CN116533817A - Charging remaining time prediction method, device and storage medium - Google Patents
Charging remaining time prediction method, device and storage medium Download PDFInfo
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- CN116533817A CN116533817A CN202210090247.1A CN202210090247A CN116533817A CN 116533817 A CN116533817 A CN 116533817A CN 202210090247 A CN202210090247 A CN 202210090247A CN 116533817 A CN116533817 A CN 116533817A
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- 238000004891 communication Methods 0.000 description 35
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- 238000013527 convolutional neural network Methods 0.000 description 6
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/68—Off-site monitoring or control, e.g. remote control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/10—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles characterised by the energy transfer between the charging station and the vehicle
- B60L53/14—Conductive energy transfer
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/30—Constructional details of charging stations
- B60L53/305—Communication interfaces
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/66—Data transfer between charging stations and vehicles
- B60L53/665—Methods related to measuring, billing or payment
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The embodiment of the application provides a method, a system and a storage medium for predicting charging remaining time, wherein the method comprises the following steps of: acquiring network state information of a vehicle; according to the network states of the vehicle and the cloud end equipment, determining that the equipment for predicting the charging remaining time is the vehicle or the cloud end equipment, so that at least one of the vehicle or the motion equipment can complete the method for predicting the charging remaining time. According to the method and the device, the multi-mode estimated charging remaining time can be combined, the charging remaining time prediction model deployed in the local vehicle machine is not completely relied on, the calculation result acquisition efficiency is improved, and the calculation result precision is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of batteries of new energy automobiles, in particular to a method, a system and a storage medium for predicting charging remaining time.
Background
At present, a charging residual time prediction model of a new energy automobile (such as an electric automobile) is generally deployed in an automobile machine of an automobile. However, if the CPU of the local vehicle simply relies on the calculation power to calculate the remaining charge time of the battery pack, the calculation power is not only excessively consumed, but also the accuracy of the calculation result is not high. And the charging residual time prediction model deployed in the local vehicle machine has low subsequent iteration speed and is inconvenient to upgrade.
Disclosure of Invention
The embodiment of the application provides a method, a system and a storage medium for predicting charging remaining time, which can combine multiple modes to predict the charging remaining time, not only improve the calculation result acquisition efficiency, but also improve the calculation result precision without completely depending on a charging remaining time prediction model deployed in a local vehicle machine.
In a first aspect, an embodiment of the present application provides a method for predicting a remaining charging time, where the method includes:
acquiring network state information of a vehicle;
according to the network states of the vehicle and the cloud end equipment, determining that the equipment for predicting the charging remaining time is the vehicle or the cloud end equipment, so that at least one of the vehicle or the motion equipment can complete the method for predicting the charging remaining time.
In a second aspect, an embodiment of the present application further provides a device for predicting a remaining charging time, where the device for predicting a remaining charging time includes: an acquisition unit and a processing unit; the acquisition unit is used for acquiring network state information of the vehicle; the processing unit is configured to determine, according to a network state of the vehicle and the cloud device, that the device for predicting the charging remaining time is the vehicle or the cloud device, so that at least one of the vehicle or the motion device completes the method for predicting the charging remaining time.
In a third aspect, an embodiment of the present application further provides a processing device, including a processor and a memory, where the memory stores a computer program, and when the processor invokes the computer program in the memory, the processor executes steps in any of the methods for predicting charging remaining time provided in the embodiments of the first aspect of the present application.
In a fourth aspect, embodiments of the present application further provide a computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the steps in any of the methods for predicting charging remaining time provided in the embodiments of the first aspect of the present application.
From the above, the method and the device can combine multi-mode prediction of the charging residual time, and not completely rely on the charging residual time prediction model deployed in the local vehicle machine, so that the calculation result acquisition efficiency is improved, and the calculation result precision is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may 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 predicting the remaining charge time in the present application;
FIG. 2 is a schematic diagram of a charging remaining time prediction system in the present application;
fig. 3 is a schematic view of a structure of the processing apparatus of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In the following description, specific embodiments of the present application will be described with reference to steps and symbols performed by one or more computers, unless otherwise indicated. Accordingly, these steps and operations will be described in terms of a computer executing an operation involving a computer processing unit representing electronic signals representing data in a structured form. This operation transforms the data or maintains it in place in the computer's memory system, which may reconfigure or otherwise alter the computer's operation in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the present application are described in the foregoing text and are not meant to be limiting, and one skilled in the art will recognize that various steps and operations described below may also be implemented in hardware.
