New method for predicating residual life of lithium-ion battery

Abstract

The invention discloses a new method for predicating the residual life of a lithium-ion battery. The method comprises the following steps of sampling an experimental sample, and determining the rated capacity and rated internal resistance of a sample battery; respectively setting three influence factors, namely different ambient temperatures, discharge rates and discharge depth; measuring the increase rates of the internal resistance of the battery under influence of a single factor and two coupled factors, and establishing a correction factor database; determining three correction factors for the internal resistance of the battery by use of a curve fitting method; and collecting the constant-current charge time in real time, obtaining real-time variable battery internal resistance by use of the three correction factors and the rated internal resistance of the battery, and predicating the residual life of the battery by use of a Monte Carlo method. The method is capable of relatively accurately reflecting the variation trend of the battery capacity along with circulation times, and can also ensure the accuracy and rapidness of estimation on the residual life of lithium-ion batteries, and effectively guiding the reasonable use and maintenance work of the batteries.

Description

technical field [0001] The invention relates to the technical field of battery management, in particular to a new method for predicting the remaining life of a lithium-ion power battery. Background technique [0002] With the development of social economy, the demand for energy is further increased, and the voice of new energy technology is getting higher and higher. At present, in the automotive industry, the further development of pure electric vehicle and hybrid vehicle technology has solved part of the energy problem. Among them, the power battery pack It is the power source of pure electric vehicles. [0003] Lithium-ion power battery is a green high-energy rechargeable battery, which is widely used in electronic communication engineering, transportation, aerospace and other fields because of its high capacity, low self-discharge rate, high safety, and long cycle life. However, the performance degradation of lithium batteries during use will affect the normal use of in...

AI Technical Summary

Problems solved by technology

This method has high prediction accuracy, but the modeling process is complicated, and the model solution is not easy to realize, so it is not widely used in actual engineering, such as a lithium-ion battery capacity estimation and remaining cycle life prediction method with patent application number 201310317219.X ; (2) Use curve fitting, neural network and other data-driven methods to analyze and model the degradation process of lithium-ion battery capacity, such as the method for predicting the remaining life of lithium-ion batteries based on wavelet noise reduction and correlation vector machine with patent number 201410833574.7

This type of method takes the monotonically increasing number of cycles as the model parameter, but the lithium-ion battery is a dynamic and nonlinear electrochemical system, which is easily affected by the external environment and its own load during operation, resulting in a certain operating condition. The performance of the battery may change from bad to good

Therefore, using the number of cycles as the remaining life prediction parameter cannot accurately reflect the energy output and loss of the battery during actual use, and the prediction accuracy is not high

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