CN110703121A - Lithium ion battery health state prediction method - Google Patents
Lithium ion battery health state prediction method Download PDFInfo
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- CN110703121A CN110703121A CN201911086995.7A CN201911086995A CN110703121A CN 110703121 A CN110703121 A CN 110703121A CN 201911086995 A CN201911086995 A CN 201911086995A CN 110703121 A CN110703121 A CN 110703121A
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
Abstract
The invention provides a lithium ion battery health state prediction method, which comprises the steps of placing a lithium ion battery in a constant temperature environment, and after standing for a time, performing constant-current charge-discharge circulation on the lithium ion battery; after each charge-discharge cycle is carried out for a preset number of times, placing the lithium ion battery in a room temperature environment, standing for a preset time, and carrying out primary capacity calibration on the lithium ion battery; performing constant current discharge on the battery at a certain multiplying power, and measuring the alternating current impedance of the lithium ion battery once after the charge state value of the lithium ion battery is reduced to a set value to establish a dynamic impedance spectrum; establishing an equivalent circuit of the lithium ion battery according to the dynamic impedance spectrum, and fitting the dynamic impedance spectrum of the lithium ion battery according to the equivalent circuit to obtain fitting data; extracting fitting data as input parameters, and substituting the fitting data into the BP neural network model to obtain the health state of the lithium ion battery; by adopting the scheme, the reliability of the health state detection is improved, the prediction error is reduced, the prediction time is shortened, the data is simple and easy to obtain, and the online detection can be realized.
Description
Technical Field
The invention belongs to the technical field of lithium ion battery health state prediction, and particularly relates to a lithium ion battery health state prediction method.
Background
Since the invention, through years of development and innovation, the lithium ion battery has higher energy density and longer cycle life, and is widely applied to the fields of smart power grids and electric automobiles; however, the actual operating environment of the electric vehicle is very complex, the battery is required to continuously maintain stable electrochemical performance in the whole life cycle, but the battery system is complex in composition, various electrochemical processes and thermodynamic processes are involved in the battery during working, long-term stability is a great challenge for the lithium ion battery, and more importantly, long-term safety and reliability in the actual use process are the premise that the large-capacity lithium ion battery is applied to the electric vehicle in a large scale and for a long time.
The State of Health (SOH) is an important index of the safety and stability of a lithium ion battery, the accurate prediction of the State of Health (SOH) is one of the preconditions and key technologies for the operation of a battery management system, is important for the safety of an electric vehicle and the prolonging of the service life of the battery, and is a hotspot and difficult problem of research all the time.
The chinese patent (CN 109444762A) estimates the state of health of the battery by using the data of the battery in the steady current charging process and using a data fusion method, but the estimation process is complicated, requires a long standing time, and is not suitable for practical engineering applications.
Based on the technical problems in the lithium ion battery health state prediction, no relevant solution is provided; there is therefore a pressing need to find effective solutions to the above problems.
Disclosure of Invention
The invention aims to provide a lithium ion battery health state prediction method aiming at overcoming the defects in the prior art.
The invention provides a lithium ion battery health state prediction method, which comprises the following processes:
s1: placing the lithium ion battery in a constant temperature environment, and setting standing time;
s2: after standing for a time, performing constant-current charge-discharge circulation on the lithium ion battery;
s3: after each charge-discharge cycle is carried out for a preset number of times, the lithium ion battery is placed in a room temperature environment to stand for a preset time, and then the lithium ion battery is subjected to primary capacity calibration;
s4: performing constant current discharge on the battery at a certain multiplying power according to the capacity calibrated by the lithium ion battery obtained in the step S3, and measuring the alternating current impedance of the primary lithium ion battery after the state of charge value of the lithium ion battery is reduced to a set value in the discharge process of the lithium ion battery, and establishing a dynamic impedance spectrum;
s5: establishing an equivalent circuit of the lithium ion battery according to the dynamic impedance spectrum obtained in the step S4, and fitting the dynamic impedance spectrum of the lithium ion battery according to the equivalent circuit to obtain fitting data;
s6: and extracting ohmic internal resistance, charge transfer internal resistance and constant phase angle element parameters in the fitting data as input parameters, and substituting the input parameters into the BP neural network model to obtain the health state of the lithium ion battery.
