CN111767672A - Lithium battery abnormal working condition data self-organizing enhancement method based on Monte Carlo method - Google Patents

Lithium battery abnormal working condition data self-organizing enhancement method based on Monte Carlo method Download PDF

Info

Publication number
CN111767672A
CN111767672A CN202010610473.9A CN202010610473A CN111767672A CN 111767672 A CN111767672 A CN 111767672A CN 202010610473 A CN202010610473 A CN 202010610473A CN 111767672 A CN111767672 A CN 111767672A
Authority
CN
China
Prior art keywords
lithium battery
particles
wavelet
monte carlo
coupling
Prior art date
Legal status (The legal status 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 status listed.)
Granted
Application number
CN202010610473.9A
Other languages
Chinese (zh)
Other versions
CN111767672B (en
Inventor
李鹏华
程家伟
柴毅
程安宇
胡向东
侯杰
朱智勤
张亚鹏
董江林
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University of Post and Telecommunications
Original Assignee
Chongqing University of Post and Telecommunications
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 Chongqing University of Post and Telecommunications filed Critical Chongqing University of Post and Telecommunications
Priority to CN202010610473.9A priority Critical patent/CN111767672B/en
Publication of CN111767672A publication Critical patent/CN111767672A/en
Application granted granted Critical
Publication of CN111767672B publication Critical patent/CN111767672B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/25—Design optimisation, verification or simulation using particle-based methods
    • 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
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • 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

Abstract

The invention relates to a lithium battery abnormal working condition data self-organizing enhancement method based on a Monte Carlo method, which belongs to the field of lithium battery detection and comprises the following steps: s1: performing wavelet decomposition on the abnormal working condition sequence of the small sample lithium battery to obtain a multi-scale wavelet coefficient; s2: mapping the multi-scale wavelet coefficients into m-dimensional hyperspace midpoints; s3: monte carlo self-grouping is performed on the multi-scale components. The method gives consideration to the authenticity of the data source and the reconfigurability of data distribution, provides a new technical means for unbalanced lithium battery data processing, can be transferred to other industrial fields, and is one of the methods for solving the problem of data imbalance.