The principles of the present application operate using many other general purpose or special purpose operations, communication environments, or configurations. Examples of well known computing systems, environments, and configurations that may be suitable for use with the application include, but are not limited to, hand-held telephones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe computers, and distributed computing environments that include any of the above systems or devices.
The terms "first," "second," and "third," etc. in this application are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
First, before describing the embodiments of the present application, the following description will refer to the relevant content of the application.
The execution main body of the prediction method of the charging remaining time provided by the application can be a vehicle machine (also can be understood as a vehicle) or cloud Equipment provided by the application, or processing Equipment such as server Equipment, a physical host, a vehicle-mounted terminal or User Equipment (UE), wherein the vehicle machine or the cloud Equipment can be realized in a hardware or software mode, and the UE can be specifically a terminal Equipment such as a smart phone, a tablet computer, a notebook computer, a palm computer, a desktop computer or a personal digital assistant (Personal Digital Assitant, PDA).
Next, a method for predicting the remaining charge time provided in the present application will be described.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for predicting charging remaining time in the present application. The method provided by the application specifically comprises the following steps:
101. acquiring network state information of a vehicle;
102. according to the network states of the vehicle and the cloud end equipment, determining that the equipment for predicting the charging remaining time is the vehicle or the cloud end equipment, so that at least one of the vehicle or the motion equipment can complete the method for predicting the charging remaining time.
In this embodiment of the present application, when it is detected that an electric vehicle is connected to an ac charging gun for charging, the remaining charging time may be predicted based on a vehicle or cloud device at this time, so as to estimate the duration required for full charge. In order to select a prediction mode with higher stability and higher precision, it is necessary to first determine whether the current network signal of the vehicle is normal to support uplink and downlink of data. If it is determined that the current network signal of the vehicle machine can normally support uplink and downlink of data (for example, a ping value of communication between the vehicle machine and the cloud device can be obtained, if the ping value does not exceed a preset ping threshold, uplink and downlink of data can be normally supported, and if the ping value exceeds the preset ping threshold, uplink and downlink of data can not be supported), it can be determined that the current network signal of the vehicle is in a normal networking state, the vehicle machine automatically switches to a cloud prediction mode, so that the obtained parameter data set is uploaded to the cloud device for prediction. If the current network signal of the vehicle is judged to be in an abnormal networking state (the abnormal networking state refers to that the ping value exceeds a preset ping threshold value, the uplink and the downlink of data cannot be supported), the vehicle is automatically switched to a local prediction mode. Therefore, the cloud prediction mode with stronger calculation power can be selected based on the condition that the current network signal of the vehicle is in a normal networking state, and the local prediction mode can be selected based on the condition that the current network signal of the vehicle is in an abnormal networking state.
For example, when the device for predicting the remaining charging time is determined to be a cloud device, step 102 includes:
transmitting a parameter data set to the cloud device, wherein the parameter data set at least comprises at least one of a battery remaining capacity percentage, a charging current, a charging voltage, a battery temperature and a battery health degree;
and the receiving cloud equipment carries out prediction operation according to the parameter data set to obtain the charging remaining time.
In the embodiment of the application, when the cloud prediction mode is selected to predict the charging residual time, the vehicle machine does not need to predict based on a local prediction algorithm at this time, and only needs to timely acquire and upload the parameter data set of the electric vehicle to the cloud device, so that cloud processing and prediction are performed in the cloud device; wherein the parameter data set includes at least one of a percentage of remaining battery power (i.e., SOC), a charge current, a charge voltage, a battery temperature, and a battery health (i.e., SOH). Because the calculation power of the cloud is stronger, and the cloud prediction model can be updated more in aspects, the acquisition efficiency and the prediction accuracy of the prediction result can be effectively improved in the cloud prediction mode.
As an alternative of step 102, after switching to the cloud prediction mode, the unique vehicle communication identifier and the vehicle parameter data set acquisition instruction may be sent to the charging pile, so as to trigger the charging pile to acquire the parameter data set after acquiring the unique vehicle communication identifier and the vehicle parameter data set acquisition instruction, and then the charging pile packages the unique vehicle communication identifier, the parameter data set and the unique charging pile equipment code and sends the packaged unique vehicle communication identifier, the parameter data set and the unique charging pile equipment code to the cloud equipment. The method is equivalent to the step of timely uploading the parameter data set to cloud equipment based on the charging pile connected with the Internet as a communication transfer station.
