WO2018112818A1 - Procédé de prédiction rapide de la durée de vie de batterie et dispositif de prédiction rapide associé - Google Patents

Procédé de prédiction rapide de la durée de vie de batterie et dispositif de prédiction rapide associé Download PDF

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WO2018112818A1
WO2018112818A1 PCT/CN2016/111431 CN2016111431W WO2018112818A1 WO 2018112818 A1 WO2018112818 A1 WO 2018112818A1 CN 2016111431 W CN2016111431 W CN 2016111431W WO 2018112818 A1 WO2018112818 A1 WO 2018112818A1
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Prior art keywords
discharge
dod
depth
state line
state
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PCT/CN2016/111431
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English (en)
Chinese (zh)
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余登超
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深圳中兴力维技术有限公司
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Priority to PCT/CN2016/111431 priority Critical patent/WO2018112818A1/fr
Priority to CN201680025104.5A priority patent/CN108064391B/zh
Publication of WO2018112818A1 publication Critical patent/WO2018112818A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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]
    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the invention relates to the technical field of battery cycle life testing, in particular to a rapid prediction method for battery cycle life and a rapid prediction device thereof.
  • Battery cycle life is an important indicator of battery performance.
  • the current lead-acid battery cycle life test mainly tests the battery by setting extreme test conditions (normal temperature cycle, low temperature cycle, high temperature cycle test, etc.), but accelerates test life results and There is no clear quantitative correspondence between routine life test results. If the accelerated life test method is not used and the test method is recommended according to national standards, the test time is too long, which is not conducive to the development of the enterprise.
  • Cida Patent Application Publication No. CN102135603A describes a battery cycle life estimating device, which comprises a measuring unit, an observer unit, an adaptive parameter unit, an internal voltage estimating unit, an open circuit voltage estimating unit, and a a battery cycle life calculation unit and a battery residual power estimator, wherein the measurement unit is configured to measure a battery operating current, an operating voltage, and an operating temperature, and the observer unit can observe a battery output end and a battery RC parallel circuit voltage,
  • the adaptive parameter unit may update a parameter value of the battery, and the internal voltage estimating unit may estimate an internal voltage of the RC parallel circuit of the battery, and the open circuit voltage estimating unit may calculate a static open circuit voltage of the battery, the battery
  • the cycle life calculation unit may calculate a battery cycle life value, and the battery residual battery estimator may estimate a battery residual charge value.
  • This method requires the construction of complex hardware, and there are too many test points, which are easily affected by the outside world and have a certain impact on the test results.
  • the Chinese patent application of the publication No. CN103399281A describes a lithium ion battery cycle life prediction method based on the ND-AR model and the EKF method of the cycle life degradation stage parameter, and relates to a lithium ion battery cycle life prediction method, and the online measurement method of the present invention Measuring the capacity data of the lithium battery, preserving the data and preprocessing the data; determining the parameters of the online lithium ion battery empirical degradation model based on the EKF method; using the pre-processed data to determine the online battery using the fusion autoregressive coefficient method AR model; offline condition simulation online conditional charge and discharge test with the same type of battery as the lithium ion battery to be tested, the capacity of the same type of battery for the predicted lithium ion battery and the lithium ion battery to be tested The degradation model is correlated, and the battery capacity data of each charge and discharge cycle is compared with the failure threshold of the lithium ion battery to be tested to obtain the RUL, and the cycle life prediction of the lithium ion
  • the main object of the present invention is to provide a rapid prediction method for battery cycle life and a rapid prediction device thereof, which aim to provide a rapid method for predicting battery cycle life and achieve good results.
  • the present invention provides a rapid prediction method for battery cycle life, which comprises the steps of:
  • the actual coordinates of the collection point are substituted into the function, and the values of the fitting coefficients a, b, c, and d corresponding to the state line at the depth of the discharge are obtained;
  • the semi-supervised method in machine learning is used to predict the unknown state and obtain a new fitted state line.
