CN115032542A - Hybrid model-based battery thermal runaway pre-judgment method for energy storage system - Google Patents
Hybrid model-based battery thermal runaway pre-judgment method for energy storage system Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The invention discloses a battery thermal runaway pre-judging method based on a hybrid model, which integrates a battery physical model and a deep learning artificial intelligence model to realize battery thermal runaway pre-judgment and alarm, estimates the internal temperature and SOC of a battery through a battery electric heating coupling model, combines the battery surface temperature, the battery voltage and the battery current measured by a battery sensor as the input of an LSTM, and accurately predicts the surface temperature and the internal temperature of the battery by using the hybrid model. According to the fault mechanism of thermal runaway, the occurrence of the thermal runaway is judged and the induction reason is determined by a threshold method, so that the accurate prediction of the thermal runaway of the battery is realized. The hybrid model method combines the thermal characteristic and the electrical characteristic of the battery, and simultaneously applies an artificial intelligence data driving method, thereby providing a new idea for a battery thermal runaway pre-judging and diagnosing method. The invention has simple fault judging process, strong practicability, quick response and better application prospect.
Description
Technical Field
The invention is suitable for application of a lithium ion battery in the field of energy storage systems, discloses a battery thermal runaway pre-judging method based on a hybrid model, and aims to reduce the occurrence of battery thermal runaway accidents in an energy storage system.
Background
The lithium ion battery has the advantages of high power density and energy density, long cycle life, low self-discharge rate, moderate price and the like, so that the lithium ion battery is widely applied to energy storage systems and electric automobiles, but along with large popularization, a series of accidents also frequently occur. Especially, accidents such as spontaneous combustion and explosion caused by thermal runaway can bring serious consequences.
Existing thermal runaway diagnostic methods can be divided into two broad categories, battery characteristic information-based and battery model-based methods. For the characteristic-based method, the voltage and temperature evolution in the thermal runaway process is mainly researched, and useful characteristic information such as voltage, temperature, impedance and the like in the early thermal runaway diagnosis process is found. Model-based methods require expertise in the physical and chemical equations of the battery, involve complex mathematical modeling and observer design, and cumbersome parameter adjustment processes.
Disclosure of Invention
The invention provides a method for prejudging battery thermal runaway based on a hybrid model, which combines a neural network and a battery model, designs an algorithm flow from a lithium ion battery thermal runaway mechanism and realizes the prediction judgment of the battery internal temperature and surface temperature abnormity.
A battery thermal runaway prejudgment method of an energy storage system based on a hybrid model combines a battery model and an LSTM neural network model to construct the following model:
a battery data acquisition model for acquiring battery related parameters;
for accurately estimating internal temperature T in A battery electrical thermal coupling model of a battery SOC;
the LSTM prediction model is used for obtaining a battery prediction internal temperature curve and a battery prediction surface temperature curve based on each moment;
and the thermal runaway pre-judging model is used for completing pre-judging to realize thermal runaway early warning.
Preferably, the first-order equivalent circuit model and the total parameter thermal model are combined to form a battery electric-thermal coupling model, and the SOC of the battery and the internal temperature of the battery are accurately estimated through the electric-thermal coupling model;
performing parameter identification on the first-order equivalent circuit model to identify an ideal voltage source Uoc and ohmic internal resistance R 0 Internal resistance to polarization R d And a polarization capacitor C d Carrying out SOC estimation by using an ampere-hour integration method; on the basis of the above, andthe total parameter thermal models are coupled, and ohmic internal resistance R is formed between the first-order equivalent circuit model and the total parameter thermal model 0 Internal polarization resistance R d And internal temperature T in Performing association;
firstly, calculating the SOC of the battery through the load current and the internal temperature of the battery;
secondly, determining ohmic internal resistance R according to the relationship among the SOC, the temperature and the internal resistance 0 Internal polarization resistance R d And calculating the heat generation amount of the battery according to the obtained resistance value;
the heat quantity Q of the lithium battery j And the ambient temperature T amb Calculating the internal temperature T of the lithium ion battery as input of the thermal model in (ii) a Then the internal temperature T is measured in The parameter is transmitted into a battery equivalent circuit model as a parameter, and a new SOC of the battery is calculated with the current I at the next moment to form a loop; realizes the real-time accurate estimation of the internal temperature T in And the battery SOC.
