CN112700135A - Charging recommendation method based on vehicle-mounted OBD intelligent box collected data - Google Patents
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Abstract
The invention discloses a charging recommendation method based on data acquired by a vehicle-mounted OBD intelligent box, which comprises the following steps: installing an OBD intelligent box; installing user service software; acquiring vehicle condition data at regular time; extracting the latest basic data in the vehicle condition data; inputting a pre-constructed GRU model to obtain the predicted endurance mileage of the electric automobile; judging whether the electric automobile sets a navigation route or not; judging whether the predicted endurance mileage of the electric automobile is enough to support the navigation route mileage or not; outputting the recommended charging electric quantity matched with the difference value; and outputting the quick charging time and the saturated charging time of the electric automobile. Has the advantages that: the driving mileage of the electric automobile can be accurately predicted, the driving experience of the user is improved, the trip route of the user can be analyzed according to the difference value between the navigation route mileage of the user and the predicted driving mileage, a reasonable charging scheme is recommended for the user, and therefore the using requirements of people can be better met.
Description
Technical Field
The invention relates to the technical field of electric automobiles, in particular to a charging recommendation method based on data collected by a vehicle-mounted OBD intelligent box.
Background
With the continuous development of the car networking technology, the popularization and application of intelligent transportation become one of the important development trends at present. Important features of the internet of vehicles include: collection of vehicle data, transmission of vehicle data. One important system for vehicle data collection is, among others, the OBD (on-board diagnostics) system. It is capable of monitoring a number of systems and components, including engines, catalytic converters, particulate traps, oxygen sensors, emission control systems, fuel systems, and the like.
The vehicle-mounted OBD equipment is used for acquiring relevant data of the automobile ECU through the OBD interface, uploading the data to the server through the network module, and providing the automobile vehicle-mounted terminal with the functions of vehicle physical examination, vehicle track, accurate travel report, oil consumption analysis, driving behavior analysis and the like.
For electric vehicles, the fuel oil vehicle has a wide market prospect, but the endurance mileage of the electric vehicles is generally shorter than that of the traditional fuel oil vehicles at present. Meanwhile, the storage battery of the electric vehicle is also affected by various factors. When the residual electric quantity of the automobile is small, the driving range of the electric automobile can be accurately predicted, the confidence that a driver smoothly drives the automobile to a destination is increased, and the driving experience of a user is improved.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a charging recommendation method based on data acquired by a vehicle-mounted OBD intelligent box, so as to overcome the technical problems in the prior related art.
Therefore, the invention adopts the following specific technical scheme:
a charging recommendation method based on data collected by a vehicle-mounted OBD intelligent box comprises the following steps:
s1, installing a preset OBD intelligent box at a corresponding position of the electric automobile, and connecting the box with a power supply and a CAN bus on the automobile;
s2, installing user service software on the mobile terminal, and establishing communication connection with the OBD intelligent box;
s3, starting the electric automobile, wherein the OBD intelligent box acquires automobile condition data regularly by using a CAN bus and sends the automobile condition data to the mobile terminal;
s4, the mobile terminal receives the vehicle condition data and extracts the latest basic data in the vehicle condition data;
s5, inputting the latest basic data into a pre-constructed GRU model to obtain the predicted endurance mileage of the electric automobile;
s6, judging whether the electric automobile is provided with a navigation route or not, if so, executing S7, and otherwise, executing S9 when the predicted driving mileage of the electric automobile exceeds the range of a preset threshold value;
s7, judging whether the predicted endurance mileage of the electric automobile is enough to support the navigation route mileage, if so, ending the process, otherwise, executing S8;
s8, calculating to obtain a difference value between the predicted driving mileage and the navigation route mileage of the electric automobile, and outputting recommended charging electric quantity matched with the difference value;
and S9, outputting the quick charging time and the saturated charging time of the electric automobile, and recommending corresponding charging station information for a user.
Further, the communication in S2 includes, but is not limited to, a mobile communication network, a bluetooth transmission mode, and a WIFI transmission mode.
Further, the step of starting the electric vehicle in S3, the OBD intelligent box using the CAN bus to obtain the vehicle condition data at regular time and sending the vehicle condition data to the mobile terminal includes the steps of:
s31, starting the electric automobile, wherein the OBD intelligent box reads automobile condition data of the electric automobile by utilizing a CAN bus;
s32, analyzing the read vehicle condition data to obtain the vehicle condition data which can be identified by the OBD intelligent box;
s33, outputting the analyzed vehicle condition data to the OBD intelligent box through an OBD interface on the electric vehicle;
and S34, the OBD intelligent box sends the analyzed vehicle condition data to the mobile terminal.