Description

Lithium battery abnormal working condition data self-organizing enhancement method based on Monte Carlo method
Technical Field
The invention belongs to the field of lithium battery detection, and relates to a lithium battery abnormal working condition data self-organizing enhancement method based on a Monte Carlo method.
Background
As an energy storage device with high energy density, long service life and clean output, the lithium battery is widely applied to energy systems such as electric automobiles, portable electronic equipment, smart grids with renewable energy sources and the like. In the last decade, research on energy density and power output of lithium batteries has been remarkably developed, but safe use of lithium batteries still faces significant challenges due to the difficulty in complete observation of internal electrochemical reactions and complex and uncertain operation conditions.
Many studies have used self-collected datasets for modeling and validation, and some have used NASA and CALCE datasets. The distribution of these measurements is not balanced, for example, in the NASA data set, the data for normal conditions is 95%, while the data for abnormal extreme environments is only 5%, and there is no analysis of what the cause is. The distribution of the CALCE dataset is similar to NASA. In most self-collected data sets, only a few studies have used outlier data to investigate the robustness of the model. Data imbalance will result in insufficient generalization capability of the trained ANN model, forcing the prediction framework not to be shared by multiple singles, i.e., the trained ANN can only be used for one singleton or one condition. There are many studies that do not realize sharing, and when a plurality of lithium batteries in the same batch need to be monitored or part of the batteries need to be replaced, a prediction framework which does not realize sharing needs to be retrained, so that more resources are wasted. The root cause of data imbalance is that the test of laboratory conditions cannot infinitely enumerate the abnormal working conditions of the lithium battery (such as extreme weather environment, bad habits of people, accidental impacts, accidents and the like).
Disclosure of Invention
In view of the above, the present invention provides a new data enhancement method for solving the problem of scarcity of high-value data in lithium batteries or other industrial fields from the perspective of industrial data, and obtains multi-scale components of abnormal working condition data through wavelet transformation, and generates artificial data synthesized from real components under specific distribution by adopting a monte carlo method to combine the multi-scale components freely.
In order to achieve the purpose, the invention provides the following technical scheme:
a lithium battery abnormal working condition data self-organizing enhancement method based on a Monte Carlo method comprises the following steps:
s1: performing wavelet decomposition on the abnormal working condition sequence of the small sample lithium battery to obtain a multi-scale wavelet coefficient;
s2: mapping the multi-scale wavelet coefficients into m-dimensional hyperspace midpoints;
s3: performing Monte Carlo self-organization on the multi-scale components;
s4: and performing wavelet coefficient reconstruction on the screened components to generate a synthesized lithium battery sequence.
Further, in step S1, for a given small sample lithium battery abnormal operating condition sequence f (t), which includes voltage, current, capacity, and internal resistance curves, the wavelet packet of the L layer is decomposed into:
Figure BDA0002560804340000021
phi (t) and psi (t) are respectively a parent wavelet and a mother wavelet, n represents the position of the wavelet packet in the layer, hkDenotes a low-pass filter, t denotes time, k denotes position coordinates, gkA high pass filter is indicated.
Further, step (ii)In S2, let x be [ x ]1,x2,…,x2L-1x2L]＝[Φ1(t),Ψ1(t),…ΦL(t),ΨL(t)]The multi-scale wavelet coefficients are mapped into m-dimensional hyperspace midpoints (called particles), and the dimension of a certain layer of wavelet coefficients is selected as a space reference according to specific sample characteristics.
Further, in step S3, consider a system of 2L particles whose structure is described by vector x, i.e., the particle state is described by { x }iDefinition, xiIs the ith wavelet coefficient; the equilibrium distribution of the system is described as π (x) widerthan exp [ -U (x)/kBT]Wherein k isBIs the thermal energy coefficient, T is the temperature corresponding to the lithium battery sequence, u (x) is the cost energy function, which is used to describe the sum of the interactions between the particles, defined as:
Figure BDA0002560804340000022
wherein ,VijIs the energy of the interaction between the ith particle and the jth particle; defining an augmented space distribution pi (x, b), wherein { x } is an initial state space, { b } is a state coupling space, and b represents a vector of coupling among particles, namely coupling among wavelet coefficients of each scale corresponding to the lithium battery sequence, and the vector has L (L-1)/2 elements, and the coupling mode is as follows:
Figure BDA0002560804340000023
the augmented spatial distribution II (x, b) and the equilibrium distribution Pi (x) of the system satisfy:
Π(x,b)＝π(x)p(b,x) (4)
where p (b, x) denotes that a coupled state b ═ b is formed at a given initial state xijThe probability of, written as:
Figure BDA0002560804340000024
the q-function represents the formation of a particular coupling probability, expressed as:
q(bij,Vij)＝bij+(1-2bij)exp[min(Vij,0)/kBΘ](6)
according to formula (6), if the interaction V between particles i and ji,jPositive, they are always not coupled, bi,jThe probability of 0 is 1; if Vi,jIs negative, then when | VijWhen | ≧ k Θ, the probability of coupling bi,j1 to 1; Θ represents the "pseudo temperature" that controls the coupling process; in practical application, the value range of theta is determined according to the characteristics of abnormal working conditions;
based on the above definition, the self-grouping between particles, i.e. the algorithm to generate new pairings starting from the initial pairing x, b, comes down in the following two modes:
① selects a new coupling state b' for all bi,jPerforming zero initialization when i is less than j and Vi,j<When 0, b isi,jThe probability set to 1 is 1-exp [ V ]ij(xi,xj)/kBΘ](ii) a This step divides the particles of the system into several groups, i.e. by a series of couplings bi,j1, mutually connecting particles into a particle set, and screening out wavelet coefficients which can be reconstructed;
② Metropolis state transition x → x' is carried out in the initial state space: selecting a particle j, and forcibly coupling the other particles with the particle j and moving the particles in a random direction under the spatial constraint of a coupling state; calculating new energy U (x') after completing the movement; meanwhile, the probability of the mobile acceptance is expressed as:
