A method for online estimation of state of health of lithium-ion batteries

Abstract

The invention belongs to the field of lithium ion batteries, and discloses an online SOH estimation method of a lithium ion battery for solving the problems that characteristic parameters are difficult to be obtained online, the dependency of a model on training data is high, the required data size is large, the complex function relationship between the battery capacity and the characteristic parameters is difficult to be described by simple linear regression, and the estimation accuracy is difficult to be guaranteed in an implementation process of the existing SOH estimation technology. According to the online SOH estimation method disclosed by the invention, the characteristic parameters are obtained from a capacity increment curve by using a capacity increment method. The method does not require the battery to undergo a complete charging and discharging process, the feature parameter extraction is simpler, and the application of the method in the BMS is facilitated. The establishment of a characteristic parameter and SOH function model is completed by using a multi-output Gaussian process regression model method, the potential correlation between different outputs is better used, and the estimation accuracy of SOH is improved. Meanwhile, the dependency of the method on the training data is small, and the online SOH estimation method has very good adaptability on different types of lithium ion batteries.

Description

technical field [0001] The invention belongs to the field of lithium ion batteries, in particular to an online estimation method for the state of health (State of Health, SOH) of a lithium ion battery. Background technique [0002] The dual pressure of energy shortage and environmental pollution has boosted the rapid development of electric vehicles. Lithium-ion batteries have become the preferred battery for electric vehicles due to their advantages of high single voltage, high energy density, long life, no memory effect and no pollution. . During the use of the battery's full life cycle, with the increase of the use time and the number of cycles, the characteristics of battery capacity, energy and power will decline. Accurate estimation of the state of health of a lithium-ion battery is of great significance for estimating its state of charge (SOC), preventing overcharge and overdischarge, and ensuring the safe and economical operation of the battery system. [0003] Sin...

AI Technical Summary

Problems solved by technology

The direct measurement method has high precision, but requires a special off-line test for the battery system, which is difficult to implement for vehicle power batteries; (2) Empirical model method: the decay curve of the target battery is estimated through a large number of decay experiments, according to However, it has poor applicability and low precision for different types of lithium-ion batteries; (3) data-driven method: use data-driven algorithm to obtain battery decline model from training data, this method is practical and the estimation accuracy is relatively low. High, but dependent on training data

[0004] The existing SOH estimation technology has difficulties in obtaining the characteristic parameters online during the implementation process. The model has a strong dependence on the training data and requires a large amount of data. It is difficult to describe the complex functional relationship between the battery capacity and the characteristic parameters by simple linear regression, and the estimation accuracy is difficult. Warranty question

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