CN113687251A - Dual-model-based lithium ion battery pack voltage abnormity fault diagnosis method - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000003745 diagnosis Methods 0.000 title claims abstract description 17
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 15
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 15
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims abstract description 17
- 229910052744 lithium Inorganic materials 0.000 claims abstract description 17
- 238000012549 training Methods 0.000 claims abstract description 4
- 230000009977 dual effect Effects 0.000 claims description 7
- 238000002474 experimental method Methods 0.000 claims description 7
- 210000002569 neuron Anatomy 0.000 claims description 6
- 230000010287 polarization Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 11
- 230000002159 abnormal effect Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 5
- 230000001186 cumulative effect Effects 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 230000005856 abnormality Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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Abstract
The invention relates to a voltage abnormity fault diagnosis method of a lithium ion battery pack based on double models, which belongs to the technical field of battery safety and comprises the following steps of 1: collecting battery charge and discharge data including current, voltage, temperature and SOC; step 2: establishing a second-order equivalent circuit model, and identifying model parameters by using a least square method to obtain various parameters of models at different temperatures and SOC; and step 3: establishing LSTM and Dense models, and training a network model by using collected normal lithium battery operation data; and 4, step 4: the SOC obtained by LSTM prediction is respectively used for the voltages of the output ends of the second-order equivalent circuit model and the Dense model, and then a more accurate terminal voltage than before is obtained by combining the terminal voltages output by the two models; and 5: and generating a residual error between the model terminal voltage and the actually operated terminal voltage, evaluating the residual error by using the CUSUM, and determining that a fault occurs if the residual error exceeds a threshold value.
Description
Technical Field
The invention belongs to the technical field of battery safety, and relates to a voltage abnormity fault diagnosis method of a lithium ion battery pack based on a dual model.
Background
With the increasing severity of energy crisis, the development of new energy sources becomes more and more important. The lithium battery technology is the core of the new energy field, and is comprehensively developed in various fields such as portable equipment, satellites, reserve power supplies, electric vehicles and the like due to the ultrahigh applicability of the lithium battery technology. In the application of the secondary power supply which is greatly advocated in China at present, the lithium battery has excellent potential and wide prospect. The lithium battery can be inevitably aged and various faults and other abnormal conditions occur after being used for a long time, if the abnormal conditions are not identified and isolated in time, serious faults are likely to occur, and further thermal runaway and even explosion are caused. The safety of the lithium battery is very important, and the development of new energy industry is restricted, so that the lithium battery fault diagnosis technology is indispensable.
Most of the existing battery management systems operate by monitoring and estimating various state parameters of the lithium battery, wherein the safety management of the battery is not mature, most of the existing fault diagnosis technologies diagnose through voltage, and the reliability of the fault diagnosis technology is influenced by the voltage precision because various faults and abnormal conditions of the lithium battery are generally expressed through terminal voltage.
Disclosure of Invention
In view of this, the present invention provides a method for diagnosing abnormal voltage faults of a lithium ion battery pack based on a dual model, so as to improve the accuracy of output voltage based on the model.
In order to achieve the purpose, the invention provides the following technical scheme:
a lithium ion battery pack voltage abnormity fault diagnosis method based on dual models comprises the following steps:
step 1: collecting battery charge and discharge data including current, voltage, temperature and SOC;
step 2: establishing a second-order equivalent circuit model, and identifying model parameters by using a least square method to obtain various parameters of models at different temperatures and SOC, wherein the parameters comprise open-circuit voltage, ohmic internal resistance, electrochemical polarization RC and concentration polarization RC;
and step 3: establishing LSTM and full connection layer Dense models, and training a network model by using collected normal lithium battery operation data;
and 4, step 4: the SOC obtained by LSTM prediction is respectively used for the output end voltages of a second-order equivalent circuit model and a full connection layer Dense model, and then a more accurate end voltage than before is obtained by combining the end voltages output by the two models;
and 5: and generating a residual error between the model terminal voltage and the actually operated terminal voltage, evaluating the residual error by using the CUSUM, and determining that a fault occurs if the residual error exceeds a threshold value.