Further, the basic data in S4 includes any one or a combination of several of electric vehicle motion data, battery data, motor control data, and environmental data.
Further, the step of receiving the vehicle condition data by the mobile terminal in S4 and extracting the latest basic data in the vehicle condition data further includes the following steps:
the mobile terminal initiates a communication request to the server and switches a corresponding working mode according to an uploading result;
and the mobile terminal uploads the vehicle condition data of the electric vehicle to the server according to the corresponding working mode.
Further, before the S5 inputs the latest basic data into a pre-constructed GRU model to obtain the predicted range of the electric vehicle, the method further includes constructing the GRU model, where constructing the GRU model includes the following steps:
acquiring historical basic data of the electric automobile to be predicted and performing data processing;
training the constructed GRU model by using the processed historical basic data;
parameters of the GRU model are updated using a minimization loss function.
Further, the data processing comprises classifying the historical basic data of the electric vehicle and performing data cleaning processing on the classified historical basic data.
Further, the training the constructed GRU model using the processed historical basic data further includes the following steps: when the GRU model is trained, the GRU model is trained in a period of 3 days, 5 days, 7 days, 9 days and 11 days respectively, and then the model with the best effect is selected for prediction.
Further, the step of calculating a difference between the predicted driving mileage and the navigation route mileage of the electric vehicle in S8, and outputting the recommended charging capacity matching the difference includes the following steps:
s81, obtaining a corresponding navigation route mileage according to the navigation route;
s82, calculating a difference value between the predicted endurance mileage and the navigation route mileage of the electric automobile;
s83, calculating to obtain the electric quantity required by the electric automobile when the electric automobile runs the difference mileage;
s84, increasing the electric quantity by preset times to obtain recommended charging electric quantity;
and S85, outputting the recommended charging electric quantity matched with the difference value.
Further, the outputting the fast charging time and the saturation charging time of the electric vehicle in S9 includes:
analyzing the charging efficiency and the current electric quantity of the electric automobile to obtain the quick charging time and the saturated charging time of the current electric automobile; the quick charging time refers to the charging time when the electric automobile reaches the highest electric quantity supported by quick charging from the current moment; the saturated charging time refers to the charging time from the current moment to the full charge of the electric automobile.
The invention has the beneficial effects that: through the use of OBD intelligence box, can regularly acquire electric automobile's vehicle condition data, and can realize the prediction to electric automobile continuation of the journey mileage according to the latest basic data and the GRU model that acquire, thereby the continuation of the journey mileage of prediction electric automobile that not only can be accurate, promote user's driving experience, but also can come to analyze user's trip route according to the difference between user's navigation route mileage and the prediction continuation of the journey mileage, and recommend reasonable charging scheme for the user, thereby can satisfy in people's user demand better.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a charging recommendation method based on data collected by a vehicle-mounted OBD smart box according to an embodiment of the present invention.
Detailed Description
For further explanation of the various embodiments, the drawings which form a part of the disclosure and which are incorporated in and constitute a part of this specification, illustrate embodiments and, together with the description, serve to explain the principles of operation of the embodiments, and to enable others of ordinary skill in the art to understand the various embodiments and advantages of the invention, and, by reference to these figures, reference is made to the accompanying drawings, which are not to scale and wherein like reference numerals generally refer to like elements.
According to the embodiment of the invention, a charging recommendation method based on data acquired by a vehicle-mounted OBD intelligent box is provided.
Referring to the drawings and the detailed description, the invention is further described, as shown in fig. 1, according to an embodiment of the invention, a charging recommendation method based on data collected by a vehicle-mounted OBD intelligent box includes the following steps:
s1, installing a preset OBD intelligent box at a corresponding position of the electric automobile, and connecting the box with a power supply and a CAN bus on the automobile;
s2, installing user service software on the mobile terminal, and establishing communication connection with the OBD intelligent box;
the communication in S2 includes, but is not limited to, a mobile communication network, a bluetooth transmission mode, and a WIFI transmission mode.
S3, starting the electric automobile, wherein the OBD intelligent box acquires automobile condition data regularly by using a CAN bus and sends the automobile condition data to the mobile terminal;
wherein, start electric automobile in S3, OBD intelligence box utilizes CAN bus timing to obtain the vehicle condition data and send to the mobile terminal includes the following step:
s31, starting the electric automobile, wherein the OBD intelligent box reads automobile condition data of the electric automobile by utilizing a CAN bus;
s32, analyzing the read vehicle condition data to obtain the vehicle condition data which can be identified by the OBD intelligent box;
s33, outputting the analyzed vehicle condition data to the OBD intelligent box through an OBD interface on the electric vehicle;
and S34, the OBD intelligent box sends the analyzed vehicle condition data to the mobile terminal.
