CN118117214A - Liquid-air mixed heat dissipation power control method and system for energy storage battery pack - Google Patents
Liquid-air mixed heat dissipation power control method and system for energy storage battery pack Download PDFInfo
- Publication number
- CN118117214A CN118117214A CN202410516498.0A CN202410516498A CN118117214A CN 118117214 A CN118117214 A CN 118117214A CN 202410516498 A CN202410516498 A CN 202410516498A CN 118117214 A CN118117214 A CN 118117214A
- Authority
- CN
- China
- Prior art keywords
- energy storage
- storage battery
- temperature
- battery pack
- sequence information
- 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
- 238000004146 energy storage Methods 0.000 title claims abstract description 230
- 230000017525 heat dissipation Effects 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000001816 cooling Methods 0.000 claims abstract description 162
- 230000005856 abnormality Effects 0.000 claims abstract description 87
- 239000007788 liquid Substances 0.000 claims abstract description 78
- 238000012544 monitoring process Methods 0.000 claims abstract description 58
- 238000005457 optimization Methods 0.000 claims abstract description 44
- 238000005265 energy consumption Methods 0.000 claims abstract description 33
- 238000004458 analytical method Methods 0.000 claims abstract description 26
- 238000005520 cutting process Methods 0.000 claims description 33
- 230000007613 environmental effect Effects 0.000 claims description 21
- 238000011156 evaluation Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 7
- 238000012549 training Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 239000002826 coolant Substances 0.000 description 1
- 239000000110 cooling liquid Substances 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Secondary Cells (AREA)
Abstract
The invention discloses a liquid-air mixed heat dissipation power control method and a system for an energy storage battery pack, which relate to the technical field of batteries, and the method comprises the following steps: performing anomaly analysis according to the monitoring temperature time sequence information and the expected temperature time sequence information to generate a first anomaly index; when the first abnormality index is greater than or equal to the abnormality index threshold, temperature prediction is performed, deviation analysis is performed by combining the expected temperature time sequence information, and a second abnormality index is generated; and when the second abnormality index is greater than or equal to the abnormality index threshold, performing energy consumption balance optimization on the air cooling control parameter and the liquid cooling control parameter, and executing liquid-air mixed heat dissipation control according to the optimized control parameter. The invention solves the technical problems that the traditional heat dissipation control of the energy storage battery pack is judged based on a single threshold value and has certain hysteresis in the prior art, and achieves the technical effects of carrying out energy consumption balance optimization and improving the timeliness of the heat dissipation control of the energy storage battery pack through temperature monitoring and prediction.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a liquid-air mixed heat dissipation power control method and system for an energy storage battery pack.
Background
The heat dissipation control of the energy storage battery pack is a key link in the battery technology, and particularly when the energy storage battery pack is applied to power batteries, energy storage batteries and container type energy storage systems on a large scale, the importance of the energy storage battery pack is more remarkable. The essence is that the heat in the battery is transferred to the external environment through the cooling medium, so that the aim of reducing the internal temperature of the battery is fulfilled. The aim of this technique is to provide a comfortable operating temperature environment for the battery, ensuring that the battery operates in an optimal operating condition.
However, in the conventional heat dissipation control of the energy storage battery pack, when the temperature of the energy storage battery pack is higher than the temperature threshold value, liquid cooling and air cooling are activated to perform heat dissipation control, so that certain hysteresis exists.
Disclosure of Invention
The application provides a liquid-air mixed heat dissipation power control method and a liquid-air mixed heat dissipation power control system for an energy storage battery pack, which are used for solving the technical problem that the traditional heat dissipation control of the energy storage battery pack in the prior art is judged based on a single threshold value and has certain hysteresis.
In a first aspect of the present application, there is provided a liquid-air hybrid heat dissipation power control method for an energy storage battery pack, the method comprising: receiving energy storage battery pack control parameters uploaded from an energy storage battery pack control end, monitoring temperature time sequence information of the energy storage battery pack uploaded from an inner temperature monitor, and monitoring environment temperature time sequence information uploaded from an outer temperature monitor; receiving an air cooling control parameter and a liquid cooling control parameter uploaded from the energy storage battery pack control end; performing anomaly analysis according to the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a first anomaly index; when the first abnormality index is greater than or equal to an abnormality index threshold, performing energy storage battery pack temperature prediction according to the energy storage battery pack control parameter, the energy storage battery pack monitoring temperature time sequence information, the environment temperature monitoring time sequence information, the air cooling control parameter and the liquid cooling control parameter, and generating energy storage battery pack prediction temperature time sequence information; performing deviation analysis according to the predicted temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a second abnormality index; when the second abnormality index is greater than or equal to the abnormality index threshold, performing energy consumption balance optimization on the air cooling control parameter and the liquid cooling control parameter to generate an air cooling optimization control parameter and a liquid cooling optimization control parameter; and executing liquid-air mixed heat dissipation control according to the air cooling optimal control parameter and the liquid cooling optimal control parameter.
