Method and system for predicting remaining service lives of different lithium ion batteries of same type

Abstract

The invention relates to a method and a system for predicting remaining service lives of different lithium ion batteries of the same type. The method comprises the following steps of 1) extracting health factors capable of reflecting the performance degradation of lithium ion batteries; 2) establishing a health factor prediction model, wherein the health factor prediction model is a relation modelof battery health factor service life early stage and service life later stage and constructed by utilizing a neural network; 3) constructing a battery capacity prediction model, wherein the batterycapacity prediction model is a relation model of the heath factors and the battery actual capacity and constructed by utilizing a neural network; and 4) taking the service life early stage health factors of different batteries of the same type to be predicted as input, obtaining a battery service life later stage battery capacity prediction value based on the health factor prediction model and thebattery capacity prediction model, and then working out the remaining service life value of each battery at the current moment. The method has the advantages that the accuracy and the adaptability are relatively high in the prediction of RUL of different batteries of the same type.

Description

technical field [0001] The invention belongs to the interdisciplinary field of lithium-ion battery technology and information technology, and relates to a method for predicting the remaining service life of lithium-ion batteries, in particular to a method and system for predicting the remaining service life of different lithium-ion batteries of the same type. Background technique [0002] Lithium-ion is the power supply component of most mainstream system devices today. 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 predicting the remaining useful life (Remaining Useful Life, RUL) of lithium-ion batteries is of great value for battery management and maintenance, and prevention of dangerous accidents. [0003] The...

AI Technical Summary

Problems solved by technology

This method avoids the problem of selecting the starting point of prediction and retraining the model, and does not require the same type of battery life to be exactly the same

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