CN111516548A - Cloud platform-based charging pile system for realizing power battery fault diagnosis - Google Patents

Cloud platform-based charging pile system for realizing power battery fault diagnosis Download PDF

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CN111516548A
CN111516548A CN202010329482.0A CN202010329482A CN111516548A CN 111516548 A CN111516548 A CN 111516548A CN 202010329482 A CN202010329482 A CN 202010329482A CN 111516548 A CN111516548 A CN 111516548A
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characteristic parameter
curve
fault
power battery
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CN111516548B (en
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曲杰
甘伟
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South China University of Technology SCUT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/68—Off-site monitoring or control, e.g. remote control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/30—Constructional details of charging stations
    • B60L53/31—Charging columns specially adapted for electric vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • 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
    • G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

Abstract

The invention provides a charging pile system for realizing power battery fault diagnosis based on a cloud platform, which comprises a charging pile, a cloud platform and a user APP, wherein the charging pile comprises a main control board, an 3/4/5G module, a WiFi module, a charging gun and a voltage measuring instrument, and the cloud platform comprises a user data receiving module, a signal noise reduction module, a characteristic parameter extraction module, a curve similarity degree calculation module and a fault diagnosis module; the charging pile acquires battery data of the power battery in a charging process in real time; transmitting the obtained data to a signal noise reduction module through an 3/4/5G module or a WiFi module to obtain battery data subjected to noise reduction; and inputting the battery data subjected to noise reduction into a characteristic parameter extraction module to obtain a characteristic parameter curve, inputting the obtained characteristic parameter curve into a curve similarity degree calculation module to obtain the charge cycle characteristic parameter curve similarity degree, and inputting the charge cycle characteristic parameter curve similarity degree into a fault diagnosis module in the cloud platform to judge whether a fault occurs.

Description

Cloud platform-based charging pile system for realizing power battery fault diagnosis
Technical Field
The invention relates to the technical field of charging piles and the field of battery fault diagnosis. In particular to a charging pile system for realizing power battery fault diagnosis based on a cloud platform.
Background
In recent years, secondary lithium ion batteries have been widely used in the fields of 3C products, electric vehicles, energy storage, and the like, due to their high energy density, long service life, low self-discharge rate, no memory effect, and the like. Particularly, with the increasingly prominent environmental protection problem, the use of lithium ion batteries in the field of electric automobiles is in an almost linear increasing trend. However, due to the instability of lithium ion batteries themselves, safety accidents frequently occur during the operation of the lithium ion batteries, which causes more and more attention. Therefore, the invention provides a method and a system which can detect battery faults in the charging process of a power battery and can predict results accurately.
The existing battery fault diagnosis method mainly comprises a fault diagnosis method based on a battery model and a battery-free model, wherein the two methods respectively exist as follows: the model has high calculation complexity and is difficult to be used for real-time online monitoring; the sample quality is seriously depended, the extrapolation capability is poor, and the like. The invention provides a charging pile system for realizing power battery fault diagnosis based on a cloud platform, which relies on the data transmission rate of 5G and the strong computing capacity of cloud service and is based on a synchronous compression continuous wavelet transform noise reduction and characteristic parameter extraction method.
Disclosure of Invention
The invention aims to provide a charging pile system for realizing power battery fault diagnosis based on a cloud platform, and online real-time fault detection of a power battery is realized.
The invention is realized by at least one of the following technical schemes.
A charging pile system for realizing power battery fault diagnosis based on a cloud platform comprises:
the charging pile comprises a main control board, an 3/4/5G module, a wireless Wi-Fi module, a charging gun and a voltage measuring instrument, wherein the 3/4/5G module, the wireless Wi-Fi module and the voltage measuring instrument are respectively connected with the main control board, the main control board comprises a switch control module, and the switch control module is connected with the charging gun;
the cloud platform comprises a user data receiving module, a signal noise reduction module, a characteristic parameter extraction module, a curve similarity degree calculation module and a fault diagnosis module, wherein the user data receiving module is used for receiving power battery data transmitted by a user charging pile, the signal noise reduction module is used for carrying out noise reduction processing on the power battery data, the characteristic parameter extraction module is used for extracting characteristic parameters in the noise-reduced power battery data, the curve similarity degree calculation module is used for calculating the curve similarity degree of a current charging cycle characteristic parameter curve and a reference characteristic parameter curve, and the fault diagnosis module is used for judging whether the curve similarity degree reaches a fault threshold value;
the charging pile acquires battery data of the power battery in a charging process in real time;
transmitting the obtained data to a signal noise reduction module of the cloud platform through an 3/4/5G module or a wireless Wi-Fi module to obtain battery data subjected to noise reduction;
inputting the battery data subjected to noise reduction into a characteristic parameter extraction module in the cloud platform to obtain a characteristic parameter curve;
inputting the obtained characteristic parameter curve into a curve similarity degree calculation module to obtain the similarity degree of the charging cycle characteristic parameter curve;
inputting the similarity degree of the obtained charging cycle characteristic parameter curve into a fault diagnosis module in the cloud platform, and judging whether a fault occurs;
if the fault occurs, the fault information is sent to the user APP, fault data and corresponding fault information are sent to a new energy automobile national monitoring and management center, and meanwhile, the information is fed back to the charging pile to stop charging.
