WO2023231356A1 - 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法 - Google Patents

一种基于光纤光栅传感器的锂离子电池荷电状态估计方法 Download PDF

Info

Publication number
WO2023231356A1
WO2023231356A1 PCT/CN2022/137071 CN2022137071W WO2023231356A1 WO 2023231356 A1 WO2023231356 A1 WO 2023231356A1 CN 2022137071 W CN2022137071 W CN 2022137071W WO 2023231356 A1 WO2023231356 A1 WO 2023231356A1
Authority
WO
WIPO (PCT)
Prior art keywords
time series
charge
data set
battery state
time
Prior art date
Application number
PCT/CN2022/137071
Other languages
English (en)
French (fr)
Inventor
杨之乐
周邦昱
郭媛君
姚文娇
刘祥飞
吴承科
Original Assignee
深圳先进技术研究院
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳先进技术研究院 filed Critical 深圳先进技术研究院
Publication of WO2023231356A1 publication Critical patent/WO2023231356A1/zh

Links

Classifications

    • 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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D5/00—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable
    • G01D5/26—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light
    • G01D5/32—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light
    • G01D5/34—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells
    • G01D5/353—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells influencing the transmission properties of an optical fibre
    • G01D5/35306—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells influencing the transmission properties of an optical fibre using an interferometer arrangement
    • G01D5/35309—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells influencing the transmission properties of an optical fibre using an interferometer arrangement using multiple waves interferometer
    • G01D5/35316—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells influencing the transmission properties of an optical fibre using an interferometer arrangement using multiple waves interferometer using a Bragg gratings
    • 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]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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
    • 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 present invention is designed in the technical field of battery analysis, and in particular relates to a method for estimating the state of charge of a lithium-ion battery based on a fiber grating sensor.
  • SOC Battery state of charge
  • Accurate and robust SOC estimation technology can avoid overcharging, over-discharging and overheating, thereby extending the service life of the battery.
  • Existing state-of-charge estimation methods have the disadvantage of relying on the accuracy of the battery model or inaccurate estimation results. Battery aging leads to overcharge. Reduced capacity also increases the difficulty of accurate state-of-charge estimation with existing technologies.
  • the technical problem to be solved by the present invention is to provide a method for estimating the state of charge of a lithium-ion battery based on a fiber grating sensor in view of the above-mentioned defects of the prior art, aiming to solve the problem that the state of charge estimation method of the prior art relies on the battery model.
  • the defects of inaccurate accuracy or estimation results, and the reduction of charge capacity caused by battery aging also increase the difficulty of accurately estimating the state of charge in the existing technology.
  • the present invention provides a method for estimating the state of charge of a lithium-ion battery based on a fiber Bragg grating sensor, wherein the method includes:
  • the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information are obtained, and the current, voltage, time, Anode strain data, temperature, and battery state of charge are used as known parameters;
  • a dynamic time warping model is trained based on the processed time series test data set to obtain a battery state of charge estimation model.
  • the current, voltage, anode strain data, and temperature are input into the battery state of charge estimation model to obtain the battery state of charge estimation model.
  • Estimated data of electrical status is input into the battery state of charge estimation model to obtain the battery state of charge estimation model.
  • At least two fiber Bragg grating sensors are provided, one of which is attached to the negative electrode surface of the lithium-ion battery for measuring anode strain data of the battery anode material, and the other is attached to the anode surface of the battery.
  • the central position of the lithium ion battery is used to measure the temperature change of the lithium ion battery.
  • the method further includes:
  • Heating the lithium-ion battery to a specified temperature repeatedly charging and discharging the lithium-ion battery, collecting current, voltage, time, anode strain data, temperature and battery state of charge, and collecting the collected current, voltage, time , anode strain data, temperature and battery state of charge as the test parameters.
  • the above-based upper and lower bound algorithm is used to delete time series in the time series test data set that do not match the time series target data set, including:
  • the distance is greater than the preset threshold, it is determined that the time series in the time series test data set whose distance is greater than the preset threshold does not match the time series target data set, and the unmatched time series is deleted.
  • the calculation of the distance between the time series test data set and the same time step in the time series target data set based on the upper and lower bound algorithm includes:
  • the method of deleting time series in the time series test data set that does not match the time series target data set based on the upper and lower bound algorithm also includes:
  • the dynamic time warping model is trained based on the processed time series test data set to obtain a battery state of charge estimation model, and the current, voltage, anode strain data, and temperature are input to the battery charge
  • the estimated data of the battery state of charge is obtained, including:
  • parameter tuning is performed on the battery state of charge estimation model.
  • embodiments of the present invention also provide a lithium-ion battery state-of-charge estimation system based on fiber Bragg grating sensors, wherein the system includes:
  • the test parameter acquisition module is used to select a lithium-ion battery with a known model, conduct charging and discharging experiments on the lithium-ion battery, and collect data within a preset time period based on the fiber Bragg grating sensor provided on the lithium-ion battery. Current, voltage, time, anode strain data, temperature and battery state of charge, and use current, voltage, time, anode strain data, temperature and battery state of charge as test parameters;
  • a known parameter acquisition module configured to obtain the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information based on the model information of the lithium-ion battery, and compare it with the model information
  • the corresponding current, voltage, time, anode strain data, temperature and battery state of charge are used as known parameters;
  • a time series processing module configured to establish a time series test data set according to the test parameters, establish a time series target data set according to the known parameters, normalize the time series test data set, and perform normalization processing on the time series test data set based on the upper and lower Boundary algorithm, delete time series that do not match the time series test data set and the time series target data set, and obtain a processed time series test data set;
  • State of charge estimation module used to train a dynamic time warping model based on the processed time series test data set, obtain a battery state of charge estimation model, and input current, voltage, anode strain data, and temperature into the battery charge state In the state estimation model, the estimated data of the battery state of charge is obtained.
  • inventions of the present invention also provide a terminal device.
  • the terminal device includes a memory, a processor, and a lithium-ion battery state-of-charge estimation program based on a fiber grating sensor that is stored in the memory and can be run on the processor, and processes
  • the device executes the fiber Bragg grating sensor-based lithium ion battery state of charge estimation program, the steps of the fiber Bragg grating sensor-based lithium ion battery state of charge estimation method in any of the above solutions are implemented.
  • embodiments of the present invention also provide a computer-readable storage medium.
  • the computer-readable storage medium stores a lithium-ion battery state-of-charge estimation program based on fiber grating sensors.
  • the lithium-ion battery charging state based on fiber Bragg grating sensors
  • the state estimation program is executed by the processor, the steps of the fiber Bragg grating sensor-based lithium-ion battery state-of-charge estimation method in any of the above solutions are implemented.
  • the present invention provides a method for estimating the state of charge of a lithium-ion battery based on a fiber grating sensor.
  • the present invention first selects a lithium-ion battery with a known model and charges the lithium-ion battery.
  • the discharge experiment based on the fiber Bragg grating sensor installed on the lithium-ion battery, collects the current, voltage, time, anode strain data, temperature and battery state of charge within a preset time period, and combines the current, voltage, time, Anode strain data, temperature, and battery state of charge were used as test parameters.
  • the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information are obtained, and the current, voltage, Time, anode strain data, temperature, and battery state of charge are used as known parameters.
  • a time series test data set is established according to the test parameters, and a time series target data set is established according to the known parameters, the time series test data set is normalized, and based on the upper and lower bound algorithm, all the time series test data sets are deleted.
  • the time series in the time series test data set that does not match the time series target data set is used to obtain a processed time series test data set.
  • a dynamic time warping model is trained based on the processed time series test data set to obtain a battery state of charge estimation model.
  • the current, voltage, anode strain data, and temperature are input into the battery state of charge estimation model to obtain Estimated data for battery state of charge.
  • This invention uses machine learning and high-precision fiber Bragg grating sensors to measure data such as anode strain and temperature, analyzes the constructed time series test data set, and deletes time series that do not match the time series target data set, thereby training A more accurate battery state-of-charge estimation model is developed, which effectively improves the accuracy of battery state-of-charge estimation.
  • FIG. 1 is a flow chart of a specific implementation of a method for estimating the state of charge of a lithium-ion battery based on a fiber Bragg grating sensor provided by an embodiment of the present invention.
  • FIG. 2 is a schematic diagram illustrating the calculation of upper and lower bound time distances in the lithium-ion battery state-of-charge estimation method based on fiber Bragg grating sensors provided by an embodiment of the present invention.
  • Figure 3 is a schematic block diagram of a lithium-ion battery state-of-charge estimation system based on a fiber Bragg grating sensor provided by an embodiment of the present invention.
  • Figure 4 is a functional block diagram of a terminal device provided by an embodiment of the present invention.
  • This embodiment provides a method for estimating the state of charge of a lithium-ion battery based on a fiber Bragg grating sensor. For specific application, this embodiment first selects a lithium-ion battery with a known model, and performs a charge and discharge experiment on the lithium-ion battery. Based on the settings The fiber Bragg grating sensor on the lithium-ion battery collects the current, voltage, time, anode strain data, temperature and battery state of charge within a preset time period, and stores the current, voltage, time, anode strain data, temperature and battery state of charge as test parameters.
