CN115327386A - Battery pack multi-fault diagnosis method based on electric-thermal coupling model - Google Patents
Battery pack multi-fault diagnosis method based on electric-thermal coupling model Download PDFInfo
- Publication number
- CN115327386A CN115327386A CN202210950451.6A CN202210950451A CN115327386A CN 115327386 A CN115327386 A CN 115327386A CN 202210950451 A CN202210950451 A CN 202210950451A CN 115327386 A CN115327386 A CN 115327386A
- Authority
- CN
- China
- Prior art keywords
- battery
- model
- fault
- residual
- residual error
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000003745 diagnosis Methods 0.000 title claims abstract description 32
- 230000008878 coupling Effects 0.000 title claims abstract description 27
- 238000010168 coupling process Methods 0.000 title claims abstract description 27
- 238000005859 coupling reaction Methods 0.000 title claims abstract description 27
- 238000012360 testing method Methods 0.000 claims abstract description 36
- 238000011156 evaluation Methods 0.000 claims abstract description 11
- 238000001514 detection method Methods 0.000 claims abstract description 8
- 238000012916 structural analysis Methods 0.000 claims abstract description 8
- 239000000178 monomer Substances 0.000 claims abstract description 7
- 238000000926 separation method Methods 0.000 claims abstract description 6
- 238000007619 statistical method Methods 0.000 claims abstract description 5
- 238000002474 experimental method Methods 0.000 claims abstract description 4
- 238000009825 accumulation Methods 0.000 claims description 10
- 230000003044 adaptive effect Effects 0.000 claims description 10
- 238000001914 filtration Methods 0.000 claims description 9
- 230000010287 polarization Effects 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000007599 discharging Methods 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 4
- 239000002245 particle Substances 0.000 claims description 4
- 238000002405 diagnostic procedure Methods 0.000 claims description 3
- 230000020169 heat generation Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 230000003068 static effect Effects 0.000 claims description 3
- 238000010998 test method Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 239000002131 composite material Substances 0.000 claims description 2
- 238000005259 measurement Methods 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
Abstract
The invention relates to a battery pack multi-fault diagnosis method based on an electric-thermal coupling model, and belongs to the technical field of batteries. The method comprises the following steps: s1: carrying out characteristic experiment test on the battery monomer to be tested, extracting characteristic parameters and establishing a battery electric-thermal coupling model; s2: performing structural analysis based on a battery model, and establishing a diagnosis test set sensitive to multiple faults; s3: based on a diagnosis test set, fusing an observer or a filter method to realize residual generation; s4: detecting and separating various faults by a residual error evaluation method; s5: residual error characteristics are extracted, and a statistical method is utilized to further separate battery short circuit and connection faults. Compared with the prior art, the method can more quickly and accurately realize the detection and separation of various faults without changing the topological structure of the voltage measurement of the battery pack.
Description
Technical Field
The invention belongs to the technical field of batteries, and relates to a battery pack multi-fault diagnosis method based on an electric-thermal coupling model.
Background
The lithium ion battery has the advantages of high energy density, high power density, long cycle life, environmental friendliness and the like, and is widely applied to electric vehicles and energy storage power stations. However, the safety problem of the battery system is still a difficult problem to be solved urgently in the industry, and the development of an advanced fault diagnosis technology is crucial to ensuring the safe operation of the battery system.
The battery system has faults such as battery short circuit, sensor and battery connection. Battery short circuit failure is a major cause of thermal runaway in battery systems; the battery voltage and temperature are not accurately monitored due to the fault of the sensor, and the battery state is estimated and noise is interfered for management; battery connection failure can lead to poor contact and localized high temperatures, and is also a potential cause of triggering thermal runaway. The fault diagnosis method widely researched at present comprises a model-based method, a signal processing method and a machine learning method, wherein characteristic parameters and statistical indexes are extracted by estimating the state of a model and the change of parameters respectively, and a fault classifier is trained by using historical data, so that fault diagnosis is realized.
However, in the prior art, most of the faults are only diagnosed for a single fault, and the similar characteristics of multiple faults of the battery system are not considered, so that a large false alarm and a large false alarm can be caused in practical application. Moreover, abnormal signals of early faults are weak, the abnormal signals are not easy to find in the early stage of the faults, and some fault diagnosis algorithms are easily interfered by model uncertainty and noise, so that the diagnosis accuracy is not high. Currently, there is a great need for battery system management that can implement multiple fault diagnosis without changing the battery pack structure and increasing hardware costs.