Further, the charging pile acquires battery data of the power battery in the charging process in real time, and specifically measures voltage change of the power battery in the charging process at a certain frequency through a voltage measuring instrument.
Further, the noise reduction processing of the signal noise reduction module comprises the following steps:
converting the obtained power battery data from a time series s (t) into a time-frequency domain, namely Continuous Wavelet Transform (CWT) coefficients, by using a CWT (CWT), wherein the obtained time-frequency domain is as follows:
Figure BDA0002464429470000021
where α denotes the continuous wavelet analysis scale, τ denotes the continuous wavelet analysis time transfer window size, t denotes time,*which represents the complex conjugate of the light source,<s,ψα,τ>time-series signal s representing input and analysis function, i.e. mother wave psiα,τThe inner product of (1) and (b),
Figure BDA0002464429470000022
representing an analysis function in a continuous wavelet transform, i.e. a parent wave;
dividing the obtained time-frequency domain into a high-energy low-frequency part and a high-energy high-frequency part, wherein the part dividing method comprises the following steps:
Figure BDA0002464429470000023
wherein, Ws(α, τ) is the resulting time-frequency domain, naFor the number of scales, the CF calculates the superposition amplitude of the CWT coefficients using all the continuous wavelet analysis scales α, obtains the distribution of the CWT coefficients along the scale axis from the CF coefficients because the existence of the low frequency feature causes the obtained distribution to have two different peaks, and divides the two different peaks by setting an optimal threshold, thereby obtaining a high-energy low-frequency part and a high-energy high-frequency part;
synchronously compressing the obtained high-frequency part and the low-frequency part respectively by using synchronous compression continuous wavelet transform (SS-CWT) to obtain instant frequency, namely a synchronous compression continuous wavelet transform coefficient (SS-CWT) coefficient, wherein the instant frequency is as follows:
Figure BDA0002464429470000031
i represents a complex number, representing the partial derivative;
for the instantaneous frequency obtained by synchronous compression of the low-frequency part and the instantaneous frequency obtained by synchronous compression of the high-frequency part, different methods are adopted for noise reduction, and the method specifically comprises the following steps:
for a low-frequency part, introducing a soft interval screening characteristic to filter noise, wherein the soft interval is as follows:
Figure BDA0002464429470000032
lambda is the set threshold value and is the threshold value,
Figure BDA0002464429470000033
indicates the characteristics after screening, omegasRepresenting continuous wavelet transform coefficients;
for the high frequency part, calculating the superposed amplitude CF of the front signal segment, screening the characteristics of the high frequency part by using a hard interval, and filtering the main noise, wherein the hard interval is as follows:
Figure BDA0002464429470000034
wherein λ isnTo set the threshold value, Mmax＝mean(max|Tn|),TnAnd TrRespectively calculating SS-CWT coefficients of narrow frequency bands corresponding to the two peak values obtained after CF calculation;
combining the SS-CWT coefficient of the high-frequency part and the SS-CWT coefficient of the low-frequency part after noise reduction to form a time-frequency domain after noise reduction, and inverting to a time sequence signal;
and (3) converting the denoised time sequence signal again through continuous wavelet transform to obtain a Continuous Wavelet Transform (CWT) coefficient, and performing post-denoising again by using a CT threshold, wherein the CT threshold is as follows:
Figure BDA0002464429470000041
where λ is the set threshold, 0<γ<λ，0 is not less than α is not more than 1, gamma is a truncation value, when the continuous wavelet transform coefficient is less than the truncation value, the coefficient is set to 0,
Figure BDA0002464429470000042
for the selected characteristics, sgn (W)s) Indicating that if the continuous wavelet transform coefficient is positive, the output is 1, otherwise-1, W is outputsRepresenting continuous wavelet transform coefficients;
and the signal denoising module outputs a Continuous Wavelet Transform (CWT) coefficient obtained by the post-denoising.