  • the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information are obtained, and the current, voltage, Time, anode strain data, temperature, and battery state of charge are used as known parameters.
  • a time series test data set is established according to the test parameters, and a time series target data set is established according to the known parameters, the time series test data set is normalized, and based on the upper and lower bound algorithm, all the time series test data sets are deleted.
  • the time series in the time series test data set that does not match the time series target data set is used to obtain a processed time series test data set.
  • a dynamic time warping model is trained based on the processed time series test data set to obtain a battery state of charge estimation model.
  • the current, voltage, anode strain data, and temperature are input into the battery state of charge estimation model to obtain Estimated data for battery state of charge.
  • This embodiment uses machine learning and high-precision fiber Bragg grating sensors to measure data such as anode strain and temperature, analyzes the constructed time series test data set, and deletes time series that do not match the time series target data set, thereby A more accurate battery state-of-charge estimation model is trained, effectively improving the estimation accuracy of battery state-of-charge (SOC).
  • the lithium-ion battery state-of-charge estimation method based on fiber grating sensors in this embodiment can be applied to terminal equipment, which is used to test and monitor lithium-ion batteries, and can also collect and collect data obtained from the test. analyze.
  • terminal equipment which is used to test and monitor lithium-ion batteries
  • it can be terminal equipment such as test terminals and computers.
  • the state-of-charge estimation of lithium batteries based on fiber Bragg grating sensors in this embodiment specifically includes the following steps:
  • Step S100 Select a lithium-ion battery with a known model, perform a charge and discharge experiment on the lithium-ion battery, and collect the current, voltage, time, anode strain data, temperature and battery state of charge, and use current, voltage, time, anode strain data, temperature and battery state of charge as test parameters.
  • the terminal equipment of this embodiment first selects a lithium-ion battery with a known model, and then retrieves the lithium-ion battery and performs a charge and discharge experiment.
  • the start time of the charge and discharge experiment is t1 and the end time is t2.
  • This embodiment can Record the current and voltage at each moment during the charge and discharge experiment (that is, the time from t1 to t2).
  • this embodiment can also collect anode strain data and temperature of the lithium-ion battery based on a fiber Bragg grating sensor (Fiber Bragg Grating, FBG) disposed on the lithium-ion battery.
  • FBG fiber Bragg grating sensor
  • this embodiment there are at least two fiber Bragg grating sensors in this embodiment, one of which is attached to the negative electrode surface of the lithium-ion battery for measuring the anode strain data of the battery anode material, and the other one is attached to the negative electrode surface of the battery anode material.
  • the central location of the lithium-ion battery is used to measure the temperature changes of the lithium-ion battery.
  • this embodiment can also allow the lithium-ion battery to be allowed to stand for a long enough time, and then measure the open circuit voltage to determine the battery state of charge of the lithium-ion battery.
  • the battery state of charge can reflect the remaining capacity state of the lithium-ion battery.
  • the battery state of charge is related to current, voltage, time, anode strain data and temperature. To a certain extent, current, voltage, time, anode strain data and temperature can directly affect the battery state of charge, and the battery The state of charge reflects the remaining capacity of the lithium-ion battery, so the battery state of charge can reflect the remaining life of the lithium-ion battery.
  • this embodiment uses the current, voltage, time, anode strain data, temperature and battery state of charge as test parameters.
  • the test parameters collected in this embodiment are used to reflect the health status of the lithium-ion battery during actual use. These data will affect the service life of the lithium-ion battery.
  • this embodiment can also heat the lithium-ion battery to a specified temperature, repeatedly charge and discharge the lithium-ion battery, and collect current, voltage, time, anode strain data, temperature and battery charge. status, and use the collected current, voltage, time, anode strain data, temperature and battery state of charge as the test parameters, so that more abundant test parameters can be obtained.
  • Step S200 According to the model information of the lithium-ion battery, obtain the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information, and convert the current, voltage corresponding to the model information , time, anode strain data, temperature, and battery state of charge as known parameters.
  • this embodiment can determine the corresponding current, voltage, time, anode strain data, temperature and battery state of charge based on the model information. These parameters can be Obtained directly from the data provided by the manufacturer, such as product instruction manual or product introduction booklet.
  • the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information are used as known parameters.
  • Each data in the known parameters in this embodiment is detected based on different times when leaving the factory. That is to say, the known parameters are tested before the lithium-ion battery is initialized. These parameters are used to reflect
  • the normal data of the lithium-ion battery under normal use conditions is a standard parameter that can be used to measure the health status of the lithium-ion battery.
  • Step S300 Establish a time series test data set according to the test parameters, and establish a time series target data set according to the known parameters, normalize the time series test data set, and delete the time series test data set based on the upper and lower bound algorithm.
  • the time series in the time series test data set that does not match the time series target data set is used to obtain a processed time series test data set.
  • this embodiment can establish time series test data based on the test parameters.
  • the established time series test data set has different time series.
  • Each time series can be used to reflect changes in different data.
  • the temperature time series reflects the changes in temperature over time
  • the voltage time series reflects the changes in voltage over time. Variety.
  • each data in the known parameters is detected based on different times when leaving the factory. Therefore, this embodiment establishes a time series target data set based on the known parameters.
  • the time series target data set is Included are time series corresponding to known parameters such as current, voltage, anode strain data, temperature, and battery state of charge.
  • this embodiment normalizes the time series test data set, and based on the upper and lower bound algorithm, deletes the time series test data set that does not match the time series target data set. time series to obtain the processed time series test data set.
  • this embodiment is based on a distance algorithm, which can determine the time series based on the distance between the same time steps between the two series. Time series in the time series test data set that do not match the time series target data set. The distance is calculated as:
  • the core of the DTW algorithm is to align two different time series in the best way.
  • the best way to align is two times.
  • the distance of the sequence is the smallest, and this smallest distance is the distance between the two time series.
  • this embodiment introduces the upper and lower bounding algorithm (Lower Bounding). Specifically, this embodiment can obtain the upper bound and lower bound of each time series in the time series target data set, and then obtain each time series in the time series test data set, and compare each time series with the corresponding time series.
  • Figure B in Figure 2 is a schematic diagram of the Q time series and C time series when the upper bound U and lower bound L are set. Schematic diagram of distance calculation for C time series.
  • the upper bound U is set based on the peak value of the Q time series
  • the lower bound L is set based on the valley value of the Q time series.
  • the corresponding Q time series is the C time series. Therefore, the distance between the C time series and the upper bound U and the lower bound L can be calculated respectively.
  • the calculation is also based on the same time step of the two sequences. calculate. At this time, the relationship between the C sequence and the upper bound U and lower bound L is calculated directly through the corresponding time step without alignment. If the calculated distance is greater than the set threshold, it is considered that the gap between the Q time series and the C time series is too large. , the two do not match, so the C time series at this time is a mismatched time series and needs to be deleted.
  • the calculation can also be based on four corresponding points. For example, directly find the four corresponding points in the Q time series and the C time series, which are the starting point, the end point, the highest point, and the lowest point, and then calculate the distance sum of these four points respectively for these four points. If it exceeds threshold, it is considered that the Q time series and the C time series do not match.
  • this embodiment can also perform distance calculation based on two corresponding points. For example, directly find two corresponding points of the Q time series and the C time series, which are the highest point and the lowest point respectively, and then target these two points respectively. Calculate the distance sum of these two points. If it exceeds the threshold, it is considered that the Q time series and the C time series do not match, which is beneficial to reducing the amount of calculation and improving calculation efficiency.
  • this embodiment can also obtain, for a time series in the time series test data set, the calculated distance between the time series and the same time step in the time series target data set. and. The calculated sum of distances is then compared with a preset distance threshold. If the sum of the distances is greater than the distance threshold, the calculation of the time series is stopped. For example, for a certain time series, after only calculating the distance of five time steps, it is found that the sum of the distances has exceeded the distance threshold, then it can be determined that the two time series do not match, and the calculation can be terminated directly. , reduce the amount of calculation and improve calculation efficiency, so as to more quickly determine the unmatched time series in the time series test data set.
  • this embodiment performs the normalization process and the steps of finding and deleting unmatched time series at the same time. That is, the distance calculation can be performed immediately for each time series normalized. If the sum of the distances is once calculated during the calculation process, If the distance threshold is exceeded, the calculation will be stopped immediately. There will be no need for normalization in the future, and the amount of calculation will be reduced. After the unmatched time series are deleted, the processed time series test data set is obtained.
  • Step S400 Train a dynamic time warping model based on the processed time series test data set to obtain a battery state of charge estimation model, and input current, voltage, anode strain data, and temperature into the battery state of charge estimation model, Get an estimate of the battery's state of charge.