After the cloud device receives the parameter data set including the parameter values such as the battery remaining capacity percentage, the charging current, the charging voltage, the battery temperature, the battery health degree and the like, the cloud device can calculate based on the cloud prediction model stored in the cloud device to obtain the charging remaining time, and the charging remaining time is sent to the vehicle and the vehicle is displayed locally in time. Specifically, the cloud prediction model may adopt a convolutional neural network, a cyclic neural network model, and other prediction models.
Illustratively, when the device that determines the predicted charge remaining time is a vehicle, step 102 includes:
and acquiring a parameter data set, inputting the parameter data set into a local prediction model for prediction operation, obtaining the charging remaining time, and displaying the charging remaining time.
In this embodiment of the present application, if it is determined that the current network signal of the vehicle machine cannot support uplink and downlink of data, it may be determined that when the current network signal of the vehicle is in an abnormal networking state (a ping value of communication between the vehicle machine and the cloud device is higher than a preset ping threshold, which may be regarded as an abnormal networking state), the vehicle machine automatically switches to a local prediction mode, so as to perform prediction based on a locally acquired parameter data set. Therefore, a local prediction mode with lower computing power than the cloud device but capable of ensuring that a prediction result is obtained can be selected based on the fact that the current network signal of the vehicle is in an abnormal networking state.
After the vehicle machine locally collects the parameter data set including the parameter values of the remaining battery capacity percentage, the charging current, the charging voltage, the battery temperature, the battery health degree and the like, the vehicle machine can operate based on a locally stored local prediction model to obtain the charging remaining time, and the charging remaining time is displayed locally in time (for example, displayed on a display screen of the vehicle machine). Specifically, the local prediction model may also adopt a convolutional neural network, a cyclic neural network model, or other prediction model, as well as the cloud prediction model.
When the user regularly maintains the system of the vehicle, the service provider can be required to write in the latest version of the local prediction model. However, because the system maintenance frequency of the user is low, the update frequency of the local prediction model in the vehicle machine is low, and the upgrading process is complex. But a local prediction model is deployed in the vehicle-mounted device, so that a prediction result with precision basically meeting actual requirements can be output under the condition of network disconnection.
In one embodiment, the acquiring parameter data set includes:
and establishing a local communication connection with the charging pile so as to acquire the parameter data set from the charging pile.
In this embodiment of the application, when detecting that the electric automobile has been connected the alternating current charging rifle and has charged, the car machine has also established local communication connection with the electric pile that fills that is connected, can be timely the mutual data transmission between car machine and the electric pile that fills like this, just also ensured to fill the electric pile and obtain parameter dataset can be timely send to the car machine to realize local prediction operation.
In one embodiment, the method for establishing a local communication connection with the charging pile comprises the following steps:
and establishing local communication connection with the charging pile by using an electric vehicle CAN protocol.
In the embodiment of the application, the vehicle machine is connected with the charging pile through the CAN bus, so that local communication connection with the charging pile through an electric vehicle CAN protocol is established. The accurate mutual transmission of data between the vehicle machine and the charging pile is ensured through an electric vehicle CAN protocol.
In one embodiment, when the device that determines the predicted charge remaining time is a vehicle, the method further comprises:
uploading the acquired parameter data set to a vehicle-mounted intelligent terminal;
and receiving the charging remaining time obtained by the terminal prediction operation of the vehicle-mounted intelligent terminal according to the parameter data set, and displaying the charging remaining time.
In this embodiment, as a first embodiment of the local auxiliary prediction mode, when it is determined that the current network information of the vehicle is in a state of networking with the vehicle-mounted intelligent terminal, that is, when it is determined that the vehicle and the vehicle-mounted intelligent terminal (such as a user smart phone) are in a wireless communication connection state and the vehicle-mounted intelligent terminal is in a normal networking state, the charging remaining time prediction can be performed by using the CPU computing power of the vehicle-mounted intelligent terminal, a parameter data set including parameter values such as a battery remaining power percentage, a charging current, a charging voltage, a battery temperature, a battery health degree and the like, which is acquired by the vehicle, is uploaded to the vehicle-mounted intelligent terminal, and then the vehicle-mounted intelligent terminal performs a charging remaining time obtained by a terminal prediction operation, and displays the charging remaining time. And the charging remaining time obtained based on terminal prediction operation in the vehicle-mounted intelligent terminal is sent to the vehicle machine for local display in time.