Preferably, the LSTM prediction model of the present invention compares the measured parameters voltage U, current I, SOC, and battery internal temperature T at each time in Battery surface temperature T amb The predicted internal temperature and the predicted surface temperature of the battery are used as an input matrix and an output matrix, and a predicted internal temperature curve and a predicted surface temperature curve of the battery based on each time are obtained.
Preferably, the thermal runaway prediction model of the invention combines with the temperature prediction model to provide a battery thermal runaway judgment process, and according to a predicted temperature curve graph obtained by the prediction model, the predicted temperature is compared with the actual measured temperature to obtain a prediction result of the battery thermal runaway and a cause of the battery thermal runaway, so that the prediction is completed to realize the thermal runaway early warning.
The invention provides a lithium battery thermal runaway pre-judging method based on a hybrid model, which adopts the hybrid model combining a battery electric thermal coupling model and an LSTM neural network, combines the thermal characteristics and the electrical characteristics of a battery, simultaneously applies a data driving method, provides a new idea for a battery thermal runaway pre-judging and diagnosing method, has simple fault judging process, strong practicability and quicker response compared with other methods, and can be widely applied to practical engineering by virtue of the advantages.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a battery thermal runaway pre-judging model;
FIG. 2 is a first order RC equivalent circuit model;
FIG. 3 is a lumped parameter thermal model;
FIG. 4 is an electrical-thermal coupling model;
FIG. 5 is an electrical measurement internal and surface temperature prediction model based on an LSTM neural network;
FIG. 6 is a schematic diagram of a thermal runaway prediction process;
FIG. 7 is data of an overcharge test;
FIG. 8 is a diagram showing the result of predicting the internal temperature of the battery according to example 1;
FIG. 9 is a diagram showing the result of predicting the external temperature of the battery according to example 1;
FIG. 10 is thermal shock experimental data;
FIG. 11 is a diagram showing the result of predicting the internal temperature of the battery in example 2;
fig. 12 is a schematic diagram showing the result of predicting the surface temperature of the battery in example 2.
Detailed Description
The technical solutions of the present invention are described in further detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
A battery thermal runaway prejudging method of an energy storage system based on a hybrid model is characterized in that a battery model is combined with an LSTM neural network model to construct the following model:
a battery data acquisition model for acquiring battery related parameters;
for accurately estimating internal temperature T in A battery electrical thermal coupling model of a battery SOC;
combining the first-order equivalent circuit model and the total parameter thermal model to form a battery electric-thermal coupling model, and accurately estimating the SOC of the battery and the internal temperature of the battery through the electric-thermal coupling model;
performing parameter identification on the first-order equivalent circuit model to identify an ideal voltage source Uoc and ohmic internal resistance R 0 Internal resistance to polarization R d And a polarization capacitor C d Carrying out SOC estimation by using an ampere-hour integration method; on the basis, the equivalent circuit is coupled with a lumped parameter thermal model through ohmic internal resistance R between a first-order equivalent circuit model and the lumped parameter thermal model 0 Internal resistance to polarization R d And internal temperature T in Performing association;
firstly, calculating the SOC of the battery through the load current and the internal temperature of the battery;
secondly, determining ohmic internal resistance R according to the relation among the SOC, the temperature and the internal resistance 0 Internal resistance to polarization R d And calculating the heat generation amount of the battery according to the obtained resistance value;
the heat quantity Q of the lithium battery j And the ambient temperature T amb Calculating the internal temperature T of the lithium ion battery as input of the thermal model in (ii) a Then the internal temperature T is measured in The parameter is transmitted into a battery equivalent circuit model as a parameter, and a new SOC of the battery is calculated with the current I at the next moment to form a loop; realizes the real-time accurate estimation of the internal temperature T in And the battery SOC.