  • this embodiment can use the current, voltage, time, anode strain data, and temperature in the processed time series test data set as input, and use the battery state of charge in the processed time series test data set as Output, train the dynamic time warping model, and obtain the battery state of charge estimation model. Since the battery state of charge is related to current, voltage, time, anode strain data and temperature, to a certain extent, current, voltage, time, anode strain data and temperature can directly affect the battery state of charge, and The battery state of charge reflects the remaining capacity of the lithium-ion battery, so the battery state of charge can reflect the remaining life of the lithium-ion battery.
  • the battery state of charge estimation model can be obtained. Therefore, when the current, voltage, anode strain data, and temperature are input into the battery state of charge estimation model, the estimated data of the battery state of charge is obtained, thereby realizing the estimation of the battery state of charge of the lithium-ion battery.
  • this embodiment can also integrate the estimated data with the processed time series test data set, ensuring that the time dimension is consistent during integration, and then input the entire data into the battery state of charge estimation model to obtain Corresponding to the predicted value of the battery state of charge, parameter tuning is performed on the battery state of charge estimation model according to the predicted value of the battery state of charge, thereby improving the accuracy of the battery state of charge estimation model.
  • this embodiment first selects a lithium-ion battery with a known model, conducts a charge and discharge experiment on the lithium-ion battery, and collects the current within a preset time period based on the fiber Bragg grating sensor provided on the lithium-ion battery. , voltage, time, anode strain data, temperature and battery state of charge, and use current, voltage, time, anode strain data, temperature and battery state of charge as test parameters. Then, according to the model information of the lithium-ion battery, the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information are obtained, and the current, voltage, Time, anode strain data, temperature, and battery state of charge are used as known parameters.
  • a time series test data set is established according to the test parameters, and a time series target data set is established according to the known parameters, the time series test data set is normalized, and based on the upper and lower bound algorithm, all the time series test data sets are deleted.
  • the time series in the time series test data set that does not match the time series target data set is used to obtain a processed time series test data set.
  • a dynamic time warping model is trained based on the processed time series test data set to obtain a battery state of charge estimation model.
  • the current, voltage, anode strain data, and temperature are input into the battery state of charge estimation model to obtain Estimated data for battery state of charge.
  • This embodiment uses machine learning and high-precision fiber Bragg grating sensors to measure data such as anode strain and temperature, analyzes the constructed time series test data set, and deletes time series that do not match the time series target data set, thereby A more accurate battery state-of-charge estimation model is trained, effectively improving the estimation accuracy of battery state-of-charge (SOC).
  • SOC battery state-of-charge
  • the present invention also provides a lithium-ion battery state-of-charge estimation system based on fiber Bragg grating sensors.
  • the system includes: a test parameter acquisition module 10, a known parameter acquisition module 20, time series processing module 30 and state of charge estimation module 40.
  • the test parameter acquisition module is used to select a lithium-ion battery with a known model, conduct charging and discharging experiments on the lithium-ion battery, and collect predetermined parameters based on the fiber Bragg grating sensor provided on the lithium-ion battery. Set the current, voltage, time, anode strain data, temperature and battery state of charge within the time period, and use the current, voltage, time, anode strain data, temperature and battery state of charge as test parameters.
  • the known parameter acquisition module 20 is used to obtain the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information according to the model information of the lithium-ion battery, and compare it with the model information.
  • the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the above model information are used as known parameters.
  • the time series processing module 30 is used to establish a time series test data set according to the test parameters, establish a time series target data set according to the known parameters, and perform normalization processing on the time series test data set, Based on the upper and lower bound algorithm, time series that do not match the time series test data set and the time series target data set are deleted to obtain a processed time series test data set.
  • the state of charge estimation module 40 is used to train a dynamic time warping model based on the processed time series test data set to obtain a battery state of charge estimation model, and input current, voltage, anode strain data, and temperature into the battery state of charge estimation module 40. In the battery state of charge estimation model, the estimated data of the battery state of charge is obtained.
  • At least two fiber Bragg grating sensors are provided, one of which is attached to the negative electrode surface of the lithium-ion battery for measuring anode strain data of the battery anode material, and the other is attached to the anode surface of the battery.
  • the central position of the lithium ion battery is used to measure the temperature change of the lithium ion battery.
  • system further includes:
  • Parameter expansion module used to heat the lithium-ion battery to a specified temperature, repeatedly charge and discharge the lithium-ion battery, collect current, voltage, time, anode strain data, temperature and battery state of charge, and collect the collected The current, voltage, time, anode strain data, temperature and battery state of charge are used as the test parameters.
  • the time series processing module 30 includes:
  • a distance calculation unit configured to calculate the distance between the same time step in the time series test data set and the time series target data set based on an upper and lower bound algorithm
  • a first distance comparison unit configured to compare the distance with a preset threshold
  • a time series deletion unit configured to determine, if the distance is greater than a preset threshold, that the time series in the time series test data set whose distance is greater than the preset threshold does not match the time series target data set, and delete the time series target data set. Mismatched time series.
  • the distance calculation unit includes:
  • the upper and lower bound obtaining subunit is used to obtain the upper and lower bounds of each time series in the time series target data set;
  • the distance determination subunit is used to obtain each time series in the time series test data set, and calculate the distance between each time series and the upper bound and lower bound of the corresponding time series at the same time step to obtain the time series.
  • the time series processing module 30 also includes:
  • a distance acquisition unit configured to obtain, for a time series in the time series test data set, the calculated sum of distances between the time series and the same time step in the time series target data set;
  • the second distance comparison unit is used to compare the calculated sum of distances with the preset distance threshold
  • the calculation stop subunit is used to stop the calculation of the time series if the sum of the distances is greater than the distance threshold.
  • the state of charge estimation module 40 includes:
  • a model training unit for taking the current, voltage, time, anode strain data, and temperature in the processed time series test data set as input, and using the battery state of charge in the processed time series test data set as output , train the dynamic time warping model to obtain the battery state of charge estimation model;
  • An estimated data determination unit configured to input current, voltage, anode strain data, and temperature into the battery state of charge estimation model to obtain estimated data of the battery state of charge
  • a parameter tuning unit is configured to perform parameter tuning on the battery state-of-charge estimation model based on the estimated data and the processed time series test data set.
  • the present invention also provides a terminal device, which is used for testing and monitoring lithium-ion batteries.
  • a terminal device which is used for testing and monitoring lithium-ion batteries.
  • it can be a test terminal, a computer and other terminal devices, and can also perform testing on the data obtained by the test. Collection and analysis.
  • it can be terminal equipment such as test terminals and computers.
  • the functional block diagram of the terminal equipment can be shown in Figure 4.
  • the terminal device includes a processor and a memory connected through a system bus, and the processor and memory are arranged in the host. Among them, the processor of the terminal device is used to provide computing and control capabilities.
  • the memory of the terminal device includes non-volatile storage media and internal memory.
  • the non-volatile storage medium stores operating systems and computer programs.
  • This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media.
  • the network interface of the terminal device is used to communicate with an external terminal through a network communication connection.
  • the computer program is executed by a processor to implement a method for estimating the state of charge of a lithium-ion battery based on a fiber Bragg grating sensor.
  • a terminal device in one embodiment, includes a memory, a processor, and a method program for estimating the state of charge of a lithium-ion battery based on a fiber grating sensor that is stored in the memory and can be run on the processor, and processes
  • the device executes the method program for lithium-ion battery state-of-charge estimation based on fiber Bragg grating sensors, the following operation instructions are implemented:
  • the current, voltage, time, anode strain data, temperature and battery state of charge corresponding to the model information are obtained, and the current, voltage, time, Anode strain data, temperature, and battery state of charge are used as known parameters;
  • a dynamic time warping model is trained based on the processed time series test data set to obtain a battery state of charge estimation model.
  • the current, voltage, anode strain data, and temperature are input into the battery state of charge estimation model to obtain the battery state of charge estimation model.
  • Estimated data of electrical status is input into the battery state of charge estimation model to obtain the battery state of charge estimation model.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual operating data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM dual operating data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous Link (Synchlink) DRAM
  • SLDRAM synchronous Link (Synchlink) DRAM
  • Rambus direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM
  • the present invention discloses a method for estimating the state of charge of a lithium-ion battery based on a fiber grating sensor.
  • the method includes: selecting a lithium-ion battery with a known model, collecting and testing based on the fiber Bragg grating sensor installed on the lithium-ion battery.
  • the battery state of charge estimation model is used to obtain the estimated data of the battery state of charge based on the battery state of charge estimation model.
  • the invention effectively improves the estimation accuracy of the battery state of charge.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)