In one embodiment, when the device for determining the predicted charge remaining time is a vehicle, the method for predicting the charge remaining time further includes:
uploading the acquired parameter data set to a vehicle-mounted intelligent terminal, so that the vehicle-mounted intelligent terminal uploads the parameter data set to cloud equipment;
and receiving the charging remaining time obtained by cloud prediction operation of the cloud device according to the parameter data set, and displaying the charging remaining time.
In this embodiment, as a second embodiment of the local auxiliary prediction mode, when it is determined that the current network information of the vehicle is in a networking state with the vehicle-mounted intelligent terminal, that is, when it is determined that the vehicle and the vehicle-mounted intelligent terminal (such as a user smart phone) are in a wireless communication connection state and the vehicle-mounted intelligent terminal is in a normal networking state, the vehicle-mounted intelligent terminal may be used as a communication transfer station to upload a parameter data set including parameter values such as a battery remaining capacity percentage, a charging current, a charging voltage, a battery temperature, a battery health degree and the like, acquired by the vehicle, to the vehicle-mounted intelligent terminal, and then the vehicle-mounted intelligent terminal packages the vehicle communication unique identification number, the parameter data set and the unique device code of the vehicle-mounted intelligent terminal and then sends the packaged parameter data set and the unique device code of the vehicle-mounted intelligent terminal to the cloud device. And the charging remaining time obtained based on cloud prediction operation in the cloud device is sent to the vehicle machine for local display in time.
Therefore, the charging remaining time prediction model deployed in the local vehicle machine is not completely relied on by combining the multi-mode prediction charging remaining time, so that the calculation result acquisition efficiency is improved, and the calculation result precision is improved.
In one embodiment, the receiving cloud device performs a prediction operation according to the parameter data set to obtain the charging remaining time, including:
and inputting the parameter data set to a cloud prediction model locally stored in a cloud device to perform cloud prediction operation to obtain charging remaining time, and sending the charging remaining time to a vehicle.
In the embodiment of the application, when the cloud device receives the parameter data set uploaded by the vehicle machine in the normal network connection state, the vehicle machine can predict the charging residual time based on the cloud prediction mode, and the vehicle machine does not need to predict based on a local prediction algorithm at this time, and only needs to timely acquire the parameter data set of the electric vehicle and upload the parameter data set to the cloud device, so that cloud processing and prediction are performed in the cloud device; wherein the parameter data set includes at least one of a percentage of remaining battery power (i.e., SOC), a charge current, a charge voltage, a battery temperature, and a battery health (i.e., SOH). Because the calculation power of the cloud is stronger, and the cloud prediction model can be updated more in aspects, the acquisition efficiency and the prediction accuracy of the prediction result can be effectively improved in the cloud prediction mode. Of course, the parameter data set is not limited to uploading by a vehicle machine, but can also be uploaded by a vehicle-mounted intelligent terminal (such as a smart phone) in communication connection with the vehicle machine or a charging pile.
When the parameter data set is subjected to cloud prediction operation to obtain the charging remaining time, a prediction model such as a convolutional neural network model and a cyclic neural network model can be adopted.
And after the charging remaining time is predicted and acquired in the cloud device, timely sending feedback of the charging remaining time to the vehicle machine so as to prompt a user for the charging remaining time in time.
In one embodiment, the method for predicting remaining charging time further includes:
when a cloud prediction model updating instruction is detected, the cloud prediction model is correspondingly updated, and an updated cloud prediction model is obtained.
In the embodiment of the application, the cloud prediction model can be iteratively trained by relying on the historical data in the cloud device so as to update the cloud prediction model and then release the cloud prediction model in real time. And the complex cloud prediction algorithm is deployed in the cloud, so that the strong calculation power of the cloud is fully utilized, and the accuracy of the prediction result is improved.