Disclosure of Invention
In view of this, the present invention provides a battery pack multiple fault diagnosis method based on an electrical-thermal coupling model.
In order to achieve the purpose, the invention provides the following technical scheme:
a battery pack multi-fault diagnosis method based on an electric-thermal coupling model comprises the following steps:
s1: performing characteristic experiment test on the battery to be tested, identifying model parameters and establishing a battery electric-thermal coupling model;
s2: performing structural analysis based on a battery model, and establishing a diagnosis test set sensitive to multiple faults;
s3: based on a diagnosis test set, fusing an adaptive filtering method, designing a residual error generator and generating a residual error;
s4: detecting and separating various faults by an accumulation and residual evaluation method based on the generated residual signals;
s5: residual error characteristics are extracted, and a statistical method is utilized to separate battery short circuit and connection faults.
Optionally, in S1, the battery characteristic testing and the electrothermal coupling model establishing include:
s11: the battery characteristic test comprises a battery static capacity test, an open-circuit voltage OCV test, a composite pulse HPPC test under different temperatures and SOC states, a dynamic stress working condition test DST, a U.S. Federal city driving working condition FUDS and a real vehicle working condition test of a city power driving working condition UDDS;
s12: establishing a battery pack model of a multi-monomer model, wherein the battery monomer model is an electric thermal coupling model, and the electric model is a first-order RC equivalent circuit model:
U t ＝U ocv -U p -RI
wherein I represents a current, U p Representing the polarization voltage, U t Represents terminal voltage, U ocv Represents an open circuit voltage, R p And C p Respectively representing polarization resistance and polarization capacitance, and R represents ohm internal resistance;
the thermal model is a concentrated mass thermal model:
Q＝I(U ocv -U t )
wherein Q represents the heat generation rate, m, c p H and A are respectively the mass, specific heat capacity, convective heat transfer coefficient and surface area of the battery, T and T ∞ Respectively representing the average temperature of the battery and the ambient temperature; establishing a relation between the electric model and the thermal model through a heat production equation so as to establish an electric-thermal coupling model;
s13: and identifying the parameters of the electric and thermal models by using a parameter identification method of least square and particle swarm optimization according to the battery characteristic test data.
Optionally, in S2, the method for analyzing the battery model is a structural analysis theory, and the multiple fault detectability and separability analysis is performed to find a structurally excessive part in the model; the constructed diagnostic test set is a series of equation sets sensitive to various faults, and a fault feature matrix FSM capable of mapping the relation between the faults and the residual errors is formed.
Optionally, in S3, the specifically adopted adaptive filtering method includes, but is not limited to, an adaptive extended kalman filtering method AEKF; the designed residual error generator comprises 2 sets of observers based on current and voltage signals and based on current and temperature signals, and 2 generated residual errors are represented as follows:
in which the subscript k denotes the time k, r 1,k And r 2,k Respectively representing 2 sets of residual generatorsThe residual error is a residual error that is,
and
respectively representing a measured terminal voltage and an estimated terminal voltage,
and
respectively representing the measured temperature and the estimated temperature.
Optionally, in S4, the residual signals are derived from 2 sets of residual generators in S3; the residual error evaluation method is an accumulation and test method, the generated decision function is compared with a set threshold value to judge faults, and multi-fault detection and fault separation of current, voltage and temperature sensors are realized by combining with the FSM established in S2;
the residual error evaluation method comprises the following steps:
in the formula g k The decision function at time k is represented,
and
representing the probability density function under the assumption of no fault and a fault, respectively.
Optionally, in S5, the extracted residual features are residual accumulation sums within a period of time window, and according to different changes of the residual accumulation sums under the charging and discharging conditions, battery short circuits and connection faults are distinguished;
in the formula r acc Representing residual cumulative sum, epsilon represents a constant, d =1 represents a short-circuit fault, otherwise a connection fault.