Further, the characteristic parameter extraction module obtains a characteristic parameter curve, and specifically includes:
calculating a parameter DF by multi-scale envelope superposition of Continuous Wavelet Transform (CWT) coefficients output by a signal denoising module, wherein DF is:
Figure BDA0002464429470000043
where n is the number of scales, E (α, tau) is the envelope function of the Continuous Wavelet Transform (CWT) coefficients, and the formula is
Figure BDA0002464429470000044
Here, the
Figure BDA0002464429470000045
Is the result of the hilbert transform of the continuous wavelet transform coefficients;
calculating an operating energy ratio ER using the DF obtained above1Said operating energy ratio ER1Comprises the following steps:
Figure BDA0002464429470000046
wherein L is the length of the energy collection window before and after the time transfer window size is tau;
using ER obtained as described above1Calculating characteristic parameter ER for representing chemical reactions with different frequencies in lithium ion battery2The ER2Comprises the following steps:
ER2(τ)＝ER1(τ)|DF(α)|
the characteristic extraction model outputs the characteristic parameter ER obtained above2。
Further, the curve similarity degree calculation module specifically includes: comparing the characteristic parameter curve obtained by the characteristic parameter extraction module with a voltage reference characteristic parameter curve by using Dynamic Time Warping (DTW) to obtain the similarity degree of the voltage characteristic curve; the voltage reference characteristic parameter curve refers to a voltage characteristic parameter curve of a first charging cycle.
Further, the fault diagnosis module judges whether the similarity degree of the voltage characteristic curve obtained by the characteristic parameter extraction module in the charging cycle and the voltage characteristic curve of the voltage reference characteristic parameter curve reaches a threshold value of open circuit fault, and if the similarity degree reaches the threshold value, the fault diagnosis module judges that the open circuit fault occurs; if the voltage characteristic curve does not reach the threshold value of the short-circuit fault, judging whether the similarity degree of the voltage characteristic curve obtained by the characteristic parameter extraction module in the electric cycle and the voltage reference characteristic parameter curve reaches the threshold value of the short-circuit fault, if so, judging that the short-circuit fault occurs, and if not, finishing the judgment.
Further, the fault diagnosis module specifically includes:
setting a fault threshold value through historical fault data in the cloud platform:
for a power battery with a short-circuit fault in historical data, calculating the similarity between a voltage characteristic parameter curve of a charging cycle and a battery voltage reference characteristic parameter curve when the battery has the short-circuit fault through dynamic time regression (DTW), and setting the obtained similarity as a short-circuit fault threshold;
for the power battery with the open circuit fault in the historical data, calculating the similarity between a voltage characteristic parameter curve of a charging cycle when the battery has the open circuit fault and a voltage reference characteristic parameter curve of the battery through dynamic time regression, and setting the obtained similarity as an open circuit fault threshold;
when the similarity degree of a characteristic parameter curve obtained by inputting battery charging cycle voltage data into a signal noise reduction module and a characteristic parameter extraction module and a voltage reference characteristic parameter curve reaches a short-circuit threshold value, determining that a short-circuit fault occurs;
and when the similarity degree of the characteristic parameter curve obtained by inputting the battery charging cycle voltage data into the signal noise reduction module and the characteristic parameter extraction module and the voltage reference characteristic parameter curve reaches the open circuit threshold value, determining that the open circuit fault occurs.
Compared with the prior art, the invention has the following beneficial effects:
by means of the 5G data transmission rate and the strong computing capacity of cloud service, based on the synchronous compression continuous wavelet transform denoising and characteristic parameter extraction method, the extracted characteristic parameters can represent the change of chemical reaction energy with different frequencies in the lithium ion battery when a fault occurs, the external macroscopic expression is associated with the internal microscopic change, and accurate, efficient and real-time power battery fault diagnosis can be achieved.
Drawings
Fig. 1 is a schematic structural diagram of a charging pile system for implementing power battery fault diagnosis based on a cloud platform according to an embodiment of the present invention;
FIG. 2 is a system flow diagram of an embodiment of the present invention;
FIG. 3 is a flowchart of the operation of a signal noise reduction module according to an embodiment of the present invention;
FIG. 4 is a flowchart of the operation of the feature parameter extraction module according to an embodiment of the present invention;
FIG. 5 is a graph showing the first charging cycle voltage curve of a failed power battery and the charging cycle voltage curve of a short-circuit fault according to an embodiment of the present invention;
fig. 6 is a first charging cycle voltage characteristic parameter curve of a failed lithium ion battery and a charging cycle voltage characteristic parameter curve when a short-circuit fault occurs according to an embodiment of the present invention;
in the figure: 1-charging pile body; 2-a charging gun; 3-a heat dissipation plate; 5-an operation panel; 6-a charging wire; a 7-Wi-Fi module.
