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Lithium battery SOH estimation method based on data driving

A healthy state, data-driven technology, applied in the direction of complex mathematical operations, measuring electricity, measuring electrical variables, etc., can solve high computing requirements, hinder practical application and other problems, achieve wide application value, improve prediction effect, save time and cost Effect

Inactive Publication Date: 2020-07-24
QINGDAO UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the model requires delicate parameters, and extremely high computational requirements hinder its practical application in BMS

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  • Lithium battery SOH estimation method based on data driving
  • Lithium battery SOH estimation method based on data driving
  • Lithium battery SOH estimation method based on data driving

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Embodiment Construction

[0033] The technical characteristics of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0034] As shown in the figure, the present invention is a data-driven lithium battery state of health SOH estimation method, including the following steps:

[0035] 1) For different types of power lithium batteries, calculate the rated capacity of the lithium batteries when they leave the factory;

[0036]2) Real-time recording of the data of different types of lithium batteries in the constant current charging mode until the end of battery life; After real-time recording of the data of different types of lithium batteries in the constant current charging mode, according to the real-time records The data form the lithium battery constant current charging voltage curve;

[0037] 3) Process and transform the constant current charging voltage curve of the lithium battery through the capacity increment analysis met...

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Abstract

The invention discloses a lithium battery SOH estimation method based on data driving. The method is characterized by comprising the following steps: 1) recording charging data of a lithium battery ina constant-current charging working mode in real time; 2) performing capacity increment curve calculation on a constant-current charging voltage curve by adopting a simplified dQ / dV processing mode through capacity increment analysis; determining the peak value intensity and the peak value position voltage of a No.2 peak of the capacity increment curve as characteristic vectors for estimating SOHthrough grey correlation analysis; 3) constructing a support vector regression model by taking the peak intensity and the peak position voltage as feature vectors as input and SOH as output; 4) fusing a differential evolution strategy with a grey wolf optimization algorithm to form an improved grey wolf optimization algorithm IGWO; and 5) performing three-parameter joint optimization on hyper-parameters in the support vector regression model through the IGWO. The method has the advantages that the defects in the prior art can be overcome, and the structural design is reasonable and novel.

Description

technical field [0001] The invention relates to a method for estimating the SOH state of health of a lithium battery based on data drive, and belongs to the field of battery power management. Background technique [0002] As a key component of electric vehicles, the battery system has a great influence on the driving performance and charging mileage of electric vehicles. Lithium batteries stand out among many types of batteries because of their inherent advantages such as high energy density, long life, and no memory effect. Battery state of health (State of health, SOH) is one of the various lithium battery performance parameters detected by the battery management system (BMS). Important reference. [0003] However, the complexity and coupling effects of the electrochemical reactions involved in the battery, and the different aging mechanisms of different types of lithium batteries, all increase the difficulty of accurately evaluating SOH. Existing SOH estimation methods...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/392G06F17/15
CPCG01R31/367G01R31/392G06F17/15
Inventor 李立伟王凯李龙刚
Owner QINGDAO UNIV
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