Further, in step 2, the model parameters are identified by a least square method, and the mathematical expression is as follows:
finally obtaining model parameters under different temperatures and SOC, and expressing the relationship of the model parameters as
X＝fX(SOC，Te)
X is a certain parameter of the model, SOC is the state of charge of the battery, and Te is the measured temperature.
Further, in step 3, the LSTM network and the full connection layer depth both include an input layer, an output layer, and an implicit layer, where the input of the LSTM network is:
wherein k and n represent the input sample time window and sample number, respectively, I represents the sample current, Ut represents the sample voltage, and T represents the sample temperature;
the output of the LSTM network is the SOC of the next moment;
the LSTM network adjusts the hyper-parameters of the network according to the data volume, wherein the hyper-parameters comprise the number of hidden layer neurons, a time window k and the size of the batch size;
the input of the full connection layer Dense network is (I) at the current momentt，Tt，SOCt) Wherein, ItRepresenting the current at the present moment, TtIndicating the temperature, SOC at the present timetAnd representing the SOC at the current moment, wherein the output of the full connection layer Dense network is terminal voltage, and the full connection layer Dense network adjusts the hyper-parameters of the network according to the data volume, including the number of hidden layer neurons and the size of the batch size.
Further, in the step 4, the equivalent circuit model ECM and the full connection layer density respectively obtain terminal voltages Ut, which are respectively expressed as UtE，UtDCombining the two models to obtain more accurate Ut, wherein the specific mathematical expression is as follows:
SOC＝LSTM(I,Ut,T)；
UtD＝Dense(I,T,SOC)；
UtE＝ECM(I，T，SOC)；
further, in step 5, generating a residual error between the model terminal voltage and the actually-operated terminal voltage, evaluating the residual error by using CUSUM, if the residual error exceeds a threshold value, determining that a fault occurs, and taking up the accumulated and calculated points: x is the number ofi- (μ + k σ), wherein xiAnd taking mu as a target value, taking 0 as the difference between the model terminal voltage and the actual operating terminal voltage, taking h sigma as a threshold value, and taking k and h as reference values and decision values respectively, wherein the difference is selected according to the operating length.
Further, the period T of voltage, current, SOC and temperature when the battery data is collected in the experiment is 1 second.
Further, the least square method with forgetting factor identifies the parameter as (1-a)1-a2)Uoc(k)，a1,a2,a3,a4,a5
The parameters of the model obtained by the final conversion are:
τ1＝R1C1，τ2＝R2C2。
the invention has the beneficial effects that: the invention adopts a double-model-based lithium ion battery pack voltage abnormity fault diagnosis method, the method adopts an equivalent circuit model and a neural network model to output voltage together, and the finally obtained voltage precision is higher than that of the voltage precision of the single model. The method needs to acquire data for lithium battery experiments, and identifies six equivalent circuit parameters, so that the model precision is high and the calculated amount is not high. The two neural network models are respectively an LSTM network for predicting SOC and a full connection layer for outputting terminal voltage, wherein the predicted SOC is respectively used as the input of an equivalent circuit model and the full connection layer model. The resulting terminal voltage is the average of the full link layer model outputs of the equivalent circuit model. And finally, in actual operation, generating a residual error with the measured voltage, and judging whether the abnormality occurs or not by accumulation and evaluation. After the identification model and the off-line training are completed, the method can be used for monitoring the energy storage battery in real time, has high reliability, provides a new solution for finding out the potential fault of the battery in time and preventing serious faults and even thermal runaway, and improves the performance of a battery management system, particularly the safety management of the battery.
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.