Detailed description of the invention
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
In order to make the aforementioned objects and features of the present invention more comprehensible, the present invention will be described in further detail with reference to the accompanying drawings and embodiments.
In the description of the present invention, it is to be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or unknown relationships based on the components or positional relationships illustrated in the drawings, are used for the purpose of describing the present invention and simplifying the description, and do not indicate or imply that the referenced positions or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
As shown in fig. 1, a charging pile system for realizing power battery fault diagnosis based on a cloud platform comprises a charging pile, wherein the charging pile comprises a main control board, a voltage measuring instrument, a charging pile body 1, a charging gun 2, a heating panel 3, an operation panel 5, charging wires 6 and 3/4/5G modules and a Wi-Fi module 7. The electric pile body 1 that fills of this embodiment is square shell, fills the positive top fixedly connected with operating panel 5 of electric pile body 1, and the operation of electric pile is filled in the control of user's accessible operating panel 5, and the bottom of rifle 2 that charges is through charging wire 6 and the bottom fixed connection who fills electric pile body 1 one side, and heating panel 3 is fixed in the one side of filling electric pile body 1, and 3/4/5G module and Wi-Fi module 7 embedding fill the inside of electric pile body 1. The main control board comprises a switch control module, and the switch control module is connected with the charging gun 2; the 3/4/5G module, the wireless Wi-Fi module 7 and the voltage measuring instrument are respectively connected with the main control board; in the embodiment, the main control board is a type 1019-1 main control board of Yumai electronic technology, Inc., the 3/4/5G modules are respectively a type H7710E DTU/type H7000-DLNZ/type Z1 of the Macro electric company, and the Wi-Fi module 7 is a type HLK-M30 of the Hi-Link company.
The cloud platform comprises a user data receiving module, a signal noise reduction module, a characteristic parameter extraction module, a curve similarity degree calculation module and a fault diagnosis module, wherein the user data receiving module is used for receiving charging data of a user charging pile in the charging process, and the charging pile is used for charging the charging process through an 3/4/5G module and a Wi-Fi module 7The data stored in real time is transmitted to the cloud platform, and the data is stored through the user data receiving module. The signal denoising module is used for denoising the power battery data, the characteristic parameter extraction module calculates a parameter DF through multi-scale enveloping superposition of continuous wavelet transform coefficients, and then calculates an operation energy proportion ER by using the obtained DF1Calculating characteristic parameter ER for representing chemical reactions with different frequencies in lithium ion battery2Obtaining a characteristic parameter ER2. The curve similarity degree calculation module is used for calculating the similarity degree of the characteristic parameter curve of the current charging cycle and the reference characteristic parameter curve, and the fault diagnosis module is used for judging whether the curve similarity degree reaches a fault threshold value;
user APP, including login module, the state of charge module, the login module is used for the user to log in, the state of charge module is used for looking over the state of charge and, whether the check breaks down.
Fig. 2 is a schematic diagram of a charging pile system for realizing power battery fault diagnosis based on a cloud platform according to an embodiment of the invention.
The charging pile acquires battery data of the power battery in a charging process in real time, namely, the voltage of the power battery in the charging process is acquired at a certain sampling frequency; transmitting the obtained data to a cloud platform through an 3/4/5G, Wi-Fi module in the charging pile, and inputting the data to a signal noise reduction module in the cloud platform to obtain battery data subjected to noise reduction; inputting the battery data subjected to noise reduction into a characteristic parameter extraction module in the cloud platform to obtain a characteristic parameter curve; inputting the characteristic parameter curve into a curve similarity degree calculation module to obtain the charge cycle characteristic parameter curve similarity degree; inputting the similarity degree of the charging cycle curve into a fault diagnosis module in the cloud platform, and judging whether a fault occurs; if the fault occurs, the fault information is sent to the user APP, fault data and corresponding fault information are sent to a new energy automobile national monitoring and management center, and meanwhile, the information is fed back to the charging pile to stop charging.