In a second aspect of the present application, there is provided a liquid-air hybrid heat dissipation power control system for an energy storage battery, the system comprising: the comprehensive data receiving module is used for receiving the control parameters of the energy storage battery pack uploaded from the control end of the energy storage battery pack, monitoring temperature time sequence information of the energy storage battery pack uploaded from the inner temperature monitor and monitoring environmental temperature time sequence information uploaded from the outer temperature monitor; the control parameter receiving module is used for receiving the air cooling control parameters and the liquid cooling control parameters uploaded from the energy storage battery pack control end; the first abnormality index generation module is used for carrying out abnormality analysis according to the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a first abnormality index; the predicted temperature time sequence information generation module is used for predicting the temperature of the energy storage battery pack according to the control parameter of the energy storage battery pack, the monitoring temperature time sequence information of the energy storage battery pack, the environment temperature monitoring time sequence information, the air cooling control parameter and the liquid cooling control parameter when the first abnormality index is larger than or equal to an abnormality index threshold value, so as to generate predicted temperature time sequence information of the energy storage battery pack; the second abnormality index generation module is used for carrying out deviation analysis according to the predicted temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a second abnormality index; the energy consumption balance optimization module is used for carrying out energy consumption balance optimization on the air cooling control parameter and the liquid cooling control parameter when the second abnormality index is greater than or equal to the abnormality index threshold value, so as to generate an air cooling optimization control parameter and a liquid cooling optimization control parameter; and the liquid-air mixed heat dissipation control module is used for executing liquid-air mixed heat dissipation control according to the air cooling optimal control parameter and the liquid cooling optimal control parameter.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The application provides a liquid-air hybrid heat dissipation power control method for an energy storage battery pack, which relates to the technical field of batteries, and comprises the steps of carrying out anomaly analysis by monitoring temperature time sequence information and expected temperature time sequence information to generate a first anomaly index, carrying out temperature prediction and deviation analysis when the first anomaly index does not meet an anomaly index threshold value to generate a second anomaly index, carrying out energy consumption balanced optimization on air cooling and liquid cooling control parameters when the second anomaly index does not meet an anomaly index threshold value, and carrying out liquid-air hybrid heat dissipation control according to the optimized control parameters.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the drawings needed in the description of the embodiments, which are merely examples of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for controlling liquid-air hybrid heat dissipation power of an energy storage battery pack according to an embodiment of the present application;
Fig. 2 is a schematic flow chart of generating a first abnormality index in a method for controlling liquid-air hybrid heat dissipation power of an energy storage battery pack according to an embodiment of the present application;
Fig. 3 is a schematic flow chart of generating an air cooling optimization control parameter and a liquid cooling optimization control parameter in the liquid-air mixing heat dissipation power control method for an energy storage battery pack according to the embodiment of the application;
Fig. 4 is a schematic structural diagram of a liquid-air hybrid heat dissipation power control system for an energy storage battery pack according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a comprehensive data receiving module 11, a control parameter receiving module 12, a first abnormality index generating module 13, a predicted temperature time sequence information generating module 14, a second abnormality index generating module 15, an energy consumption balancing optimizing module 16 and a liquid-air mixing heat dissipation control module 17.
Detailed Description
The application provides a liquid-air mixed heat dissipation power control method for an energy storage battery pack, which is used for solving the technical problem that the traditional heat dissipation control of the energy storage battery pack in the prior art is judged based on a single threshold value and has certain hysteresis.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, the terms "first," "second," and the like in the description of the present application and the above drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a liquid-wind hybrid heat dissipation power control method for an energy storage battery pack, the method comprising:
P10: receiving energy storage battery pack control parameters uploaded from an energy storage battery pack control end, monitoring temperature time sequence information of the energy storage battery pack uploaded from an inner temperature monitor, and monitoring environment temperature time sequence information uploaded from an outer temperature monitor;
It should be appreciated that receiving the energy storage battery pack control parameters uploaded from the energy storage battery pack control upload, including key information on the current operating state, power output, charge and discharge state, etc. of the battery pack may reflect the operating conditions of the battery pack and evaluate the heat dissipation requirements thereof. Meanwhile, the temperature time sequence information of the energy storage battery pack is received from the internal temperature monitor, the internal temperature monitor is usually arranged in the battery pack, the temperature change of the battery pack in the working process can be monitored in real time, and the temperature time sequence information can reflect the change condition of the internal temperature of the battery pack along with time. And, receive ambient temperature monitoring time sequence information from the external temperature monitor, the external temperature monitor is responsible for monitoring the external ambient temperature that the group battery is located, is used for knowing the heat dissipation condition, aassessment cooling system's performance and forecast group battery future temperature variation.
P20: receiving an air cooling control parameter and a liquid cooling control parameter uploaded from the energy storage battery pack control end;
Optionally, the air cooling control parameter and the liquid cooling control parameter uploaded from the energy storage battery pack control end are received. The air cooling control parameter and the liquid cooling control parameter are direct reflection of the current working state of the heat radiation system. The air cooling control parameters generally comprise the rotating speed, the wind direction, the working time and the like of the fan, and the heat dissipation effect of the air cooling system on the battery pack can be adjusted. The liquid cooling control parameters comprise flow, temperature, circulation speed and the like of the cooling liquid, and can directly influence the cooling effect of the liquid cooling system on the battery pack.
P30: performing anomaly analysis according to the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a first anomaly index;
further, as shown in fig. 2, step P30 of the embodiment of the present application further includes:
P31: cutting the expected temperature time sequence information of the energy storage battery pack from the beginning to the beginning according to the temperature time sequence information monitored by the energy storage battery pack, and generating the expected temperature time sequence information of a first sub energy storage battery pack;
P32: performing adjacent time domain temperature hierarchical clustering on the time sequence information of the expected temperature of the first sub energy storage battery pack to generate a first reference temperature sequence and a first reference time length sequence;
P33: performing adjacent time domain temperature hierarchical clustering on the monitoring temperature time sequence information of the energy storage battery pack to generate a first comparison temperature sequence and a first comparison time length sequence;
P34: and performing anomaly analysis on the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack according to the first reference temperature sequence, the first reference time length sequence, the first comparison temperature sequence and the first comparison time length sequence to generate the first anomaly index.
The abnormality analysis is performed according to the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack, and a first abnormality index is generated according to the difference between the monitoring temperature and the expected temperature, wherein the degree of deviation between the current temperature state and the expected state of the battery pack can be reflected, and the higher the index is, the larger the deviation is, and the greater the influence on the safety or performance of the battery pack is possibly caused.
Specifically, because the actually monitored temperature time sequence information and the expected temperature time sequence information may have differences in terms of time length, sampling frequency and the like, alignment cutting is needed first, consistency of two groups of data in a time dimension is ensured, and the expected temperature time sequence information of the first sub-energy storage battery pack, namely the expected temperature time sequence information after alignment cutting, is generated.
Further, adjacent time domain temperature hierarchical clustering is conducted on the expected temperature time sequence information of the first sub energy storage battery pack, the expected temperature time sequence information is divided into different temperature sequences and corresponding time length sequences according to temperature change characteristics of adjacent time domains, a first reference temperature sequence and a first reference time length sequence are generated, and each reference sequence represents a temperature range expected to be reached by the battery pack in a specific time period.
Further, adjacent time domain temperature hierarchical clustering is carried out on the monitoring temperature time sequence information of the energy storage battery pack, the monitored temperature data are divided into different temperature sequences and corresponding time length sequences, a first comparison temperature sequence and a first comparison time length sequence are generated, and the temperature change characteristics of the battery pack in the actual operation process are reflected.
Further, according to the first reference temperature sequence, the first reference time length sequence, the first comparison temperature sequence and the first comparison time length sequence, abnormality analysis is performed, and the degree of deviation between the current temperature state and the expected state of the battery pack is calculated in a quantification mode through comparing the first reference temperature sequence with the first comparison temperature sequence and the first reference time length sequence and the first comparison time length sequence, so that the first abnormality index is generated.
