CN116470190A - Power battery pack heating method, system, computer and readable storage medium - Google Patents
Power battery pack heating method, system, computer and readable storage medium Download PDFInfo
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- 238000004891 communication Methods 0.000 claims description 25
- 238000012795 verification Methods 0.000 claims description 23
- 238000012545 processing Methods 0.000 claims description 14
- 238000001514 detection method Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 9
- 238000007499 fusion processing Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/60—Heating or cooling; Temperature control
- H01M10/63—Control systems
- H01M10/633—Control systems characterised by algorithms, flow charts, software details or the like
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/60—Heating or cooling; Temperature control
- H01M10/61—Types of temperature control
- H01M10/615—Heating or keeping warm
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/60—Heating or cooling; Temperature control
- H01M10/62—Heating or cooling; Temperature control specially adapted for specific applications
- H01M10/625—Vehicles
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/60—Heating or cooling; Temperature control
- H01M10/65—Means for temperature control structurally associated with the cells
- H01M10/657—Means for temperature control structurally associated with the cells by electric or electromagnetic means
- H01M10/6571—Resistive heaters
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- 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
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Abstract
The invention provides a power battery pack heating method, a system, a computer and a readable storage medium, wherein the method comprises the following steps: the PTC heating film is arranged in the power battery pack, and each battery cell in the power battery pack is respectively provided with a temperature sensor; acquiring historical temperature data and real-time temperature data acquired by a temperature sensor, and constructing a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm; and adjusting working parameters of the PTC heating film through the temperature prediction model so as to carry out self-adaptive heating on the power battery pack through the PTC heating film. Through the mode, the working parameters of the PTC heating film can be accurately adjusted through the temperature prediction model, and then the PTC heating film can correspondingly heat the power battery pack, so that the power battery pack is in an efficient working state continuously, and the service performance of the new energy automobile is correspondingly improved.
Description
Technical Field
The invention relates to the technical field of new energy automobiles, in particular to a power battery pack heating method, a system, a computer and a readable storage medium.
Background
Along with the progress of technology and the rapid development of productivity, the technology of new energy automobiles is mature, and is accepted by people gradually, so that the new energy automobiles are popularized in the daily life of people, and the life of people is greatly facilitated.
The power battery pack is a power source of the new energy automobile, the endurance mileage of the new energy automobile is directly affected by the good or bad working performance of the power battery pack, and particularly, when the existing power battery pack works in a low-temperature environment, the problems of reduced endurance, reduced charging efficiency, reduced safety performance and the like are caused by the characteristics of battery materials.
The prior art mostly adopts two ways to complete the heating of the power battery pack, one is to use an external heater, and the other is to use an internal heater, however, the external heater can generate a phenomenon of uneven heating. Meanwhile, the occupied space is large, the use cost is high, the problem of low heating speed of the internal heater is correspondingly solved, and meanwhile, the control difficulty is high, so that the service life and the safety of the battery are influenced.
Therefore, in order to overcome the shortcomings of the prior art, it is necessary to provide a power battery pack heating method with high heating efficiency and high safety.
Disclosure of Invention
Based on the foregoing, an object of the present invention is to provide a power battery pack heating method, system, computer and readable storage medium, so as to provide a power battery pack heating method with high heating efficiency and high safety.
An embodiment of the present invention provides a power battery pack heating method, including:
the PTC heating film is arranged in the power battery pack, and a temperature sensor is respectively arranged on each battery cell in the power battery pack, and the PTC heating film is in communication connection with the temperature sensor;
acquiring historical temperature data and real-time temperature data acquired by the temperature sensor, and constructing a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm;
and adjusting working parameters of the PTC heating film through the temperature prediction model so as to carry out self-adaptive heating on the power battery pack through the PTC heating film.