Fig. 3 is a flowchart of the signal noise reduction module according to this embodiment, which specifically includes the following steps:
step 301, using Continuous Wavelet Transform (CWT) to convert the obtained lithium ion battery data from time series to time-frequency domain, i.e. Continuous Wavelet Transform (CWT) coefficients, where the obtained time-frequency domain is:
Figure BDA0002464429470000071
where α denotes the continuous wavelet analysis scale, τ denotes the continuous wavelet analysis time transfer window size,*representing the complex conjugate, t represents time,<s,ψα,τ>time-series signal s representing input and analysis function, i.e. mother wave psiα,τPhi (t) represents an analytical function in a continuous wavelet transform, i.e. the mother wave;
step 302, dividing the obtained time-frequency domain into a high-energy low-frequency part and a high-energy high-frequency part, wherein the part dividing method comprises the following steps:
Figure BDA0002464429470000072
wherein, Ws(α, τ) is the resulting time-frequency domain, naFor the number of scales, CF is to calculate the superimposed amplitude of the CWT coefficients using all the continuous wavelet analysis scales α, the distribution of the CWT coefficients along the scale axis is derived from the CF coefficients because the presence of the low frequency features causes the resulting distribution to have two different peaks, which are divided by setting an optimal threshold, thereby obtaining a high energy low frequency part and a high energy high frequency part.
Step 303, synchronously compressing the obtained high frequency part and low frequency part by using synchronous compression continuous wavelet transform (SS-CWT), respectively, to obtain an instant frequency, namely a synchronous compression continuous wavelet transform coefficient (SS-CWT) coefficient, where the instant frequency is:
Figure BDA0002464429470000081
i represents a complex number.
For the instantaneous frequency obtained by synchronous compression of the low-frequency part and the instantaneous frequency obtained by synchronous compression of the high-frequency part, different methods are adopted for noise reduction, and the method specifically comprises the following steps:
for a low-frequency part, introducing a soft interval screening characteristic to filter noise, wherein the soft interval is as follows:
Figure BDA0002464429470000082
lambda is the set threshold value and is the threshold value,
Figure BDA0002464429470000083
indicates the characteristics after screening, omegasRepresenting continuous wavelet transform coefficients;
for the high frequency part, calculating the superposed amplitude CF of the front signal segment, screening the characteristics of the high frequency part by using a hard interval, and filtering the main noise, wherein the hard interval is as follows:
Figure BDA0002464429470000084
wherein λ isnTo set the threshold value, Mmax＝mean(max|Tn|),Tn，TrRespectively calculating SS-CWT coefficients of narrow frequency bands corresponding to the two peak values obtained after CF calculation;
step 304, performing noise reduction on the instantaneous frequency obtained by synchronous compression of the low-frequency part and the instantaneous frequency obtained by synchronous compression of the high-frequency part by adopting different methods;
and 305, combining the SS-CWT coefficient of the high-frequency part and the SS-CWT coefficient of the low-frequency part after noise reduction into a time-frequency domain after noise reduction, and then reversely converting the time-frequency domain into a time sequence signal after noise reduction in a reverse order.
Step 306, converting the denoised time sequence signal again through continuous wavelet transform to obtain a Continuous Wavelet Transform (CWT) coefficient, and performing post-denoising again by using a CT threshold, wherein the CT threshold is as follows:
Figure BDA0002464429470000091
where λ is the set threshold, 0<γ<Lambda is more than or equal to 0 and less than or equal to α and less than or equal to 1, gamma is a truncated value, when the continuous wavelet transform coefficient is less than the truncated value, the coefficient is set to 0,
Figure BDA0002464429470000092
for the selected characteristics, sgn (W)s) It means that if the continuous wavelet transform coefficient is positive, the output is 1, otherwise-1 is output. WsRepresenting continuous wavelet transform coefficients.
And 307, outputting a Continuous Wavelet Transform (CWT) coefficient obtained by the post-denoising.
Fig. 4 is a flowchart of the operation of the feature parameter extraction module in this embodiment, which specifically includes the following steps:
step 401, calculating a parameter DF by multi-scale envelope superposition of Continuous Wavelet Transform (CWT) coefficients output by a signal denoising module, where DF is:
Figure BDA0002464429470000093
in the formula, naIs the number of scales, E (α, tau 1 is the envelope function of the Continuous Wavelet Transform (CWT) coefficients, and the calculation formula is
Figure BDA0002464429470000094
Here, the
Figure BDA0002464429470000095
Is the result of the hilbert transform of the continuous wavelet transform coefficients, n being the number of scales.
Step 402, calculating the running energy ratio ER using the DF obtained above1Said operating energy ratio ER1Comprises the following steps:
Figure BDA0002464429470000096
wherein L is the length of the energy collection window before and after the time transfer window size is tau;
step 403, using ER obtained as described above1Calculating characteristic parameter ER for representing chemical reactions with different frequencies in lithium ion battery2The ER2Comprises the following steps:
ER2(τ)＝ER1(τ)|DF(α)|
step 404, outputting the characteristic parameter ER obtained above2。
The curve similarity degree calculation module obtains the voltage characteristic parameter curve similarity degree, and specifically comprises the following steps:
inputting the characteristic parameter curve obtained by the characteristic parameter extraction module into a curve similarity degree calculation module, and comparing the characteristic parameter curve obtained by the characteristic parameter extraction module with a voltage reference characteristic parameter curve by using Dynamic Time Warping (DTW) to obtain the voltage characteristic curve similarity degree; the voltage reference characteristic parameter curve refers to a voltage characteristic parameter curve of a first charging cycle. The voltage reference characteristic parameter curve refers to a voltage characteristic parameter curve of a first charging cycle.