Further, step P34 of the embodiment of the present application further includes:
P34-1: aligning the head temperatures of the first reference temperature sequence and the first comparison temperature sequence, and performing Euclidean distance calculation on the first reference temperature sequence and the first comparison temperature sequence after zero padding alignment of the tail parts to generate a first distance evaluation value;
P34-2: aligning the temperature of the head part of the first reference time length sequence and the temperature of the head part of the first comparison time length sequence, and performing Euclidean distance calculation on the first reference time length sequence and the first comparison time length sequence after zero padding alignment of the tail part to generate a second distance evaluation value;
P34-3: and adding the first distance evaluation value and the second distance evaluation value to the first abnormality index.
Optionally, the first reference temperature sequence and the first comparison temperature sequence are subjected to head temperature alignment and tail zero padding alignment, so that the two sequences are ensured to be consistent in length, further, the first reference temperature sequence and the first comparison temperature sequence are subjected to euclidean distance calculation, the euclidean distance is a common similarity measurement method, and the similarity between the two vectors is reflected by calculating the linear distance of the two vectors in a multidimensional space. Here, the euclidean distance is used to quantify the difference between the desired temperature sequence and the actual monitored temperature sequence, resulting in a first distance evaluation value. The larger the first distance evaluation value, the larger the difference between the actual temperature and the desired temperature, the more likely the abnormality is in the temperature state of the battery pack.
Further, after the first reference time length sequence and the first comparison time length sequence are aligned in temperature at the head part and the tail part, the first reference time length sequence and the first comparison time length sequence are subjected to Euclidean distance calculation to obtain a second distance evaluation value, the time sequence reflects the stay time of the battery pack in different temperature intervals, and if the actual monitoring time length is obviously different from the expected time length, the temperature state of the battery pack is abnormal.
Further, the first distance evaluation value and the second distance evaluation value are used together as the first abnormality index, so that the abnormal condition of the temperature state of the battery pack can be comprehensively reflected.
P40: when the first abnormality index is greater than or equal to an abnormality index threshold, performing energy storage battery pack temperature prediction according to the energy storage battery pack control parameter, the energy storage battery pack monitoring temperature time sequence information, the environment temperature monitoring time sequence information, the air cooling control parameter and the liquid cooling control parameter, and generating energy storage battery pack prediction temperature time sequence information;
further, step P40 of the embodiment of the present application further includes:
p41: taking the model of the energy storage battery pack as constraint, and collecting environmental temperature recording time sequence information, energy storage battery pack recording temperature time sequence information, energy storage battery pack recording control parameters, air cooling recording control parameters and liquid cooling recording control parameters;
P42: performing adjacent time domain temperature hierarchical clustering on the environmental temperature recording time sequence information to obtain environmental temperature updating time sequence information;
P43: randomly cutting the recorded temperature time sequence information of the energy storage battery packs to obtain recorded temperature time sequence information of a plurality of front-stage energy storage battery packs and recorded temperature time sequence information of a plurality of rear-stage energy storage battery packs;
p44: recording temperature time sequence information according to the plurality of front-stage energy storage battery packs, and matching a plurality of environment temperature associated time sequence information with the same time sequence from the environment temperature updating time sequence information;
P45: taking the environmental temperature related time sequence information, the front energy storage battery pack recording temperature time sequence information, the energy storage battery pack recording control parameter, the air cooling recording control parameter and the liquid cooling recording control parameter as inputs, and taking the rear energy storage battery pack recording temperature time sequence information as supervision to train an energy storage battery pack temperature prediction assembly;
P46: and according to the energy storage battery pack temperature prediction component, predicting the energy storage battery pack temperature according to the energy storage battery pack control parameter, the energy storage battery pack monitoring temperature time sequence information, the environment temperature monitoring time sequence information, the air cooling control parameter and the liquid cooling control parameter, and generating the energy storage battery pack predicted temperature time sequence information.
Specifically, when the first abnormality index is greater than or equal to the abnormality index threshold, which means that there may be a problem or impending problem in the temperature state of the energy storage battery pack, in order to take measures in time to prevent further deterioration of the problem, temperature prediction is required.
Firstly, before starting temperature prediction, the model of the energy storage battery pack is required to be used as constraint, and related historical data including environmental temperature recording time sequence information, energy storage battery pack recording temperature time sequence information, energy storage battery pack recording control parameters, air cooling recording control parameters and liquid cooling recording control parameters are acquired to acquire temperature performance and corresponding control strategies of the battery pack under different environmental conditions.
Further, adjacent time domain temperature hierarchical clustering is carried out on the collected environmental temperature recording time sequence information, main characteristics of environmental temperature in different time periods are identified through cluster analysis, updated environmental temperature time sequence information is generated accordingly, noise data are filtered, and temperature prediction accuracy is improved.
Further, the temperature time sequence information recorded by the energy storage battery pack is randomly cut, a plurality of front-section energy storage battery pack temperature time sequence information recorded by the energy storage battery pack and a plurality of rear-section energy storage battery pack temperature time sequence information recorded by the energy storage battery pack are obtained, wherein the operation of random cutting can ensure that each cutting point is randomly selected, so that the obtained front-section time sequence information and rear-section time sequence information have diversity, and more possible temperature change modes can be covered.
Further, according to the temperature time sequence information recorded by the plurality of front-stage energy storage battery packs, corresponding environment temperature association time sequence information with the same time sequence is matched from the environment temperature update time sequence information so as to establish association between the environment temperature and the battery pack temperature, and necessary input data is provided for subsequent model training.
Further, using the matched environmental temperature associated time sequence information, the temperature time sequence information recorded by the front-stage energy storage battery packs, the energy storage battery pack recording control parameters, the air cooling recording control parameters and the liquid cooling recording control parameters as inputs, using the corresponding temperature time sequence information recorded by the rear-stage energy storage battery packs as supervision signals, and training to obtain the temperature prediction assembly of the energy storage battery packs based on a model of a machine learning or deep learning algorithm.
Further, the trained temperature prediction component of the energy storage battery pack is utilized to conduct temperature prediction according to the current control parameters of the energy storage battery pack, the monitoring temperature time sequence information, the environment temperature monitoring time sequence information, the air cooling control parameters and the liquid cooling control parameters, the predicted temperature time sequence information of the energy storage battery pack is obtained, and the predicted temperature time sequence information of the energy storage battery pack can reflect the temperature change trend of the battery pack in a future period of time.
