Lithium battery health state estimation and residual service life prediction method and equipment

Abstract

The invention provides a lithium battery health state estimation and residual service life prediction method and equipment. The method comprises the following steps: S1, extracting health characteristics related to capacity degradation on voltage, current and temperature curves in a battery charging period; s2, verifying the correlation degree between the extracted health features and battery capacity degradation by using a grey correlation analysis method, and proposing a multi-health feature fusion method to obtain indirect health features; s3, using an improved gravitational search algorithm to optimize the support vector regression model to estimate the state of health of the lithium battery; and S4, predicting the remaining service life of the lithium battery based on the polynomial regression model and the improved gravitational search algorithm optimized support vector regression model. The association degree between the obtained health characteristics and battery aging is higher, and the calculated amount is small; the RUL is predicted based on the estimation result of the SOH of the lithium battery, combined prediction of the RUL and the SOH is realized, and the health condition of the battery is evaluated more comprehensively.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of batteries, and in particular, to a method and device for estimating the state of health of a lithium battery and predicting its remaining service life. Background technique [0002] With the popularization of new energy vehicles, the global energy crisis and environmental pollution problems have been effectively alleviated. As the main power source of new energy vehicles, lithium batteries, battery state of health (SOH) and remaining service life (Remaining Useful Life) , RUL) has become the current research focus. SOH is generally expressed as the ratio between the current maximum available capacity of the battery and the initial capacity, and RUL is defined as the number of cycles required for the battery to decay from the current state to the end of life (EOL). Accurate SOH estimation and RUL prediction can effectively avoid overcharge, overdischarge and even thermal runaway. In...

AI Technical Summary

Problems solved by technology

However, the physical and chemical reactions in the battery discharge stage are very complex, and it is often difficult to obtain a complete charge-discharge curve in practice.

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