Abstract

一种基于光纤光栅传感器的锂离子电池荷电状态估计方法，方法包括：选取型号已知的锂离子电池，基于设置在锂离子电池上的光纤布拉格光栅传感器，采集测试参数(S100)；根据锂离子电池的型号信息，得到与型号信息对应的已知参数(S200)；根据测试参数建立时间序列测试数据集，以及根据已知参数建立时间序列目标数据集，对时间序列测试数据集进行归一化处理，并基于上下界算法，删除时间序列测试数据集中不匹配的时间序列，得到处理好的时间序列测试数据集(S300)；基于处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，并根据电池荷电状态估计模型，得到电池荷电状态的估计数据(S400)。有效提高了电池荷电状态的估计精度。

Description

一种基于光纤光栅传感器的锂离子电池荷电状态估计方法 技术领域
本发明设计电池分析技术领域，尤其涉及一种基于光纤光栅传感器的锂离子电池荷电状态估计方法。
 背景技术
电池荷电状态(SOC)是指示锂离子电池内剩余电量的重要指标。精确而稳健的SOC估算技术可避免过荷电，过放电和过热，从而延长电池的使用寿命，现有荷电状态估计方法有着依赖电池模型精度或者估计结果不准确的缺陷，电池老化导致荷电容量减少也增加了现有技术准确估计荷电状态的困难。
因此，现有技术还有待改进和提高。
 技术问题
本发明要解决的技术问题在于，针对现有技术的上述缺陷，提供一种基于光纤光栅传感器的锂离子电池荷电状态估计方法，旨在解决现有技术的荷电状态估计方法有着依赖电池模型精度或者估计结果不准确的缺陷，电池老化导致荷电容量减少也增加了现有技术准确估计荷电状态的困难的问题。
 技术解决方案
为了解决上述技术问题，本发明所采用的技术方案如下：
第一方面，本发明提供一种基于光纤光栅传感器的锂离子电池荷电状态估计方法，其中，所述方法包括：
选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，并基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数；
根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数；
根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集；
基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。
在一种实现方式中，所述光纤布拉格光栅传感器至少设置有两个，其中一个贴附在所述锂离子电池的负极表面，用于测量电池阳极材料的阳极应变数据，另一个贴附在所述锂离子电池的中央位置，用于测量所述锂离子电池的温度变化。
在一种实现方式中，所述方法还包括：
将所述锂离子电池加热至指定温度，并对所述锂离子电池重复充放电，采集电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将采集到的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为所述测试参数。
在一种实现方式中，所述基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，包括：
基于上下界算法，计算所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离；
将所述距离与预设的阈值进行比较；
若所述距离大于预设的阈值，则判定所述时间序列测试数据集中所述距离大于预设的阈值的时间序列与所述时间序列目标数据集不匹配，并删除不匹配的时间序列。
在一种实现方式中，所述基于上下界算法，计算所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离，包括：
获取所述时间序列目标数据集中每个时间序列的上界与下界；
获取所述时间序列测试数据集中每一个时间序列，并将每一个时间序列均与对应时间序列的上界以及下界通过对相同的时间步进行距离计算，得到所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离。
在一种实现方式中，所述基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，还包括：
针对所述时间序列测试数据集中的一个时间序列，获取已计算得到所述时间序列与所述时间序列目标数据集中相同时间步之间的距离之和；
将计算得到距离之和与预设的距离阈值进行比较；
若所述距离之和大于所述距离阈值，则停止所述时间序列的计算。
在一种实现方式中，所述基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据，包括：
将所述处理好的时间序列测试数据集中的电流、电压、时间、阳极应变数据、温度作为输入，将所述处理好的时间序列测试数据集中的电池荷电状态作为输出，训练所述动态时间规整模型，得到电池荷电状态估计模型；
将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据；
根据所述估计数据与所述处理好的时间序列测试数据集，对所述电池荷电状态估计模型进行参数调优。
第二方面，本发明实施例还提供一种基于光纤光栅传感器的锂离子电池荷电状态估计系统，其中，所述系统包括：
测试参数采集模块，用于选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，并基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数；
已知参数获取模块，用于根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数；
时间序列处理模块，用于根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集与所述时间序列目标数据集中不匹配的时间序列，得到处理好的时间序列测试数据集；
荷电状态估计模块，用于基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。
第三方面，本发明实施例还提供一种终端设备，终端设备包括存储器、处理器及存储在存储器中并可在处理器上运行的基于光纤光栅传感器的锂离子电池荷电状态估计程序，处理器执行基于光纤光栅传感器的锂离子电池荷电状态估计程序时，实现如上述方案中任一项的基于光纤光栅传感器的锂离子电池荷电状态估计方法的步骤。
第四方面，本发明实施例还提供一种计算机可读存储介质，计算机可读存储介质上存储有基于光纤光栅传感器的锂离子电池荷电状态估计程序，基于光纤光栅传感器的锂离子电池荷电状态估计的程序被处理器执行时，实现如上述方案中任一项的基于光纤光栅传感器的锂离子电池荷电状态估计方法的步骤。
 有益效果
有益效果：与现有技术相比，本发明提供了一种基于光纤光栅传感器的锂离子电池荷电状态估计方法，本发明首先选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数。然后，根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数。接着，根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集。最后，基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。本发明通过使用机器学习和高精度的光纤布拉格光栅传感器测量阳极应变和温度等数据，并对构建的时间序列测试数据集进行分析，删除其中与时间序列目标数据集不匹配的时间序列，从而训练出更为精确的电池荷电状态估计模型，有效提高了电池荷电状态的估计精度。
 附图说明
图1为本发明实施例提供的基于光纤光栅传感器的锂离子电池荷电状态估计方法的具体实施方式的流程图。
图2为本发明实施例提供的基于光纤光栅传感器的锂离子电池荷电状态估计方法中基于上下界时间距离计算的示意图。
图3为本发明实施例提供的基于光纤光栅传感器的锂离子电池荷电状态估计系统的原理框图。
图4为本发明实施例提供的终端设备的原理框图。
 本发明的实施方式
本实施例提供一种基于光纤光栅传感器的锂离子电池荷电状态估计方法，具体应用时，本实施例首先选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数。然后，根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数。接着，根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集。最后，基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。本实施例通过使用机器学习和高精度的光纤布拉格光栅传感器测量阳极应变和温度等数据，并对构建的时间序列测试数据集进行分析，删除其中与时间序列目标数据集不匹配的时间序列，从而训练出更为精确的电池荷电状态估计模型，有效提高了电池荷电状态（SOC）的估计精度。
示例性方法