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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 voltage abnormality fault diagnosis method of a lithium ion battery pack based on dual models, which is disclosed by the invention;
FIG. 2 is a diagram showing the result of equivalent circuit parameter identification according to the embodiment of the present invention, wherein (a) is a result diagram of parameter Em, (b) is a result diagram of parameter R0, (c) is a result diagram of parameter R1, (d) is a result diagram of parameter R2, (e) is a result diagram of parameter tau1, and (f) is a result diagram of parameter tau 2;
FIG. 3 shows the results of LSTM and Dense network tests according to an embodiment of the present invention, wherein (a) is a diagram showing the results of the LSTM network tests and (b) is a diagram showing the results of the Dense network tests;
FIG. 4 is a Ut output result of the combination of the equivalent circuit model Ut and the model according to the embodiment of the present invention, wherein (a) is an output result diagram of the equivalent circuit model Ut, and (b) is an output result diagram of the combination of the two models and the Ut;
fig. 5 shows a residual map and a cumulative sum map of an actual operation of an embodiment of the present invention, (a) shows a residual map of sample 1, (b) shows a cumulative sum map of sample 1, (c) shows a residual map of sample 2, (d) shows a cumulative sum map of sample 2, (e) shows a residual map of sample 3, and (f) shows a cumulative sum map of sample 3.
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.
Referring to fig. 1 to 5, the battery data used in the embodiment of the present invention is a public battery data set of the university of maryland. The data collected in step one of the summary of the invention is therefore the data set herein. The batteries used in the data set experiments were a123 lithium iron phosphate batteries, which were subjected to charge and discharge experiments at 0, 10, 20, 25, 30, and 40 degrees, respectively, using 3 conditions, DST, US06, and FUDS, respectively. The high-precision sensor for the experiment is used for collecting voltage, current, temperature and charge-discharge capacity.
The present embodiment is as follows:
step 1, carrying out charge and discharge experiments on the lithium battery at different temperatures, and collecting voltage, current, temperature and SOC, wherein the period is 1 second. The university of maryland public data set is used here. The data used in the data set has time, current, voltage, charge and discharge capacity and temperature.
TABLE 1
The equivalent circuit model under the identification parameters has errors under three working conditions of 25 degrees as shown in table 2:
TABLE 2
The parameters at the remaining temperatures are identified as shown in FIG. 1.
And 3, firstly establishing an LSTM network for predicting the SOC. The input format of the LSTM network is (number of samples, time step, sample characteristics). Wherein the number of samples and the number of sample characteristics are known, the sample characteristics are current, voltage and temperature, and the time step is a hyper-parameter to be adjusted. The output is the SOC. And (3) carrying out normalization processing on the DST working condition data and then using the normalized DST working condition data to train the network, and adjusting the time step, the number of hidden layer neurons, the optimizer, the size of the batchsize and the size of the epoch in a grid searching mode. The results after adjustment according to loss are shown in Table 3:
TABLE 3
The full-connection layer network is also established for the output terminal voltage, and the input format of the Dense network is (sample number, sample characteristic). Where the sample characteristics are current, temperature and SOC, the output is Ut. And obtaining the optimal number of hidden layer neurons, the optimizer, the batch size and the epoch size by adopting a grid search mode, and adjusting the result to be as shown in the table. After both networks have been trained, the results of the testing using the FUDS operating conditions are shown in FIG. 2.
And 4, in order to obtain more accurate voltage, combining the two terminal voltage outputs in the step 2 and the step 3, wherein the specific mathematical expression is as follows:
SOC＝LSTM(I,Ut,T)；
UTDense＝Dense(I,T,SOC)；
UtECM＝ECM(I,T,SOC)；
fig. 3 is the terminal voltage output by the equivalent circuit under the 20 degree FUDS operating condition and the terminal voltage output after combining the dual models.
And 5, explaining the abnormal condition of the lithium battery in actual operation by using the FUDS working condition of 20 degrees. Appropriate resistors are connected in parallel at two ends of the lithium battery to simulate the abnormal condition of the lithium battery, actual operation data are collected, residual errors are generated between the measured voltage of actual operation and the output voltage based on the dual model, and the residual errors are accumulated and evaluated. The specific process is as follows:
firstly, two ends of a lithium battery are respectively connected with 10 ohms, 30 ohms and 5000 ohms in parallel, the trigger time is 4000 seconds, 3500 seconds and 2000 seconds, charging and discharging are carried out under the FUDS working condition at 20 degrees to obtain 3 samples, and the same working condition is used for acting on two models to obtain the output voltage of the models. The difference between the sample voltage and the model output voltage is the residual. The results are shown in FIG. 4. It can be seen that for the first two samples, the method accurately identifies the abnormal condition, and the parallel resistance of the 3 rd sample is very large, which can be basically regarded as the normal condition.