Further, step P43 of the embodiment of the present application further includes:
P43-1: configuring an input data step length constraint and an output data step length constraint;
P43-2: performing self-head alignment on the recorded temperature time sequence information of the energy storage battery pack according to the input data step length constraint to obtain a cutting start point moment close to the tail;
P43-3: performing tail alignment on the recorded temperature time sequence information of the energy storage battery pack according to the constraint of the output data step length to obtain a cutting end point moment close to the head;
P43-4: and randomly cutting the recorded temperature time sequence information of the energy storage battery packs according to the cutting start point moment and the cutting end point moment to obtain the recorded temperature time sequence information of the plurality of front-stage energy storage battery packs and the recorded temperature time sequence information of the plurality of rear-stage energy storage battery packs.
It should be appreciated that the input data step size constraint and the output data step size constraint are first configured before starting the cutting operation. The input data step length constraint refers to the length requirement of the front-stage temperature time sequence information for model training, and the output data step length constraint refers to the length requirement of the corresponding back-stage temperature time sequence information. And carrying out initial alignment on the recorded temperature time sequence information of the energy storage battery pack according to the input data step length constraint, starting from the starting point of the time sequence information, moving backwards according to the length of the input data step length constraint, finding out the cutting starting point moment, and ensuring that the cutting starting point moment is close to the tail part of the time sequence information due to the step length constraint, so that the obtained front-stage temperature time sequence information has enough length for model training.
And further, performing self-tail alignment operation on the recorded temperature time sequence information of the energy storage battery pack according to the step length constraint of the output data. Starting from the end point of the time sequence information, moving forward according to the length of the constraint of the step length of the output data, and finding the cutting end point moment. The end point moment is close to the head of the time sequence information, so that the cut back-stage temperature time sequence information is ensured to have proper length and corresponds to the front-stage temperature time sequence information in time.
After determining the cutting start time and the cutting end time, randomly cutting the recorded temperature time sequence information of the energy storage battery packs to obtain the recorded temperature time sequence information of the plurality of front-stage energy storage battery packs and the recorded temperature time sequence information of the plurality of rear-stage energy storage battery packs, and training the model subsequently.
P50: performing deviation analysis according to the predicted temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a second abnormality index;
In one possible embodiment of the present application, the predicted temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack are compared one by one, a deviation between the two is calculated, for example, an absolute deviation, a relative deviation, a mean square error, etc. are calculated, and based on the result of the deviation analysis, a second abnormality index is generated by calculating a deviation average value or a weighting process, etc. to reflect the degree of deviation between the predicted temperature and the expected temperature.
P60: when the second abnormality index is greater than or equal to the abnormality index threshold, performing energy consumption balance optimization on the air cooling control parameter and the liquid cooling control parameter to generate an air cooling optimization control parameter and a liquid cooling optimization control parameter;
Further, as shown in fig. 3, step P60 of the embodiment of the present application further includes:
p61: adjusting the air cooling control parameter and the liquid cooling control parameter to generate a first air cooling control adjustment parameter and a first liquid cooling control adjustment parameter;
P62: when the first air cooling control adjustment parameter and the second abnormality index of the first liquid cooling control adjustment parameter are smaller than the abnormality index threshold, adding the first air cooling control adjustment parameter and the first liquid cooling control adjustment parameter to a plurality of groups of heat dissipation control parameters;
p63: performing energy consumption identification on the plurality of groups of heat dissipation control parameters to generate a plurality of energy consumption calibration values;
P64: and extracting the heat dissipation control parameters of the minimum values of the energy consumption calibration values, and outputting the air cooling optimization control parameters and the liquid cooling optimization control parameters.
P70: and executing liquid-air mixed heat dissipation control according to the air cooling optimal control parameter and the liquid cooling optimal control parameter.
It should be understood that when the second abnormality index is greater than or equal to the abnormality index threshold, which means that the current heat dissipation strategy cannot effectively control the battery pack temperature within the safety range, energy consumption balance optimization needs to be performed on the air cooling control parameter and the liquid cooling control parameter to find a heat dissipation scheme capable of effectively reducing the temperature and the energy consumption.
Specifically, based on industry rules or experience, the existing air cooling control parameters and liquid cooling control parameters are initially adjusted to generate first air cooling control adjustment parameters and first liquid cooling control adjustment parameters, second abnormality index calculation is performed according to the first air cooling control adjustment parameters and the first liquid cooling control adjustment parameters, when the obtained second abnormality index is smaller than the abnormality index threshold, the initial adjustment is proved to have good effect, the temperature of the battery pack is effectively controlled, and then the first air cooling control adjustment parameters and the first liquid cooling control adjustment parameters are added to a plurality of groups of heat dissipation control parameters.
Further, by simulating heat dissipation, carrying out energy consumption identification on each group of parameters added into a plurality of groups of heat dissipation control parameters, generating a plurality of energy consumption calibration values, extracting heat dissipation control parameters with minimum energy consumption calibration values from the plurality of groups of heat dissipation control parameters, and outputting the heat dissipation control parameters as the air cooling optimization control parameters and the liquid cooling optimization control parameters. And executing liquid-air mixed heat dissipation control according to the air cooling optimal control parameters and the liquid cooling optimal control parameters so as to ensure that the energy consumption is reduced as much as possible while meeting the temperature control requirement.
Further, the embodiment of the present application further includes step P80:
P80: and when the first abnormality index is smaller than the abnormality index threshold or the second abnormality index is smaller than the abnormality index threshold, performing liquid-air mixed heat dissipation control according to the air cooling control parameter and the liquid cooling control parameter.
Specifically, when the first abnormality index is smaller than the abnormality index threshold, it is indicated that the actual temperature of the battery pack is already within the safety range under the current heat dissipation control strategy, and the deviation is smaller, so that optimization is not needed. At this time, the liquid-air hybrid heat dissipation control can be directly performed according to the current air cooling control parameter and the liquid cooling control parameter, so as to maintain the temperature state of the battery pack.
And when the second abnormality index is smaller than the abnormality index threshold, the heat dissipation control parameter after optimization can effectively control the temperature of the battery pack, and the prediction result shows that the temperature deviation is smaller. In this case, the liquid-air hybrid heat radiation control may also be performed in accordance with the optimized air cooling control parameter and liquid cooling control parameter.