The invention has the beneficial effects that:
(1) The method provided by the invention can realize the detection and separation of various faults of the current, the voltage, the temperature sensor, the battery short circuit, the battery connection and the like of the battery pack, can reduce the false alarm rate of diagnosis, and does not depend on the cross voltage measurement topology;
(2) The adaptive extended Kalman filtering algorithm is integrated into the residual error generation, so that the influence of model uncertainty, inaccurate initial value, noise and the like is reduced, and the estimation precision and robustness are improved;
(3) Fault diagnosis is carried out by using a model-based residual error generation and residual error evaluation method, and tiny faults which are difficult to observe from measurement signals can be detected;
(4) Short circuit and connection faults can be separated only by carrying out statistical analysis on the existing partial residual errors, the calculated amount is reduced, and the multi-fault diagnosis method is moderate in complexity and can be applied to a real vehicle battery management system.
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 will be realized and attained by the structure particularly pointed out in the written description and claims thereof.
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 an overall process flow diagram of the present invention;
FIG. 2 is a schematic diagram of a first-order RC equivalent circuit model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a fault signature matrix mapping fault and residual relationships in an embodiment of the invention;
FIG. 4 is a flow chart of multiple fault detection and isolation in an embodiment of the present invention;
FIG. 5 is a graph of voltage sensor fault diagnosis results in an embodiment of the present invention;
fig. 6 is a schematic diagram for distinguishing between a battery short-circuit fault and a connection fault in the embodiment of the present invention.
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 embodiments may be combined with each other without conflict.
Wherein the showings are for the purpose of illustration only and not for the purpose of limiting the invention, shown in the drawings are schematic representations and not in the form of actual drawings; for a better explanation of 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 the terms "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 intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, 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 described above will be understood by those skilled in the art according to the specific circumstances.
As shown in fig. 1, the method for diagnosing multiple faults of a battery pack based on an electrical-thermal coupling model provided by the present invention can effectively detect and separate multiple different faults in the battery pack to improve the safety of a battery system, and includes the following steps:
s1: performing characteristic experiment test on the battery to be tested, identifying model parameters and establishing a battery electric-thermal coupling model;
s2: performing structural analysis based on a battery model, and establishing a diagnosis test set sensitive to multiple faults;
s3: based on a diagnosis test set, fusing an adaptive filtering method, designing a residual error generator and generating a residual error;
s4: detecting and separating various faults by a cumulative and residual evaluation method based on the generated residual signal;
s5: residual error characteristics are extracted, and a statistical method is utilized to further separate battery short circuit and connection faults.
Step S1: and (5) modeling the battery. The invention firstly carries out battery characteristic test and establishes a battery electric thermal coupling model, comprising the following steps:
s11: developing a series of battery characteristic tests aiming at the selected battery, wherein the battery characteristic tests mainly comprise a battery static capacity test and an open-circuit voltage test, the HPPC tests at different temperatures and SOC are used for identifying electric model parameters, the dynamic working condition tests such as DST, FUDS, UDDS and the like are used for identifying thermal model parameters, and the precision of the electric-thermal coupling model is verified;
s12: the battery pack model adopts a multi-monomer model, the battery monomer model is an electric-thermal coupling model, and as shown in fig. 2, the electric model adopts a first-order RC equivalent circuit model:
U t ＝U ocv -U p -RI
wherein I represents a current, U p Representing the polarization voltage, U t Represents terminal voltage, U ocv Represents the open circuit voltage, R p And C p Respectively representing polarization resistance and polarization capacitance, and R representing ohmic internal resistance.
The thermal model is a concentrated quality thermal model:
Q＝I(U ocv -U t )
wherein Q represents the heat generation rate, m, c p H and A are respectively the mass, specific heat capacity, convective heat transfer coefficient and surface area of the battery, T and T ∞ Respectively, the average temperature of the battery and the ambient temperature. And establishing the connection between the electric model and the thermal model through a heat production equation so as to establish the electric-thermal coupling model.