  • the method further includes:
  • Each of the newly fitted state lines is tested with a sampling point under a least squares criterion; if the error exceeds a preset threshold, the fitting coefficient corresponding to the newly fitted state line is re-adjusted.
  • the obtaining the status line of the battery capacity and the number of cycles of the n different depths of discharge includes:
  • converting the collected coordinates of the collection point into actual coordinates includes:
  • the obtaining a relationship between the fitting coefficient and the depth of the discharge includes:
  • state lines under other discharge depths DOD i between DOD 1 and DOD 2 discharge depths are predicted according to a i , b i , c i , d i at different discharge depths
  • the present invention further provides a device for quickly predicting the cycle life of a battery, comprising:
  • a state line obtaining unit configured to acquire n state lines of battery capacity and cycle times under different depths of discharge
  • An acquisition unit configured to collect a plurality of collection points on the state line
  • a conversion unit configured to calculate an actual coordinate (x, y) according to the collection point, where y is a battery capacity, and x is a cycle number;
  • a second calculating unit configured to acquire a relationship between the fitting coefficient and the depth of discharge
  • a testing unit configured to test each of the newly fitted state lines with sampling points under a least squares criterion; if the error exceeds a preset threshold, re-adjust the new fitting The fit factor for the status line.
  • the state line obtaining unit is configured to respectively acquire a state line of a battery capacity and a cycle number of the discharge depth of 30%, 50%, 80%, and 100%.
  • the conversion unit is configured to:
  • the second computing unit is configured to:
  • the second calculation unit is further configured to: according to a i at said different depth of discharge, b i, c i, d i DOD 1 between the predicted and the depth of discharge DOD 2 other discharge depth DOD i Status line
  • the rapid prediction method for battery cycle life proposed by the invention and the fast prediction device thereof fit the state lines under n different depths of discharge by an exponential function, and obtain an exponential relationship between the fitting constant and the depth of discharge, using machine learning
  • the semi-supervised method predicts the unknown state and predicts other state lines. It is verified by experiments that the predicted state line can be well fitted to the relationship between capacity and cycle times under different depths of discharge.
  • the error between predicted and actual values is basically maintained. Within 1%, the error is small and meets the actual needs.
  • FIG. 1 is a schematic flow chart of a method for quickly predicting a cycle life of a battery according to an embodiment of the present invention
  • FIG. 2 is a schematic flow chart of a method for quickly predicting a cycle life of a battery according to another embodiment of the present invention
  • 3 is a state line of battery capacity and cycle times under four different depths of discharge according to an embodiment of the present invention
  • Figure 5 is a coefficient of the four state lines shown in Figure 3 fitted at different depths of discharge
  • FIG. 6 is a schematic structural diagram of a device for quickly predicting a cycle life of a battery according to an embodiment of the present invention
  • FIG. 7 is a schematic structural diagram of a device for quickly predicting a cycle life of a battery according to another embodiment of the present invention.
  • a first embodiment of the present invention provides a fast prediction method for battery cycle life, including the steps of:
  • the value of the state line corresponding to the depth of 100% corresponds to the values of a, b, c, and d; the state line after the fitting of the depth of discharge is 100%;
  • the combination of the above formula S14 and the relationship in S15 can relate the capacity, the cycle time, and the depth of discharge (DOD), thereby predicting other state lines.
  • a second embodiment of the present invention provides a method for quickly predicting the cycle life of a battery. Steps S21 to S26 are the same as those of S11 to S16 mentioned in the first embodiment, and are not described herein.
  • step S26 further includes the following steps:
  • the preset threshold is, for example, 1%.
  • a third embodiment of the present invention provides a rapid prediction method for battery cycle life, which includes the same steps as S11 to S16 mentioned in the first embodiment, or the same as S21 to S27 mentioned in the second embodiment, specifically As mentioned above, it will not be described here.
