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WO2019066278A1 - Procédé de mesure d'entropie d'une batterie à l'aide d'un filtre de kalman - Google Patents

Procédé de mesure d'entropie d'une batterie à l'aide d'un filtre de kalman Download PDF

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Publication number
WO2019066278A1
WO2019066278A1 PCT/KR2018/010233 KR2018010233W WO2019066278A1 WO 2019066278 A1 WO2019066278 A1 WO 2019066278A1 KR 2018010233 W KR2018010233 W KR 2018010233W WO 2019066278 A1 WO2019066278 A1 WO 2019066278A1
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Prior art keywords
entropy
kalman filter
battery
change
unit
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Ceased
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English (en)
Korean (ko)
Inventor
이상국
기욤테네지
한석균
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Korea Advanced Institute of Science and Technology KAIST
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Korea Advanced Institute of Science and Technology KAIST
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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]
    • G01R31/371—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with remote indication, e.g. on external chargers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L50/00—Electric propulsion with power supplied within the vehicle
    • B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L50/00—Electric propulsion with power supplied within the vehicle
    • B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
    • B60L50/60—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries
    • 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
    • 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
    • G01R31/005—Testing of electric installations on transport means
    • G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
    • G01R31/007—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks using microprocessors or computers
    • 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
    • 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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Definitions