本实施例的基于光纤光栅传感器的锂离子电池荷电状态估计方法可应用于终端设备，所述终端设备为用于对锂离子电池进行测试与监控，并且还可以对测试得到的数据进行采集与分析。比如可以为测试终端、电脑等终端设备。具体地，本实施例的基于光纤光栅传感器的锂电池荷电状态估计具体包括如下步骤：
步骤S100、选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，并基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数。
具体实施时，本实施例终端设备首先选取型号已知的锂离子电池，然后调取该锂离子电池并进行充放电实验，充放电实验的开始时间为t1，结束时间为t2，本实施例可记录充放电实验过程中各个时刻（即从t1到t2之间的时间）的电流以及电压。此外，本实施例还可以基于设置在所述锂离子电池上的光纤布拉格光栅传感器（Fiber Bragg Grating,FBG），采集锂离子电池的阳极应变数据以及温度。具体地，本实施例中的光纤布拉格光栅传感器至少设置有两个，其中一个贴附在所述锂离子电池的负极表面，用于测量电池阳极材料的阳极应变数据，另一个贴附在所述锂离子电池的中央位置，用于测量所述锂离子电池的温度变化。此外，本实施例还可对锂离子电池进行静置，并在静置足够长的时间后，测量开路电压， 确定出该锂离子电池的电池荷电状态。所述电池荷电状态可反映出该锂离子电池的剩余容量状态，该数值可定义为电池剩余容量占电池总容量的比值，SOC=Q/C I，其中，Q为电池剩余容量，C I为电池以恒定电流I放电时所具有的容量。所述电池荷电状态是与电流、电压、时间、阳极应变数据以及温度有关的，在某种程度来说，电流、电压、时间、阳极应变数据以及温度可直接影响电池荷电状态，而电池荷电状态反映的是该锂离子电池的剩余容量状态，因此电池荷电状态可反映出该锂离子电池的剩余寿命。当采集得到电流、电压、时间、阳极应变数据、温度以及电池荷电状态后，本实施例将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数。本实施例中采集测试参数是用于反映出该锂离子电池在实际使用的过程中体现电池健康状态的数据，这些数据会影响锂离子电池的使用寿命。
在一种实现方式中，本实施例还可将所述锂离子电池加热至指定温度，并对所述锂离子电池重复充放电，采集电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将采集到的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为所述测试参数，这样可以得到更为丰富的测试参数。
步骤S200、根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数。
由于本实施例获取的锂离子电池的型号信息是已知的，因此本实施例可根据该型号信息确定出对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，这些参数可从厂家提供的数据中直接获取到，比如产品使用说明书，或者产品介绍书册。本实施例将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数。本实施例中的已知参数中的每一个数据都是在出厂时基于不同的时刻进行检测得到的，也就是说该已知参数是在锂离子电池初始化之前测试的，这些参数是用于反映所述锂离子电池的正常使用情况下的正常数据，是可以用于衡量出该锂离子电池的健康状态的标准参数。
步骤S300、根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集。
在本实施例中，由于该测试参数是基于开始时间为t1，结束时间为t2之间时间段来采集得到的，因此当采集得到测试参数后，本实施例可基于测试参数建立时间序列测试数据集，该建立时间序列测试数据集中具有不同时间序列，每个时间序列可用于反映不同数据的变化，比如温度时间序列反映的是温度随着时间的变化，电压时间序列反映的是电压随时间的变化。同样地，本实施例中已知参数中的每一个数据都是在出厂时基于不同的时刻进行检测得到的，因此本实施例基于已知参数建立时间序列目标数据集，该时间序列目标数据集中所包括的是已知参数中的电流、电压、阳极应变数据、温度以及电池荷电状态所对应的时间序列。当构建时间序列测试数据集后，本实施例对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集。
为了及时发现所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，本实施例基于距离算法，可通过两个序列之间中相同时间步之间的距离来判断出所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列。距离的计算方式为：
其中，Q和C为不同的时间序列，q i为Q序列中的i时刻的点，C i为C序列中的i时刻的点，可见q i和C i为相同时间步的两个点，因此通过计算两个时间序列相同时间步之间的距离就可以确定出两个时间序列的相似度。当两个序列按照时间步t完全对齐的时候，可以直接通过距离计算来评估两个算法的相似度。但是有些时候两个时间序列并未完全对齐，如果将某一时间序列进行压缩处理，此时会有信息损失，导致两个时间序列无法完全对齐。为此，本实施例引入动态时间规整算法（DTW算法），DTW算法的核心是将两个不同的时间序列按照最好的方式对齐，对齐的方式有很多，最好的对齐方式就是两个时间序列的距离最小，同时这个最小的距离就是这两个时间序列的距离。但是，当使用DTW算法的时候，需要计算两个时间序列之间不同时间步之间的距离，需要较大的计算量。为了减少计算量，本实施例引入上下界算法（Lower Bounding）。具体地，本实施例可获取所述时间序列目标数据集中每个时间序列的上界与下界，然后获取所述时间序列测试数据集中每一个时间序列，并将每一个时间序列均与对应时间序列的上界以及下界通过对相同的时间步进行距离计算，得到所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离，然后将所述距离与预设的阈值进行比较；若所述距离大于预设的阈值，则判定所述时间序列测试数据集中所述距离大于预设的阈值的时间序列与所述时间序列目标数据集不匹配，并删除不匹配的时间序列。举例说明，并结合图2进行说明，针对时间序列目标数据集中的Q时间序列（如温度时间序列），本实施例可对Q时间序列设置上界U和下界L，如图2所示，图2中的A图为不设置上界U和下界L时，Q时间序列和C时间序列进行距离计算时的示意图，图2中的B图为设置上界U和下界L时，Q时间序列和C时间序列进行距离计算时的示意图。从图2中可以看出，所述上界U是基于Q时间序列的峰值设置的，所述下界L是基于Q时间序列的谷值设置的。在时间序列测试数据集中与Q时间序列对应的是C时间序列，因此，可将C时间序列分别与上界U和下界L进行距离计算，计算的时候同样是基于两个序列相同的时间步进行计算。此时，C序列和上界U、下界L之间直接通过对应时间步计算，不用对齐，如果计算出的距离大于设置阈值，则就认为Q时间序列和C时间序列之间的差距太大了，二者不匹配，因此此时的C时间序列就属于不匹配的时间序列了，需要进行删除。
在另一种实现方式中，本实施例在计算两个时间序列的距离时，还可以基于四个对应的点进行计算。比如，直接找到Q时间序列和C时间序列中的四个对应的点，分别为起始点，终点，最高点，最低点，然后针对这四个点分别计算这四个点的距离和，如果超过阈值，则就认为Q时间序列和C时间序列不匹配。此外，本实施例还可以基于两个对应的点进行距离计算，比如，直接找到Q时间序列和C时间序列的两个对应的点，分别为最高点和最低点，然后针对这两个点分别计算这两个个点的距离和，如果超过阈值，则就认为Q时间序列和C时间序列不匹配，有利于减少计算量，提高计算效率。
在另一种实现方式中，本实施例还可以针对所述时间序列测试数据集中的一个时间序列，获取已计算得到所述时间序列与所述时间序列目标数据集中相同时间步之间的距离之和。然后将计算得到距离之和与预设的距离阈值进行比较。若所述距离之和大于所述距离阈值，则停止所述时间序列的计算。比如，针对某个时间序列，只计算了五个时间步的距离后就发现，距离之和已经超过距离阈值，则就可以确定出两个时间序列是不匹配的，此时就可以直接终止计算，减少计算量，提高计算效率，以便更快速地确定出所述时间序列测试数据集中不匹配的时间序列。此外，本实施例将归一化处理以及找出并删除不匹配的时间序列的步骤同时进行，也就是每归一化处理一个时间序列就可以立即进行距离计算，如果计算过程中距离之和一旦超过距离阈值，则立即停止计算，以后的也不用进行归一化处理了，计算量就减少了。当将不匹配的时间序列删除后，就得到了处理好的时间序列测试数据集。
步骤S400、基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。
