Indirect prediction method and device for residual life of lithium ion battery

Abstract

The invention relates to an indirect prediction method for the residual life of a lithium ion battery. The method comprises the following steps of collecting lithium ion battery monitoring data, a normalized battery capacity sequence and an equal-time voltage difference sequence; carrying out correlation analysis to extract health factors; constructing a lithium ion battery health state estimationmodel based on an echo state network; constructing a health factor prediction model based on a long short-term memory neural network, and calculating a health factor of a future cycle period; and calculating the true value of future cycle period capacity to finish the prediction of the residual life of the lithium ion battery. The invention further provides an indirect prediction device for the residual life of the lithium ion battery. According to the predication device and method, online prediction of the residual life of the lithium ion battery can be realized, and timely replacement and maintenance are carried out before the expiration of the residual life, so that normal operation of the lithium ion battery is ensured, and the prediction precision of the residual life of the lithiumion battery is improved.

Description

technical field [0001] The present invention relates to the field of lithium ion battery health management, and more particularly to an indirect prediction method and device for the remaining life of lithium ion batteries. Background technique [0002] With the development of science and technology, lithium-ion batteries are widely used in various fields such as spacecraft, electric vehicles, and portable electronic devices because of their advantages such as small size, light weight, high energy ratio, and long life. The health management of lithium-ion batteries is becoming more and more important. As an important part of the health management of lithium-ion batteries, the remaining useful life (RUL) prediction of lithium-ion batteries has become a research hotspot and challenge in the field of electronic system failure prediction and health management. one of the problems. Realizing accurate remaining service life estimation is helpful to improve system reliability, whic...

AI Technical Summary

Problems solved by technology

However, considering that there may be many factors interacting to cause performance degradation, and accurate estimation needs to take into account complex parameter calculations, it is difficult to reliably simulate the lithium-ion battery life degradation system

The data-driven method is mainly the traditional autoregressive model, but the autoregressive model is essentially a linear model, and the life decay of lithium-ion batteries is a nonlinear process, so there is still a large non-linearity in the actual prediction. linearity error

In addition, in the actual situation, the capacity will re-grow during the degradation process, and the anti-interference ability of the autoregressive model is relatively poor, so that the prediction accuracy is not high

Images