Further, in step S6, the method for calculating the state of health of the lithium ion battery according to the ohmic internal resistance, the charge transfer internal resistance and the constant phase angle element parameter includes:
s61: firstly, establishing a BP neural network model, taking ohmic internal resistance, charge transfer internal resistance and constant phase angle element parameters as input parameters and taking a corresponding health state as an output parameter, and training the BP neural network model until the error requirement is met;
s62: and substituting the parameters of the ohmic internal resistance, the charge transfer internal resistance and the constant phase angle element into the trained BP neural network model to calculate the health state of the battery.
Further, in step S3, the method for calibrating the capacity of the lithium ion battery includes:
and charging the lithium ion battery to a full-charge state, standing for a period of time, discharging to an empty state, standing for a period of time again, circulating for 3 times, and taking the last discharge capacity as the calibration capacity of the lithium ion battery.
Further, in the step S4, the set value of the state of charge value of the lithium ion battery is 5% to 95%.
Further, in the step S2, the charge-discharge multiplying power of the lithium ion battery is 0.1-3C.
Further, in the step S3, the charge-discharge multiplying power of the lithium ion battery is 0.1-1C.
Further, in step S5, fitting is performed by using a least square method when fitting the lithium ion battery dynamic impedance spectrum.
Further, in the step S1, the temperature of a constant temperature environment is-20-40 ℃; and/or the standing time is 0.5-3 h.
Further, in the step S3, standing for a preset time of 2-5 hours; and/or in the step S3, the preset times are 10-100 times.
Further, in step S4, the measurement environment conditions of the ac impedance are: the excitation signal is current, and the frequency range is 2000-0.1 Hz.
The lithium ion battery health state prediction method provided by the invention solves the problems of low accuracy and long time consumption of the conventional health state prediction method, provides a method for predicting the health state of the battery by using a dynamic impedance spectrum of direct measurement in combination with a BP neural network and using the BP neural network trained by data, thereby shortening the prediction time and improving the prediction precision.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The invention will be further explained with reference to the drawings, in which:
FIG. 1 is a diagram showing the relationship between different cycle times and the health status of a ternary lithium ion battery according to the present invention;
FIG. 2 is a dynamic impedance spectrum of the ternary lithium ion battery of the present invention at different cycle times;
FIG. 3 is an equivalent circuit diagram constructed according to dynamic impedance plot data in accordance with the present invention;
FIG. 4 is a schematic diagram of a BP neural network model established by the present invention;
FIG. 5 is a diagram illustrating the training effect of the BP neural network according to the present invention;
fig. 6 is a flowchart of a method for predicting the health status of a lithium ion battery according to the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 6, the present invention provides a method for predicting a health state of a lithium ion battery, which specifically includes the following steps:
s1: placing the lithium ion battery in a constant temperature environment, and setting standing time T; specifically, in the step, the temperature of the constant temperature environment is-20 to 40 ℃, preferably 25 ℃, 33 ℃ or 36 ℃; in the step, the standing time T is 0.5-3 h, preferably 0.7h, 1h or 2.6 h;
s2: after the standing time T is over, performing constant-current charge-discharge circulation on the lithium ion battery; specifically, the charge-discharge rate of the lithium ion battery is 0.1-3C, preferably 0.2C, 1C or 2.4C;
s3: after each charge-discharge cycle is carried out for a preset number of times N, the lithium ion battery is placed in a room temperature environment to stand for a preset time S, and then the lithium ion battery is subjected to primary capacity calibration; specifically, in the step, the charge-discharge rate of the lithium ion battery is 0.1-1C, preferably 0.2C, 0.4C or 0.8C; further, in the step, the standing is carried out for a preset time S of 2-5 hours, preferably 3 hours, 4 hours and 5 hours; further, in the step, the preset number of times N is 10-100, which can be understood as that if the preset number of times is 10, the battery performs one-time capacity calibration after 10 cycles, and then performs one-time capacity calibration after each integral multiple of 10 cycles of the battery;
s4: performing constant current discharge on the battery at a certain multiplying power according to the capacity calibrated by the lithium ion battery obtained in the step S3, and measuring the alternating current impedance of the primary lithium ion battery after the state of charge value of the lithium ion battery is reduced to a set value in the discharge process of the lithium ion battery, and establishing a dynamic impedance spectrum; specifically, in this step, the setting value of the SOC (state of charge, i.e. the ratio of the remaining capacity of the battery to the capacity of the fully charged battery, which is usually expressed in percentage and reflects the remaining capacity of the battery) value of the lithium ion battery is 5% to 95%, preferably 55%, 60%, 75% or 90%; further, it can be understood that if the set value is 95%, during the discharging process, if the SOC value of the lithium ion battery is reduced to 95%, the ac impedance of the primary battery is measured at this time; further, in the step, the discharge rate in the constant current discharge process is 0.1-3C, preferably 0.3C, 1C or 2C;
s5: and making a dynamic impedance spectrum under different cycle times, namely an Nyqusit graph according to the dynamic impedance spectrum acquired in the step of S4.