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 (7)
1. A lithium ion battery pack voltage abnormity fault diagnosis method based on dual models is characterized in that: the method comprises the following steps:
step 1: collecting battery charge and discharge data including current, voltage, temperature and SOC;
step 2: establishing a second-order equivalent circuit model, and identifying model parameters by using a least square method to obtain various parameters of models at different temperatures and SOC, wherein the parameters comprise open-circuit voltage, ohmic internal resistance, electrochemical polarization RC and concentration polarization RC;
and step 3: establishing LSTM and full connection layer Dense models, and training a network model by using collected normal lithium battery operation data;
and 4, step 4: the SOC obtained by LSTM prediction is respectively used for the output end voltages of a second-order equivalent circuit model and a full connection layer Dense model, and then a more accurate end voltage than before is obtained by combining the end voltages output by the two models;
and 5: and generating a residual error between the model terminal voltage and the actually operated terminal voltage, evaluating the residual error by using the CUSUM, and determining that a fault occurs if the residual error exceeds a threshold value.
2. The dual-model-based voltage abnormity fault diagnosis method of the lithium ion battery pack is characterized in that: in the step 2, model parameters are identified by a least square method, and a mathematical expression is as follows:
finally obtaining model parameters under different temperatures and SOC, and expressing the relationship of the model parameters as
X＝fX(SOC，Te)
X is a certain parameter of the model, SOC is the state of charge of the battery, and Te is the measured temperature.
3. The dual-model-based voltage abnormity fault diagnosis method of the lithium ion battery pack is characterized in that: in step 3, the LSTM network and the full connection layer sense both include an input layer, an output layer, and a hidden layer, where the input of the LSTM network is:
wherein k and n represent an input sample time window and a sample number, respectively, I represents a sample current, Ut represents a sample terminal voltage, and T represents a sample temperature;
the output of the LSTM network is the SOC of the next moment;
the LSTM network adjusts the hyper-parameters of the network according to the data volume, wherein the hyper-parameters comprise the number of hidden layer neurons, a time window k and the size of the batch size;
the input of the full connection layer Dense network is (I) at the current momentt，Tt，SOCt) Wherein, ItRepresenting the current at the present moment, TtIndicating the temperature, SOC at the present timetAnd representing the SOC at the current moment, wherein the output of the full connection layer Dense network is terminal voltage, and the full connection layer Dense network adjusts the hyper-parameters of the network according to the data volume, including the number of hidden layer neurons and the size of the batch size.
4. The dual-model-based voltage abnormity fault diagnosis method of the lithium ion battery pack is characterized in that: in step 4The equivalent circuit model ECM and the full connection layer Dense respectively obtain terminal voltage Ut which is respectively expressed as UtE，UtDCombining the two models to obtain more accurate Ut, wherein the specific mathematical expression is as follows:
SOC＝LSTM(I，Ut，T)；
UtD＝Dense(I，T，SOC)；
UtE＝ECM(I，T，SOC)；
5. the dual-model-based voltage abnormity fault diagnosis method of the lithium ion battery pack is characterized in that: in step 5, generating a residual error between the model terminal voltage and the actually-operated terminal voltage, evaluating the residual error by using CUSUM, if the residual error exceeds a threshold value, determining that a fault occurs, and taking accumulated and calculated points: x is the number ofi- (μ + k σ), wherein xiAnd taking mu as a target value, taking 0 as the difference between the model terminal voltage and the actual operating terminal voltage, taking h sigma as a threshold value, and taking k and h as reference values and decision values respectively, wherein the difference is selected according to the operating length.
6. The dual-model-based voltage abnormity fault diagnosis method of the lithium ion battery pack is characterized in that: the period T of voltage, current, SOC and temperature is 1 second when the experiment collects the battery data.