S4, the mobile terminal receives the vehicle condition data and extracts the latest basic data in the vehicle condition data;
specifically, the basic data in S4 includes any one or a combination of several of electric vehicle motion data, battery data, motor control data, and environmental data.
In addition, the step of receiving the vehicle condition data by the mobile terminal in S4 and extracting the latest basic data in the vehicle condition data further includes the steps of:
the mobile terminal initiates a communication request to the server and switches a corresponding working mode according to an uploading result; the operation mode includes a normal mode and a no network mode. Specifically, the method for switching to the corresponding working mode includes: if the server feeds back the communication information to the mobile terminal APP, switching to a conventional mode; and if the server does not feed back the communication information to the mobile terminal APP, switching to the no-network mode.
And the mobile terminal uploads the vehicle condition data of the electric vehicle to the server according to the corresponding working mode.
S5, inputting the latest basic data into a pre-constructed GRU model to obtain the predicted endurance mileage of the electric automobile;
wherein the step S5 of inputting the latest basic data into a pre-constructed GRU model to obtain the predicted cruising range of the electric vehicle further includes constructing the GRU model, wherein constructing the GRU model includes the steps of:
acquiring historical basic data of the electric automobile to be predicted and performing data processing;
specifically, the data processing includes classifying the historical basic data of the electric vehicle, and performing data cleaning processing on the classified historical basic data.
Training the constructed GRU model by using the processed historical basic data;
specifically, the training of the constructed GRU model using the processed historical basic data further includes the following steps: when the GRU model is trained, the GRU model is trained in a period of 3 days, 5 days, 7 days, 9 days and 11 days respectively, and then the model with the best effect is selected for prediction.
Parameters of the GRU model are updated using a minimization loss function.
S6, judging whether the electric automobile is provided with a navigation route or not, if so, executing S7, and otherwise, executing S9 when the predicted driving mileage of the electric automobile exceeds the range of a preset threshold value;
s7, judging whether the predicted endurance mileage of the electric automobile is enough to support the navigation route mileage, if so, ending the process, otherwise, executing S8;
s8, calculating to obtain a difference value between the predicted driving mileage and the navigation route mileage of the electric automobile, and outputting recommended charging electric quantity matched with the difference value;
in S8, the step of calculating a difference between the predicted driving mileage and the navigation route mileage of the electric vehicle, and outputting the recommended charging amount matched with the difference includes the following steps:
s81, obtaining a corresponding navigation route mileage according to the navigation route;
s82, calculating a difference value between the predicted endurance mileage and the navigation route mileage of the electric automobile;
s83, calculating to obtain the electric quantity required by the electric automobile when the electric automobile runs the difference mileage;
s84, increasing the electric quantity by preset times to obtain recommended charging electric quantity;
and S85, outputting the recommended charging electric quantity matched with the difference value.
And S9, outputting the quick charging time and the saturated charging time of the electric automobile, and recommending corresponding charging station information for a user.
Wherein the outputting of the fast charging time and the saturation charging time of the electric vehicle in S9 includes:
analyzing the charging efficiency and the current electric quantity of the electric automobile to obtain the quick charging time and the saturated charging time of the current electric automobile; the quick charging time refers to the charging time when the electric automobile reaches the highest electric quantity supported by quick charging from the current moment; the saturated charging time refers to the charging time from the current moment to the full charge of the electric automobile.
In summary, by means of the technical scheme of the invention, the vehicle condition data of the electric vehicle can be acquired at regular time through the use of the OBD intelligent box, and the prediction of the driving mileage of the electric vehicle can be realized according to the acquired latest basic data and the GRU model, so that the driving mileage of the electric vehicle can be accurately predicted, the driving experience of the user can be improved, the trip route of the user can be analyzed according to the difference value between the navigation route mileage of the user and the predicted driving mileage, and a reasonable charging scheme is recommended for the user, so that the use requirements of people can be better met.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A charging recommendation method based on data collected by a vehicle-mounted OBD intelligent box is characterized by comprising the following steps:
s1, installing a preset OBD intelligent box at a corresponding position of the electric automobile, and connecting the box with a power supply and a CAN bus on the automobile;
s2, installing user service software on the mobile terminal, and establishing communication connection with the OBD intelligent box;
s3, starting the electric automobile, wherein the OBD intelligent box acquires automobile condition data regularly by using a CAN bus and sends the automobile condition data to the mobile terminal;
s4, the mobile terminal receives the vehicle condition data and extracts the latest basic data in the vehicle condition data;
s5, inputting the latest basic data into a pre-constructed GRU model to obtain the predicted endurance mileage of the electric automobile;
s6, judging whether the electric automobile is provided with a navigation route or not, if so, executing S7, and otherwise, executing S9 when the predicted driving mileage of the electric automobile exceeds the range of a preset threshold value;
s7, judging whether the predicted endurance mileage of the electric automobile is enough to support the navigation route mileage, if so, ending the process, otherwise, executing S8;
s8, calculating to obtain a difference value between the predicted driving mileage and the navigation route mileage of the electric automobile, and outputting recommended charging electric quantity matched with the difference value;
and S9, outputting the quick charging time and the saturated charging time of the electric automobile, and recommending corresponding charging station information for a user.