In order to facilitate better implementation of the method of the present application, the embodiment of the present application further provides a prediction apparatus 20 for the remaining charging time.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a charging remaining time prediction system 20 according to the present application, wherein the charging remaining time prediction system 20 may specifically include the following structures: an acquisition unit 201 and a processing unit 202.
Wherein, the acquiring unit 201 is configured to acquire network state information of a vehicle;
the processing unit 202 is configured to determine, according to a network state of the vehicle and the cloud device, that the device for predicting the charging remaining time is the vehicle or the cloud device, so that at least one of the vehicle or the motion device completes the method for predicting the charging remaining time.
In this embodiment of the present application, when it is detected that an electric vehicle is connected to an ac charging gun for charging, the remaining charging time may be predicted based on a vehicle or cloud device at this time, so as to estimate the duration required for full charge. In order to select a prediction mode with higher stability and higher precision, it is necessary to firstly determine whether the current network signal of the vehicle machine is normal to support uplink and downlink of data. If it is determined that the current network signal of the vehicle machine can normally support uplink and downlink of data (for example, a ping value of communication between the vehicle machine and the cloud device can be obtained, if the ping value does not exceed a preset ping threshold, uplink and downlink of data can be normally supported, and if the ping value exceeds the preset ping threshold, uplink and downlink of data can not be supported), it can be determined that the current network signal of the vehicle machine is in a normal networking state, the vehicle machine automatically switches to a cloud prediction mode, so that the obtained parameter data set is uploaded to the cloud device for prediction. If the current network signal of the vehicle is judged to be in an abnormal networking state (the abnormal networking state refers to that the ping value exceeds a preset ping threshold value, the uplink and the downlink of data cannot be supported), the vehicle is automatically switched to a local prediction mode. Therefore, the cloud prediction mode with stronger calculation power can be selected based on the condition that the current network signal of the vehicle is in a normal networking state, and the local prediction mode can be selected based on the condition that the current network signal of the vehicle is in an abnormal networking state.
For example, when the device for determining the predicted charging remaining time is a cloud device, the processing unit 202 is specifically configured to:
transmitting a parameter data set to the cloud device, wherein the parameter data set at least comprises at least one of a battery remaining capacity percentage, a charging current, a charging voltage, a battery temperature and a battery health degree;
and the receiving cloud equipment carries out prediction operation according to the parameter data set to obtain the charging remaining time.
In the embodiment of the application, when the cloud prediction mode is selected to predict the charging residual time, the vehicle machine does not need to predict based on a local prediction algorithm at this time, and only needs to timely acquire and upload the parameter data set of the electric vehicle to the cloud device, so that cloud processing and prediction are performed in the cloud device; wherein the parameter data set includes at least one of a percentage of remaining battery power (i.e., SOC), a charge current, a charge voltage, a battery temperature, and a battery health (i.e., SOH). Because the calculation power of the cloud is stronger, and the cloud prediction model can be updated more in aspects, the acquisition efficiency and the prediction accuracy of the prediction result can be effectively improved in the cloud prediction mode.
As an alternative to the first sending unit 312, after switching to the cloud prediction mode, the unique vehicle communication identifier and the vehicle parameter data set obtaining instruction may be sent to the charging pile, so as to trigger the charging pile to obtain the parameter data set after obtaining the unique vehicle communication identifier and the vehicle parameter data set obtaining instruction, and then the charging pile packages the unique vehicle communication identifier, the parameter data set and the unique charging pile device code and sends the packaged unique vehicle communication identifier, the parameter data set and the unique charging pile device code to the cloud device. The method is equivalent to the step of timely uploading the parameter data set to cloud equipment based on the charging pile connected with the Internet as a communication transfer station.
After the cloud device receives the parameter data set including the parameter values such as the battery remaining capacity percentage, the charging current, the charging voltage, the battery temperature, the battery health degree and the like, the cloud device can calculate based on the cloud prediction model stored in the cloud device to obtain the charging remaining time, and the charging remaining time is sent to the vehicle and the vehicle is displayed locally in time. Specifically, the cloud prediction model may adopt a convolutional neural network, a cyclic neural network model, and other prediction models.
Illustratively, when the device that determines the predicted charge remaining time is a vehicle, the processing unit 202 is specifically configured to:
and acquiring a parameter data set, inputting the parameter data set into a local prediction model for prediction operation, obtaining the charging remaining time, and displaying the charging remaining time.