The LSTM prediction model is used for obtaining a battery prediction internal temperature curve and a battery prediction surface temperature curve based on each moment;
the LSTM prediction model measures the parameter voltage U, the current I, SOC and the internal temperature T of the battery at each moment in Battery surface temperature T amb The predicted internal temperature and the predicted surface temperature of the battery are used as an input matrix and an output matrix, and a predicted internal temperature curve and a predicted surface temperature curve of the battery based on each time are obtained.
And the thermal runaway pre-judging model is used for completing pre-judging to realize thermal runaway early warning.
And (3) providing a battery thermal runaway judgment process by combining a temperature prediction model, comparing the predicted temperature with the actually-measured temperature according to a predicted temperature curve graph obtained by the prediction model to obtain a prediction result of the battery thermal runaway and a cause of the battery thermal runaway, and completing prediction to realize thermal runaway early warning.
A pre-judging method for thermal runaway of an energy storage system battery based on a hybrid model comprises four modules, namely a data acquisition module, a battery electric thermal coupling model, an LSTM prediction model and a pre-judging model, wherein input parameters of the LSTM are obtained through the data acquisition module and the battery electric thermal coupling model, the output of the LSTM prediction model is used as a judgment basis of the pre-judging module, and finally pre-judging and incentive judging of the thermal runaway of the battery are obtained in the pre-judging model.
The battery voltage, the battery current and the battery surface temperature are the most easily acquired data in the battery operation, can be used as key parameters in a battery electric heating model to further estimate the internal temperature and the SOC of the battery, and have correlation with required results T 'in and T' surf in a prediction model, so the correlation is used as the input of the model.
In the existing battery fault diagnosis model, a single battery model is mostly adopted and basically developed based on the electrical characteristics of the battery. The ideal battery diagnosis method should be researched based on the electrical characteristics and the thermal effect of the battery at the same time, so that the fault alarming time can be shortened, and the fault monitoring accuracy can be improved. An electrically and thermally coupled model of a battery is therefore used herein. And the function of accurately estimating Tin and SOC is realized in the pre-judging process.
The prediction model was selected from LSTM, which was the first proposed Recurrent Neural Network (RNN) by Hochreiter et al with memory and specialized processing of time series data. The LSTM not only solves the problem of 'information loss' of judging the output state only by current input data in the traditional BP network CNN algorithm, but also solves the defect that the traditional RNN is easy to fall into 'gradient disappearance' and 'gradient explosion' due to a long-term dependence mechanism when processing long-time sequence data. The prediction output by the model is used by a diagnostic module.
And obtaining the temperature rise relation between the interior of the battery and the surface of the battery according to different thermal runaway inducers in a pre-judging module based on the prediction results T 'in and T' surf, and accordingly, providing a thermal runaway diagnosis process, wherein omega i and omega s are diagnosis threshold values. The thermal runaway determination is to determine whether thermal runaway occurs or not by comparing the difference value γ in (t) with the threshold value ω i, and when the difference value is smaller than the threshold value, it can be determined that thermal failure does not exist in the battery, and when the difference value is larger than the threshold value, it can be determined that thermal runaway occurs in the battery and an early warning signal is generated, where time t is alarm time. And secondly, detecting a thermal runaway incentive, and determining an external incentive causing the thermal runaway by comparing the difference value gamma surf (t) with a threshold value omega s.
As shown in fig. 1, in the specific implementation of the thermal runaway prediction method, first, real-time parameters in the battery operation, including battery voltage U, battery current I, and battery surface temperature T, need to be collected surf Ambient temperature T amb The method is used as a basis for prejudgment, and after the prediction is input into the battery electric-thermal coupling model and the LSTM prediction model, a battery internal temperature prediction curve and a battery surface temperature prediction curve are obtained, the thermal runaway occurrence condition of the battery is obtained through the prejudgment model, and the alarm time t and the fault reason caused by the thermal runaway are obtained under the thermal runaway occurrence condition.
