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Lithium ion battery SOH estimation method for optimizing and improving GRU neural network based on GA algorithm

A lithium-ion battery and neural network technology, applied in the field of lithium-ion battery SOH estimation, can solve problems such as inability to make full use of useful information, complex data preprocessing of original data, etc., and achieve good prediction accuracy and robustness, and the process is simple and improved. The effect of precision

Pending Publication Date: 2021-11-23
WENZHOU UNIVERSITY
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Problems solved by technology

This not only requires complex data preprocessing of the original data, but also cannot make full use of the useful information in the original data

Method used

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  • Lithium ion battery SOH estimation method for optimizing and improving GRU neural network based on GA algorithm
  • Lithium ion battery SOH estimation method for optimizing and improving GRU neural network based on GA algorithm
  • Lithium ion battery SOH estimation method for optimizing and improving GRU neural network based on GA algorithm

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Embodiment

[0040] Embodiment: optimize the lithium-ion battery SOH estimation method based on the GA algorithm to improve the GRU neural network, such as figure 1 The following steps are shown:

[0041]Step 1: Experimental data collection: set the lithium battery charging and discharging experimental working conditions to charge and discharge the lithium battery, record the voltage, current, temperature data of the lithium battery and the full capacity of the battery each discharge during the experiment; the lithium battery charging and discharging The experimental environment temperature is set to 0°C, 25°C and 45°C to simulate the low temperature, normal temperature and high temperature in actual driving respectively; the lithium battery charging and discharging experiment includes charging experiment and discharging experiment, and the charging experiment is first carried out at 1C rate Constant current charging to the upper cut-off voltage, and then constant voltage charging above th...

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Abstract

The invention discloses a lithium ion battery SOH estimation method for optimizing and improving a GRU neural network based on a GA algorithm, and the method comprises the following steps: 1, collecting experimental data: setting the charging and discharging experimental conditions of a lithium battery, carrying out the charging and discharging of the lithium battery, and recording the voltage, current and temperature data of the lithium battery in the experimental process, and the capacity of the battery when the battery is completely discharged each time; 2, preprocessing data: deleting invalid values from the collected original data and carrying out data normalization processing; 3, constructing a network model; and 4, taking the normalized data as the input of the network model, and carrying out lithium ion battery SOH estimation. The method has the advantages of simple process, accurate estimation result and high precision.

Description

technical field [0001] The invention relates to the technical field of power battery management, in particular to a lithium-ion battery SOH estimation method based on GA algorithm optimization and improved GRU neural network. Background technique [0002] The rapid development of the automobile industry has inevitably produced many negative effects: massive consumption of non-renewable energy such as petroleum, generation of automobile exhaust and greenhouse gases, etc. Faced with severe challenges such as resource shortage and environmental pollution, automobile companies have begun to vigorously develop electric vehicles that use power batteries as new energy sources to reduce dependence on petroleum energy and reduce vehicle exhaust emissions. Lithium-ion batteries are widely used in the field of electric vehicles due to their high energy density, light weight, and long charge-discharge cycle life. The state of health (SOH) of the lithium battery not only represents the ...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/392G01R31/385
CPCG01R31/367G01R31/392G01R31/385
Inventor 玄东吉陈建龙陈聪卢陈雷刘胜南谈佳淇胡浩钦
Owner WENZHOU UNIVERSITY
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