The beneficial effects of the invention are as follows: the PTC heating film is arranged in the power battery pack, and a temperature sensor is respectively arranged on each battery cell in the power battery pack, and the PTC heating film is in communication connection with the temperature sensor; further, acquiring historical temperature data and real-time temperature data acquired by the temperature sensor, and constructing a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm; and finally, the working parameters of the PTC heating film are adjusted only through the temperature prediction model, so that the power battery pack is adaptively heated through the PTC heating film. Through the mode, the working parameters of the PTC heating film can be accurately adjusted through the temperature prediction model, and then the PTC heating film can be used for correspondingly heating the power battery pack, so that the power battery pack is continuously in an efficient working state, the service performance of the new energy automobile is correspondingly improved, and the use experience of a user is improved.
Preferably, the step of constructing a corresponding temperature prediction model based on the preset algorithm according to the historical temperature data and the real-time temperature data includes:
extracting a target neural network and a predictive regression algorithm contained in the preset algorithm, and constructing a corresponding training set and a corresponding verification set according to the historical temperature data and the real-time temperature data based on a preset rule;
inputting the training set into the target neural network to optimize network parameters in the target neural network, and inputting the verification set into the predictive regression algorithm to verify the predictive accuracy of the predictive regression algorithm;
and constructing the temperature prediction model according to the optimized target neural network and the verified predictive regression algorithm.
Preferably, the step of constructing the corresponding training set and verification set according to the historical temperature data and the real-time temperature data based on the preset rule includes:
extracting a first data set from the historical temperature data according to a first preset weight, and extracting a second data set from the real-time temperature data according to a second preset weight;
performing fusion processing on the first data set and the second data set to generate a corresponding target data set;
preprocessing the target data set, and splitting the preprocessed target data set into the training set and the verification set according to a preset proportion.
Preferably, the step of inputting the training set into the target neural network to optimize network parameters in the target neural network includes:
respectively detecting a plurality of attribute values contained in the training set, and identifying a plurality of types of detection parameters contained in the training set according to the attribute values;
respectively constructing corresponding training subsets according to each type of detection parameters, and simultaneously inputting a plurality of training subsets into an encoder of the target neural network;
the encoder is adaptively trained through a number of the training subsets to optimize the network parameters in the encoder.
Preferably, the step of adaptively training the encoder through a plurality of the training subsets to optimize the network parameters in the encoder comprises:
inputting a plurality of training subsets into a first coding layer in the coder, and performing multitask learning processing on the plurality of training subsets through the first coding layer to generate a plurality of corresponding training codes;
inputting a plurality of training codes into a second coding layer in the coder, and carrying out serialization processing on the plurality of training codes through the second coding layer so as to generate a plurality of corresponding feature sequences, wherein each feature sequence comprises a plurality of training features;
optimizing the network parameters in the encoder by a number of the training features.
Preferably, the step of adaptively heating the power battery pack through the PTC heating film includes:
inputting the temperature prediction model into a preset PID controller, and establishing communication connection between the PID controller and the PTC heating film;
and the working parameters of the PTC heating film are regulated in real time through the PID controller, so that the self-adaptive heating of the power battery pack is completed.
Preferably, the method further comprises:
and establishing communication connection between the instrument panel and the PID controller as well as between the instrument panel and the PTC heating film, and displaying working parameters of the PTC heating film and working states of the PID controller in the instrument panel in real time.
A second aspect of an embodiment of the present invention provides a power battery pack heating system, the system including:
the communication module is used for arranging the PTC heating film in the power battery pack, and respectively arranging a temperature sensor on each battery cell in the power battery pack, wherein the PTC heating film is in communication connection with the temperature sensors;
the acquisition module is used for acquiring the historical temperature data and the real-time temperature data acquired by the temperature sensor and constructing a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm;
and the adjusting module is used for adjusting the working parameters of the PTC heating film through the temperature prediction model so as to carry out self-adaptive heating on the power battery pack through the PTC heating film.