  • step S11 or step S21 specifically includes: acquiring state lines of battery capacity and cycle number of discharge depths of 30%, 50%, 80%, and 100%, respectively.
  • a fourth embodiment of the present invention provides a rapid prediction method for battery cycle life, which includes the same steps as S11 to S16 mentioned in the first embodiment, or the same as S21 to S27 mentioned in the second embodiment, specifically As mentioned above, it will not be described here.
  • step S13 or step S23 if the coordinates of the collected points on a certain state line are the coordinates of the collected points, isingx' n y' n ; and y' 1 corresponds to the actual point coordinates y 1 ; y' n corresponds to the actual point coordinates y n ; Actual point coordinates corresponding to y' i y i is:
  • a fifth embodiment of the present invention provides a rapid prediction method for battery cycle life, which includes the same steps as S11 to S16 mentioned in the first embodiment, or the same as S21 to S27 mentioned in the second embodiment, specifically As mentioned above, it will not be described here.
  • step S15 or step S25 obtaining a relationship between the fitting coefficient and the depth of discharge includes:
  • step S11 or S12 the state line of the battery capacity and the number of cycles of the discharge depth of 30%, 50%, 80%, 100% is obtained, that is, the value of the DOD is 30%, 50. %, 80%, 100%.
  • step S14 or S24 as shown in FIG. 5, the fitting coefficient of the state line having a discharge depth of 30% is a 30 , b 30 , c 30 , d 30 , and the fitting coefficient of the state line having a discharge depth of 50%.
  • the fitting coefficient of the state line with a depth of 80% is a 80 , b 80 , c 80 , d 80 , and the fitting coefficient of the state line with a depth of 100% Is a 100 , b 100 , c 100 , d 100 .
  • step S15 or step S25
  • a state line at other discharge depths DOD i between 50% and 80% of the depth of discharge, and a state line at other discharge depths DOD i between 80% and 100% of the depth of discharge can be obtained.
  • a sixth embodiment of the present invention provides a fast prediction device for battery cycle life, including a state line acquisition unit 10, an acquisition unit 20, a conversion unit 30, a first calculation unit 40, and a second calculation unit 50. Prediction unit 60.
  • the state line obtaining unit 10 is configured to acquire the state lines of the battery capacity and the number of cycles of the n different depths of discharge; in a specific implementation, as shown in FIG. 3, for example, four capacities of the battery under the non-stop depth can be obtained through experiments.
  • the collecting unit 20 is configured to collect several collection points on the state line.
  • the conversion unit 30 is configured to convert the acquired coordinates of the collection point into actual coordinates (x, y), where y is the battery capacity and x is the number of cycles.
  • the values of b, c, and d; the state line of the discharge depth after fitting is 100% as shown in Fig. 4.
  • the second calculating unit 50 is configured to acquire a relationship between the fitting coefficient and the depth of discharge.
  • the prediction unit 60 is configured to predict an unknown state using a semi-supervised method in machine learning to obtain a newly fitted state line.
  • a seventh embodiment of the present invention provides a fast prediction device for battery cycle life, including an on-state line acquisition unit 10, an acquisition unit 20, a conversion unit 30, a first calculation unit 40, and a second calculation unit 50. Prediction unit 60.
  • the conversion unit 30, the first calculation unit 40, the second calculation unit 50, and the prediction unit 60 are the same, as described above, and are not described herein again.
  • the test unit 70 is further configured to test each of the newly fitted state lines with sampling points under a least squares criterion; if the error exceeds a preset threshold, then The fitting coefficient corresponding to the state line of the new fit is adjusted.
  • the preset threshold is, for example, 1%.
  • An eighth embodiment of the present invention provides a fast prediction device for battery cycle life, including an on-state line acquisition unit 10, an acquisition unit 20, a conversion unit 30, a first calculation unit 40, a second calculation unit 50, and a prediction unit 60.
  • the conversion unit 30, the first calculation unit 40, the second calculation unit 50, and the prediction unit 60 are the same, as described above, and are not described herein again.
