CN113043863A - Vehicle charging method and system, readable storage medium and electronic device - Google Patents
Vehicle charging method and system, readable storage medium and electronic device Download PDFInfo
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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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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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- 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
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/14—Plug-in electric vehicles
Abstract
The invention provides a vehicle charging method and system, a readable storage medium and an electronic device, comprising: after the vehicle starts to charge, judging whether the personalized charging function is started or not; if yes, predicting a target charging amount according to a formulated charging amount prediction rule, and stopping charging the vehicle when the electric quantity reaches the target charging amount; if not, charging the vehicle according to the charging amount set by the user or the maximum allowable charging amount; wherein the charge amount prediction rule is formulated according to vehicle battery parameters and vehicle historical usage data. Namely, through the historical behavior of learning the user with the car and user's demand input, reasonable charge strategy is taken in the automation, so alright slow down the battery ageing, promote user's use satisfaction, help extension electric motor car life, reduce electric motor car use cost.
Description
Technical Field
The present invention relates to the field of vehicle technologies, and in particular, to a vehicle charging method and system, a readable storage medium, and an electronic device.
Background
The power battery is a component which accounts for a large proportion of the cost of the electric vehicle, and the aging of the battery is an important factor influencing the value of the electric vehicle. One important factor affecting the aging of batteries for electric vehicles is overcharge and storage at high charge. The existing electric vehicle charging strategy is usually a strategy of fully charging once and then stopping even though a user does not use the electric vehicle charging strategy. For modern lithium ion batteries, the more the charge capacity, the higher the charge voltage, and the more damage to the battery. Furthermore, if the battery is stored for a long time after being fully charged, the self-discharge rate and the aging speed of the battery in a high state of charge will also be increased. On the other hand, if the charging is too little, the power consumption of the user is insufficient, and the risk of anxiety of the mileage of the user is caused.
Disclosure of Invention
The invention aims to provide a vehicle charging method and system, a readable storage medium and electronic equipment, so as to solve the problem that a power battery is easy to age.
In order to solve the above technical problem, the present invention provides a vehicle charging method, including:
after the vehicle starts to charge, judging whether the personalized charging function is started or not; if yes, predicting a target charging amount according to a formulated charging amount prediction rule, and stopping charging the vehicle when the electric quantity reaches the target charging amount; if not, charging the vehicle according to the charging amount set by the user or the maximum allowable charging amount;
wherein the charge amount prediction rule is formulated according to vehicle battery parameters and vehicle historical usage data.
Optionally, in the vehicle charging method, the vehicle charging method further includes: judging whether the current electric quantity of the vehicle meets the short-time electricity consumption demand of a user; if not, sending out charging reminding information.
Optionally, in the vehicle charging method, the method for determining whether the current electric quantity of the vehicle meets the short-time electricity consumption demand of the user includes:
predicting target short-time electricity consumption according to the formulated electricity consumption judgment rule;
and judging the current electric quantity of the vehicle and the target short-time electricity consumption, and if the current electric quantity of the vehicle is smaller than the target short-time electricity consumption, judging that the current electric quantity of the vehicle does not meet the short-time electricity consumption requirement of the user.
Optionally, in the vehicle charging method, predicting the target short-time electricity consumption according to the established electricity consumption determination rule includes:
predicting to obtain a first target short-time electricity consumption based on the maximum electricity consumption in a plurality of historical time spans;
obtaining a second target short-time electricity consumption based on probability statistics of the electricity consumption in a plurality of historical time spans;
obtaining a third target short-time electricity consumption based on the established electricity consumption prediction model;
predicting to obtain a fourth target short-time electricity consumption based on the user travel; and the number of the first and second groups,
and taking one, the maximum value, the minimum value, the average value or the weighted value of the first target short-time electricity consumption, the second target short-time electricity consumption, the third target short-time electricity consumption and the fourth target short-time electricity consumption as the final target short-time electricity consumption.
Optionally, in the vehicle charging method, the predicting the first target short-time electricity consumption based on the maximum electricity consumption in the plurality of historical time spans includes:
counting the power consumption in a plurality of historical time spans in a calculation period, and comparing to obtain the maximum power consumption;
and adding the maximum power consumption and the first safe redundancy to obtain the first target short-time power consumption.
Optionally, in the vehicle charging method, the obtaining a second target short-time electricity consumption based on probability statistics of electricity consumptions in a plurality of historical time spans includes:
counting the electricity consumption in a plurality of historical time spans in a calculation period, and calculating the average value and standard deviation of the plurality of electricity consumption obtained through statistics;
and multiplying the standard deviation obtained by calculation by a first safety factor, and adding the standard deviation and the average value obtained by calculation to obtain the second target short-time electricity consumption.