In the above power battery pack heating system, the obtaining module is specifically configured to:
extracting a target neural network and a predictive regression algorithm contained in the preset algorithm, and constructing a corresponding training set and a corresponding verification set according to the historical temperature data and the real-time temperature data based on a preset rule;
inputting the training set into the target neural network to optimize network parameters in the target neural network, and inputting the verification set into the predictive regression algorithm to verify the predictive accuracy of the predictive regression algorithm;
and constructing the temperature prediction model according to the optimized target neural network and the verified predictive regression algorithm.
In the above power battery pack heating system, the obtaining module is further specifically configured to:
extracting a first data set from the historical temperature data according to a first preset weight, and extracting a second data set from the real-time temperature data according to a second preset weight;
performing fusion processing on the first data set and the second data set to generate a corresponding target data set;
preprocessing the target data set, and splitting the preprocessed target data set into the training set and the verification set according to a preset proportion.
In the above power battery pack heating system, the obtaining module is further specifically configured to:
respectively detecting a plurality of attribute values contained in the training set, and identifying a plurality of types of detection parameters contained in the training set according to the attribute values;
respectively constructing corresponding training subsets according to each type of detection parameters, and simultaneously inputting a plurality of training subsets into an encoder of the target neural network;
the encoder is adaptively trained through a number of the training subsets to optimize the network parameters in the encoder.
In the above power battery pack heating system, the obtaining module is further specifically configured to:
inputting a plurality of training subsets into a first coding layer in the coder, and performing multitask learning processing on the plurality of training subsets through the first coding layer to generate a plurality of corresponding training codes;
inputting a plurality of training codes into a second coding layer in the coder, and carrying out serialization processing on the plurality of training codes through the second coding layer so as to generate a plurality of corresponding feature sequences, wherein each feature sequence comprises a plurality of training features;
optimizing the network parameters in the encoder by a number of the training features.
In the above power battery pack heating system, the adjusting module is specifically configured to:
inputting the temperature prediction model into a preset PID controller, and establishing communication connection between the PID controller and the PTC heating film;
and the working parameters of the PTC heating film are regulated in real time through the PID controller, so that the self-adaptive heating of the power battery pack is completed.
Among the above-mentioned power battery package heating system, power battery package heating system still includes display module, display module specifically is used for:
and establishing communication connection between the instrument panel and the PID controller as well as between the instrument panel and the PTC heating film, and displaying working parameters of the PTC heating film and working states of the PID controller in the instrument panel in real time.
A third aspect of the embodiments of the present invention proposes a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the power battery pack heating method as described above when executing the computer program.
A fourth aspect of the embodiments of the present invention proposes a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a power battery pack heating method as described above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flowchart of a power battery pack heating method according to a first embodiment of the present invention;
fig. 2 is a block diagram of a power battery pack heating system according to a third embodiment of the present invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a power battery pack heating method according to a first embodiment of the present invention is shown, and the power battery pack heating method according to the present embodiment can enable the PTC heating film to perform adaptive heating on the power battery pack, so that the power battery pack can be continuously in an efficient working state, correspondingly improving the service performance of a new energy automobile, and improving the use experience of a user.
Specifically, the heating method for the power battery pack provided by the embodiment specifically includes the following steps:
step S10, a PTC heating film is arranged in a power battery pack, a temperature sensor is respectively arranged on each battery cell in the power battery pack, and the PTC heating film is in communication connection with the temperature sensor;
specifically, in this embodiment, it should be noted first that the heating method for a power battery pack provided in this embodiment can be applied to new energy electric vehicles of different vehicle types, and is used for adaptively heating a battery pack in the new energy electric vehicle, so as to improve the service performance of the new energy electric vehicle.