S13: according to the battery characteristic test data, identifying electric and thermal model parameters by using parameter identification methods such as least square and particle swarm optimization;
taking a particle swarm optimization as an example, for the electric model parameters, the optimization target is that the root mean square error between the measured voltage and the estimated voltage is minimum:
for the thermal model parameters, the optimization objective is that the root mean square error of the measured temperature and the estimated estimate is minimal:
step S2: and (5) structural analysis. Based on a battery model, carrying out multi-fault detectability and separability analysis by utilizing a structural analysis theory, and finding out an excessive part of a structure in the model; a diagnostic test set sensitive to various faults is constructed to form a Fault Signature Matrix (FSM) which can map the relation between the faults and the residual errors, as shown in fig. 3.
And step S3: and generating a residual error. The method is to adopt an Adaptive filtering method including but not limited to an Adaptive Extended Kalman Filter (AEKF) for state estimation, a designed residual error generator comprises 2 groups of observers based on current, voltage and current and temperature signals, and the generated residual error in 2 is expressed as follows:
in which the subscript k denotes the time k, r 1,k And r 2,k Respectively representing the residuals of 2 sets of residual generators,
and
respectively representing a measured terminal voltage and an estimated terminal voltage,
and
respectively representing the measured temperature and the estimated temperature.
And step S4: and (4) multi-fault diagnosis. As shown in fig. 3 in step S2, the type 2 residuals generated in step S3 have different corresponding relationships with different faults, and the type 2 residuals can be generated by combining the single batteries in the battery pack, and the multi-fault detection and separation strategy is shown in fig. 4: under fault-free conditions, all residuals r 1 And r 2 None are reacted; if a current sensor fault occurs, all residual errors r 1 And r 2 All responses are made, namely the corresponding decision function exceeds the set threshold; if the ith voltage sensor fails, only the ith residual r 1 Making a response; if the ith temperature sensor fails, only the ith residual error r 2 Making a response; and the battery is short-circuited or the connection is failedTime, ith residual r 1 And r 2 At the same time, the reaction is performed, and a further distinction is made in step S5.
The specific fault detection is realized through residual error evaluation, the residual error evaluation method is an accumulation and test method (see the following formula), the generated decision function is compared with a set threshold value to judge the fault, and the multi-fault detection and the separation of the faults of current, voltage, temperature sensors and the like can be realized by combining the FSM established in the step S2; taking the voltage sensor failure as an example, the diagnosis result is shown in fig. 5, and only the residual error r with the failure occurs 1 In reaction, all residuals r 2 No response, consistent with previous diagnostic analysis.
In the formula g k The decision function at time k is represented,
and
representing the probability density function under the assumption of no fault and a fault, respectively.
Step S5: and (5) further separating. Residual sum in a period of time window is extracted as a feature, and battery short circuit and connection fault are distinguished according to different changes of the residual sum under charging and discharging conditions, as shown in fig. 4.
In the formula r acc Denotes the sum of residual errors, epsilon denotes a constant, d =1 denotes a short-circuit fault, otherwise a connection fault.
As shown in fig. 6, for example, when constant ∈ =0, since a short circuit consumes extra power, a residual is always negative regardless of charging and discharging, a residual sum r is accumulated acc D continues to equal 1 if d continues to decrease; while connection failure is in dischargeThe residual is negative and the residual is positive at the time of charging, so that a change occurs in which the residual accumulation has a rise and a fall, and d fluctuates between 0 and 1.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A battery pack multi-fault diagnosis method based on an electric-thermal coupling model is characterized by comprising the following steps: the method comprises the following steps:
s1: performing characteristic experiment test on the battery to be tested, identifying model parameters and establishing a battery electric-thermal coupling model;
s2: performing structural analysis based on a battery model, and establishing a diagnosis test set sensitive to multiple faults;
s3: based on a diagnosis test set, fusing an adaptive filtering method, designing a residual error generator and generating a residual error;
s4: detecting and separating various faults by an accumulation and residual evaluation method based on the generated residual signals;
s5: residual error characteristics are extracted, and a statistical method is used for separating battery short circuit and connection faults.