Further, an equivalent circuit of the lithium ion battery is established according to the dynamic impedance spectrum, and fitting is carried out on the dynamic impedance spectrum of the lithium ion battery according to the equivalent circuit to obtain fitting data; the equivalent circuit comprises an inductor, a resistor and a constant phase angle element; specifically, in the step, a least square method is adopted for fitting when the dynamic impedance spectrum of the lithium ion battery is fitted;
s6: extracting ohmic internal resistance R in fitting datasInternal resistance to charge transfer RctParameter Y of constant phase angle element Q11Constant phase angle element Q1 coefficient N1As input parameters, the corresponding SOH value is used as output parameters for training the BP neural network; and extracting ohmic internal resistance, charge transfer internal resistance and constant phase angle element parameters in the fitting data as input parameters, substituting the input parameters into the BP neural network model to obtain the health state of the lithium ion battery, and obtaining the current SOH of the battery.
Further, extracting ohm internal resistance R in the fitting datasInternal resistance to charge transfer RctParameter Y of constant phase angle element Q11Constant phase angle element Q1 coefficient N1As input parameters, the corresponding SOH value is used as output parameters for training the BP neural network; in one embodiment, the established BP neural network has three layers, namely an input layer, a hidden layer and an output layer, wherein the number of nodes of the input layer is the same as the number of input parameters in the step S6, the number of nodes of the output layer is the same as the number of output parameters in the step S6, and the number of nodes of the hidden layer is 4-12.
Preferably, in combination with the above scheme, in this embodiment, in the step S4, the measurement environment conditions of the ac impedance are: the excitation signal is current, and the frequency range is 2000-0.1 Hz.
Preferably, in combination with the above solution, in this embodiment, in step S6, the method for calculating the state of health of the lithium ion battery according to the ohmic internal resistance, the charge transfer internal resistance, and the constant phase angle element parameter includes:
s61: firstly, establishing a BP neural network model, taking ohmic internal resistance, charge transfer internal resistance and constant phase angle element parameters as input parameters and taking a corresponding health state as an output parameter, and training the BP neural network model until the error requirement is met;
s62: then substituting the ohmic internal resistance, the charge transfer internal resistance and the constant phase angle element parameters into the trained BP neural network model to calculate the SOH of the battery; the soh (state of health), that is, the state of health, refers to the ratio of the current calibrated capacity of the battery to the initial calibrated capacity of the battery, and is expressed by a percentage to reflect the current state of the battery.
Preferably, with reference to the foregoing scheme, in this embodiment, in the step S3, the method for calibrating the capacity of the lithium ion battery includes: and charging the lithium ion battery to a full-charge state, standing for a period of time, discharging to an empty state, standing for a period of time again, circulating for 3 times, and taking the last discharge capacity as the calibration capacity of the lithium ion battery.