Specifically, the battery electrical-thermal coupling model is composed of an equivalent circuit model and a total heat model, the equivalent circuit model is shown in fig. 2, and the Thevenin model is adopted, so that the polarization effect in the battery charging and discharging process is considered, and the dynamic characteristics of the battery can be better described. The parameters in the equivalent circuit include the ideal voltage source U oc Ohmic internal resistance R 0 Internal resistance to polarization R d And a polarization capacitor C d And performing parameter estimation by using the correlation of the parameters with the SOC and the temperature, and performing real-time estimation of the SOC on the model. The lumped thermal model is shown in fig. 3, which simulates the relationship between the internal temperature of the battery and the external temperature, and aims to estimate the internal temperature of the battery as an auxiliary index for judging thermal runaway of the battery. In order to reduce the complexity of the model, only the radial thermal behavior with the boundary condition of convective heat transfer is considered, and the internal temperature of the battery is assumed to be uniform, and the internal heat generation quantity comes from the internal resistance of the battery. Coupling the equivalent circuit model with lumped thermal model, as shown in FIG. 4, combining the electrical model with parameter identification and thermal model with thermal calculation to obtain the final productBattery SOC and battery internal temperature T to each time in 。
After the battery parameter preparation is completed, the internal temperature and the surface temperature of the battery are predicted, and as shown in fig. 5, the battery voltage U, the battery current I, and the battery surface temperature T are calculated surf Battery SOC and battery internal temperature T in And as an input matrix, dividing the training data into training data and test data according to time, inputting the training data into a training model, training an LSTM neural network, inputting the test data to obtain prediction data, and iterating the result into the LSTM neural network. And finally, drawing the prediction result into a temperature prediction curve taking time as an abscissa.
And after a temperature prediction curve is obtained, entering a thermal runaway pre-judging module. FIG. 6 is a thermal runaway prediction flow; the decision process first calculates the difference between the actual temperature and the predicted temperature:
γ in (t)＝T in -T’ in (1)
γ surf (t)＝T surf -T’ surf (2)
wherein T is in Is the battery internal temperature, T' in Is the predicted internal temperature, T, of the battery surf Is a battery surface temperature, T' surf Is the predicted battery surface temperature.
Specifically, the first step of the process is the determination of the occurrence of thermal runaway. The thermal runaway determination is made by comparing the difference gamma in(t) And a threshold value omega i To determine if thermal runaway has occurred, when the difference gamma is in(t) Less than a threshold value omega i Then, it can be determined that the battery has no thermal failure, when the difference value is gamma in(t) Greater than a threshold value omega i And then, judging that the battery is out of control due to heat and generating an early warning signal, wherein the time t is the warning time.
The second step of the process is to detect the cause of thermal runaway by comparing the difference gamma surf(t) And a threshold value omega s To determine the external cause of thermal runaway. When the difference value gamma is surf(t) Greater than a threshold value omega s That is, it can be judged that the cause of thermal runaway is caused by thermal shock when the difference γ is surf(t) Less than a threshold value omega s It can be determined that the cause of thermal runaway is caused by overcharge and discharge.
The present invention will be further illustrated with reference to the following specific examples.
Example 1
The method is used for verifying the experimental data of thermal runaway caused by the overcharge of the battery, the APR18650 lithium ion battery is subjected to overcharge cycling, the highest surface temperature of the battery is in a rising trend along with the increase of the cycle number, the thermal runaway occurs during the 18 th overcharge, the experimental data is processed to obtain the voltage of the battery, the surface temperature and the internal temperature of the battery are shown in figure 7, the internal temperature of the battery sharply rises around 4600s, the surface temperature of the battery begins to rise after 4600s, and the duration of the thermal runaway is longer. Partial data records are shown in table 1:
TABLE 1 temperature mutation node neighborhood data
The battery operation data is input into the thermal runaway prediction model, and the prediction results are shown in fig. 8 and 9. Wherein fig. 8 is a result of predicting the internal temperature of the battery according to example 1, and fig. 9 is a result of predicting the external temperature of the battery according to example 1; according to the thermal runaway diagnosis process, firstly, the internal temperature is compared with the prediction result, and the difference gamma between the actual temperature and the prediction temperature is determined in 4600s in Exceeds a threshold value omega i It can be determined that thermal runaway has occurred in the battery at 4600s and an alarm is generated; then comparing the surface temperature with the predicted result, the difference gamma between the actual temperature and the predicted temperature surf Does not exceed the threshold value omega s And judging that the thermal runaway incentive is electric abuse caused by internal initiation, namely the electric abuse reason such as overcharge and overdischarge according to the diagnosis process.
