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Lithium battery state-of-charge estimation method considering capacity degradation

A state of charge, lithium battery technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problem of less research on battery state of charge estimation

Active Publication Date: 2021-09-14
BEIHANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the estimation results of data-driven methods perform well in considering dynamic discharge current or dynamic ambient temperature, there are few studies on battery state of charge estimation considering capacity degradation.

Method used

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  • Lithium battery state-of-charge estimation method considering capacity degradation
  • Lithium battery state-of-charge estimation method considering capacity degradation
  • Lithium battery state-of-charge estimation method considering capacity degradation

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Experimental program
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Embodiment Construction

[0015] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation methods.

[0016] Such as figure 1 As shown, a lithium battery state of charge estimation method considering capacity degradation includes the following steps:

[0017] S1. Obtain the lithium battery experiment data set, which contains the rated capacity of the lithium battery and the voltage, current and time data during the experiment. In this example, five standard charge and discharge cycle experimental data of lithium batteries with a rated capacity of 1.1Ah provided by the CALCE Center of the University of Maryland, numbered CS2_33, CS2_34, CS2_35, CS2_36, and CS2_37, are used as data sources, wherein CS2_33, CS2_34, The experimental data of CS2_35 and CS2_36 batteries are used as the lithium battery experimental data set, and the CS2_37 battery is used as the battery whose state of charge is to be estimated. In this experiment, five ba...

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Abstract

The invention discloses a lithium battery state-of-charge estimation method considering capacity degradation, which comprises the following steps: firstly, acquiring a lithium battery experiment data set, and calculating battery state-of-charge and state-of-health data to obtain a complete data set; then constructing a hybrid neural network with two input ends, an output end, one or more cyclic network layers and one or more full-connection layers; and training the established hybrid neural network by the complete data set and then using the established hybrid neural network for performing state-of-charge estimation on the battery to be estimated. The method can be suitable for estimating the state of charge of the lithium battery in consideration of capacity degradation, namely in different health states, is high in estimation precision, and has a very high practical application value.

Description

[0001] Technical field [0002] The invention relates to lithium battery technology and the field of artificial neural network technology, and provides a method for estimating the state of charge of a lithium battery, in particular to a method for estimating the state of charge of a lithium battery considering capacity degradation. Background technique [0003] Accurate lithium battery state-of-charge estimation is crucial for lithium battery management systems, which can help battery management systems predict remaining mileage and avoid overcharging or overdischarging of batteries, thereby ensuring the safety and reliability of lithium batteries . However, it is still very challenging to accurately estimate the state of charge of Li-ion batteries during capacity degradation. [0004] Commonly used lithium battery state of charge estimation methods mainly include ampere-hour integral method, open circuit voltage method, model-based method and data-driven method. Among them,...

Claims

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Application Information

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IPC IPC(8): G01R31/387G01R31/3842G01R31/392G01R31/367
CPCG01R31/387G01R31/3842G01R31/392G01R31/367Y02E60/10
Inventor 钱诚徐炳辉任羿孙博王自力
Owner BEIHANG UNIV
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