Preferably, with reference to the above scheme, in this embodiment, the health status detection method is specifically described by taking a ternary lithium ion battery with a rated capacity of 34Ah as an example:
s10: placing the ternary lithium ion battery in a constant temperature environment of 30 ℃, standing for 2 hours, and performing constant-current charge-discharge circulation on the ternary lithium ion battery at a multiplying power of 1C;
the discharge cut-off voltage is 2.75V, the charge cut-off voltage is 4.2V, and the cycle is 750 times;
s20: placing the battery in a room temperature environment for standing for 2 hours every 50 times of circulation, then carrying out capacity calibration, and calculating according to the calibrated capacity to obtain the current SOH of the battery;
the capacity calibration method comprises the following steps: charging the battery to 4.2V at a constant current with a multiplying power of 0.3C, standing for 30min, discharging to 2.75V at a constant current with a multiplying power of 1C, standing for 30min, repeating for 3 times, taking the last discharge capacity as the current calibration capacity of the battery, and obtaining 15 groups of SOH data in total according to the step S10 and step S20 of capacity calibration after circulating for 750 times, wherein 100% SOH represents that the battery is not circulated as shown in FIG. 1;
s30: after the capacity calibration is completed each time, charging the battery to a full-charge state, standing for 2 hours, discharging at a constant current of 0.2C, and testing dynamic impedance;
in the discharging process of the battery, testing the alternating current impedance of the primary battery when the SOC is reduced to 50 percent, thereby finally obtaining 15 groups of dynamic impedance data;
s40: as shown in fig. 2, the 15 groups of dynamic impedance data are used to make Nyqusit diagrams under different cycle times, i.e. dynamic impedance spectra;
in fig. 2, the abscissa Z' represents the real part of the dynamic impedance spectrum, and the ordinate Z ″ represents the imaginary part of the dynamic impedance spectrum;
s50: as shown in fig. 3, an equivalent circuit is established according to the obtained dynamic impedance spectrum, and the dynamic impedance data is fitted by using a least square method to obtain 15 sets of fitting data;
in FIG. 3, L represents inductance, RsDenotes the ohmic resistance, RctRepresents a charge transfer resistance, Q represents a constant phase angle element;
s60: extracting ohmic internal resistance R in 15 groups of fitting datasInternal resistance to charge transfer RctParameter Y of constant phase angle element Q11Constant phase angle element Q1 coefficient N1As input parameters, the corresponding 15 sets of SOH data in step S20 are used as output parameters for training the BP neural network;
the BP neural network is shown in FIG. 4, the number of nodes of an input layer is 4, the number of nodes of a hidden layer is 8, the number of nodes of an output layer is 1, a connection function between the input layer and the hidden layer is a tansig function, a connection function between the hidden layer and the output layer is a purelin function, a training algorithm is a train lm algorithm, BP neural network training is carried out in MATLAB software, and a training result is shown in FIG. 5;
s70: when the battery circulates to 580 circles, the actual SOH calculated after the capacity is calibrated is 95.2%, the predicted value of the SOH calculated through the neural network trained in the step S60 is 95.5%, and the error is only 0.3%.
The invention provides a lithium ion battery health state prediction method, which comprises the steps of obtaining an electrochemical dynamic impedance spectrogram by testing the impedance of a battery in the charge-discharge cycle process, establishing an equivalent circuit based on the electrochemical dynamic impedance spectrogram, processing data by using a least square method, extracting the ohmic internal resistance Rs, the charge transfer internal resistance Rct, the parameter Y1 of a constant phase angle element Q1, the coefficient N1 of the constant phase angle element Q1 and the SOH value corresponding to the constant phase angle element Q1 in fitting parameters as output parameters for training a BP neural network model, and predicting the health of the battery by using the BP neural network.
The invention provides a lithium ion battery health state prediction method, which can perform dynamic impedance test on a battery when the lithium ion battery is in a running state, reflect the health state of the lithium ion battery through the dynamic characteristics of the lithium ion battery, train a BP neural network model by using measured data, improve the reliability and accuracy of prediction, greatly save time cost, shorten the standing time of the battery, improve the detection efficiency, and facilitate the quick and accurate estimation of the health state of the battery, thereby accurately acquiring the current use condition of the battery, being more beneficial to helping an electric vehicle control system to acquire the actual states of a plurality of battery cores, prolonging the service life of the battery, and being also beneficial to the safe driving of an automobile.