具体地，本实施例可将所述处理好的时间序列测试数据集中的电流、电压、时间、阳极应变数据、温度作为输入，将所述处理好的时间序列测试数据集中的电池荷电状态作为输出，训练动态时间规整模型，得到电池荷电状态估计模型。由于所述电池荷电状态是与电流、电压、时间、阳极应变数据以及温度有关的，在某种程度来说，电流、电压、时间、阳极应变数据以及温度可直接影响电池荷电状态，而电池荷电状态反映的是该锂离子电池的剩余容量状态，因此电池荷电状态可反映出该锂离子电池的剩余寿命。因此将电流、电压、时间、阳极应变数据、温度与电池荷电状态之间的对应关系均输入至动态时间规整模型中进行训练，就可以得到电池荷电状态估计模型。因此当将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据，从而实现对锂离子电池的电池荷电状态的估计。此外，本实施例还可将根据所述估计数据与所述处理好的时间序列测试数据集进行整合，整合时保证时间维度一致，然后将整个的数据输入至电池荷电状态估计模型中，得到对应电池荷电状态的预测值，根据该电池荷电状态的预测值来对所述电池荷电状态估计模型进行参数调优，从而提高该电池荷电状态估计模型的准确性。
综上，本实施例首先选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数。然后，根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数。接着，根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集。最后，基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。本实施例通过使用机器学习和高精度的光纤布拉格光栅传感器测量阳极应变和温度等数据，并对构建的时间序列测试数据集进行分析，删除其中与时间序列目标数据集不匹配的时间序列，从而训练出更为精确的电池荷电状态估计模型，有效提高了电池荷电状态（SOC）的估计精度。
示例性系统
基于上述实施例，本发明还提供一种基于光纤光栅传感器的锂离子电池荷电状态估计系统，如图3中所示，所述系统包括：测试参数采集模块10、已知参数获取模块20、时间序列处理模块30以及荷电状态估计模块40。具体地，所述测试参数采集模块，用于选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，并基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数。所述已知参数获取模块20，用于根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数。所述时间序列处理模块30，用于根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集与所述时间序列目标数据集中不匹配的时间序列，得到处理好的时间序列测试数据集。所述荷电状态估计模块40，用于基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。
在一种实现方式中，所述光纤布拉格光栅传感器至少设置有两个，其中一个贴附在所述锂离子电池的负极表面，用于测量电池阳极材料的阳极应变数据，另一个贴附在所述锂离子电池的中央位置，用于测量所述锂离子电池的温度变化。
在一种实现方式中，所述系统还包括：
参数扩充模块，用于将所述锂离子电池加热至指定温度，并对所述锂离子电池重复充放电，采集电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将采集到的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为所述测试参数。
在一种实现方式中，所述时间序列处理模块30，包括：
距离计算单元，用于基于上下界算法，计算所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离；
第一距离比较单元，用于将所述距离与预设的阈值进行比较；
时间序列删除单元，用于若所述距离大于预设的阈值，则判定所述时间序列测试数据集中所述距离大于预设的阈值的时间序列与所述时间序列目标数据集不匹配，并删除不匹配的时间序列。
在一种实现方式中，所述距离计算单元，包括：
上下界获取子单元，用于获取所述时间序列目标数据集中每个时间序列的上界与下界；
距离确定子单元，用于获取所述时间序列测试数据集中每一个时间序列，并将每一个时间序列均与对应时间序列的上界以及下界通过对相同的时间步进行距离计算，得到所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离。
在一种实现方式中，所述时间序列处理模块30，还包括：
距离之后获取单元，用于针对所述时间序列测试数据集中的一个时间序列，获取已计算得到所述时间序列与所述时间序列目标数据集中相同时间步之间的距离之和；
第二距离比较单元，用于将计算得到距离之和与预设的距离阈值进行比较；
计算停止子单元，用于若所述距离之和大于所述距离阈值，则停止所述时间序列的计算。
在一种实现方式中，所述荷电状态估计模块40，包括：
模型训练单元，用于将所述处理好的时间序列测试数据集中的电流、电压、时间、阳极应变数据、温度作为输入，将所述处理好的时间序列测试数据集中的电池荷电状态作为输出，训练所述动态时间规整模型，得到电池荷电状态估计模型；
估计数据确定单元，用于将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据；
参数调优单元，用于根据所述估计数据与所述处理好的时间序列测试数据集，对所述电池荷电状态估计模型进行参数调优。
本实施例的基于光纤光栅传感器的锂离子电池荷电状态估计系统中各个模块的工作原理与上述方法实施例中各个步骤的原理相同，此处不再赘述。
基于上述实施例，本发明还提供了一种终端设备，该终端设备为用于对锂离子电池进行测试与监控，比如可以为测试终端、电脑等终端设备，并且还可以对测试得到的数据进行采集与分析。比如可以为测试终端、电脑等终端设备。该终端设备的原理框图可以如图4所示。该终端设备包括通过系统总线连接的处理器、存储器，处理器与存储器设置在主机中。其中，该终端设备的处理器用于提供计算和控制能力。该终端设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该终端设备的网络接口用于与外部的终端通过网络通讯连接通信。该计算机程序被处理器执行时以实现一种基于光纤光栅传感器的锂离子电池荷电状态估计方法。
本领域技术人员可以理解，图4中示出的原理框图，仅仅是与本发明方案相关的部分结构的框图，并不构成对本发明方案所应用于其上的终端设备的限定，具体的终端设备以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一种终端设备，终端设备包括存储器、处理器及存储在存储器中并可在处理器上运行的基于光纤光栅传感器的锂离子电池荷电状态估计的方法程序，处理器执行基于光纤光栅传感器的锂离子电池荷电状态估计的方法程序时，实现如下操作指令：
选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，并基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数；
根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数；
根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集；
基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成的，计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本发明所提供的各实施例中所使用的对存储器、存储、运营数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器（ROM）、可编程ROM（PROM）、电可编程ROM（EPROM）、电可擦除可编程ROM（EEPROM）或闪存。易失性存储器可包括随机存取存储器（RAM）或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM（SRAM）、动态RAM（DRAM）、同步DRAM（SDRAM）、双运营数据率SDRAM（DDRSDRAM）、增强型SDRAM（ESDRAM）、同步链路（Synchlink） DRAM（SLDRAM）、存储器总线（Rambus）直接RAM（RDRAM）、直接存储器总线动态RAM（DRDRAM）、以及存储器总线动态RAM（RDRAM）等。
综上，本发明公开了一种基于光纤光栅传感器的锂离子电池荷电状态估计方法，方法包括：选取型号已知的锂离子电池，基于设置在锂离子电池上的光纤布拉格光栅传感器，采集测试参数；根据锂离子电池的型号信息，得到与型号信息对应的已知参数；根据测试参数建立时间序列测试数据集，以及根据已知参数建立时间序列目标数据集，对时间序列测试数据集进行归一化处理，并基于上下界算法，删除时间序列测试数据集中不匹配的时间序列，得到处理好的时间序列测试数据集；基于处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，并根据电池荷电状态估计模型，得到电池荷电状态的估计数据。本发明有效提高了电池荷电状态的估计精度。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。