  • the present invention relates to a technique for periodically updating an entropy model used in a Kalman filter while a Kalman filter is operating to extract entropy in real time from a battery management system (BMS). More specifically, (OCV), the Kalman filter is introduced under real-time measurement of the open-circuit voltage. By correcting the dispersion of the entropy change rate due to the measurement dispersion of the open-circuit voltage in the equilibrium state, And a method for measuring the rate of change.
  • BMS battery management system
  • Lithium-ion batteries are currently being used as energy sources for various applications due to their high output and high energy characteristics, and their application range is expected to be broader. However, battery capacity reduction and safety issues are a challenge to be solved in lithium ion batteries.
  • the degree of entropy change of the battery can be utilized as an indicator of the capacity reduction and safety of the battery, and researches thereof are actively under way. That is, since the entropy profile of the battery has a characteristic that it changes with use of the battery, if the above-mentioned differential entropy is known, it is advantageous that it can be used as an index for predicting the aging state and the risk of the battery do.
  • Electrochemical Thermodynamics Measurement is a method of measuring the entropy change of a battery.
  • the ETM provides a measure of the amount of change in open-circuit voltage (OCV) when the battery temperature is forced to change while the battery is in equilibrium.
  • OCV open-circuit voltage
  • the entropy of the battery is not defined as a single value but is measured separately for the state of charge (SoC) of the battery.
  • SoC state of charge
  • the entropy profile according to the SoC is measured by measuring the desired step within 0% to 100%.
  • the ETM measures the change of the open-circuit voltage (OCV) of the battery with temperature, which is measured by ETMS.
  • OCV open-circuit voltage
  • the OCV of the chemical equilibrium state is measured through relaxation at a battery temperature of 25 ° C and 0% SoC. Then, change the temperature of the battery to 20 ° C, 15 ° C, and 10 ° C, measure the OCV at each temperature, and measure the tilt of OCV according to the temperature. This process is performed for each SoC step, and as a result, the entropy change in the entire SoC is measured.
  • the ETMS is difficult to use as a non-commercial equipment, and the temperature changing device is specially manufactured to insert a specific battery into the device, thereby limiting the number of the battery models and the number of the batteries that can be measured at one time.
  • the battery measures the change in the OCV with respect to the measured voltage (OCV) and the temperature change in the electrical / chemical / thermal equilibrium state.
  • the battery since the time required for the battery to reach a thermal equilibrium state is long for about 24 hours, and a dynamically programmable dedicated temperature changing device is required, the battery has a limitation that is difficult to apply to BMS There is no such case applied to BMS so far.
  • An entropy model used in a Kalman filter is periodically updated by operating a Kalman filter to extract entropy in real time from a BMS.
  • an open-circuit voltage OCV an open-circuit voltage
  • the Kalman filter is introduced under real-time measurement of the open-circuit voltage, and the entropy change rate due to the measurement dispersion of the equilibrium open-circuit voltage is corrected to measure the entropy change rate in real time without reaching the equilibrium state And the like.
  • the Kalman filter unit includes: a first Kalman filter that operates according to a change in a state of charge (SoC) of the battery; And a second Kalman filter for performing a filter operation in accordance with a change in the number of times of use of the battery,
  • SoC state of charge
  • the first Kalman filter outputs a corrected actual entropy change rate in a state in which a plurality of entropy values including an actual entropy value and an estimated entropy value are input through the changed battery charging state and is repeatedly operated
  • the filter receives the actual entropy change rate of the first Kalman filter and receives the estimated change rate of the aging entropy reflecting the aging due to the increase in the battery usage count, outputs the estimated change rate of the aging entropy, and updates the entropy model periodically ,
  • the first Kalman filter tracks the entropy change by the battery charge state change information supplied from the entropy model updated through the operation of the second Kalman filter.
  • a system for measuring entropy of a battery using a Kalman filter comprising: a voltage measuring unit; A temperature measuring unit 20; An entropy calculation unit 30 for calculating entropy based on temperature and voltage information obtained through the voltage measurement unit 10 and the temperature measurement unit 20; An SOC checking unit 42 for checking the battery charging state in real time; A first modeling unit 40 for outputting the enthalpy change rate and the expected entropy change rate; A second modeling unit 50 that performs modeling with a rate of change according to the number of times of use of the battery in a state where a change occurs at a specific charging point of the battery as the battery ages; And a Kalman filter unit 60 for providing a corrected entropy change rate through data exchange between the entropy calculation unit 30 and the first and second modeling units 40 and 50.
  • the Kalman filter unit 60 Includes: a first Kalman filter that operates according to a change in a state of charge (SoC) of the battery; And a
  • the first Kalman filter 62 inputs two entropy predictive values such as the actual entropy value? Smesas supplied from the entropy calculation unit 30 and the estimated entropy value? Spred supplied from the first modeling unit 40 The actual entropy is output, the SoC is changed while the charge / discharge process is performed, and the Kalman filter is repeatedly operated.
  • the second Kalman filter 64 is provided with a predicted aging entropy change rate ( ⁇ Saged_pred) that reflects a change in aging of the battery from the second modeling unit 50, And an aging entropy change rate (? Saged_meas) as a result of the measurement.
  • ⁇ Saged_pred predicted aging entropy change rate
  • ? Saged_meas aging entropy change rate
  • the estimated aging entropy change rate ( ⁇ Saged_est) information is supplied to the first modeling unit (40) while the second Kalman filter (64) is operated.
  • the enthalpy change rate And the entropy change rate is obtained.
  • the present invention provides a method of measuring entropy of a battery in real time using the Kalman filter according to the present invention.
  • a temperature control device is required and a chemical equilibrium state is reached. This overcome the problem that it is difficult to implement in BMS due to the practical necessity.
  • the present invention eliminates the need for a temperature changing device because the entropy change rate can be easily obtained by measuring the equilibrium state OCV and the temperature when the enthalpy change is known in advance. That is, the enthalpy change is advantageous in that it can use pre-measured data in the laboratory because there is little variation among individuals in the same kind of battery and the change does not occur even when the battery ages.
  • a technique for estimating the equilibrium state OCV on an operating battery is already available, and by measuring OCV in real time, it is possible to easily obtain the entropy change rate in real time without reaching an equilibrium state.
  • the present invention introduces a Kalman filter to correct the entropy change rate dispersion caused by the equilibrium OCV measurement dispersion.
  • FIG. 1 is a diagram illustrating an entropy extraction system using a dual Kalman filter according to an embodiment of the present invention.
  • FIG. 2 is a graph showing that the Kalman filter is repeatedly operated according to a certain period of time while the SoC of the battery is changed, and gradually converges to the actual value as the number of operations increases.
  • FIG. 3 shows a graph in which a change occurs at a specific SoC point according to aging of the battery and is modeled by a rate of change according to the number of times of use.
  • thermodynamic terms of a battery used in connection with the entropy measurement in the present invention are disclosed in Korean Patent Laid-Open Nos. 10-2017-0093482 and 10-2017-0059208 filed by the present applicant.
  • OCV open circuit voltage
