WO2022237660A1 - Procédé de diagnostic de vieillissement en ligne de batterie au lithium basé sur des caractéristiques de vieillissement à deux points - Google Patents

Procédé de diagnostic de vieillissement en ligne de batterie au lithium basé sur des caractéristiques de vieillissement à deux points Download PDF

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WO2022237660A1
WO2022237660A1 PCT/CN2022/091340 CN2022091340W WO2022237660A1 WO 2022237660 A1 WO2022237660 A1 WO 2022237660A1 CN 2022091340 W CN2022091340 W CN 2022091340W WO 2022237660 A1 WO2022237660 A1 WO 2022237660A1
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capacity
lithium battery
charge
charging
charging voltage
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PCT/CN2022/091340
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English (en)
Chinese (zh)
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陈剑
刘浩
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浙江大学
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Publication of WO2022237660A1 publication Critical patent/WO2022237660A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the invention belongs to the field of lithium battery application and relates to an online aging diagnosis method for lithium batteries, and relates to an online aging diagnosis method for lithium batteries based on two-point aging characteristics.
  • lithium batteries Due to many advantages such as high energy density, low cost, fast response to power demand, and long cycle life, lithium batteries have been commercially used in various fields on a large scale. Aging diagnosis technology plays an important role in the safe and reliable operation of lithium batteries. However, due to the complex aging mechanism of lithium batteries, and the aging path is affected by many factors in the design, production and application process, it is a challenge to achieve simple, fast and accurate lithium battery aging diagnosis under complex dynamic operating conditions .
  • the present invention proposes an online aging diagnosis method for lithium batteries based on two-point aging characteristics.
  • the present invention comprises the following steps:
  • the lithium battery aging diagnosis regression model is trained to obtain the trained lithium battery aging diagnosis regression model
  • the charging voltage and charging capacity corresponding to the best charging voltage combination in the kth charging and discharging cycle of the lithium battery to be diagnosed and the current charging and discharging cycle after the kth time are respectively collected, and the best charging voltage combination is calculated.
  • the best two-point aging characteristics to be predicted input the best two-point aging characteristics to be predicted into the trained lithium battery aging diagnosis regression model for diagnosis, and output the total lithium battery capacity of the current lithium battery to be diagnosed, According to the total capacity of the lithium battery, the current aging state of the lithium battery to be diagnosed is judged.
  • Each voltage-capacity curve in the step 2) is subtracted from the voltage-capacity reference curve, and the capacity difference curve corresponding to each charge-discharge cycle is calculated and obtained, specifically:
  • the step 3) is specifically:
  • two different charging voltages in each capacity difference curve are regarded as a charging voltage combination, and the absolute value of the difference between the capacity difference vectors corresponding to a charging voltage combination is calculated and used as a two-point aging characteristic , traverse all charging voltage combinations, obtain all two-point aging characteristics of the current capacity difference curve, traverse each capacity difference curve, and calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charge-discharge cycle.
  • the step 5) is specifically:
  • the correlation coefficient matrix is formed by the correlation coefficients corresponding to all charging voltage combinations, and the charging voltage combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is taken as the optimal charging voltage Combination, and then the two-point aging characteristics corresponding to the best charging voltage combination as the best two-point aging characteristics, and finally the best two-point aging characteristics of all lithium batteries in all charge and discharge cycles and the corresponding lithium batteries in charge and discharge cycles
  • the total battery capacity constitutes the training set.
  • the correlation coefficient is the Pearson correlation coefficient, specifically calculated by the following formula:
  • ⁇ X, Y represents the correlation coefficient between the two-point aging characteristics corresponding to the same charging voltage combination in all charging and discharging cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charging and discharging cycles, and X represents all charging and discharging cycles of all lithium batteries.
  • the set of two-point aging characteristics corresponding to the same charging voltage combination in the discharge cycle, Y represents the set of the total capacity of lithium batteries of all lithium batteries in all charge and discharge cycles, and E() represents the expected operation.
  • the charging mode of each charging and discharging cycle of the lithium battery is the same as the charging mode of the voltage-capacity reference curve in the preset charging voltage range, and the charging mode varies according to different battery models.
  • the lithium battery aging diagnosis regression model selects a linear regression model and a nonlinear regression model according to the distribution relationship between the best two-point characteristics and the total capacity of the lithium battery.
  • the invention solves the problem of difficulty in online aging diagnosis of lithium batteries in practical applications.