Claims (10)

  1. 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法，其特征在于，所述方法包括：
    选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，并基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数；
    根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数；
    根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集；
    基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。
  2. 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法，其特征在于，所述方法包括：
    选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，并基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数；
    根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数；
    根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，得到处理好的时间序列测试数据集；
    基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。
  3. 根据权利要求1所述的基于光纤光栅传感器的锂离子电池荷电状态估计方法，其特征在于，所述方法还包括：
    将所述锂离子电池加热至指定温度，并对所述锂离子电池重复充放电，采集电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将采集到的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为所述测试参数。
  4. 根据权利要求1所述的基于光纤光栅传感器的锂离子电池荷电状态估计方法，其特征在于，所述基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，包括：
    基于上下界算法，计算所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离；
    将所述距离与预设的阈值进行比较；
    若所述距离大于预设的阈值，则判定所述时间序列测试数据集中所述距离大于预设的阈值的时间序列与所述时间序列目标数据集不匹配，并删除不匹配的时间序列。
  5. 根据权利要求4所述的基于光纤光栅传感器的锂离子电池荷电状态估计方法，其特征在于，所述基于上下界算法，计算所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离，包括：
    获取所述时间序列目标数据集中每个时间序列的上界与下界；
    获取所述时间序列测试数据集中每一个时间序列，并将每一个时间序列均与对应时间序列的上界以及下界通过对相同的时间步进行距离计算，得到所述时间序列测试数据集与所述时间序列目标数据集中相同时间步之间的距离。
  6. 根据权利要求4所述的基于光纤光栅传感器的锂离子电池荷电状态估计方法，其特征在于，所述基于上下界算法，删除所述时间序列测试数据集中与所述时间序列目标数据集不匹配的时间序列，还包括：
    针对所述时间序列测试数据集中的一个时间序列，获取已计算得到所述时间序列与所述时间序列目标数据集中相同时间步之间的距离之和；
    将计算得到距离之和与预设的距离阈值进行比较；
    若所述距离之和大于所述距离阈值，则停止所述时间序列的计算。
  7. 根据权利要求1所述的基于光纤光栅传感器的锂离子电池荷电状态估计方法，其特征在于，所述基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据，包括：
    将所述处理好的时间序列测试数据集中的电流、电压、时间、阳极应变数据、温度作为输入，将所述处理好的时间序列测试数据集中的电池荷电状态作为输出，训练所述动态时间规整模型，得到电池荷电状态估计模型；
    将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据；
    根据所述估计数据与所述处理好的时间序列测试数据集，对所述电池荷电状态估计模型进行参数调优。
  8. 一种基于光纤光栅传感器的锂离子电池荷电状态估计系统，其特征在于，所述系统包括：
    测试参数采集模块，用于选取型号已知的锂离子电池，对所述锂离子电池进行充放电实验，并基于设置在所述锂离子电池上的光纤布拉格光栅传感器，采集预设时间段内的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为测试参数；
    已知参数获取模块，用于根据所述锂离子电池的型号信息，得到与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态，并将与所述型号信息对应的电流、电压、时间、阳极应变数据、温度以及电池荷电状态作为已知参数；
    时间序列处理模块，用于根据所述测试参数建立时间序列测试数据集，以及根据所述已知参数建立时间序列目标数据集，对所述时间序列测试数据集进行归一化处理，并基于上下界算法，删除所述时间序列测试数据集与所述时间序列目标数据集中不匹配的时间序列，得到处理好的时间序列测试数据集；
    荷电状态估计模块，用于基于所述处理好的时间序列测试数据集训练动态时间规整模型，得到电池荷电状态估计模型，将电流、电压、阳极应变数据、温度输入至所述电池荷电状态估计模型中，得到电池荷电状态的估计数据。
  9. 一种终端设备，其特征在于，所述终端设备包括存储器、处理器及存储在所述存储器中并可在所述处理器上运行的基于光纤光栅传感器的锂离子电池荷电状态估计程序，所述处理器执行基于光纤光栅传感器的锂离子电池荷电状态估计程序时，实现如权利要求1-7任一项的基于光纤光栅传感器的锂离子电池荷电状态估计方法的步骤。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有基于光纤光栅传感器的锂离子电池荷电状态估计程序，基于光纤光栅传感器的锂离子电池荷电状态估计的程序被处理器执行时，实现如权利要求1-7任一项的基于光纤光栅传感器的锂离子电池荷电状态估计方法的步骤。
PCT/CN2022/137071 2022-05-30 2022-12-06 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法 WO2023231356A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202210598326.3 2022-05-30
CN202210598326.3A CN114935721B (zh) 2022-05-30 2022-05-30 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法