The fault diagnosis module judges whether the similarity degree of the voltage characteristic curve obtained by the characteristic parameter extraction module in the charging cycle and the voltage characteristic curve of the voltage reference characteristic parameter curve reaches a threshold value of open circuit fault, and if the similarity degree reaches the threshold value, the open circuit fault is judged to occur; if the voltage characteristic curve does not reach the threshold value of the short-circuit fault, judging whether the similarity degree of the voltage characteristic curve obtained by the characteristic parameter extraction module in the electric cycle and the voltage reference characteristic parameter curve reaches the threshold value of the short-circuit fault, if so, judging that the short-circuit fault occurs, and if not, finishing the judgment.
The fault diagnosis module specifically comprises:
setting a fault threshold value through historical fault data in the cloud platform:
for a power battery with a short-circuit fault in historical data, calculating the similarity between a voltage characteristic parameter curve of a charging cycle and a battery voltage reference characteristic parameter curve when the battery has the short-circuit fault through dynamic time regression (DTW), and setting the obtained similarity as a short-circuit fault threshold;
for the power battery with the open circuit fault in the historical data, calculating the similarity between a voltage characteristic parameter curve of a charging cycle when the battery has the open circuit fault and a voltage reference characteristic parameter curve of the battery through dynamic time regression, and setting the obtained similarity as an open circuit fault threshold;
when the similarity degree of a characteristic parameter curve obtained by inputting battery charging cycle voltage data into a signal noise reduction module and a characteristic parameter extraction module and a voltage reference characteristic parameter curve reaches a short-circuit threshold value, determining that a short-circuit fault occurs;
and when the similarity degree of the characteristic parameter curve obtained by inputting the battery charging cycle voltage data into the signal noise reduction module and the characteristic parameter extraction module and the voltage reference characteristic parameter curve reaches the open circuit threshold value, determining that the open circuit fault occurs.
In the embodiment, the similarity degree threshold corresponding to each type of fault is calculated by using a large amount of data, and a lithium ion battery fault diagnosis model is established. 2C constant current charging is carried out on a lithium ion battery with the rated capacity of 2000mAh, and each charging cycle voltage data of the battery is collected at the sampling frequency of 0.1 s/time until short circuit fault occurs, wherein a first charging cycle voltage curve of the fault lithium ion battery and a charging cycle voltage curve when the short circuit fault occurs are shown in a graph 5;
inputting the first charging cycle voltage data of the battery and the charging cycle voltage data when a short-circuit fault occurs into a noise reduction model to obtain two Continuous Wavelet Transform (CWT) curves after noise reduction, inputting the two Continuous Wavelet Transform (CWT) curves into a feature extraction model to obtain a first charging cycle characteristic parameter curve of the battery and a charging cycle curve when the short-circuit fault occurs, and fig. 6 shows the first charging cycle voltage characteristic parameter curve of the lithium ion battery and the charging cycle voltage characteristic parameter curve when the short-circuit fault occurs;
and comparing the similarity degrees of the two characteristic parameter curves, inputting the similarity degrees into a lithium ion battery fault diagnosis model, and sending a short-circuit fault alarm signal by the model when the similarity degree reaches a short-circuit fault threshold value.
The foregoing descriptions of embodiments of the present invention have been presented for purposes of illustration and description. They do not limit the invention to the details described above and many modifications and variations are possible in light of the above teaching. The examples were chosen and described in order to best explain the principles of the invention and their practical application, to thereby enable others skilled in the art to best utilize the invention in various embodiments and with various modifications as are suited to the particular use contemplated. It is, therefore, to be understood that the invention is intended to cover all modifications and equivalents within the scope of the following claims.

