Lithium ion battery remaining useful life prediction method and system based on PCA-NARX neural network

Abstract

The invention provides a lithium ion battery remaining useful life prediction method and system based on a PCA-NARX neural network, so as to solve the problems of large difficulty and high redundancyin lithium ion battery remaining useful life prediction health index measurement. The method comprises steps: 1) a constant current discharge voltage change law of the lithium ion battery at differentdischarge periods is analyzed, and parameters that reflect the degradation of battery performance are extracted; 2) the correlation between the extracted parameters and the correlation between the extracted parameters and the lithium ion battery capacity are verified, a PCA algorithm is used to remove redundancy of the parameters, and a main component obtained after redundancy removal is used asa health indicator for the lithium ion battery; and 3) the obtained health indicator for the lithium ion battery is inputted to an NARX neural network, and lithium ion battery capacity estimation andremaining useful life prediction are carried out. An experiment result proves that the method is high in prediction precision and can be used for accurate prediction on the lithium ion battery remaining useful life.

Description

technical field [0001] The invention belongs to the technical field of lithium-ion batteries, and in particular relates to a method and system for predicting the remaining service life of lithium-ion batteries based on a PCA-NARX neural network. Background technique [0002] Lithium-ion battery is an ideal power supply energy. With its advantages of high density, long life and no pollution, it has become the energy supply component of most mainstream system devices. However, in practical applications, due to the influence of temperature changes, overcharge, overdischarge, etc., the battery often cannot reach the expected life value. The safety problems caused by the degradation of battery performance pose a great threat to people's personal and property safety. Accurately estimating the state of health (State of Health, SOH) and predicting its remaining useful life (Remaining Useful Life, RUL) of lithium-ion batteries is of great value for the management and maintenance of ...

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Problems solved by technology

[0009] The present invention aims at the problem of difficulty in measuring the remaining service life of lithium-ion batteries and high redundancy in the measurement of health indicators, and proposes a method and system for predicting the remaining service life of lithium-ion batteries based on the PCA-NARX neural network. The method and system have good predictive performance

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