Optionally, in the vehicle charging method, obtaining a third target short-time electricity consumption based on the established electricity consumption prediction model includes:
counting the electricity consumption behavior characteristics in a plurality of historical time spans in a calculation period, and inputting the electricity consumption behavior characteristics into the electricity consumption prediction model to obtain the third target short-term electricity consumption;
wherein the electricity usage behavior characteristics include: one or more of maximum power consumption, average power consumption, variance of power consumption, daily power consumption, ambient temperature in the vehicle, and date information of power consumption.
Optionally, in the vehicle charging method, the predicting a fourth target short-time electricity consumption based on the user trip includes:
and receiving travel plan information planned by a user, calculating the to-be-traveled distance according to the travel plan information, and predicting the four-target short-time power consumption by combining the to-be-traveled distance and road vehicle conditions.
Optionally, in the vehicle charging method, the predicting a target charge amount according to the established charge amount prediction rule includes:
predicting a first target charge amount based on the minimum allowable capacity of the battery;
obtaining a second target charge amount based on a maximum charge amount within a plurality of historical time spans;
obtaining a third target charging quantity based on charging quantity probability statistics in a plurality of historical time spans;
obtaining a fourth target charge amount based on the established charge amount prediction model;
predicting a fifth target charge amount based on the user travel; and the number of the first and second groups,
the final target charge amount is one of the first target charge amount, the second target charge amount, the third target charge amount, the fourth target charge amount, and the fifth target charge amount, a maximum value, a minimum value, an average value, or a weighted value.
Optionally, in the vehicle charging method, the obtaining a second target charge amount based on a maximum charge amount in a plurality of historical time spans includes:
counting the electricity consumption in a plurality of historical time spans in a calculation period, and comparing to obtain the maximum charging amount;
and adding the maximum charge amount and a second safe redundancy amount to obtain a second target charge amount.
Optionally, in the vehicle charging method, the obtaining a third target charge amount based on the charge amount probability statistics in the plurality of historical time spans includes:
counting the charging quantities in a plurality of historical time spans in a calculation period, and calculating the average value and standard deviation of the plurality of counted charging quantities;
and multiplying the calculated standard deviation by a second safety factor, and adding the multiplied standard deviation and the calculated average value to obtain the third target charging amount.
Optionally, in the vehicle charging method, the obtaining a fourth target charge amount based on the established charge amount prediction model includes:
counting the charging behavior characteristics in a plurality of historical time spans in a calculation period, and inputting the charging behavior characteristics into the charging quantity prediction model to obtain the fourth target charging quantity;
wherein the charging behavior characteristics include: one or more of a maximum charge amount, a mean charge amount, a variance of charge amount, a daily charge amount, and charging date information.
Optionally, in the vehicle charging method, the predicting a fifth target charge amount based on the user trip includes:
and receiving travel plan information planned by a user, calculating the mileage to be traveled according to the travel plan information, and predicting the fifth target charging amount by combining the mileage to be traveled and road conditions.
The present invention also provides a readable storage medium having stored thereon a computer program which, when executed, implements a vehicle charging method as described above.
The invention also provides an electronic device comprising a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, implements a vehicle charging method as described above.
The present invention also provides a vehicle charging system, comprising: an onboard controller and a display interaction device, wherein,
the vehicle-mounted controller is used for controlling the vehicle to be charged by adopting the vehicle charging method and sending a charging report to the display interaction equipment;
the display interaction device is used for checking the charging report, displaying the personalized charging scheme, enabling a user to select whether to start the personalized charging function, inputting travel plan information and setting a charging amount.
Optionally, in the vehicle charging system, the method further includes: and the cloud server is used for acquiring battery parameters and vehicle use data of different vehicle clusters, establishing a power consumption prediction rule for predicting power consumption and a charging amount prediction rule for predicting charging amount, and sending the power consumption prediction rule and the charging amount prediction rule to the vehicle-mounted controller.
In summary, the vehicle charging method and system, the readable storage medium and the electronic device provided by the invention include: after the vehicle starts to charge, judging whether the personalized charging function is started or not; if yes, predicting a target charging amount according to a formulated charging amount prediction rule, and stopping charging the vehicle when the electric quantity reaches the target charging amount; if not, charging the vehicle according to the charging amount set by the user or the maximum allowable charging amount; wherein the charge amount prediction rule is formulated according to vehicle battery parameters and vehicle historical usage data. Namely, according to the vehicle charging method and system, the readable storage medium and the electronic device provided by the invention, the historical vehicle using behaviors of the user and the requirement input of the user are learned, so that a reasonable charging strategy can be automatically adopted, the aging of the battery can be slowed down, the use satisfaction of the user is improved, the service life of the electric vehicle is prolonged, and the use cost of the electric vehicle is reduced.