Figure BDA0002560804340000031
the probability ratio in equation (7) is further expressed as:
Figure BDA0002560804340000032
wherein ,
Figure BDA0002560804340000033
further, in step S4, according to the particles screened by the monte carlo simulation, i.e. the wavelet coefficients that can be coupled, wavelet coefficient reconstruction is performed on the particles according to the following formula to generate a synthesized lithium battery sequence:
Figure BDA0002560804340000034
wherein ,
Figure BDA0002560804340000035
the wavelet coefficients are screened out.
The invention has the beneficial effects that: the method gives consideration to the authenticity of the data source and the reconfigurability of data distribution, provides a new technical means for unbalanced lithium battery data processing, can be transferred to other industrial fields, and is one of the methods for solving the problem of data imbalance.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
fig. 1 is a flow chart of a multiscale lithium battery data ad hoc enhancement method based on the monte carlo method.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Aiming at the problem of unbalance of abnormal working condition data of the lithium battery, a Monte Carlo self-organizing enhancement method for the abnormal working condition data of the lithium battery under multi-scale description is researched. Designing wavelet packet transformation to obtain sequence multi-scale components and corresponding hyperspace mapping by taking actually measured small sample sequences of current, voltage, temperature, capacity, internal resistance and the like under abnormal working conditions as objects; the method is characterized in that an ultrahigh-dimensional system formed by multi-scale sequence components is taken as an object, a Monte Carlo simulation method under the constraint of the working temperature of the lithium battery is researched, and self-organizing screening of the sequence components with physical significance is achieved to synthesize a new lithium battery sequence.
The invention provides a multi-scale lithium battery data self-organizing enhancement method based on a Monte Carlo method, which comprises four steps of wavelet packet decomposition, hyperspace mapping, Monte Carlo simulation and sequence reconstruction of a lithium battery sequence as shown in figure 1.
1) Wavelet packet decomposition of original sequence
The abnormal working condition sequence f (t) of the small sample lithium battery is determined, which can be curves of voltage, current, capacity, internal resistance and the like, and the wavelet packet of the L layer is decomposed into:
Figure BDA0002560804340000051
wherein phi (t) and psi (t) are respectively parent wavelet and mother wavelet, hk and gkRespectively a low and a high pass filter.
2) Superspace mapping of multiscale components
Let x be [ x ]1,x2,…,x2L-1x2L]＝[Φ1(t),Ψ1(t),…ΦL(t),ΨL(t)]The multi-scale wavelet coefficients are mapped to m-dimensional hyperspace midpoints (called particles). In practical research, the dimension of a certain layer of wavelet coefficient is selected as a spatial reference according to specific sample characteristics.
3) Multi-scale component Monte Carlo self-organization
Consider a system of 2L particles whose structure is described by vector x, i.e. the particle state is described by { x }iDefinition (x)iThe ith wavelet coefficient). The equilibrium distribution of this system is described as π (x) widerthan exp [ -U (x)/kBT]Wherein k isBIs the thermal energy coefficient, T is the temperature corresponding to the lithium battery sequence, u (x) is the cost energy function, which is used to describe the sum of the interactions between the particles, defined as:
Figure BDA0002560804340000052
wherein ,VijIs the energy of the interaction between the ith particle and the jth particle. Meanwhile, an augmented spatial distribution [ (. x.,. b) ] is defined, where { x } is the initial state space and { b } is the state coupling space. b represents the vector of coupling between particles: (Coupling among wavelet coefficients of all scales corresponding to the lithium battery sequence) which has L (L-1)/2 elements, and the coupling mode is as follows:
Figure BDA0002560804340000053
the augmented spatial distribution pi (x, b) and the equilibrium distribution pi (x) of the system satisfy:
Π(x,b)＝π(x)p(b,x) (4)
where p (b, x) denotes that a coupled state b ═ b is formed at a given initial state xijThe possibility of (c) } can be written as:
Figure BDA0002560804340000054
here, the q function represents the formation of a particular coupling probability, which can be expressed as:
q(bij,Vij)＝bij+(1-2bij)exp[min(Vij,0)/kBΘ](6)
according to formula (6), if the interaction V between particles i and ji,jPositive, they are always not coupled (b)i,jThe probability of 0 is 1); if Vi,jIs negative, then when | VijWhen | ≧ k Θ, the probability of coupling (b)i,j1) to 1.Θ represents the "pseudo temperature" that controls the coupling process. In practical application, the value range of theta is determined according to the characteristics of abnormal working conditions.
Based on the above definition, the self-grouping between particles, i.e. the algorithm to generate new pairings starting from the initial pairing x, b, can be summarized in the following two modes:
① selects a new coupling state b' for all bi,jPerforming zero initialization when i is less than j and Vi,j<When 0, b isi,jThe probability set to 1 is 1-exp [ V ]ij(xi,xj)/kBΘ]. This step divides the particles of the system into several groups, i.e. by a series of couplings (b)i,j1), the particles are connected to each other into a set of particles, thereby screening out wavelet coefficients that can be reconstructed.
② Metropolis state transition x → x' is carried out in the initial state space: and selecting a particle j, and forcibly coupling the rest particles with the particle j and moving the particles in a random direction under the spatial constraint of a coupling state. When the movement is completed, a new energy U (x') can be calculated. At the same time, the probability that the move is acceptable is expressed as:
Figure BDA0002560804340000061
the probability ratio in equation (7) can be further expressed as:
Figure BDA0002560804340000062
wherein ,
Figure BDA0002560804340000063
4) artificial sequence generation of screening components
And according to the particles (namely possible coupled wavelet coefficients) screened by the Monte Carlo simulation, performing wavelet coefficient reconstruction on the particles according to the following formula to generate a synthesized lithium battery sequence.
Figure BDA0002560804340000064
wherein ,
Figure BDA0002560804340000065
the wavelet coefficients are screened out.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (5)