In summary, the embodiment of the application has at least the following technical effects:
According to the application, the temperature time sequence information and the expected temperature time sequence information are monitored to perform anomaly analysis to generate a first anomaly index, when the first anomaly index is greater than or equal to an anomaly index threshold value, temperature prediction and deviation analysis are performed to generate a second anomaly index, and when the second anomaly index is greater than or equal to the anomaly index threshold value, energy consumption balance optimization is performed on air cooling and liquid cooling control parameters, and liquid-air mixing heat dissipation control is performed according to the optimized control parameters.
The technical effects of performing energy consumption balance optimization and improving timeliness of heat dissipation control of the energy storage battery pack through temperature monitoring and prediction are achieved.
Example two
Based on the same inventive concept as the liquid-air hybrid heat dissipation power control method for the energy storage battery pack in the foregoing embodiment, as shown in fig. 4, the present application provides a liquid-air hybrid heat dissipation power control system for the energy storage battery pack, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
The comprehensive data receiving module 11 is used for receiving the control parameters of the energy storage battery pack uploaded from the control end of the energy storage battery pack, monitoring temperature time sequence information of the energy storage battery pack uploaded from the inner temperature monitor and monitoring environmental temperature time sequence information uploaded from the outer temperature monitor;
A control parameter receiving module 12, where the control parameter receiving module 12 is configured to receive an air cooling control parameter and a liquid cooling control parameter uploaded from the energy storage battery pack control end;
the first abnormality index generation module 13 is configured to perform abnormality analysis according to the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack, and generate a first abnormality index;
the predicted temperature timing information generating module 14, where the predicted temperature timing information generating module 14 is configured to predict a temperature of the energy storage battery pack according to the energy storage battery pack control parameter, the energy storage battery pack monitoring temperature timing information, the ambient temperature monitoring timing information, the air cooling control parameter, and the liquid cooling control parameter when the first abnormality index is greater than or equal to an abnormality index threshold, and generate predicted temperature timing information of the energy storage battery pack;
The second abnormality index generation module 15 is configured to perform deviation analysis according to the predicted temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack, so as to generate a second abnormality index;
the energy consumption balance optimization module 16, where the energy consumption balance optimization module 16 is configured to perform energy consumption balance optimization on the air cooling control parameter and the liquid cooling control parameter when the second abnormality index is greater than or equal to the abnormality index threshold value, so as to generate an air cooling optimization control parameter and a liquid cooling optimization control parameter;
The liquid-air mixed heat dissipation control module 17, wherein the liquid-air mixed heat dissipation control module 17 is used for executing liquid-air mixed heat dissipation control according to the air cooling optimal control parameter and the liquid cooling optimal control parameter.
Further, the first abnormality index generating module 13 is further configured to perform the following steps:
cutting the expected temperature time sequence information of the energy storage battery pack from the beginning to the beginning according to the temperature time sequence information monitored by the energy storage battery pack, and generating the expected temperature time sequence information of a first sub energy storage battery pack;
Performing adjacent time domain temperature hierarchical clustering on the time sequence information of the expected temperature of the first sub energy storage battery pack to generate a first reference temperature sequence and a first reference time length sequence;
Performing adjacent time domain temperature hierarchical clustering on the monitoring temperature time sequence information of the energy storage battery pack to generate a first comparison temperature sequence and a first comparison time length sequence;
And performing anomaly analysis on the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack according to the first reference temperature sequence, the first reference time length sequence, the first comparison temperature sequence and the first comparison time length sequence to generate the first anomaly index.
Further, the first abnormality index generating module 13 is further configured to perform the following steps:
aligning the head temperatures of the first reference temperature sequence and the first comparison temperature sequence, and performing Euclidean distance calculation on the first reference temperature sequence and the first comparison temperature sequence after zero padding alignment of the tail parts to generate a first distance evaluation value;
Aligning the temperature of the head part of the first reference time length sequence and the temperature of the head part of the first comparison time length sequence, and performing Euclidean distance calculation on the first reference time length sequence and the first comparison time length sequence after zero padding alignment of the tail part to generate a second distance evaluation value;
And adding the first distance evaluation value and the second distance evaluation value to the first abnormality index.
Further, the predicted temperature time sequence information generating module 14 is further configured to perform the following steps:
taking the model of the energy storage battery pack as constraint, and collecting environmental temperature recording time sequence information, energy storage battery pack recording temperature time sequence information, energy storage battery pack recording control parameters, air cooling recording control parameters and liquid cooling recording control parameters;
performing adjacent time domain temperature hierarchical clustering on the environmental temperature recording time sequence information to obtain environmental temperature updating time sequence information;
randomly cutting the recorded temperature time sequence information of the energy storage battery packs to obtain recorded temperature time sequence information of a plurality of front-stage energy storage battery packs and recorded temperature time sequence information of a plurality of rear-stage energy storage battery packs;
Recording temperature time sequence information according to the plurality of front-stage energy storage battery packs, and matching a plurality of environment temperature associated time sequence information with the same time sequence from the environment temperature updating time sequence information;
Taking the environmental temperature related time sequence information, the front energy storage battery pack recording temperature time sequence information, the energy storage battery pack recording control parameter, the air cooling recording control parameter and the liquid cooling recording control parameter as inputs, and taking the rear energy storage battery pack recording temperature time sequence information as supervision to train an energy storage battery pack temperature prediction assembly;
and according to the energy storage battery pack temperature prediction component, predicting the energy storage battery pack temperature according to the energy storage battery pack control parameter, the energy storage battery pack monitoring temperature time sequence information, the environment temperature monitoring time sequence information, the air cooling control parameter and the liquid cooling control parameter, and generating the energy storage battery pack predicted temperature time sequence information.
Further, the predicted temperature time sequence information generating module 14 is further configured to perform the following steps:
Configuring an input data step length constraint and an output data step length constraint;
performing self-head alignment on the recorded temperature time sequence information of the energy storage battery pack according to the input data step length constraint to obtain a cutting start point moment close to the tail;
performing tail alignment on the recorded temperature time sequence information of the energy storage battery pack according to the constraint of the output data step length to obtain a cutting end point moment close to the head;
And randomly cutting the recorded temperature time sequence information of the energy storage battery packs according to the cutting start point moment and the cutting end point moment to obtain the recorded temperature time sequence information of the plurality of front-stage energy storage battery packs and the recorded temperature time sequence information of the plurality of rear-stage energy storage battery packs.