In addition, in the present embodiment, it should be noted that the heating method of the power battery pack provided in the present embodiment is implemented based on the existing PTC heating film, temperature sensor and deep machine learning algorithm, where it should be noted that the PTC heating film provided in the present embodiment is specifically installed inside the power battery pack, and preferably, the PTC heating film is attached to the power battery pack. Furthermore, in this embodiment, a thermistor, that is, the above temperature sensor, is further installed on each battery cell in the current power battery pack, so as to collect the temperature of each battery cell in real time. Specifically, the embodiment replaces a precise thermocouple through thermistor training, so that implementation cost can be effectively reduced, and subsequent mass production is facilitated.
In this regard, in this step, after the PTC heating film and the temperature sensor are previously set, the communication connection between the PTC heating film and the temperature sensor is further established, preferably, the communication connection between the PTC heating film and the temperature sensor is established through the CAN line. On the basis, the signal interaction between the PTC heating film and the temperature sensor can be realized.
Step S20, acquiring historical temperature data and real-time temperature data acquired by the temperature sensor, and constructing a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm;
further, in this step, it should be noted that, after the PTC heating film and the temperature sensor are respectively disposed in the above manner, the current temperature sensor may further collect the historical temperature data and the real-time temperature data generated by the current power battery pack, and specifically, both the historical temperature data and the real-time temperature data include actual values.
Based on the above, a required temperature prediction model can be correspondingly constructed based on the current historical temperature data and the real-time temperature data according to a preset algorithm, and specifically, the temperature prediction model exists in the vehicle-mounted controller so as to realize a corresponding prediction function.
And step S30, adjusting working parameters of the PTC heating film through the temperature prediction model so as to carry out self-adaptive heating on the power battery pack through the PTC heating film.
Finally, in this step, after the required temperature prediction model is obtained in the above step, the working parameters of the PTC heating film can be further adjusted by the temperature prediction model, so that the current power battery pack can be adaptively heated correspondingly by the PTC heating film. Specifically, the working voltage and the working current of the PTC heating film are specifically adjusted in this embodiment, so that the working state of the PTC heating film is correspondingly adjusted.
It should be noted that, the PTC heating film provided in this embodiment can rapidly, uniformly and accurately heat the power battery pack.
When the PTC heating film is used, the PTC heating film is arranged in the power battery pack, and a temperature sensor is respectively arranged on each battery cell in the power battery pack, and the PTC heating film is in communication connection with the temperature sensor; further, acquiring historical temperature data and real-time temperature data acquired by the temperature sensor, and constructing a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm; and finally, the working parameters of the PTC heating film are adjusted only through the temperature prediction model, so that the power battery pack is adaptively heated through the PTC heating film. Through the mode, the working parameters of the PTC heating film can be accurately adjusted through the temperature prediction model, and then the PTC heating film can be used for correspondingly heating the power battery pack, so that the power battery pack is continuously in an efficient working state, the service performance of the new energy automobile is correspondingly improved, and the use experience of a user is improved.
It should be noted that the foregoing implementation procedure is only for illustrating the feasibility of the present application, but this does not represent that the power battery pack heating method of the present application is only one implementation procedure, and may be incorporated into the feasible embodiment of the present application as long as the power battery pack heating method of the present application can be implemented.
In summary, the heating method for the power battery pack provided by the embodiment of the invention can enable the PTC heating film to perform self-adaptive heating on the power battery pack, so that the power battery pack can be in a high-efficiency working state continuously, the service performance of a new energy automobile is correspondingly improved, and the use experience of a user is improved.
The second embodiment of the present invention also provides a power battery pack heating method, which is different from the power battery pack heating method provided in the first embodiment in that:
specifically, in this embodiment, it should be noted that the step of constructing the corresponding temperature prediction model based on the historical temperature data and the real-time temperature data based on the preset algorithm includes:
extracting a target neural network and a predictive regression algorithm contained in the preset algorithm, and constructing a corresponding training set and a corresponding verification set according to the historical temperature data and the real-time temperature data based on a preset rule;
inputting the training set into the target neural network to optimize network parameters in the target neural network, and inputting the verification set into the predictive regression algorithm to verify the predictive accuracy of the predictive regression algorithm;
and constructing the temperature prediction model according to the optimized target neural network and the verified predictive regression algorithm.
