Rapid detection method for health degree of battery module

Abstract

The invention discloses a rapid detection method for the health degree of a battery module. The method comprises the following steps: acquiring platform voltage data of a battery module sample with known available capacity in a charging and discharging process; establishing a probability density function PDF curve of a known available capacity battery module sample according to the platform voltage data, and calculating a peak area of a set voltage interval; fitting and establishing a battery module health degree SOH-probability density function PDF curve; obtaining the relationship between the voltage data and the battery aging degree in the charging and discharging process of the battery module, wherein only platform voltage data in the charging and discharging process of the to-be-detected battery module needs to be detected in the detection process; and establishing a battery module health degree SOH value of a battery module sample corresponding to the consistency of the peak areain the battery module health degree SOH-probability density function PDF curve and the detection peak area, wherein the battery module health degree SOH value is the battery module health degree SOHvalue of the to-be-detected battery module. The method is simple, the detection speed is high, the battery module does not need to be independently modeled and detected, and the detection result is accurate.

Description

technical field [0001] The invention relates to a battery operation and maintenance technology, in particular to a method for quickly detecting the health of a battery module. Background technique [0002] Energy storage provides important technical support for the construction of the ubiquitous power Internet of Things, including clean energy consumption and comprehensive energy services, and more and more lithium battery energy storage power stations are becoming an important part of power grid transmission, transformation and distribution. component. After the energy storage power station has been in operation for a period of time, the performance of lithium batteries with good consistency gradually becomes uneven, which brings potential risks to the safe and efficient operation of the energy storage power station. Low-cost and fast online detection of battery health is the core technology that the operation and maintenance personnel of energy storage power stations and ...

AI Technical Summary

Problems solved by technology

Yan et al. used six selected decommissioned lithium iron phosphate batteries to test the cycle life of the battery under three typical energy storage load curves, and used the least square method to evaluate the battery life and input the IC curve characteristics into the regression model. The research results showed that the minimum The error of the quadratic model for evaluating battery life is within 3%, but the above-mentioned existing methods for battery life detection have low detection efficiency, require independent modeling or independent detection of the battery, and the cost of development programs is high. A new battery is urgently needed Life Testing and Evaluation Method

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