Compared with the prior art, the lithium ion battery health state prediction method provided by the invention has the following beneficial effects:
the lithium ion battery health state prediction method provided by the invention solves the problems of low accuracy and long time consumption of the conventional health state prediction method, provides a method for predicting the health state of the battery by using a dynamic impedance spectrum of direct measurement in combination with a BP neural network and using the BP neural network trained by data, thereby shortening the prediction time and improving the prediction precision.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way. Those skilled in the art can make numerous possible variations and modifications to the described embodiments, or modify equivalent embodiments, without departing from the scope of the invention. Therefore, any modification, equivalent change and modification made to the above embodiments according to the technology of the present invention are within the protection scope of the present invention, unless the content of the technical solution of the present invention is departed from.
Claims (10)
1. A lithium ion battery health state prediction method is characterized by comprising the following processes:
s1: placing the lithium ion battery in a constant temperature environment, and setting standing time;
s2: after standing for a time, performing constant-current charge-discharge circulation on the lithium ion battery;
s3: after each charge-discharge cycle is carried out for a preset number of times, the lithium ion battery is placed in a room temperature environment to stand for a preset time, and then the lithium ion battery is subjected to primary capacity calibration;
s4: performing constant current discharge on the battery at a certain multiplying power according to the calibrated capacity of the lithium ion battery obtained in the step S3, and measuring the alternating current impedance of the lithium ion battery once after the state of charge value of the lithium ion battery is reduced to a set value in the discharge process of the lithium ion battery to establish a dynamic impedance spectrum;
s5: establishing an equivalent circuit of the lithium ion battery according to the dynamic impedance spectrum obtained in the step S4, and fitting the dynamic impedance spectrum of the lithium ion battery according to the equivalent circuit to obtain fitting data;
s6: and extracting ohmic internal resistance, charge transfer internal resistance and constant phase angle element parameters in the fitting data as input parameters, and substituting the input parameters into a BP neural network model to obtain the health state of the lithium ion battery.
2. The method for predicting the health status of a lithium ion battery according to claim 1, wherein in the step S6, the method for calculating the health status of a lithium ion battery according to the ohmic internal resistance, the charge transfer internal resistance and the constant phase angle element parameters comprises:
s61: firstly, establishing a BP neural network model, taking ohmic internal resistance, charge transfer internal resistance and constant phase angle element parameters as input parameters and taking a corresponding health state as an output parameter, and training the BP neural network model until the error requirement is met;
s62: and substituting the parameters of the ohmic internal resistance, the charge transfer internal resistance and the constant phase angle element into the trained BP neural network model to calculate the health state of the battery.
3. The method for predicting the state of health of a lithium ion battery according to claim 1, wherein in the step S3, the method for calibrating the capacity of the lithium ion battery comprises:
and charging the lithium ion battery to a full-charge state, standing for a period of time, discharging to an empty state, standing for a period of time again, circulating for 3 times, and taking the last discharge capacity as the calibration capacity of the lithium ion battery.
4. The method according to claim 1, wherein in the step S4, the set value of the state of charge value of the lithium ion battery is 5% to 95%.
5. The method for predicting the state of health of a lithium ion battery according to claim 1, wherein in the step S2, the charge-discharge rate of the lithium ion battery is 0.1 to 3C.
6. The method for predicting the state of health of a lithium ion battery according to claim 1, wherein in the step S3, the charge-discharge rate of the lithium ion battery is 0.1 to 1C.
7. The method for predicting the state of health of a lithium ion battery according to claim 1, wherein in the step S5, a least square method is used for fitting the lithium ion battery dynamic impedance spectrum.
8. The method for predicting the health status of a lithium ion battery according to claim 1, wherein in the step S1, the temperature of the constant temperature environment is-20 to 40 ℃; and/or the standing time is 0.5-3 h.
9. The lithium ion battery health state prediction method according to claim 1, wherein in the step S3, the preset standing time is 2-5 hours; and/or the preset times are 10-100 times.
10. The method for predicting the state of health of a lithium ion battery according to claim 1, wherein in the step S4, the measurement environmental conditions of the ac impedance are: the excitation signal is current, and the frequency range is 2000-0.1 Hz.
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