1. A lithium battery abnormal working condition data self-organizing enhancing method based on a Monte Carlo method is characterized in that: the method comprises the following steps:
s1: performing wavelet decomposition on the abnormal working condition sequence of the small sample lithium battery to obtain a multi-scale wavelet coefficient;
s2: mapping the multi-scale wavelet coefficients into m-dimensional hyperspace midpoints;
s3: performing Monte Carlo self-organization on the multi-scale components;
s4: and performing wavelet coefficient reconstruction on the screened components to generate a synthesized lithium battery sequence.
2. The lithium battery abnormal working condition data self-organizing enhancing method based on the Monte Carlo method as claimed in claim 1, wherein: in step S1, for a given small sample lithium battery abnormal operating condition sequence f (t), including voltage, current, capacity, and internal resistance curves, the wavelet packet of the L layer is decomposed into:
Figure FDA0002560804330000011
phi (t) and psi (t) are respectively a parent wavelet and a mother wavelet, n represents the position of the wavelet packet in the layer, hkDenotes a low-pass filter, t denotes time, k denotes position coordinates, gkA high pass filter is indicated.
3. The lithium battery abnormal working condition data self-organizing enhancing method based on the Monte Carlo method as claimed in claim 1, wherein: in step S2, x is made [ x ]1,x2,…,x2L-1x2L]＝[Φ1(t),Ψ1(t),…ΦL(t),ΨL(t)]The multi-scale wavelet coefficients are mapped into m-dimensional hyperspace midpoints (called particles), and the dimension of a certain layer of wavelet coefficients is selected as a space reference according to specific sample characteristics.
4. The lithium battery abnormal working condition data self-organizing enhancing method based on the Monte Carlo method as claimed in claim 3, wherein the method is characterized in thatThe method comprises the following steps: in step S3, consider a system of 2L particles whose structure is described by vector x, i.e., the particle state is described by { x }iDefinition, xiIs the ith wavelet coefficient; the equilibrium distribution of the system is described as π (x) widerthan exp [ -U (x)/kBT]Wherein k isBIs the thermal energy coefficient, T is the temperature corresponding to the lithium battery sequence, u (x) is the cost energy function, which is used to describe the sum of the interactions between the particles, defined as:
Figure FDA0002560804330000012
wherein ,VijIs the energy of the interaction between the ith particle and the jth particle; defining an augmented space distribution pi (x, b), wherein { x } is an initial state space, { b } is a state coupling space, and b represents a vector of coupling among particles, namely coupling among wavelet coefficients of each scale corresponding to the lithium battery sequence, and the vector has L (L-1)/2 elements, and the coupling mode is as follows:
Figure FDA0002560804330000021
the augmented spatial distribution pi (x, b) and the equilibrium distribution pi (x) of the system satisfy:
Π(x,b)＝π(x)p(b,x) (4)
where p (b, x) denotes that a coupled state b ═ b is formed at a given initial state xijThe probability of, written as:
Figure FDA0002560804330000022
the q-function represents the formation of a particular coupling probability, expressed as:
q(bij,Vij)＝bij+(1-2bij)exp[min(Vij,0)/kBΘ](6)
according to formula (6), if the interaction V between particles i and ji,jPositive, they are always not coupled, bi,jThe probability of 0 is 1; if Vi,jIs negative in the number of the positive lines,then when | VijWhen | ≧ k Θ, the probability of coupling bi,j1 to 1; Θ represents the "pseudo temperature" that controls the coupling process; in practical application, the value range of theta is determined according to the characteristics of abnormal working conditions;
based on the above definition, the self-grouping between particles, i.e. the algorithm to generate new pairings starting from the initial pairing x, b, comes down in the following two modes:
① selects a new coupling state b' for all bi,jPerforming zero initialization when i is less than j and Vi,j<When 0, b isi,jThe probability set to 1 is 1-exp [ V ]ij(xi,xj)/kBΘ](ii) a This step divides the particles of the system into several groups, i.e. by a series of couplings bi,j1, mutually connecting particles into a particle set, and screening out wavelet coefficients which can be reconstructed;
② Metropolis state transition x → x' is carried out in the initial state space: selecting a particle j, and forcibly coupling the other particles with the particle j and moving the particles in a random direction under the spatial constraint of a coupling state; calculating new energy U (x') after completing the movement; meanwhile, the probability of the mobile acceptance is expressed as:
Figure FDA0002560804330000023
the probability ratio in equation (7) is further expressed as:
Figure FDA0002560804330000024
wherein ,
Figure FDA0002560804330000025
5. the lithium battery abnormal working condition data self-organizing enhancing method based on the Monte Carlo method as claimed in claim 4, wherein: in step S4, wavelet coefficients, i.e., possible coupled wavelet coefficients, are reconstructed according to the particles screened by the monte carlo simulation to generate a synthesized lithium battery sequence:
Figure FDA0002560804330000031
wherein ,
Figure FDA0002560804330000032
the wavelet coefficients are screened out.
CN202010610473.9A 2020-06-29 2020-06-29 Monte Carlo method-based lithium battery abnormal working condition data self-organizing enhancement method Active CN111767672B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010610473.9A CN111767672B (en) 2020-06-29 2020-06-29 Monte Carlo method-based lithium battery abnormal working condition data self-organizing enhancement method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010610473.9A CN111767672B (en) 2020-06-29 2020-06-29 Monte Carlo method-based lithium battery abnormal working condition data self-organizing enhancement method