Publications (1)

Publication Number Publication Date
WO2023231356A1 true WO2023231356A1 (zh) 2023-12-07

Family

ID=82866061

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/137071 WO2023231356A1 (zh) 2022-05-30 2022-12-06 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法

Country Status (2)

Country Link
CN (1) CN114935721B (zh)
WO (1) WO2023231356A1 (zh)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114935721B (zh) * 2022-05-30 2023-03-24 深圳先进技术研究院 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法
CN116643181B (zh) * 2022-08-25 2024-04-16 浙江长兴震革科技有限公司 一种蓄电池状态监测系统
CN117521857A (zh) * 2024-01-05 2024-02-06 宁德时代新能源科技股份有限公司 一种电芯析锂分析方法、装置、可读存储介质及电子设备

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019144211A (ja) * 2018-02-23 2019-08-29 株式会社デンソーテン 推定装置および推定方法
CN110308403A (zh) * 2019-07-03 2019-10-08 中国民用航空飞行学院 动力锂离子电池多参数探测与采集方法
CN112014738A (zh) * 2020-10-13 2020-12-01 北京理工大学 一种基于内嵌光纤传感器的锂离子电池荷电状态估计方法
CN112067998A (zh) * 2020-09-10 2020-12-11 昆明理工大学 一种基于深度神经网络的锂离子电池荷电状态估计方法
CN113312527A (zh) * 2021-07-28 2021-08-27 太平金融科技服务(上海)有限公司深圳分公司 采购数据处理方法、装置、计算机设备和存储介质
CN114280490A (zh) * 2021-09-08 2022-04-05 国网湖北省电力有限公司荆门供电公司 一种锂离子电池荷电状态估计方法及系统
CN114935721A (zh) * 2022-05-30 2022-08-23 深圳先进技术研究院 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法

Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU2019200162B2 (en) * 2012-07-20 2021-10-07 Verinata Health, Inc. Detecting and classifying copy number variation
CN103226513B (zh) * 2013-04-25 2015-12-09 中国人民解放军信息工程大学 基于二次规划模型的非线性下标表达式的依赖测试方法
CN204215026U (zh) * 2014-11-27 2015-03-18 温州大学 车用蓄电池充放电参数实时检测装置
CN105134484A (zh) * 2015-07-28 2015-12-09 国家电网公司 风电功率异常数据点的识别方法
CN106655250A (zh) * 2016-10-31 2017-05-10 国家电网公司 一种大型光伏电站直流系统过电压风险指数预测方法
CN107169062A (zh) * 2017-05-02 2017-09-15 江苏大学 一种基于始末距离的时间序列符号聚合近似表示方法
CN107592422B (zh) * 2017-09-20 2019-07-02 上海交通大学 一种基于手势特征的身份认证方法及系统
CN107546809B (zh) * 2017-09-29 2020-01-03 北京航空航天大学 一种基于应变及温度和电压调控的智能充电系统及方法
CN108197011B (zh) * 2018-01-29 2021-06-01 上海洞识信息科技有限公司 一种基于人工智能大数据平台的单指标预测和预警方法
CN109325060B (zh) * 2018-07-27 2021-10-12 山东大学 一种基于数据特征的时间序列流数据快速搜索方法
US11899069B2 (en) * 2018-12-21 2024-02-13 Cummins Inc. SOC and SOH co-estimation systems and methods for electric vehicles
CN111092257A (zh) * 2019-12-17 2020-05-01 天津力神电池股份有限公司 一种智能锂离子电池
CN111241208B (zh) * 2019-12-31 2024-03-29 合肥城市云数据中心股份有限公司 一种周期性时序数据的异常监测方法及装置
CN113076349A (zh) * 2020-01-06 2021-07-06 阿里巴巴集团控股有限公司 数据异常检测方法、装置、系统及电子设备
CN111812515A (zh) * 2020-07-17 2020-10-23 广西师范大学 基于XGboost模型的锂离子电池荷电状态估算
CN112083333A (zh) * 2020-08-11 2020-12-15 昆明理工大学 一种基于机器学习模型的动力电池组荷电状态估计方法

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019144211A (ja) * 2018-02-23 2019-08-29 株式会社デンソーテン 推定装置および推定方法
CN110308403A (zh) * 2019-07-03 2019-10-08 中国民用航空飞行学院 动力锂离子电池多参数探测与采集方法
CN112067998A (zh) * 2020-09-10 2020-12-11 昆明理工大学 一种基于深度神经网络的锂离子电池荷电状态估计方法
CN112014738A (zh) * 2020-10-13 2020-12-01 北京理工大学 一种基于内嵌光纤传感器的锂离子电池荷电状态估计方法
CN113312527A (zh) * 2021-07-28 2021-08-27 太平金融科技服务(上海)有限公司深圳分公司 采购数据处理方法、装置、计算机设备和存储介质
CN114280490A (zh) * 2021-09-08 2022-04-05 国网湖北省电力有限公司荆门供电公司 一种锂离子电池荷电状态估计方法及系统
CN114935721A (zh) * 2022-05-30 2022-08-23 深圳先进技术研究院 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法

Also Published As

Publication number Publication date
CN114935721A (zh) 2022-08-23
CN114935721B (zh) 2023-03-24

Similar Documents

Publication Publication Date Title
WO2023231356A1 (zh) 一种基于光纤光栅传感器的锂离子电池荷电状态估计方法
US11408942B2 (en) Method for predicting service life of retired power battery
CN108550928B (zh) 电动汽车、充放电设备、电池soh的检测方法及系统
WO2021057917A1 (zh) 电池温度估算方法、装置、电子设备及存储介质
CN111448467A (zh) 用于对电池容量进行建模和估计的方法及系统
WO2021143592A1 (zh) 电池等效电路模型的建立方法、健康状态估算方法及装置
WO2022213789A1 (zh) 锂电池soc估计方法、装置及计算机可读存储介质
TWI831145B (zh) 電池狀態判定方法及電池狀態判定裝置
WO2022164127A1 (ko) 디지털트윈 장치 및 디지털트윈 기반의 배터리 온도감시방법
Chang et al. Micro-fault diagnosis of electric vehicle batteries based on the evolution of battery consistency relative position
WO2019042416A1 (zh) 电池均衡方法、系统、车辆及电子设备
JP2022545033A (ja) 電池診断装置および方法
WO2024066055A1 (zh) 一种双向锂离子电池的寿命预测与状态估计方法
CN116203490A (zh) 传感器故障诊断方法、装置、设备及存储介质
US11505088B2 (en) Vehicle and control method thereof
CN114930609A (zh) 用于估计电池单元表面温度的系统和方法
KR20220100442A (ko) 배터리 진단 장치, 배터리 시스템 및 배터리 진단 방법
CN116718921A (zh) 基于多模型的电池荷电状态预测方法及装置
CN116047339A (zh) 基于热电耦合模型的锂离子电池组soc估计方法及装置
CN116482539A (zh) 基于多维数据驱动的电池荷电状态估计方法、系统、设备及介质
CN113125965B (zh) 电池析锂检测方法、装置、设备及存储介质
CN114720901A (zh) 一种锂离子电池健康状态估计的方法
JP2024501510A (ja) バッテリー管理装置及び方法
JP2023524846A (ja) バッテリー異常診断装置及び方法
Yang et al. A battery capacity estimation method using surface temperature change under constant-current charge scenario

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22944657

Country of ref document: EP

Kind code of ref document: A1