2. The charging recommendation method based on the vehicle-mounted OBD intelligent box collected data according to claim 1, wherein the communication in S2 includes but is not limited to communication connection modes of a mobile communication network, Bluetooth transmission and WIFI transmission.
3. The charging recommendation method based on the data collected by the on-board OBD intelligent box according to claim 1, wherein the step S3 of starting the electric vehicle, the OBD intelligent box using the CAN bus to periodically obtain the vehicle condition data and send the vehicle condition data to the mobile terminal includes the following steps:
s31, starting the electric automobile, wherein the OBD intelligent box reads automobile condition data of the electric automobile by utilizing a CAN bus;
s32, analyzing the read vehicle condition data to obtain the vehicle condition data which can be identified by the OBD intelligent box;
s33, outputting the analyzed vehicle condition data to the OBD intelligent box through an OBD interface on the electric vehicle;
and S34, the OBD intelligent box sends the analyzed vehicle condition data to the mobile terminal.
4. The charging recommendation method based on the vehicle-mounted OBD intelligent box collected data according to claim 1, wherein the basic data in S4 comprises any one or a combination of electric vehicle motion data, battery data, motor control data and environment data.
5. The charging recommendation method based on the vehicle-mounted OBD intelligent box collected data according to claim 1, wherein the step of receiving the vehicle condition data by the mobile terminal in S4 and extracting the latest basic data in the vehicle condition data further comprises the following steps:
the mobile terminal initiates a communication request to the server and switches a corresponding working mode according to an uploading result;
and the mobile terminal uploads the vehicle condition data of the electric vehicle to the server according to the corresponding working mode.
6. The vehicle-mounted OBD intelligent box data collection-based charge recommendation method according to claim 1, wherein the step S5 of inputting the latest basic data into a pre-constructed GRU model to obtain the predicted range of the electric vehicle further comprises constructing the GRU model, wherein the step of constructing the GRU model comprises the following steps:
acquiring historical basic data of the electric automobile to be predicted and performing data processing;
training the constructed GRU model by using the processed historical basic data;
parameters of the GRU model are updated using a minimization loss function.
7. The charging recommendation method based on the vehicle-mounted OBD intelligent box collected data is characterized in that the data processing comprises classifying historical basic data of the electric vehicle and performing data cleaning processing on the classified historical basic data.
8. The method of claim 6, wherein the training of the constructed GRU model using the processed historical base data further comprises the steps of: when the GRU model is trained, the GRU model is trained in a period of 3 days, 5 days, 7 days, 9 days and 11 days respectively, and then the model with the best effect is selected for prediction.
9. The charging recommendation method based on the vehicle-mounted OBD intelligent box collected data according to claim 1, wherein the step of calculating the difference value between the predicted driving mileage and the navigation route mileage of the electric vehicle in S8 and outputting the recommended charging capacity matched with the difference value comprises the following steps:
s81, obtaining a corresponding navigation route mileage according to the navigation route;
s82, calculating a difference value between the predicted endurance mileage and the navigation route mileage of the electric automobile;
s83, calculating to obtain the electric quantity required by the electric automobile when the electric automobile runs the difference mileage;
s84, increasing the electric quantity by preset times to obtain recommended charging electric quantity;
and S85, outputting the recommended charging electric quantity matched with the difference value.
10. The charging recommendation method based on the vehicle-mounted OBD intelligent box collected data according to claim 1, wherein the step of outputting the fast charging time and the saturation charging time of the electric vehicle in the step S9 comprises the following steps:
analyzing the charging efficiency and the current electric quantity of the electric automobile to obtain the quick charging time and the saturated charging time of the current electric automobile; the quick charging time refers to the charging time when the electric automobile reaches the highest electric quantity supported by quick charging from the current moment; the saturated charging time refers to the charging time from the current moment to the full charge of the electric automobile.
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| CN113869988A (en) * | 2021-10-28 | 2021-12-31 | 重庆金康赛力斯新能源汽车设计院有限公司 | Intelligent charging method and device for electric automobile and electronic terminal equipment |
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