  • the State of Charge represents the state of charge and is equivalent to the fuel gauge of the battery, in units of percentage points. Specifically, 0% indicates fullness and 100% indicates fullness.
  • the SoC is mainly used to indicate the current state of charge of the battery in use.
  • a battery management system is an electronic system that manages a rechargeable battery (cell or battery pack), which protects the battery from operating outside the safe operating area, monitors the status of the battery, (Not limited to this task) of computing, reporting, reporting its data, controlling its environment, authenticating and / or balancing the battery.
  • the enthalpy is equal to the total calorific value of the system, equal to the internal energy of the system plus the products of pressure and volume.
  • the change in enthalpy of the system is associated with a particular chemical process.
  • Entropy is a thermodynamic quantity (state function) that represents the thermal energy of a system that is not useful for converting to mechanical work, and is often interpreted as the degree of randomness or randomness of the system.
  • the present invention focuses on overcoming the technical limitations in applying electrochemical thermodynamic measurements (ETM) to a battery management system (BMS).
  • ETM electrochemical thermodynamic measurements
  • BMS battery management system
  • the entropy of a battery is presumed to measure the level of change of the OCV with respect to the open-circuit voltage and the temperature change, which is the voltage measured in the electric / chemical / thermal equilibrium state of the battery.
  • the battery has a limitation that is difficult to apply to BMS There is no case applied to the BMS until now, and it is intended to improve the entropy change rate measurement by using the correction system using the Kalman filter.
  • n represents the exchange capacity of electrons in a typical basic reaction
  • F is a Faraday constant
  • Equation (2) a temperature controller is required to obtain the OCV slope with respect to temperature, and it takes a long time to reach the chemical equilibrium state, which makes it difficult to implement in the BMS.
  • the present invention eliminates the necessity of the temperature changing device through the following equation (3) since the entropy change rate can be easily obtained by measuring the equilibrium state OCV and temperature when the enthalpy change is known in advance.
  • the entropy extraction system includes an OCV acquisition unit 12 for extracting OCV based on data measured by the voltage measurement unit 10 and the voltage measurement unit 10, a temperature measurement unit 20, a voltage measurement unit 10, An entropy calculation unit 30 for calculating an entropy based on the temperature and voltage information acquired through the unit 20, an SOC checking unit 42 for checking the battery charging status in real time, an initial value input unit 44, A first modeling unit 40 for outputting an enthalpy change rate and an expected entropy change rate based on the information of the unit 42 and the initial value input unit 44, A dual Kalman filter unit 60 for providing a corrected entropy change rate through data exchange between the entropy calculation unit 30 and the first and second modeling units 40 and 50, .
  • the dual Kalman filter unit 60 includes a first Kalman filter 62 that operates according to the SoC change and a second Kalman filter 64 that operates according to the change in the number of times the battery is used.
  • the first modeling unit 40 measures the entropy profile of the battery in a laboratory environment and models the model as a function according to the SoC. That is, through the modeling information linearized for each section through the charge state information of the battery, which is checked in real time by the SOC checking unit 42, the initial enthalpy change rate and the initial estimated entropy change rate, which are confirmed by the initial value input unit 44, The predicted enthalpy change rate and the estimated entropy change rate ( ⁇ Spred) are obtained in the SOC section of the battery. The predicted enthalpy change rate and the estimated entropy change rate are provided to the entropy calculation unit 30 and the dual Kalman filter unit 60, respectively.
  • the measured entropy change rate can be obtained.
  • the first Kalman filter 62 receives the two entropy predicted values such as the actual entropy value? Smesas supplied from the entropy calculation unit 30 and the estimated entropy value? Spred supplied from the first modeling unit 40, As the entropy is output, the SoC is changed and the Kalman filter is repeatedly operated as it is charged and discharged.
  • FIG. 2 shows that the Kalman filter is repeatedly operated according to a constant period as the SoC of the battery is changed, and gradually converges to the actual value as the number of operations increases.
  • the axis of abscissa shows the variation axis of the SoC as the number of times of battery usage increases
  • the axis of ordinate shows the change rate of entropy.
  • a change occurs at a specific SoC point according to aging of the battery, and is modeled by a rate of change according to the number of times of use. That is, the entropy profile is measured separately according to the state of charge (SoC) of the battery formed along the horizontal axis, and the entropy profile according to the SoC is measured by measuring the desired step within 0% to 100%. In the above, it reflects the degree of change at a specific SoC point as the battery ages.
  • SoC state of charge
  • the predicted aging entropy change rate ( ⁇ Saged_pred) reflecting the change with aging of the battery is supplied from the second modeling unit 50 to the second Kalman filter 64 and the corrected result of the measurement derived from the first Kalman filter 62
  • the aging entropy change rate ([Delta] Saged_meas) is input to the measured value of the second Kalman filter 64.
  • the process of measuring the entropy of the battery according to an embodiment of the present invention does not consider the aging when the SoC is changed in the same cycle so that the first modeling unit through the second Kalman filter 64 40 is operated every one cycle or a plurality of cycles. Therefore, at the calculation of entropy at one point, the first Kalman filter 62 functions sufficiently to operate once without considering the aging through the second Kalman filter 64.
  • first and second Kalman filters 62 and 64 are operated and updated at every measurement time when the SoC is changed in the same cycle.
  • the estimated aging entropy change rate ( ⁇ Saged_est) information as the second Kalman filter 64 is operated is supplied to the first modeling unit 40.
  • the enthalpy change rate and the estimated entropy change rate Perform the process of obtaining again.
  • the first Kalman filter 62 receives the two entropy predicted values, such as the actual entropy value supplied from the entropy calculator 30 and the predicted entropy value supplied to the first modeling unit 40, and outputs the actual entropy,
  • the SoC is changed and the Kalman filter is repeatedly operated.
  • the first and second Kalman filters 62 and 64 of the dual Kalman filter unit 60 periodically update the entropy model through the first and second modeling units 40 and 50, Successfully track changes.
  • the present invention provides a method of detecting a true value by correcting an error of a measured value due to inaccuracy or other reasons of an existing measurement sensor using a dual Kalman filter.
  • the method of directly measuring a desired value in a process of using a dual Kalman filter, and the method of predicting through a model by modeling a system to be measured are used in an overlapping manner.
  • the direct measurement method generally has a large measurement error, the error in each measurement is maintained at the same level.
  • the modeling prediction method is to predict and model the system to be measured. If the modeling is accurate, the prediction error is small, but when the value is continuously predicted through the modeling, the error is increased as the previously predicted error continues to accumulate By using the dual Kalman filter, the measurement error is minimized by periodically receiving the two values of the direct measurement method and the modeling prediction method.
  • the error may be initially large after the operation of the Kalman filter, the iteration is repeated over time, and finally the measurement value having the minimum error is calculated.
  • a temperature control device is required to obtain the OCV slope with respect to the temperature, and a chemical equilibrium state is reached at the same time. This overcome the problem that it is difficult to implement in BMS due to the practical necessity