  • the state line acquiring unit 10 can be specifically configured to obtain a state line of battery capacity and cycle number of discharge depths of 30%, 50%, 80%, and 100%, respectively.
  • test unit 70 may be further included, as described in the seventh embodiment, and details are not described herein again.
  • a ninth embodiment of the present invention provides a fast prediction device for battery cycle life, including an on-state line acquisition unit 10, an acquisition unit 20, a conversion unit 30, a first calculation unit 40, a second calculation unit 50, and a prediction unit 60.
  • the conversion unit 30, the first calculation unit 40, the second calculation unit 50, and the prediction unit 60 are the same, as described above, and are not described herein again.
  • test unit 70 may be further included, as described in the seventh embodiment, and details are not described herein again.
  • a tenth embodiment of the present invention provides a fast prediction device for battery cycle life, including an on-state line acquisition unit 10, an acquisition unit 20, a conversion unit 30, a first calculation unit 40, a second calculation unit 50, and a prediction unit 60.
  • the state line acquisition unit 10 the acquisition unit 20, the conversion unit 30, the first calculation unit 40, the second calculation unit 50, the prediction unit 60 in the present embodiment and the state line acquisition in the sixth embodiment described above
  • the unit 10, the acquisition unit 20, the conversion unit 30, the first calculation unit 40, the second calculation unit 50, and the prediction unit 60 are the same, as described above, and are not described herein again.
  • the second calculating unit 50 is specifically configured to:
  • the fitting coefficient of the state line having a discharge depth of 30% is a 30 , b 30 , c 30 , and d 30
  • the fitting coefficient of the state line having a discharge depth of 50% is a 50 , b 50 , c 50 , d 50
  • the fitting coefficient of the state line with the depth of discharge of 80% is a 80 , b 80 , c 80 , d 80
  • the fitting coefficient of the state line with the depth of discharge of 100% is a 100 , b 100 , c 100 , d 100 .
  • the second calculating unit 50 is configured to:
  • the second calculating unit 50 obtains 30% of the values based on the calculated values of m a30 and n a30 , the values of m b30 and n b30 , the values of m c30 and n c30 , the values of m d30 and n d30 .
  • 50% of the state of the discharge depth DOD i between the line coefficients a i, b i, c i , d i can be further predicted discharge of 30% and 50% in the other line when the discharge state between the depth of the depth DOD i
  • a state line at other discharge depths DOD i between 50% and 80% of the depth of discharge, and a state line at other discharge depths DOD i between 80% and 100% of the depth of discharge can be obtained.
  • test unit 70 may be further included, as described in the seventh embodiment, and details are not described herein again.
  • the rapid prediction method of the battery cycle life and the rapid prediction device provided by the invention have simple and rapid test methods, and the error between the predicted value and the actual value is small, which satisfies the actual needs.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
  • Implementation Based on such understanding, the technical solution of the present invention, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a cell phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present invention.
  • the rapid prediction method for battery cycle life proposed by the invention and the fast prediction device thereof fit the state lines under n different depths of discharge by an exponential function, and obtain an exponential relationship between the fitting constant and the depth of discharge, using machine learning
  • the semi-supervised method predicts the unknown state and predicts other state lines. It is verified by experiments that the predicted state line can be well fitted to the relationship between capacity and cycle times under different depths of discharge.
  • the error between predicted and actual values is basically maintained. Within 1%, the error is small and meets the actual needs. Therefore, it has industrial applicability.