7. The dual-model-based voltage abnormity fault diagnosis method of the lithium ion battery pack is characterized in that: the least square method identified parameter with forgetting factor is (1-a)1-a2)Uoc(k)，a1，a2，a3，a4，a5
The parameters of the model obtained by the final conversion are:
τ1＝R1C1，τ2＝R2C2。
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114994543A (en) * | 2022-08-01 | 2022-09-02 | 湖南华大电工高科技有限公司 | Energy storage power station battery fault diagnosis method and device and storage medium |
| CN116068479A (en) * | 2023-03-07 | 2023-05-05 | 潍柴动力股份有限公司 | Abnormality detection method and device for output performance signal in fuel cell endurance test |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107390138A (en) * | 2017-09-13 | 2017-11-24 | 山东大学 | Electrokinetic cell equivalent circuit model parameter iteration new method for identifying |
| CN110208704A (en) * | 2019-04-29 | 2019-09-06 | 北京航空航天大学 | A kind of lithium battery modeling method and system based on voltage delay effect |
| CN110568360A (en) * | 2019-09-12 | 2019-12-13 | 华中科技大学 | lithium battery aging diagnosis method based on fuzzy logic algorithm |
| CN111220921A (en) * | 2020-01-08 | 2020-06-02 | 重庆邮电大学 | Lithium battery capacity estimation method based on improved convolution-long-and-short-term memory neural network |
| CN111999657A (en) * | 2020-10-29 | 2020-11-27 | 北京航空航天大学 | Method for evaluating driving mileage of lithium ion battery of electric vehicle in residual life |
| CN112285569A (en) * | 2020-10-29 | 2021-01-29 | 哈尔滨工业大学（威海） | Electric vehicle fault diagnosis method based on dynamic threshold model |
| CN112946480A (en) * | 2021-01-28 | 2021-06-11 | 中国矿业大学 | Lithium battery circuit model simplification method for improving SOC estimation real-time performance |
| CN113049962A (en) * | 2021-03-24 | 2021-06-29 | 国网浙江省电力有限公司电力科学研究院 | LSTM-based energy storage device operation situation deduction method |
-
2021
- 2021-08-23 CN CN202110993005.9A patent/CN113687251A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107390138A (en) * | 2017-09-13 | 2017-11-24 | 山东大学 | Electrokinetic cell equivalent circuit model parameter iteration new method for identifying |
| CN110208704A (en) * | 2019-04-29 | 2019-09-06 | 北京航空航天大学 | A kind of lithium battery modeling method and system based on voltage delay effect |
| CN110568360A (en) * | 2019-09-12 | 2019-12-13 | 华中科技大学 | lithium battery aging diagnosis method based on fuzzy logic algorithm |
| CN111220921A (en) * | 2020-01-08 | 2020-06-02 | 重庆邮电大学 | Lithium battery capacity estimation method based on improved convolution-long-and-short-term memory neural network |
| CN111999657A (en) * | 2020-10-29 | 2020-11-27 | 北京航空航天大学 | Method for evaluating driving mileage of lithium ion battery of electric vehicle in residual life |
| CN112285569A (en) * | 2020-10-29 | 2021-01-29 | 哈尔滨工业大学（威海） | Electric vehicle fault diagnosis method based on dynamic threshold model |
| CN112946480A (en) * | 2021-01-28 | 2021-06-11 | 中国矿业大学 | Lithium battery circuit model simplification method for improving SOC estimation real-time performance |
| CN113049962A (en) * | 2021-03-24 | 2021-06-29 | 国网浙江省电力有限公司电力科学研究院 | LSTM-based energy storage device operation situation deduction method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114994543A (en) * | 2022-08-01 | 2022-09-02 | 湖南华大电工高科技有限公司 | Energy storage power station battery fault diagnosis method and device and storage medium |
| CN116068479A (en) * | 2023-03-07 | 2023-05-05 | 潍柴动力股份有限公司 | Abnormality detection method and device for output performance signal in fuel cell endurance test |
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