Method for estimating state of health (SOH) of lithium battery based on grey neural network

Abstract

The invention discloses a method for estimating the state of health (SOH) of a lithium battery based on a grey neural network. The method of the invention includes the following steps of: respectivelyperforming constant-current discharge and pulse discharge on the battery, and recording battery capacity data in the constant-current discharge process and the battery terminal voltage and dischargecurrent in the pulse discharge process of the battery; analyzing the characteristics of the battery terminal voltage, and building a third-order RC model in Simscape as an equivalent circuit model ofthe battery; automatically estimating internal impedance parameters of the battery through the battery model; constructing a battery SOH estimation model combining the grey theory with a neural network, and training the model according to the recorded internal impedance parameters of the battery and the battery capacity; and estimating the battery capacity by the model, and further calculating theSOH of the battery. The method of the invention can adapt to the highly nonlinear characteristics of an electrochemical system of the battery, and has the advantages of small data calculation amount,less required sample data, high prediction accuracy and the like.

Description

Technical field [0001] The invention belongs to the technical field of lithium batteries, and relates to a method for estimating the health state of lithium batteries based on a gray neural network. Background technique [0002] Compared with traditional batteries, lithium-ion batteries charge faster, last longer, have higher power density, and can provide longer battery life in a lighter volume. But all rechargeable batteries are consumables and have a limited life span. With the increase in the number of battery charge and discharge cycles, the chemical age of the battery continues to grow, and the amount of power that the battery can store will decrease, resulting in the gradual shortening of the battery's use time, thereby affecting the normal operation of the battery. Therefore, an accurate estimation of the battery SOH can promptly replace the battery for the product to ensure product performance and safety. [0003] The battery SOH value cannot be measured directly like vo...

AI Technical Summary

Problems solved by technology

[0003] The battery SOH value cannot be directly measured like voltage, current, temperature and other characteristics, it can only be calculated indirectly

At present, a variety of SOH estimation methods have been realized at home and abroad, mainly including: (1) Direct discharge method: Let the single battery be actually discharged once, test the released capacity, and estimate the battery SOH through the definition of the battery health state. It is only used for offline testing, and the discharge time is long, and the operation is inconvenient; (2) Internal resistance method: estimate SOH by establishing the relationship between internal resistance and SOH, but the internal resistance of the battery is at the milliohm level, which is difficult to be accurate Measurement; (3) Electrochemical impedance analysis: Apply multiple sinusoidal signals of different frequencies to the battery, and then use fuzzy theory to analyze the

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