Claims (7)

1. The utility model provides a fill electric pile system based on cloud platform realizes power battery failure diagnosis which characterized in that includes:
the charging pile comprises a main control board, an 3/4/5G module, a wireless Wi-Fi module, a charging gun and a voltage measuring instrument, wherein the 3/4/5G module, the wireless Wi-Fi module and the voltage measuring instrument are respectively connected with the main control board, the main control board comprises a switch control module, and the switch control module is connected with the charging gun;
the cloud platform comprises a user data receiving module, a signal noise reduction module, a characteristic parameter extraction module, a curve similarity degree calculation module and a fault diagnosis module, wherein the user data receiving module is used for receiving power battery data transmitted by a user charging pile, the signal noise reduction module is used for carrying out noise reduction processing on the power battery data, the characteristic parameter extraction module is used for extracting characteristic parameters in the noise-reduced power battery data, the curve similarity degree calculation module is used for calculating the curve similarity degree of a current charging cycle characteristic parameter curve and a reference characteristic parameter curve, and the fault diagnosis module is used for judging whether the curve similarity degree reaches a fault threshold value;
the charging pile acquires battery data of the power battery in a charging process in real time;
transmitting the obtained data to a signal noise reduction module of the cloud platform through an 3/4/5G module or a wireless Wi-Fi module to obtain battery data subjected to noise reduction;
inputting the battery data subjected to noise reduction into a characteristic parameter extraction module in the cloud platform to obtain a characteristic parameter curve;
inputting the obtained characteristic parameter curve into a curve similarity degree calculation module to obtain the similarity degree of the charging cycle characteristic parameter curve;
inputting the similarity degree of the obtained charging cycle characteristic parameter curve into a fault diagnosis module in the cloud platform, and judging whether a fault occurs;
if the fault occurs, the fault information is sent to the user APP, fault data and corresponding fault information are sent to a new energy automobile national monitoring and management center, and meanwhile, the information is fed back to the charging pile to stop charging.
2. The charging pile system for realizing fault diagnosis of the power battery based on the cloud platform as claimed in claim 1, wherein the charging pile obtains the battery data of the power battery in the charging process in real time, specifically, measures the voltage change of the power battery in the charging process at a certain frequency through a voltage measuring instrument.
3. The cloud platform-based charging pile system for realizing power battery fault diagnosis according to claim 1, wherein the noise reduction processing of the signal noise reduction module comprises the following steps:
converting the obtained power battery data from a time series s (t) into a time-frequency domain, namely Continuous Wavelet Transform (CWT) coefficients, by using a CWT (CWT), wherein the obtained time-frequency domain is as follows:
Figure FDA0002464429460000011
where α denotes the continuous wavelet analysis scale, τ denotes the continuous wavelet analysis time transfer window size, t denotes time, denotes the complex conjugate,<s,ψα,τ>time-series signal s representing input and analysis function, i.e. mother wave psiα,τThe inner product of (a) is,
Figure FDA0002464429460000021
representing an analysis function in a continuous wavelet transform, i.e. a parent wave;
dividing the obtained time-frequency domain into a high-energy low-frequency part and a high-energy high-frequency part, wherein the part dividing method comprises the following steps:
Figure FDA0002464429460000022
wherein, Ws(α, τ) is the resulting time-frequency domain, naFor the number of scales, the CF calculates the superposition amplitude of the CWT coefficients using all the continuous wavelet analysis scales α, obtains the distribution of the CWT coefficients along the scale axis from the CF coefficients because the existence of the low frequency feature causes the obtained distribution to have two different peaks, and divides the two different peaks by setting an optimal threshold, thereby obtaining a high-energy low-frequency part and a high-energy high-frequency part;
synchronously compressing the obtained high-frequency part and the low-frequency part respectively by using synchronous compression continuous wavelet transform (SS-CWT) to obtain instant frequency, namely a synchronous compression continuous wavelet transform coefficient (SS-CWT) coefficient, wherein the instant frequency is as follows:
Figure FDA0002464429460000023
i represents a complex number, representing the partial derivative;
for the instantaneous frequency obtained by synchronous compression of the low-frequency part and the instantaneous frequency obtained by synchronous compression of the high-frequency part, different methods are adopted for noise reduction, and the method specifically comprises the following steps:
for a low-frequency part, introducing a soft interval screening characteristic to filter noise, wherein the soft interval is as follows:
Figure FDA0002464429460000024
lambda is the set threshold value and is the threshold value,
Figure FDA0002464429460000025
indicates the characteristics after screening, omegasRepresenting continuous wavelet transform coefficients;
for the high frequency part, calculating the superposed amplitude CF of the front signal segment, screening the characteristics of the high frequency part by using a hard interval, and filtering the main noise, wherein the hard interval is as follows:
Figure FDA0002464429460000031
wherein λ isnTo set the threshold value, Mmax＝mean(max|Tn|),TnAnd TrRespectively calculating SS-CWT coefficients of narrow frequency bands corresponding to the two peak values obtained after CF calculation;
combining the SS-CWT coefficient of the high-frequency part and the SS-CWT coefficient of the low-frequency part after noise reduction to form a time-frequency domain after noise reduction, and inverting to a time sequence signal;
and (3) converting the denoised time sequence signal again through continuous wavelet transform to obtain a Continuous Wavelet Transform (CWT) coefficient, and performing post-denoising again by using a CT threshold, wherein the CT threshold is as follows:
Figure FDA0002464429460000032
where λ is the set threshold, 0<γ<Lambda is more than or equal to 0 and less than or equal to α and less than or equal to 1, gamma is a truncation value, when the continuous wavelet transform coefficient is less than the truncation value, the coefficient is set to 0,
Figure FDA0002464429460000033
for the selected characteristics, sgn (W)s) Indicating that if the continuous wavelet transform coefficient is positive, the output is 1, otherwise-1, W is outputsRepresenting continuous wavelet transform coefficients;
and the signal denoising module outputs a Continuous Wavelet Transform (CWT) coefficient obtained by the post-denoising.