Specifically, in this embodiment, it should be noted that, the preset algorithm provided in this embodiment is a deep machine learning algorithm, and specifically, the deep machine learning algorithm is a regression algorithm based on a neural network, where the inside of the algorithm includes the target neural network and the predictive regression algorithm, and further, in this embodiment, a required training set and a verification set are further constructed in the historical temperature data and the real-time temperature data according to a rule of 8:2.
Based on this, the embodiment further inputs the training set into the target neural network, so that the network parameters in the current target neural network can be correspondingly optimized, and correspondingly, the embodiment also inputs the verification set into the predictive regression algorithm, so that the prediction accuracy of the current predictive regression algorithm can be verified.
On the basis, the temperature prediction model can be further constructed according to the optimized target neural network and the verified predictive regression algorithm. Specifically, the target neural network provided in this embodiment is specifically configured as a neural network formed by an ERNIE-DPCNN model, where the ERNIE-DPCNN model is a composite model and has a powerful algorithm function, and further, the ERNIE-DPCNN model includes a convolution layer and a pooling layer, and the training set is sequentially input into the convolution layer and the pooling layer in this embodiment, so that network parameters of the convolution layer and the pooling layer in the current target neural network can be optimized in the training process.
In addition, the verification set is further input into the predictive regression algorithm, specifically, the predictive regression algorithm provided in this embodiment is specifically set to be SVM (Suppot Vertor Machine) algorithm, and the algorithm can predict the accuracy of the result output by the target neural network according to the verification set input in real time, so that the temperature prediction model can be effectively constructed only by inputting the ERNIE-DPCNN model and the SVM algorithm into the existing fully connected layer architecture at the same time.
Specifically, in this embodiment, it should be further noted that the step of constructing the corresponding training set and verification set based on the historical temperature data and the real-time temperature data based on the preset rule includes:
extracting a first data set from the historical temperature data according to a first preset weight, and extracting a second data set from the real-time temperature data according to a second preset weight;
performing fusion processing on the first data set and the second data set to generate a corresponding target data set;
preprocessing the target data set, and splitting the preprocessed target data set into the training set and the verification set according to a preset proportion.
In particular, in this embodiment, it should be further noted that, since the reference values of the historical temperature data and the real-time temperature data are different, the amount of data used in the training process is also different. Based on this, the embodiment extracts the first data set needed in the historical temperature data based on the first preset weight, and correspondingly, extracts the second data set in the real-time temperature data based on the second preset weight. Preferably, the importance of the real-time temperature data provided in this embodiment is higher than that of the historical temperature data, so the first preset weight may be set to 40%, and correspondingly, the second preset weight may be set to 60%.
Furthermore, in order to improve the training effectiveness, the embodiment further performs fusion processing on the current first data set and the second data set, so as to correspondingly generate a final target data set. Further, the current target data set is subjected to filtering and noise reduction processing, and the current target data set is further split into the training set and the verification set according to the proportion of 8:2.
In addition, in this embodiment, it should be noted that the step of inputting the training set into the target neural network to optimize the network parameters in the target neural network includes:
respectively detecting a plurality of attribute values contained in the training set, and identifying a plurality of types of detection parameters contained in the training set according to the attribute values;
respectively constructing corresponding training subsets according to each type of detection parameters, and simultaneously inputting a plurality of training subsets into an encoder of the target neural network;
the encoder is adaptively trained through a number of the training subsets to optimize the network parameters in the encoder.
In addition, in this embodiment, after the required training set is obtained in the above manner, since the current training set includes multiple types of parameters, such as the ambient temperature, the battery pack temperature, and the battery pack state, and since the training modes of each parameter are different, the training needs to be performed separately based on this.