Publications (2)

Publication Number Publication Date
CN111767672A true CN111767672A (en) 2020-10-13
CN111767672B CN111767672B (en) 2023-10-20

Family

ID=72724096

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010610473.9A Active CN111767672B (en) 2020-06-29 2020-06-29 Monte Carlo method-based lithium battery abnormal working condition data self-organizing enhancement method

Country Status (1)

Country Link
CN (1) CN111767672B (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110307438A1 (en) * 2010-06-14 2011-12-15 Fern E Acu A Ee Ndez Mart E Acu I Ee Nez Juan Luis High-dimensional data analysis
CN102749573A (en) * 2012-07-27 2012-10-24 重庆大学 Analog circuit fault diagnosis method based on wavelet packet analysis and Hopfield network
CN104077763A (en) * 2014-07-08 2014-10-01 中国科学院高能物理研究所 TOF-PET image reconstruction method based on compressed sensing theory
WO2016001752A2 (en) * 2014-06-30 2016-01-07 Cgg Services Sa Ensemble-based multi-scale history-matching device and method for reservoir characterization
CN105915297A (en) * 2014-12-03 2016-08-31 上海特金无线技术有限公司 Monte Carlo spectrum band occupancy rate measurement method
US9554738B1 (en) * 2016-03-30 2017-01-31 Zyomed Corp. Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing
CN109657364A (en) * 2018-12-24 2019-04-19 北京华大九天软件有限公司 A method of circuit optimization is realized using quick monte carlo method
CN109660553A (en) * 2019-01-07 2019-04-19 重庆邮电大学 Downlink safe transmission strategy based on time reversal technology
CN209085657U (en) * 2017-08-02 2019-07-09 强力物联网投资组合2016有限公司 For data gathering system related or industrial environment with chemical production technology