2. The battery pack multi-fault diagnosis method based on the electrical-thermal coupling model according to claim 1, characterized in that: in S1, the battery characteristic testing and the electrothermal coupling model establishing include:
s11: the battery characteristic test comprises a battery static capacity test, an open-circuit voltage OCV test, a composite pulse HPPC test under different temperatures and SOC states, a dynamic stress working condition test DST, a U.S. Federal city driving working condition FUDS and a real vehicle working condition test of a city power driving working condition UDDS;
s12: establishing a battery pack model of a multi-monomer model, wherein the battery monomer model is an electric thermal coupling model, and the electric model is a first-order RC equivalent circuit model:
U t ＝U ocv -U p -RI
wherein I represents a current, U p Representing the polarization voltage, U t Represents terminal voltage, U ocv Represents an open circuit voltage, R p And C p Respectively representing polarization resistance and polarization capacitance, and R representing ohm internal resistance;
the thermal model is a concentrated quality thermal model:
Q＝I(U ocv -U t )
wherein Q represents a heat generation rate, m, c p H and A are the mass, specific heat capacity, convective heat transfer coefficient and surface area of the cell, T and T respectively ∞ Respectively representing the average temperature of the battery and the ambient temperature; establishing a relation between the electric model and the thermal model through a heat production equation so as to establish an electric-thermal coupling model;
s13: and identifying the parameters of the electric and thermal models by using a parameter identification method of least square and particle swarm optimization according to the battery characteristic test data.
3. The battery pack multi-fault diagnosis method based on the electrical-thermal coupling model according to claim 2, characterized in that: in the S2, the method for analyzing the battery model is a structural analysis theory, multi-fault detectability and separability analysis is carried out, and over-part of the structure in the model is found; the constructed diagnostic test set is a series of equation sets sensitive to various faults, and a fault feature matrix FSM capable of mapping the relation between the faults and the residual errors is formed.
4. The battery pack multi-fault diagnosis method based on the electrical-thermal coupling model according to claim 3, characterized in that: in the S3, the specifically adopted adaptive filtering method includes, but is not limited to, an adaptive extended kalman filtering method AEKF; the designed residual error generator comprises 2 sets of observers based on current, voltage and current and temperature signals, and the generated 2-in-2 residual error is expressed as follows:
in which the index k denotes the time k, r 1,k And r 2,k Respectively representing the residuals of 2 sets of residual generators,
and
respectively representing a measured terminal voltage and an estimated terminal voltage,
and
respectively representing the measured temperature and the estimated temperature.
5. The battery pack multi-fault diagnosis method based on the electrical thermal coupling model according to claim 4, characterized in that: in S4, the residual signals are derived from 2 sets of residual generators in S3; the residual error evaluation method is an accumulation and test method, the generated decision function is compared with a set threshold value to judge faults, and multi-fault detection and fault separation of current, voltage and temperature sensors are realized by combining with the FSM established in S2;
the residual error evaluation method comprises the following steps:
in the formula g k The decision function at time k is represented,
and
representing the probability density function under the assumption of no fault and a fault, respectively.
6. The battery pack multi-fault diagnosis method based on the electrical thermal coupling model according to claim 5, characterized in that: in the S5, the extracted residual error features are residual error accumulation sums in a time window, and battery short circuit and connection faults are distinguished according to different changes of the residual error accumulation sums under the charging and discharging working conditions;
in the formula r acc Denotes the sum of residual errors, epsilon denotes a constant, d =1 denotes a short-circuit fault, otherwise a connection fault.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210950451.6A CN115327386A (en) | 2022-08-09 | 2022-08-09 | Battery pack multi-fault diagnosis method based on electric-thermal coupling model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210950451.6A CN115327386A (en) | 2022-08-09 | 2022-08-09 | Battery pack multi-fault diagnosis method based on electric-thermal coupling model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115327386A true CN115327386A (en) | 2022-11-11 |
Family
ID=83921894