Further, the energy consumption balancing optimization module 16 is further configured to perform the following steps:
Adjusting the air cooling control parameter and the liquid cooling control parameter to generate a first air cooling control adjustment parameter and a first liquid cooling control adjustment parameter;
When the first air cooling control adjustment parameter and the second abnormality index of the first liquid cooling control adjustment parameter are smaller than the abnormality index threshold, adding the first air cooling control adjustment parameter and the first liquid cooling control adjustment parameter to a plurality of groups of heat dissipation control parameters;
Performing energy consumption identification on the plurality of groups of heat dissipation control parameters to generate a plurality of energy consumption calibration values;
and extracting the heat dissipation control parameters of the minimum values of the energy consumption calibration values, and outputting the air cooling optimization control parameters and the liquid cooling optimization control parameters.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the application and its equivalents.
Claims (8)
1. The liquid-air hybrid heat dissipation power control method for the energy storage battery pack is characterized by being applied to a liquid-air hybrid heat dissipation power control system of the energy storage battery pack, wherein the system is in communication connection with an inner temperature monitor, an outer temperature monitor and an energy storage battery pack control end, and comprises the following steps:
receiving energy storage battery pack control parameters uploaded from an energy storage battery pack control end, monitoring temperature time sequence information of the energy storage battery pack uploaded from an inner temperature monitor, and monitoring environment temperature time sequence information uploaded from an outer temperature monitor;
Receiving an air cooling control parameter and a liquid cooling control parameter uploaded from the energy storage battery pack control end;
performing anomaly analysis according to the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a first anomaly index;
When the first abnormality index is greater than or equal to an abnormality index threshold, performing energy storage battery pack temperature prediction according to the energy storage battery pack control parameter, the energy storage battery pack monitoring temperature time sequence information, the environment temperature monitoring time sequence information, the air cooling control parameter and the liquid cooling control parameter, and generating energy storage battery pack prediction temperature time sequence information;
Performing deviation analysis according to the predicted temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a second abnormality index;
when the second abnormality index is greater than or equal to the abnormality index threshold, performing energy consumption balance optimization on the air cooling control parameter and the liquid cooling control parameter to generate an air cooling optimization control parameter and a liquid cooling optimization control parameter;
And executing liquid-air mixed heat dissipation control according to the air cooling optimal control parameter and the liquid cooling optimal control parameter.
2. The method as recited in claim 1, further comprising:
And when the first abnormality index is smaller than the abnormality index threshold or the second abnormality index is smaller than the abnormality index threshold, performing liquid-air mixed heat dissipation control according to the air cooling control parameter and the liquid cooling control parameter.
3. The method of claim 1, wherein performing anomaly analysis based on the energy storage battery monitoring temperature timing information and energy storage battery desired temperature timing information to generate a first anomaly index comprises:
cutting the expected temperature time sequence information of the energy storage battery pack from the beginning to the beginning according to the temperature time sequence information monitored by the energy storage battery pack, and generating the expected temperature time sequence information of a first sub energy storage battery pack;
Performing adjacent time domain temperature hierarchical clustering on the time sequence information of the expected temperature of the first sub energy storage battery pack to generate a first reference temperature sequence and a first reference time length sequence;
Performing adjacent time domain temperature hierarchical clustering on the monitoring temperature time sequence information of the energy storage battery pack to generate a first comparison temperature sequence and a first comparison time length sequence;
And performing anomaly analysis on the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack according to the first reference temperature sequence, the first reference time length sequence, the first comparison temperature sequence and the first comparison time length sequence to generate the first anomaly index.
4. The method of claim 3, wherein performing anomaly analysis on the energy storage battery monitoring temperature timing information and energy storage battery desired temperature timing information based on the first reference temperature sequence, the first reference time duration sequence, the first comparison temperature sequence, and the first comparison time duration sequence, generating the first anomaly index comprises:
aligning the head temperatures of the first reference temperature sequence and the first comparison temperature sequence, and performing Euclidean distance calculation on the first reference temperature sequence and the first comparison temperature sequence after zero padding alignment of the tail parts to generate a first distance evaluation value;
Aligning the temperature of the head part of the first reference time length sequence and the temperature of the head part of the first comparison time length sequence, and performing Euclidean distance calculation on the first reference time length sequence and the first comparison time length sequence after zero padding alignment of the tail part to generate a second distance evaluation value;
And adding the first distance evaluation value and the second distance evaluation value to the first abnormality index.
5. The method of claim 1, wherein performing energy storage battery pack temperature prediction based on the energy storage battery pack control parameter, the energy storage battery pack monitoring temperature timing information, the ambient temperature monitoring timing information, the air cooling control parameter, and the liquid cooling control parameter, generating energy storage battery pack predicted temperature timing information, comprising:
taking the model of the energy storage battery pack as constraint, and collecting environmental temperature recording time sequence information, energy storage battery pack recording temperature time sequence information, energy storage battery pack recording control parameters, air cooling recording control parameters and liquid cooling recording control parameters;
performing adjacent time domain temperature hierarchical clustering on the environmental temperature recording time sequence information to obtain environmental temperature updating time sequence information;
randomly cutting the recorded temperature time sequence information of the energy storage battery packs to obtain recorded temperature time sequence information of a plurality of front-stage energy storage battery packs and recorded temperature time sequence information of a plurality of rear-stage energy storage battery packs;
Recording temperature time sequence information according to the plurality of front-stage energy storage battery packs, and matching a plurality of environment temperature associated time sequence information with the same time sequence from the environment temperature updating time sequence information;
Taking the environmental temperature related time sequence information, the front energy storage battery pack recording temperature time sequence information, the energy storage battery pack recording control parameter, the air cooling recording control parameter and the liquid cooling recording control parameter as inputs, and taking the rear energy storage battery pack recording temperature time sequence information as supervision to train an energy storage battery pack temperature prediction assembly;
and according to the energy storage battery pack temperature prediction component, predicting the energy storage battery pack temperature according to the energy storage battery pack control parameter, the energy storage battery pack monitoring temperature time sequence information, the environment temperature monitoring time sequence information, the air cooling control parameter and the liquid cooling control parameter, and generating the energy storage battery pack predicted temperature time sequence information.