Further, the vehicle charging method further includes: and judging whether the current electric quantity of the vehicle meets the short-time power consumption requirement of the user or not by utilizing the formulated power consumption judgment rule, and if not, sending charging reminding information. The vehicle charging method and system, the readable storage medium and the electronic equipment provided by the invention also have a function of predicting the short-term power consumption of the user, and the power consumption judgment rule is formulated according to the historical use data of the vehicle, so that whether the vehicle needs to be charged or not can be judged reasonably according to the requirement of the user, the battery aging caused by charging can be reduced, the use satisfaction degree of the user is improved, the service life of the electric vehicle is further prolonged, and the use cost of the electric vehicle is reduced. In addition, the vehicle charging method and system, the readable storage medium and the electronic device provided by the invention also have a user charging reminding function, so that the situation that the electric quantity is insufficient in the use process of the vehicle can be avoided.
Drawings
Fig. 1 is a flowchart of a vehicle charging method according to an embodiment of the present invention.
Detailed Description
To make the objects, advantages and features of the present invention more apparent, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It is to be noted that the drawings are in greatly simplified form and are not to scale, but are merely intended to facilitate and clarify the explanation of the embodiments of the present invention. Further, the structures illustrated in the drawings are often part of actual structures. In particular, the drawings may have different emphasis points and may sometimes be scaled differently. It should be further understood that the terms "first," "second," "third," and the like in the description are used for distinguishing between various components, elements, steps, and the like, and are not intended to imply a logical or sequential relationship between various components, elements, steps, or the like, unless otherwise indicated or indicated.
As shown in fig. 1, an embodiment of the present invention provides a vehicle charging method, including the following steps:
s1, after the vehicle starts to charge, judging whether the personalized charging function is started; if yes, go to step S2, otherwise go to step S3;
s2, predicting a target charge amount according to the established charge amount prediction rule, and stopping charging the vehicle when the amount of electricity reaches the target charge amount;
s3, the vehicle is charged by the charge amount or the maximum allowable charge amount set by the user.
In step S2, the charge amount prediction rule is made according to vehicle battery parameters and vehicle historical usage data.
According to the vehicle charging method provided by the embodiment of the invention, through learning the historical vehicle using behaviors of the user and the requirement input of the user, a reasonable charging strategy can be automatically adopted, so that the aging of the battery can be slowed down, the use satisfaction of the user is improved, the service life of the electric vehicle is prolonged, and the use cost of the electric vehicle is reduced.
Preferably, the vehicle charging method provided by the embodiment of the present invention further includes: and judging whether the current electric quantity of the vehicle meets the short-time power consumption requirement of the user, and if not, sending charging reminding information. The charging reminding information is used for reminding a user of charging the vehicle, so that the condition that the electric quantity of the vehicle is insufficient in the using process can be avoided.
The above steps S1 to S3 may be executed when the user receives the reminder information and then charges the battery, or may be executed when the user actively charges the battery.
The vehicle charging method provided by the embodiment of the invention can be executed by a computer program, and the computer program can be configured on an on-board controller. When the application program is configured on the vehicle-mounted controller, the vehicle-mounted controller can send the charging reminding information to a display interaction device, and the display interaction device can be a display interaction device carried by a mobile terminal, a personal computer, a vehicle-mounted display/navigation device.
The vehicle-mounted controller comprises a battery management system, a vehicle controller, a domain controller, vehicle-mounted entertainment navigation equipment and the like, the vehicle-mounted controller acquires relevant information of a battery and/or a whole vehicle on the one hand and acquires user behavior characteristics (battery voltage, charging current, temperature, state of charge, vehicle position, vehicle speed, weather, ambient temperature/humidity, charging pile information, vehicle running and charging state and the like) so as to be used for formulating charging quantity prediction rules, and on the other hand, the charging quantity prediction rules are utilized for carrying out charging quantity depth prediction, the vehicle is charged based on prediction results, further, a charging report is generated after the vehicle is charged and sent to the display interaction equipment, so that a user can know the charging condition at each time. The charge amount prediction rule may be formulated by a cloud server. Specifically, the vehicle-mounted controller sends the acquired related information and user behavior characteristics (namely vehicle use data) of the battery and/or the whole vehicle to the cloud server, so that the cloud server can collect the related information and the user behavior characteristics of the battery and/or the whole vehicle of different vehicle clusters, so that a charging amount prediction rule can be established based on big data, and then the charging amount prediction rule is sent to the vehicle-mounted controller for predicting the charging amount depth.
In other embodiments, the charging amount prediction rule may also be formulated by the onboard controller itself, and the onboard controller collects relevant information and user behavior characteristics of a battery and/or a vehicle of the vehicle where the onboard controller is located, and then establishes the charging amount prediction rule based on the collected data, which is not limited in this application.
In addition, preferably, after the battery of the vehicle and/or the related information of the whole vehicle and the user behavior characteristics are updated, the charging amount prediction rule is correspondingly updated, namely, the vehicle charging strategy can change along with the user using habits, so that the charging is always in a reasonable state, the battery aging can be slowed down, and the user using satisfaction is improved.
As is apparent from the above description, the vehicle charging method provided by the present embodiment includes the following functions: the method comprises the steps of predicting short-time electricity consumption of a user, reminding charging of the user, predicting charge amount of the user, adjusting charging depth of the user and generating a charging report. The following describes each function of the vehicle charging method provided in the present embodiment in further detail.
(1) User short-term power consumption prediction
The function is to predict the amount of power usage by the user over a time span in the future, which can be configured based on user input and defined as the amount of power usage for the current driving cycle, the current day, and the next days.
In this embodiment, the target short-term electricity consumption can be predicted by formulating an electricity consumption judgment rule, and the method for judging whether the current electricity quantity of the vehicle meets the short-term electricity consumption demand of the user includes:
predicting target short-time electricity consumption according to the formulated electricity consumption judgment rule;
and judging the current electric quantity of the vehicle and the target short-time electricity consumption, and if the current electric quantity of the vehicle is smaller than the target short-time electricity consumption, judging that the current electric quantity of the vehicle does not meet the short-time electricity consumption requirement of the user.
The power consumption judgment rule is formulated according to historical use data of the vehicle, so that whether charging is needed or not can be reasonably judged according to user demands, the aging of a battery caused by charging can be slowed down, the use satisfaction of a user can be improved, the service life of the electric vehicle can be further prolonged, and the use cost of the electric vehicle can be reduced.
The method comprises the steps of obtaining a plurality of predicted values by adopting a plurality of power consumption judgment rules for prediction, and then adopting one, the maximum value, the minimum value, the average value or the weighted value of the plurality of predicted values as a final target short-time power consumption for judging whether the current electric quantity of a vehicle meets the short-time power consumption requirement of a user.
In this embodiment, the power consumption determination rule may include: based on maximum power usage, based on probabilistic statistics, based on predictive models and based on user travel. Further, the predicting the target short-time electricity consumption according to the established electricity consumption judgment rule comprises:
predicting to obtain a first target short-time electricity consumption based on the maximum electricity consumption in a plurality of historical time spans;
obtaining a second target short-time electricity consumption based on probability statistics of the electricity consumption in a plurality of historical time spans;
obtaining a third target short-time electricity consumption based on the established electricity consumption prediction model;
predicting to obtain a fourth target short-time electricity consumption based on the user travel; and the number of the first and second groups,
and taking one, the maximum value, the minimum value, the average value or the weighted value of the first target short-time electricity consumption, the second target short-time electricity consumption, the third target short-time electricity consumption and the fourth target short-time electricity consumption as the final target short-time electricity consumption.
Each electricity usage determination rule will be described in detail below.
1.1 based on maximum power consumption
The predicting a first target short term power usage based on a maximum power usage over a plurality of historical time spans comprises:
counting the power consumption in a plurality of historical time spans in a calculation period, and comparing to obtain the maximum power consumption;
and adding the maximum power consumption and the first safe redundancy to obtain the first target short-time power consumption.
The first safe redundancy amount may be the amount of power required for a fixed driving range (e.g., 20 km driving range), or a fixed amount of power set by a user.
1.2 based on probability statistics
The obtaining of the second target short-time electricity consumption based on the probability statistics of the electricity consumption in the plurality of historical time spans comprises:
counting the electricity consumption in a plurality of historical time spans in a calculation period, and calculating the average value and standard deviation of the plurality of electricity consumption obtained through statistics;
and multiplying the standard deviation obtained by calculation by a first safety factor, and adding the standard deviation and the average value obtained by calculation to obtain the second target short-time electricity consumption.
Wherein the first safety factor is adjustable, for example, can be set to 95%.
1.3 based on predictive models
The obtaining of the third target short-term power consumption based on the established power consumption prediction model comprises:
counting the electricity consumption behavior characteristics in a plurality of historical time spans in a calculation period, and inputting the electricity consumption behavior characteristics into the electricity consumption prediction model to obtain the third target short-term electricity consumption;
wherein the electricity usage behavior characteristics include: one or more of maximum power consumption, average power consumption, variance of power consumption, daily power consumption, ambient temperature in the vehicle, and date information of power consumption.
The power consumption prediction model can be obtained by utilizing linear regression statistical learning or machine learning algorithm training, and the linear regression statistical learning comprises the following steps: the machine learning algorithm comprises the following steps of multivariate linear regression, nonlinear regression, survival analysis, time series, a mixed model, a Bayesian prediction model and the like, wherein the machine learning algorithm comprises the following steps: decision trees, Bagging, Boosting, random forests, GDRT, XGbost, naive bayes, bayesian network models, SVMs, (deep) neural networks, etc., to which the present application is not limited.
1.4 user based itineraries
The step of predicting the fourth target short-time electricity consumption based on the user travel comprises the following steps:
and receiving travel plan information planned by a user, calculating the to-be-traveled distance according to the travel plan information, and predicting the four-target short-time power consumption by combining the to-be-traveled distance and road vehicle conditions.
Specifically, the method comprises the following steps:
obtaining the driving mileage required by the user according to the user navigation information, and calculating by combining the road vehicle condition to obtain the prediction of the power consumption of the user;
and obtaining user destination information according to the user schedule, obtaining driving mileage by combining navigation information, and calculating by combining road vehicle conditions to obtain the current power consumption prediction of the user.
In addition, if the trip plan planned by the user is to enter a vacation mode, the expected parking time of the vehicle is obtained, and the prediction of the power consumption of the vehicle is obtained according to the static electricity consumption of the vehicle.
(2) User charging reminder
If the current residual electric quantity of the vehicle does not meet the short-time power consumption requirement, charging reminding information is sent out to remind a user of timely charging to avoid the situation that the power consumption requirement cannot be met. And if the current residual electric quantity of the vehicle meets the demand of the short-time electricity consumption, the charging reminding information is not sent. Specifically, when the vehicle is in a parking or running state, a user short-time electricity consumption prediction function is performed, if the current remaining electricity quantity is not enough to meet the short-time electricity consumption demand, a charging reminding message is sent, and the charging reminding message is displayed through the display interactive device to prompt the user to charge. And if the current residual electric quantity meets the short-time electricity consumption requirement, the charging reminding information is not sent out.
(3) User charge amount prediction
The function is to predict the user's charge depth to avoid charging higher than the user needs, so that battery aging due to charging or high charge storage can be reduced.
As described above, the target charge amount is predicted by the charge amount prediction rule made. In this embodiment, the predicting the target charge amount according to the established charge amount prediction rule may include:
predicting a first target charge amount based on the minimum allowable capacity of the battery;
obtaining a second target charge amount based on a maximum charge amount within a plurality of historical time spans;
obtaining a third target charging quantity based on charging quantity probability statistics in a plurality of historical time spans;
obtaining a fourth target charge amount based on the established charge amount prediction model;
predicting a fifth target charge amount based on the user travel; and the number of the first and second groups,
the final target charge amount is one of the first target charge amount, the second target charge amount, the third target charge amount, the fourth target charge amount, and the fifth target charge amount, a maximum value, a minimum value, an average value, or a weighted value.
Preferably, a maximum value among the first target charge amount, the second target charge amount, the third target charge amount, the fourth target charge amount, and the fifth target charge amount is a final target charge amount, and if the final target charge amount is larger than a maximum allowable charge amount allowed by a battery, the vehicle is charged by the maximum allowable charge amount.
The charge amount prediction rules will be described in detail below.
2.1 minimum Capacity based on Battery tolerance
The minimum capacity allowed for the battery is given by the battery manufacturer, a safety threshold set to prevent over-discharge of the battery.
2.2 maximum charge
The deriving a second target amount of charge based on maximum amounts of charge over a plurality of historical time spans comprises:
counting the electricity consumption in a plurality of historical time spans in a calculation period, and comparing to obtain the maximum charging amount;
and adding the maximum charge amount and a second safe redundancy amount to obtain a second target charge amount.
Similarly, the second safe redundancy amount may adopt an amount of power required for a fixed driving distance, or a fixed amount of power set by a user.
2.3 based on probability statistics
The obtaining a third target charge amount based on the charge amount probability statistics over a plurality of historical time spans comprises:
counting the charging quantities in a plurality of historical time spans in a calculation period, and calculating the average value and standard deviation of the plurality of counted charging quantities;
and multiplying the calculated standard deviation by a second safety factor, and adding the multiplied standard deviation and the calculated average value to obtain the third target charging amount.
Likewise, the second safety factor may be adjustable, for example, set to 95%.
2.4 Charge-based prediction model
The deriving a fourth target charge amount based on the established charge amount prediction model includes:
counting the charging behavior characteristics in a plurality of historical time spans in a calculation period, and inputting the charging behavior characteristics into the charging quantity prediction model to obtain the fourth target charging quantity;
wherein the charging behavior characteristics include: one or more of a maximum charge amount, a mean charge amount, a variance of charge amount, a daily charge amount, and charging date information.
The charging amount prediction model can be obtained by training with the same algorithm as that used for obtaining the power consumption prediction model, and details are not repeated here.
2.5 user based itineraries
The predicting of the fifth target charge amount based on the user trip includes:
and receiving travel plan information planned by a user, calculating the mileage to be traveled according to the travel plan information, and predicting the fifth target charging amount by combining the mileage to be traveled and road conditions.
Specifically, the method comprises the following steps:
obtaining the required driving mileage of the user according to the navigation information of the user, and calculating by combining road vehicle conditions to obtain the prediction of the current charging amount of the user;
and obtaining user destination information according to the user schedule, obtaining driving mileage by combining navigation information, and calculating by combining road vehicle conditions to obtain the prediction of the current charging amount of the user.
In addition, if the trip plan planned by the user is to enter a vacation mode, the expected parking time of the vehicle is obtained, and the prediction of the vehicle charging amount is obtained according to the static electricity consumption of the vehicle.
(4) User charging depth adjustment
The predicted value of the user charge amount is set as the charge depth. When the vehicle is in a charging state, if the personalized charging function is not started, charging to the maximum allowable charging amount or charging to the user-set charging amount; and if the individual charging depth function is started, predicting the power consumption of the user, and if the current charged residual capacity of the battery reaches the predicted value of the power consumption of the user, automatically stopping charging. Otherwise, the charging state is continued.
(5) Generating a charging report
After the charging is finished, sending a charging report to the display interaction device, including: whether to turn on personalized charging, the current charge amount, etc. Through the generated charging report, the user can intuitively know the charging condition every time, so that the use feeling of the user can be improved.
The present embodiment also provides a readable storage medium having stored thereon a computer program that, when executed, implements the vehicle charging method as provided by the present embodiment.
The readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device, such as, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, and any suitable combination of the foregoing.
The present embodiment also provides an electronic device, including: a processor and a memory, the memory having stored thereon a computer program that, when executed by the processor, implements the vehicle charging method as provided by the present implementations. The electronic device is, for example, the vehicle-mounted controller described above.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The processor is a control center of the electronic equipment and is connected with each part of the whole electronic equipment by various interfaces and lines.
In addition to the processor and memory, the electronic device may also include a user interface, a network interface, and a communication bus. The user interface is used to receive information input by a user, for example, using a display interaction device. The network interface is used for the server side to communicate with the outside. The network interface mainly comprises a wired interface and a wireless interface, such as an RS232 module, a radio frequency module, a WIFI module and the like. The communication bus is used for communication among the components in the electronic device, and may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. The present embodiment also provides a vehicle charging system including: an onboard controller and a display interaction device, wherein,
the vehicle-mounted controller is used for controlling a vehicle to be charged by adopting the vehicle charging method provided by the embodiment and sending a charging report to the display interaction equipment;
the display interaction device is used for checking the charging report, displaying the personalized charging scheme, enabling a user to select whether to start the personalized charging function, inputting travel plan information and setting a charging amount.
Wherein the displaying the personalized charging profile comprises: if the charging amount prediction rule is multiple, the user can select one or more of the display interaction devices according to needs to predict the charging amount, and if the charging amount is predicted by multiple display interaction devices, a large value, a minimum value, an average value or a weighted value is selected as the final target short-time electricity consumption. And if the user does not select and only starts the personalized charging function, predicting the target short-time electricity consumption according to default settings.
Optionally, the vehicle charging system may further include: and the cloud server is used for acquiring battery parameters and vehicle use data of different vehicle clusters, establishing a power consumption prediction rule for predicting power consumption and a charging amount prediction rule for predicting charging amount, and sending the power consumption prediction rule and the charging amount prediction rule to the vehicle-mounted controller.
The work flow of the vehicle charging system provided by the embodiment is as follows:
the cloud server collects battery parameters and vehicle use data (including vehicle electricity utilization behavior characteristics and vehicle charging behavior characteristics) of different vehicle clusters by using the vehicle-mounted controller, formulates an electricity consumption judgment rule and a charging amount prediction rule according to the collected data, and sends the formulated electricity consumption judgment rule and charging amount prediction rule to the display interactive equipment so as to display an individualized charging scheme on the display interactive equipment;
in the using or parking process of the vehicle, judging whether the current electric quantity of the vehicle meets the short-time power consumption requirement of a user in real time, if not, sending charging reminding information to the display interactive equipment to remind the user of charging;
when a user receives charging reminding information and wants to charge or actively wants to charge, selecting whether to start an individualized charging function and whether to customize an individualized scheme, judging whether the individualized charging function is started or not by the vehicle-mounted controller after a vehicle starts to charge, if so, predicting a target charging amount according to a formulated charging amount prediction rule, and stopping charging the vehicle when the electric quantity reaches the target charging amount; if the vehicle is not started, charging the vehicle according to the charging amount or the maximum allowable charging amount set by the user; and after the charging is finished, the vehicle-mounted controller generates a charging report and sends the charging report to the display interaction equipment.
In summary, the vehicle charging method and system, the readable storage medium and the electronic device provided in the embodiments of the present invention include: after the vehicle starts to charge, judging whether the personalized charging function is started or not; if yes, predicting a target charging amount according to a formulated charging amount prediction rule, and stopping charging the vehicle when the electric quantity reaches the target charging amount; if not, charging the vehicle according to the charging amount set by the user or the maximum allowable charging amount; wherein the charge amount prediction rule is formulated according to vehicle battery parameters and vehicle historical usage data. Namely, according to the vehicle charging method and system, the readable storage medium and the electronic device provided by the invention, the historical vehicle using behaviors of the user and the requirement input of the user are learned, so that a reasonable charging strategy and a reasonable charging prompt of the user can be automatically adopted, the aging of the battery can be slowed down, the use satisfaction degree of the user is improved, the service life of the electric vehicle is prolonged, and the use cost of the electric vehicle is reduced.
It should be understood, however, that the intention is not to limit the invention to the particular embodiments described. It will be apparent to those skilled in the art from this disclosure that many changes and modifications can be made, or equivalents modified, in the embodiments of the invention without departing from the scope of the invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the protection scope of the technical solution of the present invention, unless the content of the technical solution of the present invention is departed from.
Claims (17)
1. A vehicle charging method, characterized by comprising:
after the vehicle starts to charge, judging whether the personalized charging function is started or not; if yes, predicting a target charging amount according to a formulated charging amount prediction rule, and stopping charging the vehicle when the electric quantity reaches the target charging amount; if not, charging the vehicle according to the charging amount set by the user or the maximum allowable charging amount;
wherein the charge amount prediction rule is formulated according to vehicle battery parameters and vehicle historical usage data.
2. The vehicle charging method according to claim 1, further comprising:
and judging whether the current electric quantity of the vehicle meets the short-time power consumption requirement of the user, and if not, sending charging reminding information.
3. The vehicle charging method according to claim 2, wherein the method of determining whether the current amount of electricity of the vehicle satisfies the user demand for short-term electricity consumption includes:
predicting target short-time electricity consumption according to the formulated electricity consumption judgment rule;
and judging the current electric quantity of the vehicle and the target short-time electricity consumption, and if the current electric quantity of the vehicle is smaller than the target short-time electricity consumption, judging that the current electric quantity of the vehicle does not meet the short-time electricity consumption requirement of the user.
4. The vehicle charging method according to claim 3, wherein the predicting the target short-time electricity usage according to the formulated electricity usage determination rule includes:
predicting to obtain a first target short-time electricity consumption based on the maximum electricity consumption in a plurality of historical time spans;
obtaining a second target short-time electricity consumption based on probability statistics of the electricity consumption in a plurality of historical time spans;
obtaining a third target short-time electricity consumption based on the established electricity consumption prediction model;
predicting to obtain a fourth target short-time electricity consumption based on the user travel; and the number of the first and second groups,
and taking one, the maximum value, the minimum value, the average value or the weighted value of the first target short-time electricity consumption, the second target short-time electricity consumption, the third target short-time electricity consumption and the fourth target short-time electricity consumption as the final target short-time electricity consumption.
5. The vehicle charging method of claim 4, wherein the predicting a first target short term power usage based on a maximum power usage over a plurality of historical time spans comprises:
counting the power consumption in a plurality of historical time spans in a calculation period, and comparing to obtain the maximum power consumption;
and adding the maximum power consumption and the first safe redundancy to obtain the first target short-time power consumption.
6. The vehicle charging method according to claim 4, wherein the deriving a second target short term electricity usage based on probabilistic statistics of electricity usage over a plurality of historical time spans comprises:
counting the electricity consumption in a plurality of historical time spans in a calculation period, and calculating the average value and standard deviation of the plurality of electricity consumption obtained through statistics;
and multiplying the standard deviation obtained by calculation by a first safety factor, and adding the standard deviation and the average value obtained by calculation to obtain the second target short-time electricity consumption.
7. The vehicle charging method according to claim 4, wherein the deriving a third target short-time power usage based on the established power usage prediction model comprises:
counting the electricity consumption behavior characteristics in a plurality of historical time spans in a calculation period, and inputting the electricity consumption behavior characteristics into the electricity consumption prediction model to obtain the third target short-term electricity consumption;
wherein the electricity usage behavior characteristics include: one or more of maximum power consumption, average power consumption, variance of power consumption, daily power consumption, ambient temperature in the vehicle, and date information of power consumption.
8. The vehicle charging method according to claim 4, wherein the predicting a fourth target short-time electricity usage based on the user trip includes:
and receiving travel plan information planned by a user, calculating the to-be-traveled distance according to the travel plan information, and predicting the four-target short-time power consumption by combining the to-be-traveled distance and road vehicle conditions.
9. The vehicle charging method according to claim 1, wherein the predicting the target charge amount according to the formulated charge amount prediction rule includes:
predicting a first target charge amount based on the minimum allowable capacity of the battery;
obtaining a second target charge amount based on a maximum charge amount within a plurality of historical time spans;
obtaining a third target charging quantity based on charging quantity probability statistics in a plurality of historical time spans;
obtaining a fourth target charge amount based on the established charge amount prediction model;
predicting a fifth target charge amount based on the user travel; and the number of the first and second groups,
the final target charge amount is one of the first target charge amount, the second target charge amount, the third target charge amount, the fourth target charge amount, and the fifth target charge amount, a maximum value, a minimum value, an average value, or a weighted value.
10. The vehicle charging method according to claim 9, wherein the deriving the second target charge amount based on the maximum charge amount over the plurality of historical time spans includes:
counting the electricity consumption in a plurality of historical time spans in a calculation period, and comparing to obtain the maximum charging amount;
and adding the maximum charge amount and a second safe redundancy amount to obtain a second target charge amount.
11. The vehicle charging method according to claim 9, wherein the statistically deriving the third target charge amount based on the charge amount probabilities over a plurality of historical time spans comprises:
counting the charging quantities in a plurality of historical time spans in a calculation period, and calculating the average value and standard deviation of the plurality of counted charging quantities;
and multiplying the calculated standard deviation by a second safety factor, and adding the multiplied standard deviation and the calculated average value to obtain the third target charging amount.
12. The vehicle charging method according to claim 9, wherein the deriving the fourth target charge amount based on the established charge amount prediction model includes:
counting the charging behavior characteristics in a plurality of historical time spans in a calculation period, and inputting the charging behavior characteristics into the charging quantity prediction model to obtain the fourth target charging quantity;
wherein the charging behavior characteristics include: one or more of a maximum charge amount, a mean charge amount, a variance of charge amount, a daily charge amount, and charging date information.
13. The vehicle charging method according to claim 9, wherein the predicting of the fifth target amount of charge based on the user's trip includes:
and receiving travel plan information planned by a user, calculating the mileage to be traveled according to the travel plan information, and predicting the fifth target charging amount by combining the mileage to be traveled and road conditions.
14. A readable storage medium having stored thereon a computer program which, when executed, implements a vehicle charging method as claimed in any one of claims 1 to 13.
15. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program that, when executed by the processor, implements a vehicle charging method as claimed in any one of claims 1 to 13.
16. A vehicle charging system, comprising: an onboard controller and a display interaction device, wherein,
the vehicle-mounted controller is used for controlling a vehicle to be charged by adopting the vehicle charging method according to any one of claims 1-13 and sending a charging report to the display interaction device;
the display interaction device is used for checking the charging report, displaying the personalized charging scheme, enabling a user to select whether to start the personalized charging function, inputting travel plan information and setting a charging amount.
17. The vehicle charging system of claim 16, further comprising: and the cloud server is used for acquiring battery parameters and vehicle use data of different vehicle clusters, establishing a power consumption prediction rule for predicting power consumption and a charging amount prediction rule for predicting charging amount, and sending the power consumption prediction rule and the charging amount prediction rule to the vehicle-mounted controller.
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