It should be noted that, because the attribute values of each type of parameter are different, based on this, the embodiment can correspondingly detect the types of parameters by respectively detecting the types of attribute values included in the current training set, further, respectively construct corresponding training subsets according to each type of parameter, and on this basis, input the constructed training subsets into the encoder of the target neural network at the same time, so that the current encoder can be adaptively trained through the current training subsets, so as to optimize the network parameters in the current encoder.
In addition, in this embodiment, it should also be noted that the step of adaptively training the encoder through the plurality of training subsets to optimize the network parameters in the encoder includes:
inputting a plurality of training subsets into a first coding layer in the coder, and performing multitask learning processing on the plurality of training subsets through the first coding layer to generate a plurality of corresponding training codes;
inputting a plurality of training codes into a second coding layer in the coder, and carrying out serialization processing on the plurality of training codes through the second coding layer so as to generate a plurality of corresponding feature sequences, wherein each feature sequence comprises a plurality of training features;
optimizing the network parameters in the encoder by a number of the training features.
In addition, in this embodiment, it should be further noted that, after the required several training subsets are obtained in the above manner, the present embodiment further inputs the current training subset into the first coding layer and the second coding layer in the above encoder in sequence, where the level of the second coding layer is higher than that of the first coding layer, and specifically, the present embodiment performs the existing multi-task learning process on the current several training subsets through the current first coding layer, so that the current encoder can adapt to different processing tasks, that is, adapt to different working states of the power battery pack.
Further, after the generated plurality of training codes are obtained, the current training codes are correspondingly input into a second coding layer of the current encoder, and meanwhile, the current plurality of training codes are further subjected to serialization processing through a DTW algorithm in the second coding layer, so that a plurality of corresponding feature sequences can be generated, specifically, each feature sequence comprises a plurality of corresponding training features, more specifically, each training feature is a code which can be identified by the current encoder.
Based on this, the network parameters in the current encoder can be correspondingly optimized through the current several training features.
In this embodiment, it should be noted that the step of adaptively heating the power battery pack through the PTC heating film includes:
inputting the temperature prediction model into a preset PID controller, and establishing communication connection between the PID controller and the PTC heating film;
and the working parameters of the PTC heating film are regulated in real time through the PID controller, so that the self-adaptive heating of the power battery pack is completed.
In this embodiment, it is noted that, after the desired temperature prediction model is finally obtained in the above manner, the present embodiment further inputs the current temperature prediction model into a PID controller installed in advance in the vehicle interior, and a communication connection is established between the PID controller and the PTC heater.
Based on the method, under the processing of the temperature prediction model, the working parameters of the current PTC heating film can be correspondingly adjusted in real time through the PID controller, so that the self-adaptive heating of the current power battery pack can be correspondingly completed.
In this embodiment, it should be noted that, the method further includes:
and establishing communication connection between the instrument panel and the PID controller as well as between the instrument panel and the PTC heating film, and displaying working parameters of the PTC heating film and working states of the PID controller in the instrument panel in real time.
In this embodiment, it should be noted that, in order to enable the driver to observe the heating state of the PTC heating film and the control condition of the PID controller in real time, the present embodiment further establishes a communication connection between the dashboard inside the vehicle and the PID controller and the PTC heater, so that the operating parameters of the PTC heating film and the operating state of the PID controller can be displayed in real time in the dashboard correspondingly.
It should be noted that, for the sake of brevity, the method according to the second embodiment of the present invention, which implements the same principle and some of the technical effects as the first embodiment, is not mentioned here, and reference is made to the corresponding content provided by the first embodiment.
In summary, the heating method for the power battery pack provided by the embodiment of the invention can enable the PTC heating film to perform self-adaptive heating on the power battery pack, so that the power battery pack can be in a high-efficiency working state continuously, the service performance of a new energy automobile is correspondingly improved, and the use experience of a user is improved.
Referring to fig. 2, a power battery pack heating system according to a third embodiment of the present invention is shown, the system includes:
the communication module 12 is used for arranging the PTC heating film in the power battery pack, and respectively arranging a temperature sensor on each battery cell in the power battery pack, wherein the PTC heating film is in communication connection with the temperature sensors;
the acquiring module 22 is configured to acquire historical temperature data and real-time temperature data acquired by the temperature sensor, and construct a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm;
and the adjusting module 32 is used for adjusting the working parameters of the PTC heating film through the temperature prediction model so as to adaptively heat the power battery pack through the PTC heating film.
In the above power battery pack heating system, the obtaining module 22 is specifically configured to:
extracting a target neural network and a predictive regression algorithm contained in the preset algorithm, and constructing a corresponding training set and a corresponding verification set according to the historical temperature data and the real-time temperature data based on a preset rule;
inputting the training set into the target neural network to optimize network parameters in the target neural network, and inputting the verification set into the predictive regression algorithm to verify the predictive accuracy of the predictive regression algorithm;
and constructing the temperature prediction model according to the optimized target neural network and the verified predictive regression algorithm.
In the above power battery pack heating system, the obtaining module 22 is further specifically configured to:
extracting a first data set from the historical temperature data according to a first preset weight, and extracting a second data set from the real-time temperature data according to a second preset weight;
performing fusion processing on the first data set and the second data set to generate a corresponding target data set;
preprocessing the target data set, and splitting the preprocessed target data set into the training set and the verification set according to a preset proportion.
In the above power battery pack heating system, the obtaining module 22 is further specifically configured to:
respectively detecting a plurality of attribute values contained in the training set, and identifying a plurality of types of detection parameters contained in the training set according to the attribute values;
respectively constructing corresponding training subsets according to each type of detection parameters, and simultaneously inputting a plurality of training subsets into an encoder of the target neural network;
the encoder is adaptively trained through a number of the training subsets to optimize the network parameters in the encoder.
In the above power battery pack heating system, the obtaining module 22 is further specifically configured to:
inputting a plurality of training subsets into a first coding layer in the coder, and performing multitask learning processing on the plurality of training subsets through the first coding layer to generate a plurality of corresponding training codes;
inputting a plurality of training codes into a second coding layer in the coder, and carrying out serialization processing on the plurality of training codes through the second coding layer so as to generate a plurality of corresponding feature sequences, wherein each feature sequence comprises a plurality of training features;
optimizing the network parameters in the encoder by a number of the training features.
In the above power battery pack heating system, the adjusting module 32 is specifically configured to:
inputting the temperature prediction model into a preset PID controller, and establishing communication connection between the PID controller and the PTC heating film;
and the working parameters of the PTC heating film are regulated in real time through the PID controller, so that the self-adaptive heating of the power battery pack is completed.
Among them, in the above-mentioned power battery package heating system, the power battery package heating system still includes display module 42, display module 42 specifically is used for:
and establishing communication connection between the instrument panel and the PID controller as well as between the instrument panel and the PTC heating film, and displaying working parameters of the PTC heating film and working states of the PID controller in the instrument panel in real time.
A fourth embodiment of the present invention provides a computer including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the power battery pack heating method provided in the above embodiments when executing the computer program.
A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program when executed by a processor implements the power battery pack heating method provided by the above embodiments.
In summary, the method, the system, the computer and the readable storage medium for heating the power battery pack provided by the embodiment of the invention can enable the PTC heater to perform self-adaptive heating on the power battery pack, so that the power battery pack can be continuously in an efficient working state, the service performance of the new energy automobile is correspondingly improved, and the use experience of a user is improved.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A method of heating a power battery pack, the method comprising:
the PTC heating film is arranged in the power battery pack, and a temperature sensor is respectively arranged on each battery cell in the power battery pack, and the PTC heating film is in communication connection with the temperature sensor;
acquiring historical temperature data and real-time temperature data acquired by the temperature sensor, and constructing a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm;
and adjusting working parameters of the PTC heating film through the temperature prediction model so as to carry out self-adaptive heating on the power battery pack through the PTC heating film.