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Abstract

L'invention concerne un procédé et un dispositif de prédiction rapide de la durée de vie d'une batterie. Le procédé consiste : à acquérir n lignes d'état de la capacité de batterie et le nombre de cycles sous différentes profondeurs de décharge (S11) ; à collecter plusieurs points de collecte sur les lignes d'état (S12) ; à convertir des coordonnées de collecte des points de collecte en coordonnées réelles (x, y) (S13), y étant la capacité de la batterie, et x étant le nombre de cycles ; à sélectionner, dans le critère des moindres carrés, une fonction exponentielle y = 100 - (a * eb * x + c * ed * x) pour effectuer un ajustement de ligne d'état, et à substituer les coordonnées réelles d'au moins quatre points de collecte sur chaque ligne d'état sous une profondeur de décharge dans la fonction de façon à obtenir les valeurs de coefficients d'ajustement a, b, c et d correspondant à la ligne d'état sous la profondeur de décharge (S14) ; à acquérir une relation entre les coefficients d'ajustement et les profondeurs de décharge (S15) ; et à prédire un état inconnu à l'aide d'un procédé semi-dirigé dans l'apprentissage machine, et à acquérir une ligne d'état nouvellement ajustée (S16). La ligne d'état prédite au moyen de ce procédé peut bien s'ajuster à la relation entre la capacité et le nombre de cycles sous différentes profondeurs de décharge, et l'erreur entre une valeur prédite et une valeur réelle est très petite, ce qui permet de répondre aux exigences réelles.
PCT/CN2016/111431 2016-12-22 2016-12-22 Procédé de prédiction rapide de la durée de vie de batterie et dispositif de prédiction rapide associé WO2018112818A1 (fr)

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CN201680025104.5A CN108064391B (zh) 2016-12-22 2016-12-22 一种电池循环寿命的快速预测方法及其快速预测装置

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CN112462275A (zh) * 2019-09-09 2021-03-09 河南森源重工有限公司 一种电池包循环寿命测试方法
CN112798974A (zh) * 2020-12-22 2021-05-14 中国船舶重工集团公司第七0九研究所 孤立基站混合供电系统蓄电池soh在线监测方法及系统
CN113761716A (zh) * 2021-08-12 2021-12-07 惠州市豪鹏科技有限公司 一种锂离子电池循环寿命预测方法及其应用
US11340306B2 (en) * 2017-11-16 2022-05-24 Semiconductor Energy Laboratory Co., Ltd. Lifetime estimation device, lifetime estimation method, and abnormality detection method of secondary battery
CN116381540A (zh) * 2023-06-05 2023-07-04 石家庄学院 一种计算机运行状态下电池健康监控系统
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WO2024183743A1 (fr) * 2023-03-06 2024-09-12 中国华能集团清洁能源技术研究院有限公司 Procédé et appareil de prédiction en ligne pour la durée de vie résiduelle d'une batterie de stockage d'énergie

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US11340306B2 (en) * 2017-11-16 2022-05-24 Semiconductor Energy Laboratory Co., Ltd. Lifetime estimation device, lifetime estimation method, and abnormality detection method of secondary battery
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US11740290B2 (en) 2020-01-14 2023-08-29 Battelle Energy Alliance, Llc Energy storage cell qualification and related systems, methods, and devices
CN112798974A (zh) * 2020-12-22 2021-05-14 中国船舶重工集团公司第七0九研究所 孤立基站混合供电系统蓄电池soh在线监测方法及系统
CN113761716A (zh) * 2021-08-12 2021-12-07 惠州市豪鹏科技有限公司 一种锂离子电池循环寿命预测方法及其应用
CN113761716B (zh) * 2021-08-12 2024-02-02 惠州市豪鹏科技有限公司 一种锂离子电池循环寿命预测方法及其应用
WO2024183743A1 (fr) * 2023-03-06 2024-09-12 中国华能集团清洁能源技术研究院有限公司 Procédé et appareil de prédiction en ligne pour la durée de vie résiduelle d'une batterie de stockage d'énergie
CN116381540A (zh) * 2023-06-05 2023-07-04 石家庄学院 一种计算机运行状态下电池健康监控系统
CN116381540B (zh) * 2023-06-05 2023-08-22 石家庄学院 一种计算机运行状态下电池健康监控系统

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