycles.Based on the NASA's public data set,the proposed two-dimensional region of support transductive modeling method has a prediction error of less than 1.56％on the four batteries,and has realized the accurate prediction of the early history of lithium batteries and the point of regeneration.
lithium battery SOHfew charge-discharge cycles2-D region of supporttransductive learning
国家科技期刊平台
NSTL
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