  • the two-point aging characteristics can be calculated by monitoring the capacity values corresponding to two fixed charging voltage points in each charge-discharge cycle of the lithium battery during online application, and then Realize accurate lithium battery aging diagnosis, reduce data storage burden, calculation burden and cost burden, and do not need to rely on specific discharge test data that does not exist in actual lithium battery applications, and are more suitable for online aging diagnosis of lithium batteries in actual application scenarios. Contribute to the safer and more reliable operation of lithium batteries.
  • Fig. 1 is the overall flowchart of the present invention.
  • Fig. 2 is a schematic diagram of calculating the capacity difference vectors of the first charge-discharge cycle and the sixth charge-discharge cycle in the same charging mode and the same stage of the lithium battery in the embodiment of the present invention and within the same voltage range.
  • Fig. 3 is a position diagram of two charging voltages corresponding to the optimal two-point aging characteristics selected in the embodiment of the present invention on different capacity difference curves of a lithium battery.
  • Fig. 4 is a graph showing the distribution relationship between the best two-point aging characteristics of all lithium batteries selected in the embodiment of the present invention in all charge and discharge cycles and the corresponding total capacity of lithium batteries on the logarithmic axis of 10.
  • the present invention comprises the following steps:
  • step 2) each voltage capacity curve is subtracted from the voltage capacity reference curve to calculate and obtain the capacity difference curve corresponding to each voltage capacity curve, specifically:
  • Step 3) is specifically:
  • the preset charging voltage range two different charging voltages in each capacity difference curve are regarded as a charging voltage combination, and the absolute value of the difference between the capacity difference vectors corresponding to a charging voltage combination is calculated and used as a two-point aging characteristic , traverse all charging voltage combinations, obtain all two-point aging characteristics of the current capacity difference curve, traverse each capacity difference curve, and calculate the two-point aging characteristics corresponding to all charging voltage combinations of each charge-discharge cycle.
  • the preset charging voltage range is preferably 3.05V-4.20V. The higher the accuracy of the charging voltage within the range allowed by the sensor accuracy, the better. The higher the accuracy of the charging voltage, the more charging voltage combinations, and the more corresponding two-point aging characteristics.
  • Step 5) is specifically:
  • the correlation coefficient of all charging voltage combinations is obtained through traversal calculation, and the correlation coefficient matrix is formed by the correlation coefficients of all charging voltage combinations.
  • the correlation coefficient matrix is used as the correlation between the two-point aging characteristics corresponding to different charging voltage combinations and the total capacity of the lithium battery.
  • the charging voltage combination corresponding to the correlation coefficient with the largest absolute value in the correlation coefficient matrix is taken as the best charging voltage combination, and then the two aging characteristics corresponding to the best charging voltage combination are taken as the best two points Aging characteristics.
  • the best two-point aging characteristics of all lithium batteries in all charge and discharge cycles and the total capacity of lithium batteries in the corresponding charge and discharge cycles constitute a training set.
  • the best two-point aging characteristics are specifically the two-point aging characteristics of all lithium batteries under the best charging voltage combination in different charge and discharge cycles, and the best charging voltage combination in each charge and discharge cycle
  • the label of the two-point aging characteristic is the total lithium battery capacity of the current lithium battery in the current charge and discharge cycle.
  • the row number and column number of the correlation coefficient in the correlation coefficient matrix respectively represent the two charging voltages in the charging voltage combination corresponding to the two-point aging characteristics, and the row and column of the correlation coefficient matrix both represent the preset charging voltage range .
  • the correlation coefficient is the Pearson correlation coefficient, which is calculated by the following formula:
  • ⁇ X, Y represents the correlation coefficient between the two-point aging characteristics corresponding to the same charging voltage combination in all charging and discharging cycles of all lithium batteries and the total capacity of lithium batteries in the corresponding charging and discharging cycles, and X represents all charging and discharging cycles of all lithium batteries.
  • the set of two-point aging characteristics corresponding to the same charging voltage combination in the discharge cycle, Y represents the set of the total capacity of lithium batteries of all lithium batteries in all charge and discharge cycles, and E() represents the expected operation.
  • the lithium battery aging diagnosis regression model selects the linear regression model and the nonlinear regression model according to the distribution relationship between the best two-point aging characteristics and the total capacity of the lithium battery.
  • the charging voltage and charging capacity corresponding to the best charging voltage combination in the kth charging and discharging cycle of the lithium battery to be diagnosed and the current charging and discharging cycle after the kth time are respectively collected, and the best charging voltage combination is calculated.
  • the best two-point aging characteristics to be predicted input the best two-point aging characteristics to be predicted into the trained lithium battery aging diagnosis regression model for diagnosis, and output the total lithium battery capacity of the current lithium battery to be diagnosed, According to the total capacity of the lithium battery, the current aging state of the lithium battery to be diagnosed is judged.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