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210950451.6A Pending CN115327386A (en) | 2022-08-09 | 2022-08-09 | Battery pack multi-fault diagnosis method based on electric-thermal coupling model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115327386A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116106771A (en) * | 2023-03-15 | 2023-05-12 | 华能澜沧江水电股份有限公司 | Battery pack fault detection method and device based on structural analysis method and electronic equipment |
| CN116482560A (en) * | 2023-06-21 | 2023-07-25 | 中国华能集团清洁能源技术研究院有限公司 | Battery fault detection method and device, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112147512A (en) * | 2020-09-17 | 2020-12-29 | 北京理工大学 | Diagnosis and separation method for short-circuit and abuse faults of lithium ion battery |
| CN114158276A (en) * | 2020-02-05 | 2022-03-08 | 株式会社Lg新能源 | Method of detecting lithium plating and method and apparatus for managing battery by using the same |
-
2022
- 2022-08-09 CN CN202210950451.6A patent/CN115327386A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114158276A (en) * | 2020-02-05 | 2022-03-08 | 株式会社Lg新能源 | Method of detecting lithium plating and method and apparatus for managing battery by using the same |
| CN112147512A (en) * | 2020-09-17 | 2020-12-29 | 北京理工大学 | Diagnosis and separation method for short-circuit and abuse faults of lithium ion battery |
Non-Patent Citations (2)
| Title |
|---|
| KAI ZHANG 等: "Multi-fault Detection and Isolation for Lithium-Ion Battery Systems", 《IEEE TRANSACTIONS ON POWER ELECTRONICS》, vol. 37, no. 1, 31 January 2022 (2022-01-31), pages 971 - 989, XP011879401, DOI: 10.1109/TPEL.2021.3098445 * |
| 许俊雄 等: "电动车辆锂离子电池传感器故障诊断方法", 《重庆大学学报》, vol. 45, no. 6, 23 April 2021 (2021-04-23), pages 27 - 39 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116106771A (en) * | 2023-03-15 | 2023-05-12 | 华能澜沧江水电股份有限公司 | Battery pack fault detection method and device based on structural analysis method and electronic equipment |
| CN116482560A (en) * | 2023-06-21 | 2023-07-25 | 中国华能集团清洁能源技术研究院有限公司 | Battery fault detection method and device, electronic equipment and storage medium |
| CN116482560B (en) * | 2023-06-21 | 2023-09-12 | 中国华能集团清洁能源技术研究院有限公司 | Battery fault detection method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110187225B (en) | Method and system for detecting abnormal short-circuit voltage and current in lithium battery | |
| CN106526488B (en) | Tandem power battery pack inner sensor method for diagnosing faults | |
| EP3933422B1 (en) | Diagnosis method for distinguishing micro-short-circuit fault of battery from small-capacity fault of battery | |
| CN115327386A (en) | Battery pack multi-fault diagnosis method based on electric-thermal coupling model | |
| KR102106775B1 (en) | Diagnosis method of battery using Deep Learning | |
| CN109061537B (en) | Electric vehicle lithium ion battery sensor fault diagnosis method based on observer | |
| US10094806B2 (en) | Method and apparatus for monitoring battery state | |
| Lin et al. | Voltage-correlation based multi-fault diagnosis of lithium-ion battery packs considering inconsistency | |
| CN112906744B (en) | Fault single battery identification method based on isolated forest algorithm | |
| CN110441695A (en) | A kind of battery pack multiple faults error comprehensive diagnosis method combined based on model and signal processing | |
| CN112345955B (en) | Multi-fault online diagnosis method and system for power battery | |
| CN113933732A (en) | New energy automobile power battery health state analysis method, system and storage medium | |
| CN114559819B (en) | Electric automobile battery safety early warning method based on signal processing | |
| CN111257753B (en) | Battery system fault diagnosis method | |
| CN113447828A (en) | Lithium battery temperature estimation method and system based on Bayesian neural network | |
| CN112068018A (en) | Power battery pack fault diagnosis method based on improved Grubbs criterion and battery electric thermal coupling model | |
| CN114814691A (en) | Lithium battery system voltage sensor fault diagnosis method based on digital twinning | |
| CN112285570A (en) | Electric vehicle fault diagnosis method based on attenuation memory filter | |
| Li et al. | Data-driven thermal anomaly detection for batteries using unsupervised shape clustering | |
| CN111537893A (en) | Method and system for evaluating operation safety of lithium ion battery module and electronic equipment | |
| CN113687251A (en) | Dual-model-based lithium ion battery pack voltage abnormity fault diagnosis method | |
| CN112009252B (en) | Fault diagnosis and fault-tolerant control method for power battery system | |
| Yang et al. | An online multifault diagnosis scheme for battery packs based on voltage envelope relationship | |
| CN116190818A (en) | Lithium battery device based on multidimensional sensing fusion, lithium battery monitoring device and method | |
| CN115659799A (en) | Lithium battery energy storage power station fault diagnosis method with threshold self-adaption function |
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 |