6. The method of claim 5, wherein randomly cutting the stored energy battery pack recorded temperature timing information to obtain a plurality of front stored energy battery pack recorded temperature timing information and a plurality of rear stored energy battery pack recorded temperature timing information, comprising:
Configuring an input data step length constraint and an output data step length constraint;
performing self-head alignment on the recorded temperature time sequence information of the energy storage battery pack according to the input data step length constraint to obtain a cutting start point moment close to the tail;
performing tail alignment on the recorded temperature time sequence information of the energy storage battery pack according to the constraint of the output data step length to obtain a cutting end point moment close to the head;
And randomly cutting the recorded temperature time sequence information of the energy storage battery packs according to the cutting start point moment and the cutting end point moment to obtain the recorded temperature time sequence information of the plurality of front-stage energy storage battery packs and the recorded temperature time sequence information of the plurality of rear-stage energy storage battery packs.
7. The method of claim 1, wherein when the second anomaly index is greater than or equal to the anomaly index threshold value, performing energy consumption equalization optimization on the air-cooling control parameter and the liquid-cooling control parameter to generate an air-cooling optimization control parameter and a liquid-cooling optimization control parameter, comprising:
Adjusting the air cooling control parameter and the liquid cooling control parameter to generate a first air cooling control adjustment parameter and a first liquid cooling control adjustment parameter;
When the first air cooling control adjustment parameter and the second abnormality index of the first liquid cooling control adjustment parameter are smaller than the abnormality index threshold, adding the first air cooling control adjustment parameter and the first liquid cooling control adjustment parameter to a plurality of groups of heat dissipation control parameters;
Performing energy consumption identification on the plurality of groups of heat dissipation control parameters to generate a plurality of energy consumption calibration values;
and extracting the heat dissipation control parameters of the minimum values of the energy consumption calibration values, and outputting the air cooling optimization control parameters and the liquid cooling optimization control parameters.
8. A liquid-air hybrid heat dissipation power control system for an energy storage battery, the system comprising:
the comprehensive data receiving module is used for receiving the control parameters of the energy storage battery pack uploaded from the control end of the energy storage battery pack, monitoring temperature time sequence information of the energy storage battery pack uploaded from the inner temperature monitor and monitoring environmental temperature time sequence information uploaded from the outer temperature monitor;
The control parameter receiving module is used for receiving the air cooling control parameters and the liquid cooling control parameters uploaded from the energy storage battery pack control end;
the first abnormality index generation module is used for carrying out abnormality analysis according to the monitoring temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a first abnormality index;
The predicted temperature time sequence information generation module is used for predicting the temperature of the energy storage battery pack according to the control parameter of the energy storage battery pack, the monitoring temperature time sequence information of the energy storage battery pack, the environment temperature monitoring time sequence information, the air cooling control parameter and the liquid cooling control parameter when the first abnormality index is larger than or equal to an abnormality index threshold value, so as to generate predicted temperature time sequence information of the energy storage battery pack;
The second abnormality index generation module is used for carrying out deviation analysis according to the predicted temperature time sequence information of the energy storage battery pack and the expected temperature time sequence information of the energy storage battery pack to generate a second abnormality index;
The energy consumption balance optimization module is used for carrying out energy consumption balance optimization on the air cooling control parameter and the liquid cooling control parameter when the second abnormality index is greater than or equal to the abnormality index threshold value, so as to generate an air cooling optimization control parameter and a liquid cooling optimization control parameter;
And the liquid-air mixed heat dissipation control module is used for executing liquid-air mixed heat dissipation control according to the air cooling optimal control parameter and the liquid cooling optimal control parameter.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410516498.0A CN118117214A (en) | 2024-04-28 | 2024-04-28 | Liquid-air mixed heat dissipation power control method and system for energy storage battery pack |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410516498.0A CN118117214A (en) | 2024-04-28 | 2024-04-28 | Liquid-air mixed heat dissipation power control method and system for energy storage battery pack |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118117214A true CN118117214A (en) | 2024-05-31 |
Family
ID=91220959
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410516498.0A Pending CN118117214A (en) | 2024-04-28 | 2024-04-28 | Liquid-air mixed heat dissipation power control method and system for energy storage battery pack |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118117214A (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103676998A (en) * | 2012-09-12 | 2014-03-26 | 英业达科技有限公司 | Temperature control system and temperature control method thereof |
| JP2016220310A (en) * | 2015-05-15 | 2016-12-22 | 日産自動車株式会社 | Battery temperature control apparatus and battery temperature control method |
| CN109818101A (en) * | 2017-11-20 | 2019-05-28 | 明创能源股份有限公司 | The heat management system of independent big electric energy electrical equipment |
| JP2019161791A (en) * | 2018-03-09 | 2019-09-19 | 株式会社日立製作所 | Secondary battery temperature control system and temperature control method |
| CN112519635A (en) * | 2019-09-17 | 2021-03-19 | 深圳市英维克科技股份有限公司 | Control method and related device for battery thermal management |
| CN112825106A (en) * | 2019-11-20 | 2021-05-21 | 维斯塔斯风力系统集团公司 | Model-based battery system monitoring |
| CN113128110A (en) * | 2021-04-12 | 2021-07-16 | 吉林大学 | Thermal management optimization method for power battery of intelligent network-connected electric automobile in alpine region |
| WO2022121298A1 (en) * | 2020-12-11 | 2022-06-16 | 青岛海尔空调器有限总公司 | Air conditioner alarm control method and apparatus, and electronic device and storage medium |