In this embodiment of the present application, if it is determined that the current network signal of the vehicle machine cannot support uplink and downlink of data, it may be determined that when the current network signal of the vehicle is in an abnormal networking state (a ping value of communication between the vehicle machine and the cloud device is higher than a preset ping threshold, which may be regarded as an abnormal networking state), the vehicle machine automatically switches to a local prediction mode, so as to perform prediction based on a locally acquired parameter data set. Therefore, a local prediction mode with lower computing power than the cloud device but capable of ensuring that a prediction result is obtained can be selected based on the fact that the current network signal of the vehicle is in an abnormal networking state.
After the vehicle machine locally collects the parameter data set including the parameter values of the remaining battery capacity percentage, the charging current, the charging voltage, the battery temperature, the battery health degree and the like, the vehicle machine can operate based on a locally stored local prediction model to obtain the charging remaining time, and the charging remaining time is displayed locally in time (for example, displayed on a display screen of the vehicle machine). Specifically, the local prediction model may also adopt a convolutional neural network, a cyclic neural network model, or other prediction model, as well as the cloud prediction model.
When the user regularly maintains the system of the vehicle, the service provider can be required to write in the latest version of the local prediction model. However, because the system maintenance frequency of the user is low, the update frequency of the local prediction model in the vehicle machine is low, and the upgrading process is complex. But a local prediction model is deployed in the vehicle-mounted device, so that a prediction result with precision basically meeting actual requirements can be output under the condition of network disconnection.
In one embodiment, the processing unit 202 is further specifically configured to:
and establishing a local communication connection with the charging pile so as to acquire the parameter data set from the charging pile.
In this embodiment of the application, when detecting that the electric automobile has been connected the alternating current charging rifle and has charged, the car machine has also established local communication connection with the electric pile that fills that is connected, can be timely the mutual data transmission between car machine and the electric pile that fills like this, just also ensured to fill the electric pile and obtain parameter dataset can be timely send to the car machine to realize local prediction operation.
In one embodiment, the establishing a local communication connection with the charging pile further specifically includes:
and establishing local communication connection with the charging pile by using an electric vehicle CAN protocol.
In the embodiment of the application, the vehicle machine is connected with the charging pile through the CAN bus, so that local communication connection with the charging pile through an electric vehicle CAN protocol is established. The accurate mutual transmission of data between the vehicle machine and the charging pile is ensured through an electric vehicle CAN protocol.
Illustratively, when the device that determines the predicted charge remaining time is a vehicle, the processing unit 202 is further specifically configured to:
uploading the acquired parameter data set to a vehicle-mounted intelligent terminal;
and receiving the charging remaining time obtained by the terminal prediction operation of the vehicle-mounted intelligent terminal according to the parameter data set, and displaying the charging remaining time.
In this embodiment, as a first embodiment of the local auxiliary prediction mode, when it is determined that the current network information of the vehicle is in a state of networking with the vehicle-mounted intelligent terminal, that is, when it is determined that the vehicle and the vehicle-mounted intelligent terminal (such as a user smart phone) are in a wireless communication connection state and the vehicle-mounted intelligent terminal is in a normal networking state, the charging remaining time prediction can be performed by using the CPU computing power of the vehicle-mounted intelligent terminal, a parameter data set including parameter values such as a battery remaining power percentage, a charging current, a charging voltage, a battery temperature, a battery health degree and the like, which is acquired by the vehicle, is uploaded to the vehicle-mounted intelligent terminal, and then the vehicle-mounted intelligent terminal performs a charging remaining time obtained by a terminal prediction operation, and displays the charging remaining time. And the charging remaining time obtained based on terminal prediction operation in the vehicle-mounted intelligent terminal is sent to the vehicle machine for local display in time.
Illustratively, when the device that determines the predicted charge remaining time is a vehicle, the processing unit 202 is further specifically configured to:
uploading the acquired parameter data set to a vehicle-mounted intelligent terminal, so that the vehicle-mounted intelligent terminal uploads the parameter data set to cloud equipment;
and receiving the charging remaining time obtained by cloud prediction operation of the cloud device according to the parameter data set, and displaying the charging remaining time.