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110307438A1 (en) * 2010-06-14 2011-12-15 Fern E Acu A Ee Ndez Mart E Acu I Ee Nez Juan Luis High-dimensional data analysis
CN102749573A (en) * 2012-07-27 2012-10-24 重庆大学 Analog circuit fault diagnosis method based on wavelet packet analysis and Hopfield network
WO2016001752A2 (en) * 2014-06-30 2016-01-07 Cgg Services Sa Ensemble-based multi-scale history-matching device and method for reservoir characterization
CN104077763A (en) * 2014-07-08 2014-10-01 中国科学院高能物理研究所 TOF-PET image reconstruction method based on compressed sensing theory
CN105915297A (en) * 2014-12-03 2016-08-31 上海特金无线技术有限公司 Monte Carlo spectrum band occupancy rate measurement method
US9554738B1 (en) * 2016-03-30 2017-01-31 Zyomed Corp. Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing
CN209085657U (en) * 2017-08-02 2019-07-09 强力物联网投资组合2016有限公司 For data gathering system related or industrial environment with chemical production technology
CN109657364A (en) * 2018-12-24 2019-04-19 北京华大九天软件有限公司 A method of circuit optimization is realized using quick monte carlo method
CN109660553A (en) * 2019-01-07 2019-04-19 重庆邮电大学 Downlink safe transmission strategy based on time reversal technology

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
RATHIN CHANDRA SHIT .ETC: "Ubiquitous Localization（UbiLoc）：A Survey and Taxonomy on Device Free Localization for Smart World", 《IEEE COMMUNICATIONS SURVEYS & TUTORIALS》, vol. 21, no. 4, pages 3532 - 3564, XP011758784, DOI: 10.1109/COMST.2019.2915923 *
裴洪等: "基于机器学习的设备剩余寿命预测方法综述", 《机械工程学报》, vol. 55, no. 8, pages 1 - 13 *

Also Published As

Publication number Publication date
CN111767672B (en) 2023-10-20

Similar Documents

Publication Publication Date Title
Zhou et al. Remaining useful life prediction for supercapacitor based on long short-term memory neural network
Ma et al. The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach
Chandran et al. State of charge estimation of lithium-ion battery for electric vehicles using machine learning algorithms
Chang et al. CLEASE: a versatile and user-friendly implementation of cluster expansion method
Jang et al. Structure-based synthesizability prediction of crystals using partially supervised learning
Mistry et al. How machine learning will revolutionize electrochemical sciences
Sun et al. Data-driven ICA-Bi-LSTM-combined lithium battery SOH estimation
Ruan et al. Generalised diagnostic framework for rapid battery degradation quantification with deep learning
CN111595485B (en) Lithium ion battery online temperature distribution observer design method based on reduced order model
Effendy et al. Analysis, design, and generalization of electrochemical impedance spectroscopy (EIS) inversion algorithms
Zhang et al. Intelligent state of charge estimation of battery pack based on particle swarm optimization algorithm improved radical basis function neural network
Keshun et al. Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process
Wang et al. Health diagnosis for lithium-ion battery by combining partial incremental capacity and deep belief network during insufficient discharge profile
CN113888351A (en) Power prediction method and device of photovoltaic system and electronic equipment
Fanoro et al. A review of the impact of battery degradation on energy management systems with a special emphasis on electric vehicles
Li et al. Toward group applications: a critical review of the classification strategies of lithium-ion batteries
Rodemann A many-objective configuration optimization for building energy management
CN111767672B (en) Monte Carlo method-based lithium battery abnormal working condition data self-organizing enhancement method
CN111900717B (en) Operation risk assessment method for alternating current-direct current hybrid power grid containing high-dimensional random variables
Li et al. The state of charge estimation of lithium-ion battery based on battery capacity
CN114636933B (en) Composite neural network-based retired power battery capacity detection method and system
Cao Recent advances in the application of machine-learning algorithms to predict adsorption energies
Teichert et al. Li $ _x $ CoO $ _2 $ phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories
Li et al. Data-driven state of health estimation for lithium-ion batteries based on universal feature selection
Zhao et al. Online state of health estimation with deep learning frameworks based on short and random battery charging data segments

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant