CN117169744A - Method, system, equipment and medium for estimating residual electric quantity of electric automobile - Google Patents
Method, system, equipment and medium for estimating residual electric quantity of electric automobile Download PDFInfo
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Abstract
The invention belongs to the field of batteries of electric vehicles, and particularly relates to a method, a system, equipment and a medium for estimating the residual electric quantity of an electric vehicle. When the initial state of the automobile is included, collecting running history data of the electric automobile; the electric vehicle driving history data comprises electric vehicle driving history basic data and battery data; the battery data comprises initial battery power and residual battery power after driving; taking the collected driving history basic data of the electric vehicle and the initial electric quantity of the battery as inputs, and taking the residual electric quantity of the battery after driving as output to manufacture a data set; building a two-dimensional convolutional neural network according to the data set, and training to obtain a trained network model; acquiring real-time driving data of the electric automobile, inputting the real-time driving data into a trained network model, and carrying out simulation prediction to obtain residual electric quantity after driving; and outputting the residual electric quantity after the electric automobile runs. And collecting historical data, and using the historical data as a data set model to improve the stability of the average electric quantity data of the predicted electric automobile.
Description
Technical Field
The invention belongs to the field of batteries of electric vehicles, and particularly relates to a method, a system, equipment and a medium for estimating the residual electric quantity of an electric vehicle.
Background
In the running process of the electric automobile, the battery is in a complete discharging state from the current running state, and the distance that the automobile can run is the remaining driving mileage of the automobile. The remaining range of the vehicle is mainly determined by two factors, namely the remaining available energy and the future electric quantity of the automobile. In the previous research, the SOC can be accurately predicted by using methods such as ampere integration method, speed-time integration, KNN regression prediction and the like, and the available energy of the battery can be estimated by combining the voltage. The running resistance of the vehicle is affected by the factors of the mass, structure and performance of parts thereof, motor efficiency, internal resistance consumption of the battery, tire pressure and modeling, and then the running electric quantity of the vehicle is affected. In addition, different drivers have different demands on the interior of the vehicle, and the use of the air conditioner has obvious influence on the electric quantity of the electric automobile.
At present, the electric quantity of the automobile is difficult to predict according to the existing data, so that the residual electric quantity of the electric automobile cannot be predicted, and the cruising mileage of the automobile can be estimated.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method, a system, equipment and a medium for estimating the residual electric quantity of an electric automobile, so as to solve the technical problem that the residual electric quantity of the electric automobile cannot be accurately predicted in real time in the prior art, and the endurance mileage of the automobile is estimated.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a remaining power estimation method of an electric vehicle, including:
s1: when the automobile is in an initial state, collecting running history data of the electric automobile; the method comprises the steps of carrying out a first treatment on the surface of the The electric vehicle driving history data comprises electric vehicle driving history basic data and battery data; the battery data comprises initial battery power and residual battery power after driving;
s2: taking collected electric vehicle running history basic data and initial battery electric quantity as inputs, and taking residual battery electric quantity after running as output to manufacture a data set;
s3: building a two-dimensional convolutional neural network according to the data set, and training to obtain a trained network model;
s4: acquiring real-time driving data of the electric automobile, inputting the real-time driving data into a trained network model, and carrying out simulation prediction to obtain the residual electric quantity of the electric automobile after driving;
s5: and outputting the residual electric quantity after the electric automobile runs.
Further, the electric vehicle driving history basic data comprises driving initial time, driving speed, driving distance and driving ending time.
Further, the two-dimensional convolutional neural network includes: four convolution layers and two full connection layers;
the four convolution layers are used for extracting characteristic information;
and the two full-connection layers are used for carrying out data regression prediction.
Further, the real-time data of the electric vehicle in step S4 includes a driving initial time, a driving speed, a driving distance, a driving end time and a battery initial power.
Further, the training is used for calculating the accuracy of the two-dimensional convolutional neural network;
the accuracy is obtained by subtracting the absolute value of the real result from the predicted result, dividing the absolute value by the real result, and averaging the entropy of all working conditions.
Further, after the simulation prediction is performed according to the automobile running basic data information collected by each sensor of the automobile, the network model predicts the residual electric quantity of the electric automobile according to the accuracy.
And further, after the output residual electric quantity is predicted through the network model, transmitting data to a central control large screen, and displaying the residual electric quantity through central control large screen display and voice system broadcasting.
In a second aspect, there is provided a remaining power estimation system of an electric vehicle, including:
and a reading module: when the method is used in an initial state of an automobile, collecting running history data of the electric automobile; the electric vehicle driving history data comprises electric vehicle driving history basic data and battery data; the battery data comprises initial battery power and residual battery power after driving;
and a recording module: the method comprises the steps that collected electric vehicle running history basic data and initial electric quantity of a battery are used as inputs, and residual electric quantity after running is used as output, so that a data set is manufactured;
training module: the method comprises the steps of constructing a two-dimensional convolutional neural network according to a data set, and training to obtain a trained network model;
simulation prediction module: the method comprises the steps of acquiring real-time driving data of the electric automobile, inputting the real-time driving data into a trained network model, and carrying out simulation prediction to obtain the residual electric quantity of the electric automobile;
and an output module: and the device is used for outputting the residual electric quantity after the electric automobile runs.
In a third aspect, an electronic device is provided, including a processor and a memory, where the processor is configured to execute a computer program stored in the memory to implement the method for estimating a remaining power of an electric vehicle.
In a fourth aspect, a computer readable storage medium is provided, where at least one instruction is stored, and when the at least one instruction is executed by a processor, the method for estimating a remaining power of an electric vehicle is implemented.
The invention has at least the following beneficial effects:
the invention provides a method for estimating the residual electric quantity of an electric automobile, which comprises the following steps: s1: when the automobile is in an initial state, collecting running history data of the electric automobile; the electric vehicle driving history data comprises electric vehicle driving history basic data and battery data; the battery data comprises initial battery power and residual battery power after driving; s2: taking collected electric vehicle running history basic data and initial battery electric quantity as inputs, and taking residual battery electric quantity after running as output to manufacture a data set; s3: building a two-dimensional convolutional neural network according to the data set, and training to obtain a trained network model; s4: acquiring real-time driving data of the electric automobile, inputting the real-time driving data into a trained network model, and carrying out simulation prediction to obtain the residual electric quantity of the electric automobile after driving; s5: and outputting the residual electric quantity after the electric automobile runs. And collecting historical data, and using the historical data as a data set model to improve the stability of the average electric quantity data of the predicted electric automobile.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is an algorithm flow chart of a residual electric quantity estimation method of an electric vehicle according to the present invention;
FIG. 2 is a flowchart of a method, system, device and medium for estimating remaining power of an electric vehicle according to the present invention;
FIG. 3 is a flow chart of a method, system, device and medium for estimating the residual electric quantity of an electric vehicle;
fig. 4 is a block diagram of an electronic device according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical 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 is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
Example 1
Referring to fig. 1, the method for estimating the remaining power of an electric vehicle according to the present invention includes: s1: when the automobile is in an initial state, collecting running history data of the electric automobile; the electric vehicle driving history data comprises electric vehicle driving history basic data and battery data; the battery data comprises initial battery power and residual battery power after driving; s2: taking collected electric vehicle running history basic data and initial battery electric quantity as inputs, and taking residual battery electric quantity after running as output to manufacture a data set; s3: building a two-dimensional convolutional neural network according to the data set, and training to obtain a trained network model; s4: acquiring real-time driving data of the electric automobile, inputting the real-time driving data into a trained network model, and carrying out simulation prediction to obtain the residual electric quantity of the electric automobile after driving; s5: and outputting the residual electric quantity after the electric automobile runs. Collecting historical data includes: the method comprises the steps of performing data classification and data cleaning on historical basic data to obtain historical cleaning data, training by using the historical cleaning data as training data and using a deep learning framework to construct a deep neural network model, inputting known basic data into the trained deep neural network model when each sensor of the electric automobile obtains a plurality of new basic data to be predicted, and outputting a predicted value of residual electric quantity.
Referring to fig. 2, the neural network model includes four convolution layers and two full connection layers, each time charging is performed, a data set describes 7 numbers of average speed, maximum vehicle speed, minimum vehicle speed, charging voltage, discharging voltage, motor power and ambient temperature, and outputs 1 number of remaining power. After preprocessing the matrix arrangement of the data set input matrix, processing the vector with the length of 7 into a matrix, traversing the matrix by a convolution kernel with the size of 2×2 through four layers of convolution layers, changing the matrix with the thickness of 1 into a thickness matrix with the thickness of 32 by the first layer of convolution, changing the number of characteristic pictures into 32 after the first layer of convolution, and changing the characteristic pictures into 64 after the second, third and fourth layers of convolution. The characteristic information of the fourth layer of convolution layer is extracted and then transmitted to the first layer of the full-connection layer, the first layer of full-connection layer pulls up 64 matrixes obtained by the convolution layer into a one-dimensional array of 512, and the second layer of full-connection layer compresses the one-dimensional array of 512 into an array with the length of 1. The array with the length of 1 is the final output result after the neural network carries out data regression prediction.
Example 2
Referring to fig. 3, the remaining power estimating system of an electric vehicle according to the present invention includes: and a reading module: the method comprises the steps of collecting automobile driving history data; and a recording module: the method comprises the steps of using collected historical data as input, using residual electric quantity as output, and manufacturing a data set; training module: the two-dimensional convolutional neural network is used for building a two-dimensional convolutional neural network according to the data set to train; simulation prediction module: the vehicle data input method comprises the steps of reading vehicle data in a neural network, inputting the vehicle data into a network model, and performing simulation prediction according to training results; and an output module: and the method is used for predicting the output residual capacity according to the simulation of the network model. And collecting and analyzing vehicle historical data through each module, and outputting the residual electric quantity of the electric automobile. The acquired history basic data includes a travel initial time, a travel speed, a travel distance, and a travel end time.
Example 3
Referring to fig. 4, the present invention provides a remaining power estimating apparatus 100 of an electric vehicle, where the electronic apparatus 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
The memory 101 may be used to store the computer program 103, and the processor 102 may implement the remaining power estimating method step of the electric vehicle according to embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data) created according to the use of the electronic device 100, and the like. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), at least one disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one processor 102 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, the processor 102 being a control center of the electronic device 100, the various interfaces and lines being utilized to connect various portions of the overall electronic device 100.
The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for estimating a remaining power of an electric vehicle, and the processor 102 may execute the plurality of instructions to implement:
s1: when the automobile is in an initial state, collecting running history data of the electric automobile; the electric vehicle driving history data comprises electric vehicle driving history basic data and battery data; the battery data comprises initial battery power and residual battery power after driving;
s2: taking collected electric vehicle running history basic data and initial battery electric quantity as inputs, and taking residual battery electric quantity after running as output to manufacture a data set;
s3: building a two-dimensional convolutional neural network according to the data set, and training to obtain a trained network model;
s4: acquiring real-time driving data of the electric automobile, inputting the real-time driving data into a trained network model, and carrying out simulation prediction to obtain the residual electric quantity of the electric automobile after driving;
s5: and outputting the residual electric quantity after the electric automobile runs.
Example 4
The invention provides a computer readable storage medium, which is characterized in that the computer readable storage medium stores at least one instruction, and the at least one instruction realizes a residual electric quantity estimation method of an electric automobile when being executed by a processor.
The modules/units integrated in the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, and a Read-Only Memory (ROM).
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (9)
1. The method for estimating the residual electric quantity of the electric automobile is characterized by comprising the following steps of:
s1: when the automobile is in an initial state, collecting running history data of the electric automobile; the electric vehicle driving history data comprises electric vehicle driving history basic data and battery data; the battery data comprises initial battery power and residual battery power after driving;
s2: taking collected electric vehicle running history basic data and initial battery electric quantity as inputs, and taking residual battery electric quantity after running as output to manufacture a data set;
s3: building a two-dimensional convolutional neural network according to the data set, and training to obtain a trained network model;
s4: acquiring real-time driving data of the electric automobile, inputting the real-time driving data into a trained network model, and carrying out simulation prediction to obtain the residual electric quantity of the electric automobile after driving;
s5: and outputting the residual electric quantity after the electric automobile runs.
2. The method for estimating a remaining power of an electric vehicle according to claim 1, wherein the electric vehicle traveling history basic data includes a traveling initial time, a traveling speed, a traveling distance, and a traveling end time.
3. The method for estimating a remaining power of an electric vehicle according to claim 1, wherein the two-dimensional convolutional neural network comprises: four convolution layers and two full connection layers;
the four convolution layers are used for extracting characteristic information;
and the two full-connection layers are used for carrying out data regression prediction.
4. The method for estimating remaining power of an electric vehicle according to claim 3, wherein the real-time data of the electric vehicle in step S4 includes a travel initial time, a travel speed, a travel distance, a travel end time, and a battery initial power.
5. The method for estimating the residual capacity of the electric automobile according to claim 1, wherein the training is used for calculating the accuracy of a two-dimensional convolutional neural network;
the accuracy is obtained by subtracting the absolute value of the real result from the predicted result, dividing the absolute value by the real result, and averaging the entropy of all working conditions.
6. The method for estimating the residual electric quantity of the electric automobile according to claim 1, wherein the output residual electric quantity is predicted through a network model and then data is transmitted to a central control large screen, and the residual electric quantity is displayed through the central control large screen display and the voice system broadcast.
7. A remaining power estimation system of an electric vehicle, comprising:
and a reading module: when the method is used in an initial state of an automobile, collecting running history data of the electric automobile; the electric vehicle driving history data comprises electric vehicle driving history basic data and battery data; the battery data comprises initial battery power and residual battery power after driving;
and a recording module: the method comprises the steps that collected electric vehicle running history basic data and initial battery electric quantity are used as inputs, and battery residual electric quantity after running is used as output, so that a data set is manufactured;
training module: the method comprises the steps of constructing a two-dimensional convolutional neural network according to a data set, and training to obtain a trained network model;
simulation prediction module: the method comprises the steps of acquiring real-time driving data of the electric automobile, inputting the real-time driving data into a trained network model, and carrying out simulation prediction to obtain the residual electric quantity of the electric automobile after driving;
and an output module: and the device is used for outputting the residual electric quantity after the electric automobile runs.
8. An electronic device comprising a processor and a memory, the processor configured to execute a computer program stored in the memory to implement the method for estimating the remaining power of an electric vehicle according to any one of claims 1 to 6.
9. A computer-readable storage medium storing at least one instruction that when executed by a processor implements the method of estimating remaining power of an electric vehicle according to any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117556589A (en) * | 2024-01-04 | 2024-02-13 | 江阴飞阳电子科技有限公司 | Intelligent calibration method and system for electric quantity of instrument |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117556589A (en) * | 2024-01-04 | 2024-02-13 | 江阴飞阳电子科技有限公司 | Intelligent calibration method and system for electric quantity of instrument |
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