2. The power cell pack heating method according to claim 1, wherein: the step of constructing a corresponding temperature prediction model based on the preset algorithm according to the historical temperature data and the real-time temperature data comprises the following steps:
extracting a target neural network and a predictive regression algorithm contained in the preset algorithm, and constructing a corresponding training set and a corresponding verification set according to the historical temperature data and the real-time temperature data based on a preset rule;
inputting the training set into the target neural network to optimize network parameters in the target neural network, and inputting the verification set into the predictive regression algorithm to verify the predictive accuracy of the predictive regression algorithm;
and constructing the temperature prediction model according to the optimized target neural network and the verified predictive regression algorithm.
3. The power cell pack heating method according to claim 2, wherein: the step of constructing the corresponding training set and verification set based on the preset rule according to the historical temperature data and the real-time temperature data comprises the following steps:
extracting a first data set from the historical temperature data according to a first preset weight, and extracting a second data set from the real-time temperature data according to a second preset weight;
performing fusion processing on the first data set and the second data set to generate a corresponding target data set;
preprocessing the target data set, and splitting the preprocessed target data set into the training set and the verification set according to a preset proportion.
4. The power cell pack heating method according to claim 2, wherein: the step of inputting the training set into the target neural network to optimize network parameters in the target neural network includes:
respectively detecting a plurality of attribute values contained in the training set, and identifying a plurality of types of detection parameters contained in the training set according to the attribute values;
respectively constructing corresponding training subsets according to each type of detection parameters, and simultaneously inputting a plurality of training subsets into an encoder of the target neural network;
the encoder is adaptively trained through a number of the training subsets to optimize the network parameters in the encoder.
5. The power cell pack heating method according to claim 4, wherein: the step of adaptively training the encoder through the plurality of training subsets to optimize the network parameters in the encoder comprises:
inputting a plurality of training subsets into a first coding layer in the coder, and performing multitask learning processing on the plurality of training subsets through the first coding layer to generate a plurality of corresponding training codes;
inputting a plurality of training codes into a second coding layer in the coder, and carrying out serialization processing on the plurality of training codes through the second coding layer so as to generate a plurality of corresponding feature sequences, wherein each feature sequence comprises a plurality of training features;
optimizing the network parameters in the encoder by a number of the training features.
6. The power cell pack heating method according to claim 1, wherein: the step of adaptively heating the power battery pack through the PTC heating film includes:
inputting the temperature prediction model into a preset PID controller, and establishing communication connection between the PID controller and the PTC heating film;
and the working parameters of the PTC heating film are regulated in real time through the PID controller, so that the self-adaptive heating of the power battery pack is completed.
7. The power cell pack heating method of claim 6, wherein: the method further comprises the steps of:
and establishing communication connection between the instrument panel and the PID controller as well as between the instrument panel and the PTC heating film, and displaying working parameters of the PTC heating film and working states of the PID controller in the instrument panel in real time.
8. A power battery pack heating system, the system comprising:
the communication module is used for arranging the PTC heating film in the power battery pack, and respectively arranging a temperature sensor on each battery cell in the power battery pack, wherein the PTC heating film is in communication connection with the temperature sensors;
the acquisition module is used for acquiring the historical temperature data and the real-time temperature data acquired by the temperature sensor and constructing a corresponding temperature prediction model according to the historical temperature data and the real-time temperature data based on a preset algorithm;
and the adjusting module is used for adjusting the working parameters of the PTC heating film through the temperature prediction model so as to carry out self-adaptive heating on the power battery pack through the PTC heating film.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the power cell pack heating method of any one of claims 1 to 7 when the computer program is executed.
10. A readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the power battery pack heating method according to any one of claims 1 to 7.
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