In this embodiment, as a second embodiment of the local auxiliary prediction mode, when it is determined that the current network information of the vehicle is in a networking state with the vehicle-mounted intelligent terminal, that is, when it is determined that the vehicle and the vehicle-mounted intelligent terminal (such as a user smart phone) are in a wireless communication connection state and the vehicle-mounted intelligent terminal is in a normal networking state, the vehicle-mounted intelligent terminal may be used as a communication transfer station to upload a parameter data set including parameter values such as a battery remaining capacity percentage, a charging current, a charging voltage, a battery temperature, a battery health degree and the like, acquired by the vehicle, to the vehicle-mounted intelligent terminal, and then the vehicle-mounted intelligent terminal packages the vehicle communication unique identification number, the parameter data set and the unique device code of the vehicle-mounted intelligent terminal and then sends the packaged parameter data set and the unique device code of the vehicle-mounted intelligent terminal to the cloud device. And the charging remaining time obtained based on cloud prediction operation in the cloud device is sent to the vehicle machine for local display in time.
In one embodiment, the processing unit 202 is further specifically configured to:
and inputting the parameter data set to a cloud prediction model locally stored in a cloud device to perform cloud prediction operation to obtain charging remaining time, and sending the charging remaining time to a vehicle.
In the embodiment of the application, when the cloud device receives the parameter data set uploaded by the vehicle machine in the normal network connection state, the vehicle machine can predict the charging residual time based on the cloud prediction mode, and the vehicle machine does not need to predict based on a local prediction algorithm at this time, and only needs to timely acquire the parameter data set of the electric vehicle and upload the parameter data set to the cloud device, so that cloud processing and prediction are performed in the cloud device; wherein the parameter data set includes at least one of a percentage of remaining battery power (i.e., SOC), a charge current, a charge voltage, a battery temperature, and a battery health (i.e., SOH). Because the calculation power of the cloud is stronger, and the cloud prediction model can be updated more in aspects, the acquisition efficiency and the prediction accuracy of the prediction result can be effectively improved in the cloud prediction mode. Of course, the parameter data set is not limited to uploading by a vehicle machine, but can also be uploaded by a vehicle-mounted intelligent terminal (such as a smart phone) in communication connection with the vehicle machine or a charging pile.
When the parameter data set is subjected to cloud prediction operation to obtain the first charging remaining time, a prediction model such as a convolutional neural network model and a cyclic neural network model can be adopted.
And after the first charging remaining time is predicted and acquired in the cloud device, timely sending feedback of the first charging remaining time to the vehicle to prompt a user for the charging remaining time in time.
In one embodiment, the processing unit 202 is further specifically configured to:
when a cloud prediction model updating instruction is detected, the cloud prediction model is correspondingly updated, and an updated cloud prediction model is obtained.
In the embodiment of the application, the cloud prediction model can be iteratively trained by relying on the historical data in the cloud device so as to update the cloud prediction model and then release the cloud prediction model in real time. And the complex cloud prediction algorithm is deployed in the cloud, so that the strong calculation power of the cloud is fully utilized, and the accuracy of the prediction result is improved.
The present application further provides a processing device, referring to fig. 3, and fig. 3 shows a schematic structural diagram of the processing device, and specifically, the processing device provided in the present application includes a processor, where the processor is configured to implement steps in the corresponding embodiment as shown in fig. 1 when executing a computer program stored in a memory; alternatively, the processor may be configured to implement the functions of the modules in the corresponding embodiment as shown in fig. 3 when executing the computer program stored in the memory.
For example, a computer program may be split into one or more modules/units, which are stored in a memory and executed by a processor to complete the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions to describe the execution of the computer program in a computer device.
The processing device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the illustrations are merely examples of processing devices, and are not limiting of processing devices, and may include more or less components than illustrated, or may combine some components, or different components, e.g., processing devices may also include input and output devices, network access devices, buses, etc., through which processors, memories, input and output devices, network access devices, etc. are connected.
The processor 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), off-the-shelf 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. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is a control center for a processing device that utilizes various interfaces and lines to connect various parts of the overall processing device.
The memory may be used to store computer programs and/or modules, and the processor implements various functions of the computer device by running or executing the computer programs and/or modules stored in the memory, and invoking data stored in the memory. The memory 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, video data, etc.) created according to the use of the processing device, and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The display screen is used for displaying characters of at least one character type output by the input-output unit.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific operation of the apparatus, the processing device and the corresponding modules described above may refer to the description in the corresponding embodiment as shown in fig. 1, and will not be described in detail herein.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.