The diagnosis result is as follows: and (4) generating thermal runaway, wherein the alarm time t is 4600s, and the cause of the thermal runaway is battery overcharge and discharge. The diagnosis result is identical with the experiment, and the effectiveness of the diagnosis method is verified.
Example 2
The method is used for verifying thermal runaway experimental data caused by thermal shock of the battery, and the lithium ion battery monomer is processed before the thermal shock experiment: and (3) using a cyclic charge-discharge instrument to enable the full-charge state to be achieved, discharging at the current rate of 0.5C to the end voltage of 3.0V at room temperature, standing for 60min, then charging at the current rate of 0.5C to the charging end voltage of 4.2V, converting to constant-voltage charging, stopping charging, and standing for 60 min. After the charge and discharge cycle is carried out according to QC/T743 standard, the experimental battery is placed in an adiabatic accelerated calorimeter for stable discharge, a heating device is heated to 150 ℃ at the speed of 2 ℃/min, then the heating is stopped, and experimental data are recorded. The battery is ignited and burnt in the later heating period, and the shell is broken. The experimental data are processed to obtain the voltage of the battery, the surface temperature and the internal temperature of the battery are shown in fig. 10, the internal temperature of the battery is rapidly increased about 4400s, the surface temperature of the battery starts to be increased after 4400s, the thermal runaway of the battery occurs, the combustion temperature reaches up to 900 ℃, the temperature is gradually reduced after the combustion is finished, and the duration time of the thermal runaway process is longer. Part of the experimental data is recorded as shown in table 2:
TABLE 2 temperature mutation node neighborhood data
The battery operation data is input into the thermal runaway prediction model, and the prediction results are shown in fig. 11 and 12. Wherein fig. 11 is a result of predicting the internal temperature of the battery of example 2, and fig. 12 is a result of predicting the surface temperature of the battery of example 2; according to the thermal runaway diagnosis process, the internal temperature is compared with the predicted result, and at 4440s, the difference gamma between the actual temperature and the predicted temperature in Exceeds a threshold value omega i It can be determined that thermal runaway of the battery occurred at 4440s and an alarm is generated; then comparing the surface temperature with the predicted result, the difference gamma between the actual temperature and the predicted temperature surf Greater than a threshold value omega s And judging the thermal runaway cause as thermal abuse caused by thermal shock according to the diagnosis process.
The diagnosis result is as follows: and (4) thermal runaway occurs, the alarm time t is 4440s, and the cause of the thermal runaway is external thermal shock. The diagnosis result is matched with the experiment, and the effectiveness of the proposed diagnosis method is verified.
Claims (4)
1. A method for pre-judging thermal runaway of an energy storage system battery based on a hybrid model is characterized in that a battery model is combined with an LSTM neural network model to construct the following model:
a battery data acquisition model for acquiring battery related parameters;
for accurately estimating internal temperature T in A battery electrical thermal coupling model of a battery SOC;
the LSTM prediction model is used for obtaining a battery prediction internal temperature curve and a battery prediction surface temperature curve based on each moment;
and the thermal runaway pre-judging model is used for completing pre-judging to realize thermal runaway early warning.
