Lithium ion battery service life prediction method based on traceless particle filtering

Abstract

The invention discloses a lithium ion battery service life prediction method based on traceless particle filtering, and the method can achieve more accurate estimation of the capacity state of a battery and improves the prediction accuracy of the service life of the battery. The method comprises the steps: enabling a double-index capacity attenuation model as a lithium ion battery capacity degradation model, and obtaining a state transfer equation and measurement equation of the lithium ion battery capacity; obtaining the distribution of the initial value of a state variable of the double-index capacity attenuation model according to the known service life attenuation data of other batteries; determining a corresponding prediction starting point for a to-be-measured battery, wherein the service life of the to-be-measured battery needs to be predicted; carrying out the state tracking of to-be-measured battery capacity data of charging and discharging times through employing a traceless particle filtering method, updating the state variable in the capacity attenuation model, and obtaining a corresponding state variable after the charging; predicting the corresponding state variable and battery capacity after the charging and discharging, plotting a capacity prediction curve, and determining the service life of the to-be-measured battery.

Description

technical field [0001] The invention belongs to the technical field of lithium-ion battery fault prediction and health management, and in particular relates to a lithium-ion battery life prediction method based on traceless particle filtering. Background technique [0002] As a new type of storage battery, lithium-ion batteries have great application prospects, especially in occasions where the electrical performance and reliability of energy storage are required to be high, such as aerospace equipment such as low earth orbit, geosynchronous orbit, and space stations. [0003] The remaining service life of the battery is also called the cycle life, which refers to the number of charge and discharge cycles that the battery undergoes before the capacity drops to the specified value under a certain charge and discharge system. For many applications of lithium-ion batteries, the lithium-ion battery is considered to be invalid when the actual capacity drops to 70%-80% of the rate...

AI Technical Summary

Problems solved by technology

However, the particle filter (PF)-based life prediction method commonly used in the prior art has certain limitations in practical applications, specifically: in the standard particle filter algorithm, the prior distribution is generally taken as the suggested distribution, This method does not take into account the latest measurement information. When the system model is inaccurate or the measurement noise changes suddenly, the prediction result cannot effectively represent the real value.

In addition, the complex electrochemical reaction process inside the lithium-ion battery is difficult to characterize, and it is difficult to establish a battery capacity decay model that combines the entire life-span degradation process of the battery and the characteristics of the degradation data, which brings certain difficulties to life prediction. The estimation of battery life is inaccurate and fails to truly reflect the law of battery life, which brings many difficulties to the failure prediction and health management of lithium-ion batteries used in the future.

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