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Abstract

Selon un procédé fourni par la présente invention, un ensemble filtre de Kalman comprend : un premier filtre de Kalman qui effectue une opération de filtre conformément à un changement d'un état chargé d'une batterie ; et un second filtre de Kalman qui effectue une opération de filtre en fonction d'un changement du nombre d'utilisations de la batterie, le premier filtre de Kalman, dans un état dans lequel il reçoit une pluralité de valeurs d'entropie comprenant une valeur d'entropie de mesure réelle et une valeur d'entropie attendue, en fonction de l'état de charge modifié de la batterie, délivrant un taux de changement d'entropie de mesure réelle corrigé, en même temps que le premier filtre de Kalman est exploité de manière répétée ; le second filtre de Kalman mettant à jour périodiquement un modèle d'entropie, dans un état dans lequel il reçoit à la fois le taux de changement d'entropie de mesure réelle du premier filtre de Kalman et un taux de changement d'entropie de vieillissement attendu reflétant le vieillissement dû à une augmentation du nombre d'utilisations de la batterie ; et le premier filtre de Kalman suivant un changement d'entropie au moyen d'informations concernant le changement de l'état chargé de la batterie fourni par le modèle d'entropie.
PCT/KR2018/010233 2017-09-29 2018-09-03 Procédé de mesure d'entropie d'une batterie à l'aide d'un filtre de kalman Ceased WO2019066278A1 (fr)

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CN113030764B (zh) * 2021-03-04 2022-01-25 武汉大学 一种电池组健康状态估计方法及系统
KR20230028709A (ko) 2021-08-19 2023-03-02 한국과학기술원 전기화학소자의 엔트로피 측정 방법 및 장치
CN116203441B (zh) * 2023-03-24 2023-10-27 广州巨湾技研有限公司 锂离子电池温熵系数的测试方法及装置

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