L'invention concerne un procédé de diagnostic de vieillissement en ligne de batterie au lithium basé sur des caractéristiques de vieillissement à deux points. La présente invention comprend les étapes suivantes consistant à : 1 collecter et calculer une courbe de référence de capacité de tension d'une batterie au lithium actuelle; 2 calculer et obtenir une courbe de différence de capacité correspondant à chaque cycle de charge-décharge de la batterie au lithium actuelle; 3 calculer des caractéristiques de vieillissement à deux points correspondant à toutes les combinaisons de tension de charge de chaque cycle de charge-décharge de la batterie au lithium actuelle; 4 répéter 1 à 3 pour obtenir des caractéristiques de vieillissement à deux points de chaque cycle de charge-décharge de chaque batterie au lithium et les capacités totales des batteries au lithium; 5 sélectionner une combinaison optimale de tension de charge et une caractéristique de vieillissement à deux points optimale, et former un ensemble d'apprentissage; 6 obtenir un modèle de régression de diagnostic de vieillissement de batterie au lithium entraîné; et 7 collecter et calculer, pendant le diagnostic en ligne, la caractéristique de vieillissement à deux points optimale à prévoir d'une batterie au lithium à diagnostiquer, et obtenir la capacité totale de la batterie au lithium après le diagnostic de façon à déterminer l'état de vieillissement de la batterie au lithium à diagnostiquer. La présente invention permet d'obtenir un diagnostic de vieillissement de batterie au lithium précis, et la charge de stockage de données, la charge de calcul et la charge de coût sont réduites.
PCT/CN2022/091340 2021-05-13 2022-05-07 Procédé de diagnostic de vieillissement en ligne de batterie au lithium basé sur des caractéristiques de vieillissement à deux points WO2022237660A1 (fr)

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CN202110522337.9A CN113447816B (zh) 2021-05-13 2021-05-13 一种基于两点老化特征的锂电池在线老化诊断方法
CN202110522337.9 2021-05-13

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CN113447816B (zh) * 2021-05-13 2022-04-08 浙江大学 一种基于两点老化特征的锂电池在线老化诊断方法
CN116610926A (zh) * 2023-07-20 2023-08-18 中国第一汽车股份有限公司 电池老化特征的确定方法、装置、存储介质和车辆

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CN104701924B (zh) * 2014-09-22 2018-05-18 深圳市沛城电子科技有限公司 智能电池平衡管理的方法
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