4. The cloud platform-based charging pile system for realizing power battery fault diagnosis according to claim 1, wherein the characteristic parameter extraction module obtains a characteristic parameter curve, and specifically comprises:
calculating a parameter DF by multi-scale envelope superposition of Continuous Wavelet Transform (CWT) coefficients output by a signal denoising module, wherein DF is:
Figure FDA0002464429460000034
where n is the number of scales, E (α, tau) is the envelope function of the Continuous Wavelet Transform (CWT) coefficients, and the formula is
Figure FDA0002464429460000041
Here, the
Figure FDA0002464429460000042
Is the result of the hilbert transform of the continuous wavelet transform coefficients;
calculating an operating energy ratio ER using the DF obtained above1Said operating energy ratio ER1Comprises the following steps:
Figure FDA0002464429460000043
wherein L is the length of the energy collection window before and after the time transfer window size is tau;
using ER obtained as described above1Calculating characteristic parameter ER for representing chemical reactions with different frequencies in lithium ion battery2The ER2Comprises the following steps:
ER2(τ)＝ER1(τ)|DF(α)|
the characteristic extraction model outputs the characteristic parameter ER obtained above2。
5. The cloud platform-based charging pile system for achieving power battery fault diagnosis according to claim 1, wherein the curve similarity degree calculation module specifically comprises: comparing the characteristic parameter curve obtained by the characteristic parameter extraction module with a voltage reference characteristic parameter curve by using Dynamic Time Warping (DTW) to obtain the similarity degree of the voltage characteristic curve; the voltage reference characteristic parameter curve refers to a voltage characteristic parameter curve of a first charging cycle.
6. The cloud platform-based charging pile system for realizing power battery fault diagnosis according to claim 1, wherein the fault diagnosis module judges whether the similarity degree of the voltage characteristic curve obtained by the characteristic parameter extraction module in the charging cycle and the voltage characteristic curve of the voltage reference characteristic parameter curve reaches an open circuit fault threshold value, and if the similarity degree reaches the threshold value, the open circuit fault is judged to occur; if the voltage characteristic curve does not reach the threshold value of the short-circuit fault, judging whether the similarity degree of the voltage characteristic curve obtained by the characteristic parameter extraction module in the electric cycle and the voltage reference characteristic parameter curve reaches the threshold value of the short-circuit fault, if so, judging that the short-circuit fault occurs, and if not, finishing the judgment.
7. The cloud platform-based charging pile system for achieving fault diagnosis of the power battery according to claim 1, wherein the fault diagnosis module specifically comprises:
setting a fault threshold value through historical fault data in the cloud platform:
for a power battery with a short-circuit fault in historical data, calculating the similarity between a voltage characteristic parameter curve of a charging cycle and a battery voltage reference characteristic parameter curve when the battery has the short-circuit fault through dynamic time regression (DTW), and setting the obtained similarity as a short-circuit fault threshold;
for the power battery with the open circuit fault in the historical data, calculating the similarity between a voltage characteristic parameter curve of a charging cycle when the battery has the open circuit fault and a voltage reference characteristic parameter curve of the battery through dynamic time regression, and setting the obtained similarity as an open circuit fault threshold;
when the similarity degree of a characteristic parameter curve obtained by inputting battery charging cycle voltage data into a signal noise reduction module and a characteristic parameter extraction module and a voltage reference characteristic parameter curve reaches a short-circuit threshold value, determining that a short-circuit fault occurs;
and when the similarity degree of the characteristic parameter curve obtained by inputting the battery charging cycle voltage data into the signal noise reduction module and the characteristic parameter extraction module and the voltage reference characteristic parameter curve reaches the open circuit threshold value, determining that the open circuit fault occurs.
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