2. The hybrid model-based energy storage system battery thermal runaway prediction method of claim 1, wherein a first-order equivalent circuit model and a lumped parameter thermal model are combined to form a battery electrical thermal coupling model, and the battery SOC and the internal temperature of the battery are accurately estimated through the electrical thermal coupling model;
performing parameter identification on the first-order equivalent circuit model to identify an ideal voltage source Uoc and ohmic internal resistance R 0 Internal resistance to polarization R d And a polarization capacitor C d Carrying out SOC estimation by using an ampere-hour integration method; on the basis, the equivalent circuit is coupled with a lumped parameter thermal model through ohmic internal resistance R between a first-order equivalent circuit model and the lumped parameter thermal model 0 Internal polarization resistance R d And internal temperature T in Performing association;
firstly, calculating the SOC of the battery through the load current and the internal temperature of the battery;
secondly, determining ohmic internal resistance R according to the relation among the SOC, the temperature and the internal resistance 0 Internal polarization resistance R d And calculating the heat generation amount of the battery according to the obtained resistance value;
the heat quantity Q of the lithium battery j And ambient temperature T amb As input of the thermal model, the internal temperature T of the lithium ion battery is calculated in (ii) a Then the internal temperature T is measured in The parameter is transmitted into a battery equivalent circuit model as a parameter, and a new SOC of the battery is calculated with the current I at the next moment to form a loop; realizes the real-time accurate estimation of the internal temperature T in And the battery SOC.
3. The hybrid model-based thermal runaway prediction method for energy storage system batteries according to claim 2, wherein the LSTM prediction model is used for predicting the measured parameters at each moment, namely voltage U, current I, SOC and internal temperature T of the battery in Battery surface temperature T amb The predicted internal temperature and the predicted surface temperature of the battery are used as an input matrix and an output matrix, and a predicted internal temperature curve and a predicted surface temperature curve of the battery based on each time are obtained.
4. The hybrid model-based energy storage system battery thermal runaway anticipation method of claim 1, wherein the thermal runaway anticipation model provides a battery thermal runaway determination process in combination with a temperature prediction model, and according to a predicted temperature curve graph obtained by the prediction model, a predicted temperature is compared with an actual temperature to obtain a battery thermal runaway anticipation result and a battery thermal runaway inducement, so that anticipation is completed to realize thermal runaway early warning.
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| CN115267589A (en) * | 2022-09-26 | 2022-11-01 | 陕西汽车集团股份有限公司 | Multi-parameter joint diagnosis method for battery faults of electric vehicle |
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| CN115453367A (en) * | 2022-08-29 | 2022-12-09 | 中国人民解放军陆军工程大学 | Hybrid model-based battery thermal runaway pre-judgment method for energy storage system |
| CN115575830A (en) * | 2022-10-10 | 2023-01-06 | 中国标准化研究院 | Method and system for identifying inducement of thermal runaway of lithium battery |
| CN116295934A (en) * | 2023-05-18 | 2023-06-23 | 中国华能集团清洁能源技术研究院有限公司 | Battery temperature anomaly detection method and system based on dynamic threshold |
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| CN115453367A (en) * | 2022-08-29 | 2022-12-09 | 中国人民解放军陆军工程大学 | Hybrid model-based battery thermal runaway pre-judgment method for energy storage system |
| CN115267589A (en) * | 2022-09-26 | 2022-11-01 | 陕西汽车集团股份有限公司 | Multi-parameter joint diagnosis method for battery faults of electric vehicle |
| CN115267589B (en) * | 2022-09-26 | 2023-01-06 | 陕西汽车集团股份有限公司 | Multi-parameter joint diagnosis method for battery faults of electric vehicle |
| CN115575830A (en) * | 2022-10-10 | 2023-01-06 | 中国标准化研究院 | Method and system for identifying inducement of thermal runaway of lithium battery |
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| CN116295934A (en) * | 2023-05-18 | 2023-06-23 | 中国华能集团清洁能源技术研究院有限公司 | Battery temperature anomaly detection method and system based on dynamic threshold |
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| CN116500451A (en) * | 2023-05-29 | 2023-07-28 | 海南蓄能发电有限公司 | Online monitoring system for storage battery |
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