For this reason, the embodiment of the present application provides a computer readable storage medium, in which a plurality of instructions capable of being loaded by a processor are stored, so as to execute steps in the embodiment of the present application corresponding to fig. 1, and specific operations may refer to the description in the embodiment corresponding to fig. 1, which is not repeated herein.
Wherein the computer-readable storage medium may comprise: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
Since the steps in the embodiment of the present application corresponding to fig. 1 may be performed by the instructions stored in the computer readable storage medium, the beneficial effects that can be achieved in the embodiment of the present application corresponding to fig. 1 may be achieved, which are described in detail in the foregoing description and are not repeated herein.
The foregoing describes in detail a method, a system and a storage medium for predicting charging remaining time provided in the present application, and specific examples are applied in the embodiments of the present application to illustrate principles and implementations of the present application, where the foregoing description of the embodiments is only used to help understand the method and core idea of the present application; meanwhile, those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, and the present description should not be construed as limiting the present application in view of the above.
Claims (10)
1. A method of predicting charge remaining time, the method comprising:
acquiring network state information of a vehicle;
according to the network states of the vehicle and the cloud end equipment, determining that the equipment for predicting the charging remaining time is the vehicle or the cloud end equipment, so that at least one of the vehicle or the motion equipment can complete the method for predicting the charging remaining time.
2. The method of claim 1, wherein when the device that determines the predicted charge remaining time is a cloud device, the method further comprises:
transmitting a parameter data set to the cloud device, wherein the parameter data set at least comprises at least one of a battery remaining capacity percentage, a charging current, a charging voltage, a battery temperature and a battery health degree;
and the receiving cloud equipment carries out prediction operation according to the parameter data set to obtain the charging remaining time.
3. The method of claim 1, wherein when the device that determines the predicted charge remaining time is a vehicle, the method further comprises:
and acquiring a parameter data set, inputting the parameter data set into a local prediction model for prediction operation, obtaining the charging remaining time, and displaying the charging remaining time.
4. The method of claim 1, wherein when the device that determines the predicted charge remaining time is a vehicle, the method further comprises:
uploading the acquired parameter data set to a vehicle-mounted intelligent terminal;
and receiving the charging remaining time obtained by the terminal prediction operation of the vehicle-mounted intelligent terminal according to the parameter data set, and displaying the charging remaining time.
5. The method according to claim 1, wherein the method further comprises:
uploading the acquired parameter data set to a vehicle-mounted intelligent terminal, so that the vehicle-mounted intelligent terminal uploads the parameter data set to cloud equipment;
and receiving the charging remaining time obtained by cloud prediction operation of the cloud device according to the parameter data set, and displaying the charging remaining time.
6. The method of claim 2, wherein the receiving cloud device performing a prediction operation according to the parameter data set to obtain the charging remaining time comprises:
and inputting the parameter data set to a cloud prediction model locally stored in a cloud device to perform cloud prediction operation to obtain charging remaining time, and sending the charging remaining time to a vehicle.
7. The method of claim 6, wherein the method further comprises:
when a cloud prediction model updating instruction is detected, the cloud prediction model is correspondingly updated, and an updated cloud prediction model is obtained.
8. A prediction apparatus of a remaining charge time, characterized in that the prediction apparatus of a remaining charge time includes: an acquisition unit and a processing unit;
the acquisition unit is used for acquiring network state information of the vehicle;
the processing unit is configured to determine, according to a network state of the vehicle and the cloud device, that the device for predicting the charging remaining time is the vehicle or the cloud device, so that at least one of the vehicle or the motion device completes the method for predicting the charging remaining time.
9. A processing device comprising a processor and a memory, the memory having stored therein a computer program, the processor executing the method of any of claims 1 to 7 when invoking the computer program in the memory.
10. A computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the method of any one of claims 1 to 7.
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| CN117933433B (en) * | 2024-03-22 | 2024-05-31 | 成都信息工程大学 | Charging pile reservation charging scheduling method and device based on block chain |
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| CN117933433B (en) * | 2024-03-22 | 2024-05-31 | 成都信息工程大学 | Charging pile reservation charging scheduling method and device based on block chain |
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