| CN115020875A (en) * | 2022-07-12 | 2022-09-06 | 吉林大学 | Optimization method for thermal management system of power battery of automatic driving electric automobile |
| CN115295927A (en) * | 2022-07-22 | 2022-11-04 | 清安储能技术(重庆)有限公司 | Temperature control method, device, equipment and storage medium of energy storage system |
| CN116505141A (en) * | 2023-06-14 | 2023-07-28 | 应雪汽车科技(常熟)有限公司 | Thermal management control system for energy storage battery and control method thereof |
| CN116776746A (en) * | 2023-08-18 | 2023-09-19 | 深圳市贝视特科技有限公司 | Energy storage liquid cooling temperature control optimizing system based on fluid dynamics |
| CN116826253A (en) * | 2023-08-10 | 2023-09-29 | 浙江兴创新能源有限公司 | Active thermal management method and system for prolonging service life of immersed liquid-cooled energy storage battery |
| CN116879788A (en) * | 2023-07-04 | 2023-10-13 | 广东鸿昊升能源科技有限公司 | Energy storage electric cabinet safety detection method, device, equipment and storage medium |
| CN116933666A (en) * | 2023-09-19 | 2023-10-24 | 深圳康普盾科技股份有限公司 | Thermal management optimization method, system and medium for container energy storage system |
| US20240111319A1 (en) * | 2020-11-17 | 2024-04-04 | Changchun Jetty Automotive Parts Corporation | Temperature control system for device and temperature control method |
| JP7467570B1 (en) * | 2022-11-04 | 2024-04-15 | 本田技研工業株式会社 | Battery temperature control method and battery temperature control system |
| CN117936993A (en) * | 2023-12-20 | 2024-04-26 | 浙江一舟电子科技股份有限公司 | Thermal management method and system applied to container energy storage system |
-
2024
- 2024-04-28 CN CN202410516498.0A patent/CN118117214A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103676998A (en) * | 2012-09-12 | 2014-03-26 | 英业达科技有限公司 | Temperature control system and temperature control method thereof |
| JP2016220310A (en) * | 2015-05-15 | 2016-12-22 | 日産自動車株式会社 | Battery temperature control apparatus and battery temperature control method |
| CN109818101A (en) * | 2017-11-20 | 2019-05-28 | 明创能源股份有限公司 | The heat management system of independent big electric energy electrical equipment |
| JP2019161791A (en) * | 2018-03-09 | 2019-09-19 | 株式会社日立製作所 | Secondary battery temperature control system and temperature control method |
| CN112519635A (en) * | 2019-09-17 | 2021-03-19 | 深圳市英维克科技股份有限公司 | Control method and related device for battery thermal management |
| CN112825106A (en) * | 2019-11-20 | 2021-05-21 | 维斯塔斯风力系统集团公司 | Model-based battery system monitoring |
| US20240111319A1 (en) * | 2020-11-17 | 2024-04-04 | Changchun Jetty Automotive Parts Corporation | Temperature control system for device and temperature control method |
| WO2022121298A1 (en) * | 2020-12-11 | 2022-06-16 | 青岛海尔空调器有限总公司 | Air conditioner alarm control method and apparatus, and electronic device and storage medium |
| CN113128110A (en) * | 2021-04-12 | 2021-07-16 | 吉林大学 | Thermal management optimization method for power battery of intelligent network-connected electric automobile in alpine region |
| CN115020875A (en) * | 2022-07-12 | 2022-09-06 | 吉林大学 | Optimization method for thermal management system of power battery of automatic driving electric automobile |
| CN115295927A (en) * | 2022-07-22 | 2022-11-04 | 清安储能技术(重庆)有限公司 | Temperature control method, device, equipment and storage medium of energy storage system |
| JP7467570B1 (en) * | 2022-11-04 | 2024-04-15 | 本田技研工業株式会社 | Battery temperature control method and battery temperature control system |
| CN116505141A (en) * | 2023-06-14 | 2023-07-28 | 应雪汽车科技(常熟)有限公司 | Thermal management control system for energy storage battery and control method thereof |
| CN116879788A (en) * | 2023-07-04 | 2023-10-13 | 广东鸿昊升能源科技有限公司 | Energy storage electric cabinet safety detection method, device, equipment and storage medium |
| CN116826253A (en) * | 2023-08-10 | 2023-09-29 | 浙江兴创新能源有限公司 | Active thermal management method and system for prolonging service life of immersed liquid-cooled energy storage battery |
| CN116776746A (en) * | 2023-08-18 | 2023-09-19 | 深圳市贝视特科技有限公司 | Energy storage liquid cooling temperature control optimizing system based on fluid dynamics |
| CN116933666A (en) * | 2023-09-19 | 2023-10-24 | 深圳康普盾科技股份有限公司 | Thermal management optimization method, system and medium for container energy storage system |
| CN117936993A (en) * | 2023-12-20 | 2024-04-26 | 浙江一舟电子科技股份有限公司 | Thermal management method and system applied to container energy storage system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102103006B1 (en) | Method and Apparatus for Operating Optimal of Equipment based on Machine Learning Model | |
| CN116991215A (en) | Causal learning-based data center foundation structure optimization method | |
| CN112632773B (en) | Reliability prediction method for wind turbine generator | |
| CN110874665B (en) | Control device and method for wind generating set | |
| CN116683061B (en) | Power battery thermal runaway prediction and suppression integrated system, method and storage medium | |
| CN111723925A (en) | Method, device, equipment and medium for fault diagnosis of on-road intelligent train air conditioning unit | |
| Liang et al. | Evaluation of battery modules state for electric vehicle using artificial neural network and experimental validation | |
| CN115130266A (en) | Method and system for thermal management control of a vehicle | |
| CN117317408A (en) | Battery and heat optimization management method based on big data and artificial intelligence | |
| CN110059938B (en) | Power distribution network planning method based on association rule driving | |
| CN113487086B (en) | Method, device, computer equipment and medium for predicting residual service life of equipment | |
| CN117080624B (en) | Battery heating control method and system | |
| CN118117214A (en) | Liquid-air mixed heat dissipation power control method and system for energy storage battery pack | |
| CN116780042A (en) | Battery temperature control method, device, system, equipment and storage medium | |
| CN113589682B (en) | Main transformer cooler switching method and system | |
| CN115935790A (en) | Temperature prediction method and device for motor rotor, storage medium and processor | |
| CN112801815B (en) | Power communication network fault early warning method based on federal learning | |
| CN115081308A (en) | Method for accurately predicting transient temperature field of electrically-driven gearbox by considering space-time correlation characteristic | |
| Branco et al. | A design methodology to employ digital twins for remaining useful lifetime prediction in electric vehicle batteries | |
| Yan et al. | Remaining Useful Life Interval Prediction for Complex System Based on BiGRU Optimized by Log-Norm | |
| CN117540330B (en) | Power distribution cabinet system based on self-learning function | |
| CN117277917B (en) | Temperature monitoring method and system for high-speed magnetic suspension permanent magnet motor | |
| US20240168093A1 (en) | Device and Method for Predicting Low Voltage Failure of Secondary Battery, and Battery Control System Comprising Same Device | |
| CN114650190B (en) | Energy-saving method, system, terminal equipment and storage medium for data center network | |
| Liu et al. | From Motor to Battery: A Digital Twin Model of Electric Vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |