WO2022111480A1 - 车辆充电意图确定方法、装置及车辆 - Google Patents

车辆充电意图确定方法、装置及车辆 Download PDF

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Publication number
WO2022111480A1
WO2022111480A1 PCT/CN2021/132492 CN2021132492W WO2022111480A1 WO 2022111480 A1 WO2022111480 A1 WO 2022111480A1 CN 2021132492 W CN2021132492 W CN 2021132492W WO 2022111480 A1 WO2022111480 A1 WO 2022111480A1
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Prior art keywords
charging
target
trip
vehicle
historical
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PCT/CN2021/132492
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English (en)
French (fr)
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张巍
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北京车和家信息技术有限公司
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Publication of WO2022111480A1 publication Critical patent/WO2022111480A1/zh
Priority to US18/322,068 priority Critical patent/US20230286416A1/en

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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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
    • 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
    • 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
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/60—Navigation input
    • B60L2240/62—Vehicle position
    • B60L2240/622—Vehicle position by satellite navigation
    • 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
    • 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
    • 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

Definitions

  • the present disclosure relates to the field of vehicles, and in particular, to a method, a device, and a vehicle for determining a vehicle charging intention.
  • the charging intention is generally judged by the surrounding environment of the vehicle, or the charging of the vehicle is guided and planned from the aspects of charging distance cost, time cost, charging station equipment utilization, etc., which cannot take into account the driver's charging habits, resulting in inability to accurately judge.
  • the actual charging intention of the driver is revealed.
  • the purpose of the present disclosure is to provide a method, device and vehicle for determining vehicle charging intention, so as to improve the prediction accuracy of the user's driving charging intention.
  • a method for determining a vehicle charging intention comprising:
  • the user's charging intention confidence level in the target travel is determined, and the charging intention confidence level is determined by the charging probability of at least one preset dimension, wherein the preset dimension includes the charging position dimension, charging time dimension and battery state dimension;
  • the confidence level of the charging intention it is determined whether the user has a charging intention in the target trip.
  • the target travel information includes a target end position of the target travel
  • the determining, according to the target travel information, the confidence level of the user's charging intention in the target travel includes:
  • the confidence level of the charging intention is determined according to the charging probability of the at least one preset dimension.
  • the target travel information further includes a target starting point position of the target travel and a target driving route of the target travel;
  • the charging probability of the vehicle in the charging location dimension is obtained in the following manner:
  • the charging probability of the vehicle in the charging position dimension is determined.
  • the determining the probability that the vehicle is charged at the target charging point, as the first probability includes:
  • the first probability is determined according to the ratio of the first number of charging times to the number of trips.
  • the determining the probability that the target end position is the charging point, as the second probability includes:
  • the designated charging trip is a historical charging trip whose trip start point matches the target starting point position in each historical charging trip;
  • the second probability is determined according to the similarity corresponding to each designated charging trip.
  • the target travel information further includes a target start time point of the target travel
  • the charging probability of the vehicle in the charging time dimension is obtained in the following manner:
  • the charging starting time interval is predefined based on the starting time points of each historical charging journey
  • the charging probability of the vehicle in the charging time dimension is determined according to the ratio of the second charging times to the first charging times.
  • the target trip information further includes a target initial SOC value of the vehicle battery at the beginning of the target trip;
  • the charging probability of the vehicle in the battery state dimension is obtained in the following manner:
  • the SOC interval is predefined based on initial SOC values of each historical charging trip
  • the charging probability of the vehicle in the battery state dimension is determined.
  • the determining the confidence level of the charging intention according to the charging probability of the at least one preset dimension includes:
  • the charging intention confidence level is determined according to the product of the charging probabilities of the at least one preset dimension.
  • the determining whether the user has a charging intention in the target trip according to the charging intention confidence includes:
  • the confidence of the charging intention is greater than or equal to the confidence threshold, it is determined that the user has a charging intention in the target journey;
  • the confidence level of the charging intention is less than the confidence level threshold, it is determined that the user has no charging intention in the target trip.
  • the target travel information includes a target end position of the target travel, a target start time point, and a target initial SOC value of the vehicle battery at the start of the target travel;
  • the method further includes:
  • the preset condition includes at least one of the following:
  • the pre-defined charging points there is at least one charging point within a preset range around the target end position;
  • the predefined charging starting time interval there is a charging starting time interval including the target starting time point;
  • the target itinerary is determined by:
  • the historical travel information includes a historical starting time point, a historical starting SOC value, and a historical charging point of each historical charging trip, and further includes the vehicle using each historical charging point the number of historical trips at the end of the trip and the number of historical charges the vehicle has been charged at each historical charging point;
  • the target itinerary is determined.
  • the determining the target travel according to the predicted travel includes:
  • the predicted travel is determined as the target travel.
  • the target itinerary is determined by:
  • the stroke indicated by the setting instruction is determined as the target stroke.
  • the method further includes:
  • the battery temperature of the vehicle is controlled to be at a target temperature, where the target temperature is A temperature favorable for charging the vehicle battery.
  • a vehicle charging intention determination device comprising:
  • the first acquisition module is used to acquire the itinerary information of the target itinerary as the target itinerary information
  • the first determination module is configured to determine, according to the target travel information, the user's charging intention confidence in the target travel, the charging intention confidence is determined by the charging probability of at least one preset dimension, wherein the The preset dimensions include charging position dimension, charging time dimension and battery state dimension;
  • the second determination module is configured to determine whether the user has a charging intention in the target trip according to the charging intention confidence level.
  • the target travel information includes a target end position of the target travel
  • the first determining module includes:
  • the first determination sub-module is used to determine the charging point that can match the target end position as the target charging point, wherein the charging point is the position where the vehicle is charged in the historical charging journey, and the historical charging journey is the charging point. the trip for which the vehicle was overcharged;
  • a second determination submodule configured to determine the charging probability of the vehicle in at least one preset dimension according to the target charging point and the target travel information
  • the third determination sub-module is configured to determine the confidence level of the charging intention according to the charging probability of the at least one preset dimension.
  • the target travel information further includes a target starting point position of the target travel and a target driving route of the target travel;
  • the charging probability of the vehicle in the charging position dimension is obtained through the following modules:
  • a fourth determination sub-module configured to determine the probability that the vehicle is charged at the target charging point as a first probability
  • a fifth determination sub-module configured to determine the probability that the target end position is a charging point as the second probability
  • the sixth determination sub-module is configured to determine the charging probability of the vehicle in the charging position dimension according to the product of the first probability and the second probability.
  • the fourth determination sub-module is used for:
  • the first probability is determined according to the ratio of the first number of charging times to the number of trips.
  • the fifth determination submodule is used for:
  • the designated charging trip is a historical charging trip whose trip start point matches the target starting point position in each historical charging trip;
  • the second probability is determined according to the similarity corresponding to each designated charging trip.
  • the target travel information further includes a target start time point of the target travel
  • the charging probability of the vehicle in the charging time dimension is obtained through the following modules:
  • the seventh determination sub-module is used to determine the charging starting time interval where the target starting time point is located as the target time interval, wherein the charging starting time interval is pre-defined based on the starting time points of each historical charging journey of;
  • the first obtaining sub-module is configured to obtain the second charging times of the vehicle being charged in the target time interval during the historical charging journey and the first charging times that the vehicle has been overcharged at the target charging point during the historical charging journey frequency;
  • the eighth determination sub-module is configured to determine the charging probability of the vehicle in the charging time dimension according to the ratio of the second charging times to the first charging times.
  • the target trip information further includes a target initial SOC value of the vehicle battery at the beginning of the target trip;
  • the charging probability of the vehicle in the battery state dimension is obtained through the following modules:
  • a ninth determination sub-module configured to determine an SOC interval in which the target initial SOC value is located as a target SOC interval, wherein the SOC interval is predefined based on initial SOC values of each historical charging trip;
  • the second obtaining sub-module is configured to obtain the third charging times for the vehicle to be charged in the target SOC interval during the historical charging journey and the first charging for the vehicle to be overcharged at the target charging point during the historical charging journey frequency;
  • a tenth determination sub-module is configured to determine the charging probability of the vehicle in the battery state dimension according to the ratio of the third charging times to the first charging times.
  • the third determining sub-module is configured to determine the charging intention confidence level according to the product of the charging probabilities of the at least one preset dimension.
  • the second determining module includes:
  • an eleventh determination sub-module configured to determine that the user has a charging intention in the target itinerary if the confidence level of the charging intention is greater than or equal to a confidence level threshold;
  • a twelfth determination sub-module is configured to determine that the user does not have a charging intention in the target journey if the confidence level of the charging intention is less than the confidence level threshold.
  • the target travel information includes a target end position of the target travel, a target start time point, and a target initial SOC value of the vehicle battery at the start of the target travel;
  • the first determining module is configured to determine that the target travel information satisfies a preset condition before determining the confidence level of the user's charging intention in the target travel information according to the target travel information;
  • the preset condition includes at least one of the following:
  • the pre-defined charging points there is at least one charging point within a preset range around the target end position;
  • the predefined charging starting time interval there is a charging starting time interval including the target starting time point;
  • the target itinerary is determined by the following modules:
  • a second acquisition module used for acquiring vehicle location information
  • the third acquiring module is configured to acquire historical travel information of the vehicle, where the historical travel information includes a historical starting time point, a historical starting SOC value and a historical charging point of each historical charging trip, and further includes the The number of historical journeys of the vehicle with each historical charging point as the end of the journey and the historical charging times of the vehicle being charged at each historical charging point;
  • a generating module configured to generate a predicted itinerary according to the historical itinerary information and the vehicle position information
  • the fourth determining module is configured to determine the target travel according to the predicted travel.
  • the fourth determining module includes:
  • an output sub-module for outputting predicted travel information used to characterize the predicted travel
  • the thirteenth determination sub-module is configured to determine the predicted itinerary as the target itinerary if a confirmation instruction for the predicted itinerary information from the user is received.
  • the target itinerary is determined by the following modules:
  • a receiving module configured to receive a user's setting instruction for a target itinerary, where the setting instruction is used to indicate the itinerary information of the target itinerary;
  • the fifth determination module is configured to determine the travel indicated by the setting instruction as the target travel.
  • the apparatus further comprises:
  • a temperature control module configured to control the battery temperature of the vehicle to be at a target temperature during the process of driving the vehicle on the target journey when it is determined that the user has an intention to charge during the target journey, wherein , the target temperature is a temperature favorable for charging the vehicle battery.
  • a vehicle for performing the method of the first aspect of the present disclosure According to a third aspect of the present disclosure, there is provided a vehicle for performing the method of the first aspect of the present disclosure.
  • the itinerary information of the target trip is obtained as the target trip information, according to the target trip information, the confidence level of the user's charging intention in the target trip is determined, and according to the charging intent confidence degree, it is determined whether the user has the charging intention in the target trip.
  • the charging intention confidence is determined by the charging probability of at least one preset dimension, and the preset dimension includes a charging position dimension, a charging time dimension, and a battery state dimension.
  • the user's charging intention confidence level is determined, and then whether the user has charging intention is determined according to the confidence level, which can effectively improve the accuracy of charging intention determination. , and provide a reliable basis for battery thermal management to improve the charging effect of the vehicle.
  • FIG. 1 is a flowchart of a method for determining a vehicle charging intention provided according to an embodiment of the present disclosure
  • FIG. 2 is an exemplary flowchart of the steps of determining the confidence level of the user's charging intention in the target travel according to the target travel information in the vehicle charging intention determination method provided according to the present disclosure
  • FIG. 3 is a flowchart of a method for determining a vehicle charging intention provided according to another embodiment of the present disclosure
  • FIG. 4 is a flowchart of a method for determining a vehicle charging intention provided according to another embodiment of the present disclosure
  • FIG. 5 is a schematic structural diagram of a system to which a method for determining a vehicle charging intention provided according to an embodiment of the present disclosure is applied;
  • FIG. 6 is a block diagram of a vehicle charging intention determination device provided according to an embodiment of the present disclosure.
  • FIG. 1 is a flowchart of a method for determining a vehicle charging intention according to an embodiment of the present disclosure.
  • the method provided by the present disclosure can be applied to the vehicle end, and can also be applied to a server or cloud capable of communicating with the vehicle. As shown in Figure 1, the method may include the following steps.
  • step 11 the travel information of the target travel is acquired as the target travel information.
  • step 12 the confidence level of the user's charging intention in the target journey is determined according to the target journey information.
  • step 13 it is determined whether the user has a charging intention in the target itinerary according to the confidence level of the charging intention.
  • the charging intention confidence is determined by the charging probability of at least one preset dimension, and the preset dimension includes a charging position dimension, a charging time dimension, and a battery state dimension.
  • the trip information may include, but is not limited to, at least one of the following: the starting position of the trip, the ending position of the trip, the starting time point of the trip, the ending time point of the trip, and the vehicle battery at the beginning of the trip SOC (State of Charge, state of charge) value, travel route of the trip.
  • SOC State of Charge, state of charge
  • the start of the trip is where the vehicle is at the start of the trip.
  • the end position of the vehicle refers to the position of the vehicle at the end of the trip.
  • the start time point of the trip refers to the time when the trip starts.
  • the end time point of the trip refers to the time when the trip ends.
  • the vehicle battery SOC value at the beginning of the trip refers to the SOC value corresponding to the vehicle power battery at the beginning of the trip.
  • the driving route value of the trip is the driving track of the vehicle during the driving process corresponding to the trip.
  • the vehicle position may be acquired periodically, and the vehicle position acquired in the itinerary constitutes the travel track of the vehicle in the itinerary, that is, the driving route, according to the sequence of acquisition.
  • the target travel information may accordingly include a target starting point position, a target end point position, a target starting time point, a target ending time point, a target SOC value, a target driving route, and the like.
  • the target itinerary may be determined in the following manner:
  • the target itinerary is determined.
  • a future trip is actually predicted based on the user's historical driving records or habits, and a target trip is determined based on the predicted trip.
  • the vehicle location information refers to the current location of the vehicle.
  • the historical itinerary information of the vehicle can represent the user's historical driving habits. Therefore, based on the historical itinerary information, it is beneficial to improve the accuracy of the itinerary prediction. Therefore, in the initial stage, it is necessary to collect the relevant data of the vehicle in the historical driving process, so as to form the historical itinerary information, that is, to obtain the historical itinerary information based on the big data. Since this solution focuses on the prediction of vehicle charging intention, when collecting data, the data related to vehicle charging can be focused on. For example, the daily driving charging data of the user is recorded, which may include the starting time of the user's charging trip, the starting time of charging, the charging location, the SOC value of the battery at the beginning of the trip (or the beginning of charging), and the charging time.
  • the historical itinerary information can represent the user's charging habits to a certain extent.
  • the collected data can be statistically analyzed according to the following rules, so as to facilitate subsequent data processing.
  • Count the SOC status of the power battery at the starting point of the historical charging journey (which can be reflected by the SOC value) when the vehicle goes to each charging point to charge.
  • it can also divide different SOC intervals based on the SOC values that appear, and count the occurrence of each SOC interval. frequency.
  • the historical trip information may correspondingly include: the historical starting time point, historical starting SOC value and historical charging point of each historical charging trip, and also include the historical trip of the vehicle with each historical charging point as the end of the trip. The number and the number of historical charges the vehicle has been charged at each historical charging point.
  • the predicted itinerary can be generated according to the historical itinerary information and the vehicle position information.
  • the frequency of occurrence of the specified starting point to the specified ending point within a certain time period can be identified according to the historical travel information, and the travel corresponding to the combination of the ⁇ starting point, ending point> with the highest frequency of occurrence is used as the travel for the time period Predicted itinerary.
  • the starting point position in the historical travel information that matches the vehicle position information can be determined according to the vehicle position information, and then the starting point position corresponding to the starting point position can be determined from the historical charging trip. , select the historical charging trip with the highest frequency in the time period of the trip to be predicted, and use the ⁇ starting point, end point> corresponding to the historical charging trip as the predicted trip.
  • the user's travel habits within a specified period of time can be counted according to historical travel information. For example, on weekdays from 8:00 to 9:00, the user drives from point A to point B, and at the same time, from 18:00 to 19:00 on weekdays, the user drives from point B to point A.
  • the start point, end point and time of the itinerary can refer to the itinerary habits that have been calculated.
  • the target itinerary can be determined according to the predicted itinerary.
  • the predicted trip may be directly used as the target trip.
  • determining the target itinerary according to the predicted itinerary may include the following steps:
  • the predicted travel is determined as the target travel.
  • the predicted itinerary after the predicted itinerary is generated, it will also be pushed to the user for confirmation, and the itinerary will be regarded as a valid itinerary, that is, the target itinerary, only when the user's confirmation is obtained.
  • the predicted itinerary information may include the starting point position, the ending point position, the starting time point, and the like of the itinerary.
  • the predicted itinerary information can be pushed to the user's APP in the form of a schedule reminder, and the user can confirm whether he has a current travel plan.
  • the vehicle uses the predicted trip data as valid data as the basis for subsequent data processing, for example, for the subsequent identification of the confidence level of the charging intention of the target trip. If the user refuses to accept the itinerary, the data related to the predicted itinerary will be deleted.
  • the user can also set the target itinerary by himself.
  • the target itinerary can be determined in the following manner:
  • the stroke indicated by the setting command is determined as the target stroke.
  • the setting command is used to indicate the travel information of the target travel
  • the user can preset multiple itinerary plans, and the itinerary plan can set the repetition time and the start time and end time of the reminder.
  • Step 12 is explained in detail below.
  • step 12 may include the following steps, as shown in FIG. 2 .
  • step 21 a charging point that can match the target end position is determined as the target charging point.
  • the charging point is the location where the vehicle is charged during the historical charging journey
  • the historical charging journey is the journey where the vehicle is overcharged during driving. Relevant explanations have been given above, and are not repeated here.
  • step 22 the charging probability of the vehicle in at least one preset dimension is determined according to the target charging point and the target travel information.
  • the charging intention confidence is determined by the charging probability of at least one preset dimension, and the preset dimension includes a charging location dimension, a charging time dimension, and a battery state dimension.
  • the charging probability of the vehicle in the charging position dimension is obtained in the following manner:
  • the charging probability of the vehicle in the charging position dimension is determined.
  • determining the probability that the vehicle is charged at the target charging point, as the first probability may include the following steps:
  • the first probability is determined according to the ratio of the first charging times to the number of trips.
  • the ratio of the first charging times to the number of trips may be used as the first probability.
  • a preset operation eg, multiplying by a preset coefficient
  • the obtained result may be used as the first probability.
  • determining the probability that the target end position is the charging point, as the second probability may include the following steps:
  • the second probability is determined according to the degree of similarity corresponding to each designated charging trip.
  • the designated charging trip is a historical charging trip in which the trip start point matches the target starting point position in each historical charging trip.
  • the matching can be that the starting point of the historical charging journey is the same as the target starting point, or the distance between the starting point of the historical charging journey and the target starting point can be less than the specified and preset threshold, or it can be The starting point of the historical charging journey and the target starting point belong to the same area (which can be pre-defined).
  • the similarity between the target driving route and each specified charging trip is determined.
  • the travel end point of each designated charging travel corresponds to a probability that the travel end point is a charging point.
  • the above-mentioned probability corresponding to the specified charging journey corresponding to the maximum similarity may be used as the second probability.
  • the charging probability of the vehicle in the charging position dimension can be determined according to the product of the two.
  • the product of the first probability and the second probability may be used as the charging probability of the vehicle in the charging position dimension.
  • a preset operation may be performed (eg, multiplied by a preset coefficient), and the obtained result may be used as the charging probability of the vehicle in the charging position dimension.
  • the charging probability of the vehicle in the dimension of the charging position can represent the probability that the vehicle may be charged at the charging point involved in the target trip, so that the charging intention of the user can be reflected from the perspective of the charging position.
  • the charging probability of the vehicle in the charging time dimension is obtained in the following manner:
  • the charging probability of the vehicle in the charging time dimension is determined.
  • the charging start time interval is pre-defined based on the start time points of each historical charging trip. As mentioned above, different time periods may be divided according to the starting time points that appear in the statistical process, as different charging starting time intervals.
  • the ratio of the second charging times to the first charging times may be used as the charging probability of the vehicle in the charging time dimension.
  • a preset operation may be performed (eg, multiplied by a preset coefficient), and the obtained result may be used as the charging probability of the vehicle in the charging time dimension.
  • the charging probability of the vehicle in the charging time dimension can represent the probability that the vehicle may be charged during the journey starting from the target starting point, so as to reflect the user's charging intention from the perspective of charging time.
  • the charging probability of the vehicle in the battery state dimension is obtained in the following manner:
  • the charging probability of the vehicle in the battery state dimension is determined.
  • the SOC interval is pre-defined based on the initial SOC value of each historical charging trip. As described above, different SOC intervals may be divided according to the initial SOC values appearing in the statistical process as different SOC intervals.
  • the ratio of the third charging times to the first charging times may be used as the charging probability of the vehicle in the battery state dimension.
  • a preset operation for example, multiplying by a preset coefficient
  • the obtained result may be used as the charging probability of the vehicle in the battery state dimension.
  • the charging probability of the vehicle in the battery state dimension can represent the probability that the vehicle may be charged during the journey starting from the target initial SOC value, so as to reflect the user's charging intention from the perspective of the battery state.
  • step 23 the confidence level of the charging intention is determined according to the charging probability of at least one preset dimension.
  • the confidence level of the charging intention can be determined.
  • step 23 may include the following steps:
  • the charging intention confidence level is determined according to the product of charging probabilities in at least one preset dimension.
  • the product of the charging probabilities of each preset dimension may be used as the charging intention confidence.
  • a preset operation eg, multiplying by a preset coefficient
  • the obtained result may be used as the confidence level of the charging intention.
  • the confidence level of the charging intention can be determined from multiple dimensions, which is beneficial to improve the accuracy of the judgment of the charging intention, and the more preset dimensions used, the more accurate the judgment of the charging intention.
  • step 13 it is determined whether the user has a charging intention in the target journey, which may include the following steps:
  • the confidence of the charging intention is less than the confidence threshold, it is determined that the user does not have the charging intention in the target journey.
  • the confidence threshold may be preset according to an empirical value.
  • the travel information of the target travel for which the charging intention is to be determined is obtained as the target travel information. Whether there is a charging intent in the .
  • the charging intention confidence is determined by the charging probability of at least one preset dimension, and the preset dimension includes a charging position dimension, a charging time dimension, and a battery state dimension. In this way, for the dimensions of charging location, charging time, and battery status, based on the user's charging habits, determine the user's charging intention confidence, and then determine whether the user has charging intention according to the confidence, which can effectively improve the accuracy of charging intention determination. , and provide a reliable basis for battery thermal management to improve vehicle charging effect.
  • the method provided by the present disclosure may further include the following steps:
  • the target travel information After obtaining the target travel information, it is first judged whether the target travel information satisfies the preset conditions, and if it is determined that the target travel information meets the preset conditions, the confidence level of the charging intention corresponding to the target travel is further calculated.
  • the preset condition may include at least one of the following:
  • the pre-defined charging points there is at least one charging point within a preset range around the target end position;
  • the pre-defined charging starting time interval there is a charging starting time interval including the target starting time point;
  • predefined SOC intervals there are SOC intervals including the target start SOC value.
  • the end position of the target trip should be close to the existing charging point. Therefore, if there is at least one charging point within the preset range around the target end position, the charging intention of the vehicle can obviously be determined because the charging point is too far away. Insufficient, there is no need to make subsequent confidence judgments to save data processing pressure. And, the judgment idea based on the charging start time interval and the SOC interval is the same as the above-mentioned content, and will not be repeated here.
  • the method provided by the present disclosure may further include the following steps:
  • step 31 when it is determined that the user has an intention to charge during the target trip, the battery temperature of the vehicle is controlled to be at the target temperature while the vehicle is traveling on the target trip.
  • the target temperature is a temperature favorable for charging the vehicle battery.
  • the battery temperature of the vehicle may be controlled to be at the target temperature for a preset period of time before the vehicle reaches the target charging point.
  • the temperature of the battery can be controlled in advance to reach a temperature that is favorable for charging the battery, which is conducive to better charging the battery.
  • the flowchart of the method provided by the present disclosure may be as shown in FIG. 4 .
  • the specific implementation manner of each step has been correspondingly given above, and will not be repeated here.
  • the travel information of the target travel for which the charging intention is to be determined is obtained as the target travel information. Whether there is a charging intent in the .
  • the charging intention confidence is determined by the charging probability of at least one preset dimension, and the preset dimension includes a charging position dimension, a charging time dimension, and a battery state dimension. In this way, for the dimensions of charging location, charging time, and battery status, based on the user's charging habits, determine the user's charging intention confidence, and then determine whether the user has charging intention according to the confidence, which can effectively improve the accuracy of charging intention determination. , and provide a reliable basis for battery thermal management to improve vehicle charging effect.
  • the method provided by the present disclosure can be applied to a cloud server, the cloud server can be provided with a cloud data system and a cloud prediction system, and the cloud server can communicate with the user's mobile phone and vehicle.
  • a schematic diagram of the structure of these devices may be shown in FIG. 5 .
  • the direction of the arrow may indicate the direction of information transmission, and the content enclosed by the dotted box near the arrow indicates the transmitted information.
  • the cloud data system is used for data collection and data processing. For example, you can refer to the above-mentioned steps of collecting relevant data of vehicles in the historical formation process to form historical itinerary information.
  • the cloud data system can collect the above data and perform statistical analysis to obtain historical itinerary information. And, as shown in FIG. 5 , the cloud data system will synchronize the historical travel information to the cloud prediction system.
  • the cloud prediction system is the main carrier for executing the vehicle charging intention determination method provided by the present disclosure, which includes 4 functional modules, namely, a module for predicting trip generation (referred to as M1 hereinafter), a module for target trip A module for determination (hereinafter referred to as M2), a module for determining the confidence of charging intention (hereinafter referred to as M3), and a module for vehicle information acquisition (hereinafter referred to as M4).
  • M1-M4 can determine the user's charging intention in the target itinerary in the following ways:
  • M1 generates a predicted itinerary based on historical itinerary information
  • M1 sends the predicted itinerary information corresponding to the predicted itinerary to M2, and also sends the predicted itinerary information to the user's mobile phone, so that the user can confirm whether to accept the itinerary;
  • M2 After M2 receives the user's confirmation instruction for the predicted itinerary information, it will determine the target itinerary according to the predicted itinerary information, and then obtain the target itinerary information;
  • M2 sends the target itinerary information to M3;
  • M3 determines the confidence level of the user's charging intent in the target trip according to the target trip information and the parameters obtained from M4, so as to determine whether the user has charging intent in the target trip, and sends the determination result to the vehicle end;
  • M4 obtains the relevant parameters required by M3 in the data processing process through the vehicle end.
  • FIG. 6 is a block diagram of an apparatus for determining a vehicle charging intention provided according to an embodiment of the present disclosure. As shown in FIG. 6 , the apparatus 40 includes:
  • the first acquisition module 41 is used to acquire the itinerary information of the target itinerary as the target itinerary information
  • the first determination module 42 is configured to determine, according to the target travel information, the confidence level of the user's charging intention in the target travel, where the confidence level of the charging intention is determined by the charging probability of at least one preset dimension, wherein,
  • the preset dimensions include a charging position dimension, a charging time dimension and a battery state dimension;
  • the second determination module 43 is configured to determine, according to the confidence level of the charging intention, whether the user has a charging intention in the target journey.
  • the target travel information includes a target end position of the target travel
  • the first determining module 42 includes:
  • the first determination sub-module is used to determine the charging point that can match the target end position as the target charging point, wherein the charging point is the position where the vehicle is charged in the historical charging journey, and the historical charging journey is the charging point. the trip for which the vehicle was overcharged;
  • a second determination submodule configured to determine the charging probability of the vehicle in at least one preset dimension according to the target charging point and the target travel information
  • the third determination sub-module is configured to determine the confidence level of the charging intention according to the charging probability of the at least one preset dimension.
  • the target travel information further includes a target starting point position of the target travel and a target driving route of the target travel;
  • the charging probability of the vehicle in the charging position dimension is obtained through the following modules:
  • a fourth determination sub-module configured to determine the probability that the vehicle is charged at the target charging point as a first probability
  • a fifth determination sub-module configured to determine the probability that the target end position is a charging point as the second probability
  • the sixth determination sub-module is configured to determine the charging probability of the vehicle in the charging position dimension according to the product of the first probability and the second probability.
  • the fourth determination sub-module is used for:
  • the first probability is determined according to the ratio of the first number of charging times to the number of trips.
  • the fifth determination submodule is used for:
  • the designated charging trip is a historical charging trip whose trip start point matches the target starting point position in each historical charging trip;
  • the second probability is determined according to the similarity corresponding to each designated charging trip.
  • the target travel information further includes a target start time point of the target travel
  • the charging probability of the vehicle in the charging time dimension is obtained through the following modules:
  • the seventh determination sub-module is used to determine the charging starting time interval where the target starting time point is located as the target time interval, wherein the charging starting time interval is pre-defined based on the starting time points of each historical charging journey of;
  • the first obtaining sub-module is configured to obtain the second charging times of the vehicle being charged in the target time interval during the historical charging journey and the first charging times that the vehicle has been overcharged at the target charging point during the historical charging journey frequency;
  • the eighth determination sub-module is configured to determine the charging probability of the vehicle in the charging time dimension according to the ratio of the second charging times to the first charging times.
  • the target trip information further includes a target initial SOC value of the vehicle battery at the beginning of the target trip;
  • the charging probability of the vehicle in the battery state dimension is obtained through the following modules:
  • a ninth determination sub-module configured to determine an SOC interval in which the target initial SOC value is located as a target SOC interval, wherein the SOC interval is predefined based on initial SOC values of each historical charging trip;
  • the second obtaining sub-module is configured to obtain the third charging times for the vehicle to be charged in the target SOC interval during the historical charging journey and the first charging for the vehicle to be overcharged at the target charging point during the historical charging journey frequency;
  • a tenth determining submodule is configured to determine the charging probability of the vehicle in the battery state dimension according to the ratio of the third charging times to the first charging times.
  • the third determining sub-module is configured to determine the charging intention confidence level according to the product of the charging probabilities of the at least one preset dimension.
  • the second determining module 43 includes:
  • an eleventh determination sub-module configured to determine that the user has a charging intention in the target itinerary if the confidence level of the charging intention is greater than or equal to a confidence level threshold;
  • a twelfth determination sub-module is configured to determine that the user does not have a charging intention in the target journey if the confidence level of the charging intention is less than the confidence level threshold.
  • the target travel information includes a target end position of the target travel, a target start time point, and a target initial SOC value of the vehicle battery at the start of the target travel;
  • the first determining module is configured to determine that the target travel information satisfies a preset condition before determining the confidence level of the user's charging intention in the target travel information according to the target travel information;
  • the preset condition includes at least one of the following:
  • the pre-defined charging points there is at least one charging point within a preset range around the target end position;
  • the predefined charging starting time interval there is a charging starting time interval including the target starting time point;
  • the target itinerary is determined by the following modules:
  • a second acquisition module used for acquiring vehicle location information
  • the third acquiring module is configured to acquire historical travel information of the vehicle, where the historical travel information includes a historical starting time point, a historical starting SOC value and a historical charging point of each historical charging trip, and further includes the The number of historical journeys of the vehicle with each historical charging point as the end of the journey and the historical charging times of the vehicle being charged at each historical charging point;
  • a generating module configured to generate a predicted itinerary according to the historical itinerary information and the vehicle position information
  • the fourth determining module is configured to determine the target travel according to the predicted travel.
  • the fourth determining module includes:
  • an output sub-module for outputting predicted travel information used to characterize the predicted travel
  • the thirteenth determination sub-module is configured to determine the predicted itinerary as the target itinerary if a confirmation instruction for the predicted itinerary information from the user is received.
  • the target itinerary is determined by the following modules:
  • a receiving module configured to receive a user's setting instruction for a target itinerary, where the setting instruction is used to indicate the itinerary information of the target itinerary;
  • the fifth determination module is configured to determine the travel indicated by the setting instruction as the target travel.
  • the apparatus 40 further includes:
  • a temperature control module configured to control the battery temperature of the vehicle to be at a target temperature during the process of driving the vehicle on the target journey when it is determined that the user has an intention to charge during the target journey, wherein , the target temperature is a temperature favorable for charging the vehicle battery.
  • the present disclosure further provides a vehicle for executing the method for determining vehicle charging intention provided by any embodiment of the present disclosure.

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Abstract

一种车辆充电意图确定方法，包括：获取目标行程的行程信息，作为目标行程信息；根据目标行程信息，确定用户在目标行程中的充电意图置信度，充电意图置信度是通过至少一个预设维度的充电概率确定的，其中，预设维度包括充电位置维度、充电时间维度和电池状态维度；根据充电意图置信度，确定用户在目标行程中是否存在充电意图。

Description

车辆充电意图确定方法、装置及车辆
相关申请的交叉引用
本申请基于申请号为202011334649.9、申请日为2020年11月24日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本公开涉及车辆领域，具体地，涉及一种车辆充电意图确定方法、装置及车辆。
背景技术
目前，在车辆行驶过程中，通常需要对车辆的充电意图进行一定程度的预测，以便于辅助车辆进行充电准备，达到更好的充电效果。相关技术中，一般通过车辆周边环境判断充电意图，或者，从充电距离成本、时间成本、充电站设备利用率等方面对车辆充电进行引导、规划，无法兼顾驾驶员的充电习惯，导致无法准确判断出驾驶员的真实充电意图。
发明内容
本公开的目的是提供一种车辆充电意图确定方法、装置及车辆，以提升用户行车充电意图的预测准确性。
为了实现上述目的，根据本公开的第一方面，提供一种车辆充电意图确定方法，所述方法包括：
获取目标行程的行程信息，作为目标行程信息；
根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度，所述充电意图置信度是通过至少一个预设维度的充电概率确定的，其中，所述预设维度包括充电位置维度、充电时间维度和电池状态维度；
根据所述充电意图置信度，确定所述用户在所述目标行程中是否存在充电意图。
在一个实施方式中，所述目标行程信息包括所述目标行程的目标终点位置；
所述根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度，包括：
确定能够与所述目标终点位置相匹配的充电点，作为目标充电点，其中，充电点为所述车辆在历史充电行程中进行充电的位置，历史充电行程为所述车辆进行过充电的行程；
根据所述目标充电点和所述目标行程信息，确定所述车辆在至少一个预设维度的充电概率；
根据所述至少一个预设维度的充电概率，确定所述充电意图置信度。
在一个实施方式中，所述目标行程信息还包括所述目标行程的目标起点位置以及所述目标行程的目标行车路线；
若所述预设维度包括所述充电位置维度，所述车辆在所述充电位置维度的充电概率通过如下方式获得：
确定所述车辆在所述目标充电点进行充电的概率，作为第一概率；
确定所述目标终点位置为充电点的概率，作为第二概率；
根据所述第一概率与所述第二概率的乘积，确定所述车辆在所述充电位置维度的充电概率。
在一个实施方式中，所述确定所述车辆在所述目标充电点进行充电的概率，作为第一概率，包括：
获取所述车辆在历史充电行程中在所述目标充电点进行过充电的第一充电次数以及所述历史充电行程中以所述目标充电点为终点的匹配行程的行程数量；
根据所述第一充电次数与所述行程数量的比值，确定所述第一概率。
在一个实施方式中，所述确定所述目标终点位置为充电点的概率，作为第二概率，包括：
确定所述目标行车路线与指定充电行程的行车路线的相似度，其中，指定充电行程为各历史充电行程中行程起点与所述目标起点位置相匹配的历史充电行程；
根据各指定充电行程对应的相似度，确定所述第二概率。
在一个实施方式中，所述目标行程信息还包括所述目标行程的目标起始时间点；
若所述预设维度包括所述充电时间维度，所述车辆在所述充电时间维度的充电概率通过如下方式获得：
确定所述目标起始时间点所处的充电起始时间区间，作为目标时间区间，其中，充电起始时间区间是基于各个历史充电行程的起始时间点预先定义的；
获取所述车辆在历史充电行程中在所述目标时间区间进行充电的第二充电次数以及所述车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
根据所述第二充电次数与所述第一充电次数的比值，确定所述车辆在所述充电时间维度的充电概率。
在一个实施方式中，所述目标行程信息还包括所述目标行程开始时车辆电池的目标起始SOC值；
若所述预设维度包括所述电池状态维度，所述车辆在所述电池状态维度的充电概率通过如下方式获得：
确定所述目标起始SOC值所处的SOC区间，作为目标SOC区间，其中，SOC区间是基于各个历史充电行程的起始SOC值预先定义的；
获取所述车辆在历史充电行程中在所述目标SOC区间进行充电的第三充电次数以及所述车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
根据所述第三充电次数与所述第一充电次数的比值，确定所述车辆在所述电池状态维度的充电概率。
在一个实施方式中，所述根据所述至少一个预设维度的充电概率，确定所述充电意图置信度，包括：
根据所述至少一个预设维度的充电概率的乘积，确定所述充电意图置信度。
在一个实施方式中，所述根据所述充电意图置信度，确定所述用户在所述目标行程中是否存在充电意图，包括：
若所述充电意图置信度大于或者等于置信度阈值，确定所述用户在所述目标行程中存在充电意图；
若所述充电意图置信度小于所述置信度阈值，确定所述用户在所述目标行程中不存在充电意图。
在一个实施方式中，所述目标行程信息包括所述目标行程的目标终点位置、目标起始时间点以及所述目标行程开始时车辆电池的目标起始SOC值；
在所述根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度的步骤之前，所述方法还包括：
确定所述目标行程信息满足预设条件；
其中，所述预设条件包括以下中的至少一者：
在预先定义的充电点中，在所述目标终点位置周边的预设范围内存在至少一个充电点；
在预先定义的充电起始时间区间中，存在包含所述目标起始时间点的充电起始时间区间；
在预先定义的SOC区间中，存在包含所述目标起始SOC值的SOC区间。
在一个实施方式中，所述目标行程通过如下方式确定：
获取车辆位置信息；
获取所述车辆的历史行程信息，所述历史行程信息包括每一历史充电行程的历史起始时间点、历史起始SOC值和历史充电点，以及，还包括所述车辆以每一历史充电点为行程终点的历史行程数量和所述车辆在每一历史充电点进行充电的历史充电次数；
根据所述历史行程信息和所述车辆位置信息，生成预测行程；
根据所述预测行程，确定所述目标行程。
在一个实施方式中，所述根据所述预测行程，确定所述目标行程，包括：
输出用于表征所述预测行程的预测行程信息；
若接收到用户针对所述预测行程信息的确认指令，将所述预测行程确定为所述目标行程。
在一个实施方式中，所述目标行程通过如下方式确定：
接收用户针对目标行程的设置指令，所述设置指令用于指示所述目标行程的行程信息；
将所述设置指令所指示的行程确定为所述目标行程。
在一个实施方式中，所述方法还包括：
在确定所述用户在所述目标行程中存在充电意图的情况下，在所述车辆行驶于所述目标行程的过程中，控制所述车辆的电池温度处于目标温度，其中，所述目标温度为有利于车辆电池充电的温度。
根据本公开的第二方面，提供一种车辆充电意图确定装置，所述装置包括：
第一获取模块，用于获取目标行程的行程信息，作为目标行程信息；
第一确定模块，用于根据所述目标行程信息，确定用户在所述目标行程中的充电意图置 信度，所述充电意图置信度是通过至少一个预设维度的充电概率确定的，其中，所述预设维度包括充电位置维度、充电时间维度和电池状态维度；
第二确定模块，用于根据所述充电意图置信度，确定所述用户在所述目标行程中是否存在充电意图。
在一个实施方式中，所述目标行程信息包括所述目标行程的目标终点位置；
所述第一确定模块包括：
第一确定子模块，用于确定能够与所述目标终点位置相匹配的充电点，作为目标充电点，其中，充电点为所述车辆在历史充电行程中进行充电的位置，历史充电行程为所述车辆进行过充电的行程；
第二确定子模块，用于根据所述目标充电点和所述目标行程信息，确定所述车辆在至少一个预设维度的充电概率；
第三确定子模块，用于根据所述至少一个预设维度的充电概率，确定所述充电意图置信度。
在一个实施方式中，所述目标行程信息还包括所述目标行程的目标起点位置以及所述目标行程的目标行车路线；
若所述预设维度包括所述充电位置维度，所述车辆在所述充电位置维度的充电概率通过如下模块获得：
第四确定子模块，用于确定所述车辆在所述目标充电点进行充电的概率，作为第一概率；
第五确定子模块，用于确定所述目标终点位置为充电点的概率，作为第二概率；
第六确定子模块，用于根据所述第一概率与所述第二概率的乘积，确定所述车辆在所述充电位置维度的充电概率。
在一个实施方式中，所述第四确定子模块用于：
获取所述车辆在历史充电行程中在所述目标充电点进行过充电的第一充电次数以及所述历史充电行程中以所述目标充电点为终点的匹配行程的行程数量；
根据所述第一充电次数与所述行程数量的比值，确定所述第一概率。
在一个实施方式中，所述第五确定子模块用于：
确定所述目标行车路线与每一指定充电行程的行车路线的相似度，其中，指定充电行程为各历史充电行程中行程起点与所述目标起点位置相匹配的历史充电行程；
根据各指定充电行程对应的相似度，确定所述第二概率。
在一个实施方式中，所述目标行程信息还包括所述目标行程的目标起始时间点；
若所述预设维度包括所述充电时间维度，所述车辆在所述充电时间维度的充电概率通过如下模块获得：
第七确定子模块，用于确定所述目标起始时间点所处的充电起始时间区间，作为目标时间区间，其中，充电起始时间区间是基于各个历史充电行程的起始时间点预先定义的；
第一获取子模块，用于获取所述车辆在历史充电行程中在所述目标时间区间进行充电的第二充电次数以及所述车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
第八确定子模块，用于根据所述第二充电次数与所述第一充电次数的比值，确定所述车辆在所述充电时间维度的充电概率。
在一个实施方式中，所述目标行程信息还包括所述目标行程开始时车辆电池的目标起始SOC值；
若所述预设维度包括所述电池状态维度，所述车辆在所述电池状态维度的充电概率通过如下模块获得：
第九确定子模块，用于确定所述目标起始SOC值所处的SOC区间，作为目标SOC区间，其中，SOC区间是基于各个历史充电行程的起始SOC值预先定义的；
第二获取子模块，用于获取所述车辆在历史充电行程中在所述目标SOC区间进行充电的第三充电次数以及所述车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
第十确定子模块，用于根据所述第三充电次数与所述第一充电次数的比值，确定所述车辆在所述电池状态维度的充电概率。
在一个实施方式中，所述第三确定子模块用于根据所述至少一个预设维度的充电概率的乘积，确定所述充电意图置信度。
在一个实施方式中，所述第二确定模块包括：
第十一确定子模块，用于若所述充电意图置信度大于或者等于置信度阈值，确定所述用户在所述目标行程中存在充电意图；
第十二确定子模块，用于若所述充电意图置信度小于所述置信度阈值，确定所述用户在所述目标行程中不存在充电意图。
在一个实施方式中，所述目标行程信息包括所述目标行程的目标终点位置、目标起始时间点以及所述目标行程开始时车辆电池的目标起始SOC值；
所述第一确定模块用于在根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度之前，确定所述目标行程信息满足预设条件；
其中，所述预设条件包括以下中的至少一者：
在预先定义的充电点中，在所述目标终点位置周边的预设范围内存在至少一个充电点；
在预先定义的充电起始时间区间中，存在包含所述目标起始时间点的充电起始时间区间；
在预先定义的SOC区间中，存在包含所述目标起始SOC值的SOC区间。
在一个实施方式中，所述目标行程通过如下模块确定：
第二获取模块，用于获取车辆位置信息；
第三获取模块，用于获取所述车辆的历史行程信息，所述历史行程信息包括每一历史充电行程的历史起始时间点、历史起始SOC值和历史充电点，以及，还包括所述车辆以每一历史充电点为行程终点的历史行程数量和所述车辆在每一历史充电点进行充电的历史充电次数；
生成模块，用于根据所述历史行程信息和所述车辆位置信息，生成预测行程；
第四确定模块，用于根据所述预测行程，确定所述目标行程。
在一个实施方式中，所述第四确定模块包括：
输出子模块，用于输出用于表征所述预测行程的预测行程信息；
第十三确定子模块，用于若接收到用户针对所述预测行程信息的确认指令，将所述预测行程确定为所述目标行程。
在一个实施方式中，所述目标行程通过如下模块确定：
接收模块，用于接收用户针对目标行程的设置指令，所述设置指令用于指示所述目标行程的行程信息；
第五确定模块，用于将所述设置指令所指示的行程确定为所述目标行程。
在一个实施方式中，所述装置还包括：
温度控制模块，用于在确定所述用户在所述目标行程中存在充电意图的情况下，在所述车辆行驶于所述目标行程的过程中，控制所述车辆的电池温度处于目标温度，其中，所述目标温度为有利于车辆电池充电的温度。
根据本公开的第三方面，提供一种车辆，所述车辆用于执行本公开第一方面所述的方法。
通过上述技术方案，获取目标行程的行程信息，作为目标行程信息，根据目标行程信息，确定用户在目标行程中的充电意图置信度，根据充电意图置信度，确定用户在目标行程中是否存在充电意图。其中，充电意图置信度是通过至少一个预设维度的充电概率确定的，预设维度包括充电位置维度、充电时间维度和电池状态维度。这样，针对充电位置、充电时间、电池状态这几个维度，基于用户的充电习惯，确定用户的充电意图置信度，再根据置信度确定用户是否存在充电意图，能够有效提升充电意图确定的准确性，并为电池热管理提供可靠依据，提升车辆充电效果。
本公开的其他特征和优点将在随后的具体实施方式部分予以详细说明。
附图说明
附图是用来提供对本公开的进一步理解，并且构成说明书的一部分，与下面的具体实施方式一起用于解释本公开，但并不构成对本公开的限制。在附图中：
图1是根据本公开的一种实施方式提供的车辆充电意图确定方法的流程图；
图2是根据本公开提供的车辆充电意图确定方法中，根据目标行程信息，确定用户在目标行程中的充电意图置信度的步骤的一种示例性的流程图；
图3是根据本公开的另一种实施方式提供的车辆充电意图确定方法的流程图；
图4是根据本公开的另一种实施方式提供的车辆充电意图确定方法的流程图；
图5是根据本公开的一种实施方式提供的车辆充电意图确定方法所应用的系统的结构示意图；
图6是根据本公开的一种实施方式提供的车辆充电意图确定装置的框图。
具体实施方式
以下结合附图对本公开的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本公开，并不用于限制本公开。
图1是根据本公开的一种实施方式提供的车辆充电意图确定方法的流程图。本公开提供的方法可以应用于车辆端，也可以应用于能够与车辆通信的服务器或云端。如图1所示，该方法可以包括以下步骤。
在步骤11中，获取目标行程的行程信息，作为目标行程信息。
在步骤12中，根据目标行程信息，确定用户在目标行程中的充电意图置信度。
在步骤13中，根据充电意图置信度，确定用户在目标行程中是否存在充电意图。
其中，充电意图置信度是通过至少一个预设维度的充电概率确定的，预设维度包括充电位置维度、充电时间维度和电池状态维度。
在本公开的方案中，行程信息可以包括但不限于以下中的至少一者：行程的起点位置、行程的终点位置、行程的起始时间点、行程的结束时间点、行程开始时的车辆电池SOC(State of Charge，荷电状态)值、行程的行车路线。
行程的起点位置指行程开始时车辆所处的位置。车辆的终点位置指行程结束时车辆所处的位置。行程的起始时间点指行程开始时的时间。行程的结束时间点指行程结束时的时间。行程开始时的车辆电池SOC值指行程开始时车辆动力电池所对应的SOC值。行程的行车路线值在行程所对应的行车过程中，车辆的行驶轨迹。在一种实施例中，可以周期性地获取车辆位置，在行程内获取到的车辆位置按照其获取的先后顺序构成车辆在行程内的行驶轨迹，即行车路线。
因此，基于上述行程信息的定义，目标行程信息可以相应包括目标起点位置、目标终点位置、目标起始时间点、目标结束时间点、目标SOC值、目标行车路线等。
在一种可能的实施方式中，目标行程可以通过如下方式确定：
获取车辆位置信息；
获取车辆的历史行程信息；
根据历史行程信息和车辆位置信息，生成预测行程；
根据预测行程，确定目标行程。
在这一实施方式中，实际上是基于用户的历史行车记录或习惯，预测出未来的行程，并基于预测出的行程，确定出目标行程。
其中，车辆位置信息指车辆当前所处的位置。
车辆的历史行程信息可以表征用户的历史行车习惯，因此，基于历史行程信息，有利于提升行程预测的准确性。因此，在初始阶段，需要收集车辆在历史行车过程中的相关数据，用于构成历史行程信息，即，基于大数据得到历史行程信息。由于本方案集中在对车辆充电意图的预测，因此，收集数据时，可以重点收集与车辆充电相关的数据。例如，对用户日常的行车充电数据进行记录，可以包括用户进行充电的行程开始时间、充电开始时间、充电地点、行程开始(或充电开始)时的电池SOC值、充电时长等。
基于大数据(收集到的相关数据)，按照一定的规律进行统计分析，获得历史行程信息。其中，历史行程信息可以在一定程度上代表用户的充电习惯。示例地，可以按照如下规律对收集到的数据进行统计分析，以便于后续的数据处理。
统计车辆所有在终点进行过充电的行程，记录该行程的起点和终点，将该行程定义为充电行程(历史充电行程)，同时，将该行程的终点(进行充电操作的位置)定义为充电点。
统计车辆以各个充电点为行程终点的行程数量。
统计车辆在各个充电点进行充电的次数。
统计车辆在前往各个充电点充电时，历史充电行程的起点的动力电池SOC状态(可通过SOC值体现)，另外，还可以基于出现的SOC值划分不同的SOC区间，并统计每个SOC区间出现的频次。
统计车辆在前往各个充电点充电时，历史充电行程的起始时间点，另外，还可以基于出现的起始时间点划分不同的充电起始时间区间，并统计每个充电起始时间区间出现的频次。
基于上述内容，历史行程信息可以相应包括：每一历史充电行程的历史起始时间点、历史起始SOC值和历史充电点，以及，还包括车辆以每一历史充电点为行程终点的历史行程数量和车辆在每一历史充电点进行充电的历史充电次数。
基于上文内容，进一步地，可以根据历史行程信息和车辆位置信息，生成预测行程。
在一种实施例中，可以根据历史行程信息，识别其中指定起点到指定终点在某个时间段内的出现频率，将出现频率最高的<起点，终点>的组合对应的行程作为针对该时间段预测的行程。
再例如，可以根据车辆位置信息，确定历史行程信息中与车辆位置信息相匹配(例如，二者之间距离小于某一距离阈值)的起点位置，再从与该起点位置对应的历史充电行程中，选取在待预测行程的时段出现频次最高的历史充电行程，将该历史充电行程对应的<起点，终点>作为预测行程。
再例如，可以根据历史行程信息，统计出用户在指定时段内的行程习惯。例如，在工作日8：00～9：00，用户驾车从A地行驶到B地，同时，在工作日18：00～19:00，用户驾车从B地行驶到A地，则可以生成针对工作日的预测行程，行程的起点、终点、时间均可参考已统计出的行程习惯。
在获得预测行程后，可以根据预测行程，确定目标行程。
在一种实施例中，可以直接将预测行程作为目标行程。
再例如，根据预测行程，确定目标行程，可以包括以下步骤：
输出用于表征预测行程的预测行程信息；
若接收到用户针对预测行程信息的确认指令，将预测行程确定为目标行程。
在这一实施例中，在生成预测行程后，还会推送给用户进行确认，在获得用户的确认时，该行程才会被作为有效行程，即目标行程。
其中，预测行程信息可以包括行程的起点位置、终点位置、起始时间点等。
在一种实施例中，预测行程信息可以通过日程提醒的形式推送至用户APP端，用户可确认自己是否有当前出行计划。当用户确认出行，则车辆将此预测行程数据作为有效数据，作为后续数据处理的依据，例如，供后续识别目标行程的充电意图置信度使用。若用户拒绝接受该行程，则删除该预测行程相关的数据。
在一种可能的实施方式中，用户还可自行设置目标行程，在这一实施方式中，目标行程可以通过如下方式确定：
接收用户针对目标行程的设置指令；
将设置指令所指示的行程确定为目标行程。
其中，设置指令用于指示目标行程的行程信息
在一种实施例中，用户可以预设多个行程计划，行程计划可以设置重复时间以及提醒的开始时间和结束时间。
下面对步骤12进行详细的解释说明。
在一种可能的实施方式中，步骤12可以包括以下步骤，如图2所示。
在步骤21中，确定能够与目标终点位置相匹配的充电点，作为目标充电点。
其中，充电点为车辆在历史充电行程中进行充电的位置，历史充电行程为车辆在行车过程中进行过充电的行程。相关的解释说明在前文已经给出，此处不再赘述。
在步骤22中，根据目标充电点和目标行程信息，确定车辆在至少一个预设维度的充电概率。
如上文所述，充电意图置信度是通过至少一个预设维度的充电概率确定的，并且，预设维度包括充电位置维度、充电时间维度和电池状态维度。
在一种可能的实施例中，若预设维度包括充电位置维度，车辆在充电位置维度的充电概率通过如下方式获得：
确定车辆在目标充电点进行充电的概率，作为第一概率；
确定目标终点位置为充电点的概率，作为第二概率；
根据第一概率与第二概率的乘积，确定车辆在充电位置维度的充电概率。
在一种实施例中，确定车辆在目标充电点进行充电的概率，作为第一概率，可以包括以下步骤：
获取车辆在历史充电行程中在目标充电点进行过充电的第一充电次数以及历史充电行程中以目标充电点为终点的匹配行程的行程数量；
根据第一充电次数与行程数量的比值，确定第一概率。
在一种实施例中，可以将第一充电次数与行程数量的比值作为第一概率。再例如，可以在得到第一充电次数与行程数量的比值后，进行预设运算(例如，乘以预设系数)，将得到的结果作为第一概率。
在一种实施例中，确定目标终点位置为充电点的概率，作为第二概率，可以包括以下步骤：
确定目标行车路线与指定充电行程的行车路线的相似度；
根据各指定充电行程对应的相似度，确定第二概率。
其中，指定充电行程为各历史充电行程中行程起点与目标起点位置相匹配的历史充电行程。在这里，相匹配可以为历史充电行程的行程起点与目标起点位置相同，或者，可以为历史充电行程的行程起点与目标起点位置之间的距离小于制定与预先设定的阈值，或者，可以 为历史充电行程的行程起点与目标起点位置同属于一个区域(可以预先划定)。
根据目标行车路线，以及指定充电行程对应的行车路线(通过如前文所描述的大数据方式获得)，确定目标行车路线与每一指定充电行程的相似度。同时，每一指定充电行程的行程终点均对应有该行程终点为充电点的概率。在一种实施例中，可以将最大相似度对应的指定充电行程所对应的上述概率作为第二概率。
在获得第一概率和第二概率之后，根据二者的乘积，即可确定车辆在充电位置维度的充电概率。
在一种实施例中，可以将第一概率和第二概率的乘积作为车辆在充电位置维度的充电概率。再例如，可以在得到第一概率和第二概率的乘积后，进行预设运算(例如，乘以预设系数)，将得到的结果作为车辆在充电位置维度的充电概率。
通过上述计算方式，车辆在充电位置维度的充电概率能够表征车辆可能在目标行程中所涉及到的充电点进行充电的概率，从而能够从充电位置的角度反映用户的充电意图。
在一种可能的实施例中，若预设维度包括充电时间维度，车辆在充电时间维度的充电概率通过如下方式获得：
确定目标起始时间点所处的充电起始时间区间，作为目标时间区间；
获取车辆在历史充电行程中在目标时间区间进行充电的第二充电次数以及车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
根据第二充电次数与第一充电次数的比值，确定车辆在充电时间维度的充电概率。
其中，充电起始时间区间是基于各个历史充电行程的起始时间点预先定义的。如前文所述，可以根据统计过程中出现的起始时间点划分不同的时间段，作为不同的充电起始时间区间。
在一种实施例中，可以将第二充电次数与第一充电次数的比值作为车辆在充电时间维度的充电概率。再例如，可以在得到第二充电次数与第一充电次数的比值后，进行预设运算(例如，乘以预设系数)，将得到的结果作为车辆在充电时间维度的充电概率。
通过上述计算方式，车辆在充电时间维度的充电概率能够表征车辆可能在以目标起点位置为起点的行程中进行充电的概率，从而能够从充电时间的角度反映用户的充电意图。
在一种可能的实施例中，若预设维度包括电池状态维度，车辆在电池状态维度的充电概率通过如下方式获得：
确定目标起始SOC值所处的SOC区间，作为目标SOC区间；
获取车辆在历史充电行程中在目标SOC区间进行充电的第三充电次数以及车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
根据第三充电次数与第一充电次数的比值，确定车辆在电池状态维度的充电概率。
其中，SOC区间是基于各个历史充电行程的起始SOC值预先定义的。如前文所述，可以根据统计过程中出现的起始SOC值划分不同的SOC区间，作为不同的SOC区间。
在一种实施例中，可以将第三充电次数与第一充电次数的比值作为车辆在电池状态维度的充电概率。再例如，可以在得到第三充电次数与第一充电次数的比值后，进行预设运算(例 如，乘以预设系数)，将得到的结果作为车辆在电池状态维度的充电概率。
通过上述计算方式，车辆在电池状态维度的充电概率能够表征车辆可能在以目标起始SOC值开始的行程中进行充电的概率，从而能够从电池状态的角度反映用户的充电意图。
进一步地，在步骤23中，根据至少一个预设维度的充电概率，确定充电意图置信度。
在获取到每一预设维度对应的充电概率之后，即可确定充电意图置信度。
在一种可能的实施例中，步骤23可以包括以下步骤：
根据至少一个预设维度的充电概率的乘积，确定充电意图置信度。
在一种实施例中，可以将各个预设维度的充电概率的乘积作为充电意图置信度。再例如，可以在得到各个预设维度的充电概率的乘积后，进行预设运算(例如，乘以预设系数)，将得到的结果作为充电意图置信度。
这样，能够从多个维度确定充电意图置信度，有利于提升充电意图判断的准确性，并且，所使用的预设维度越多，对于充电意图的判断越精确。
回到图1，在步骤13中，根据充电意图置信度，确定用户在目标行程中是否存在充电意图，可以包括以下步骤：
若充电意图置信度大于或者等于置信度阈值，确定用户在目标行程中存在充电意图；
若充电意图置信度小于置信度阈值，确定用户在目标行程中不存在充电意图。
其中，置信度阈值可以根据经验值预先设定。
通过上述技术方案，获取待判断充电意图的目标行程的行程信息，作为目标行程信息，根据目标行程信息，确定用户在目标行程中的充电意图置信度，根据充电意图置信度，确定用户在目标行程中是否存在充电意图。其中，充电意图置信度是通过至少一个预设维度的充电概率确定的，预设维度包括充电位置维度、充电时间维度和电池状态维度。这样，针对充电位置、充电时间、电池状态这几个维度，基于用户的充电习惯，确定用户的充电意图置信度，再根据置信度确定用户是否存在充电意图，能够有效提升充电意图确定的准确性，并为电池热管理提供可靠依据，提升车辆充电效果。
在一个实施方式中，在步骤12之前，本公开提供的方法还可以包括以下步骤：
确定目标行程信息满足预设条件。
也就是说，在获得目标行程信息之后，首先判断其是否满足预设条件，若确定目标行程信息满足预设条件，再进一步地计算目标行程对应的充电意图置信度。
而在确定目标行程信息不满足预设条件的情况下，可以直接确定用户在目标行程中不存在充电意图，节省后续的数据处理步骤。
其中，预设条件可以包括以下中的至少一者：
在预先定义的充电点中，在目标终点位置周边的预设范围内存在至少一个充电点；
在预先定义的充电起始时间区间中，存在包含目标起始时间点的充电起始时间区间；
在预先定义的SOC区间中，存在包含目标起始SOC值的SOC区间。
目标行程的终点位置应当在已有充电点周边较近的位置，因此，若在目标终点位置周边的预设范围内存在至少一个充电点，则由于充电点距离过远，明显可以确定车辆充电意图不 足，也就无需进行后续的置信度判断，以节省数据处理压力。以及，基于充电起始时间区间、SOC区间的判断思路与上述内容同理，此处不赘述。
在一个实施方式中，如图3所示，在图1所示各步骤的基础上，本公开提供的方法还可以包括以下步骤：
在步骤31中，在确定用户在目标行程中存在充电意图的情况下，在车辆行驶于目标行程的过程中，控制车辆的电池温度处于目标温度。
其中，目标温度为有利于车辆电池充电的温度。
在一种实施例中，可以在车辆到达目标充电点之前的预设时段内控制车辆的电池温度处于目标温度。
基于上述步骤，在确定车辆在目标行程中存在充电意图的情况下，可以提前控制电池的温度达到有利于电池充电的温度，有利于更好地为电池充电。
在一种可能的实施例中，本公开提供的方法的流程图可以如图4所示。其中各步骤的具体实施方式在上文中已经对应给出，此处不赘述。
通过上述技术方案，获取待判断充电意图的目标行程的行程信息，作为目标行程信息，根据目标行程信息，确定用户在目标行程中的充电意图置信度，根据充电意图置信度，确定用户在目标行程中是否存在充电意图。其中，充电意图置信度是通过至少一个预设维度的充电概率确定的，预设维度包括充电位置维度、充电时间维度和电池状态维度。这样，针对充电位置、充电时间、电池状态这几个维度，基于用户的充电习惯，确定用户的充电意图置信度，再根据置信度确定用户是否存在充电意图，能够有效提升充电意图确定的准确性，并为电池热管理提供可靠依据，提升车辆充电效果。
在一种可能的实施例中，本公开提供的方法可以应用于云端服务器，云端服务器可以设有云端数据系统和云端预测系统，并且，云端服务器可以与用户的手机端和车辆端进行通信，上述几者的结构示意图可以如图5所示，在图5中，箭头方向可以表示信息传输的方向，箭头附近的虚线框所圈住的内容表示所传输的信息。
云端数据系统用于数据收集、数据处理。例如，可以参照前文所给出的收集车辆在历史形成过程中的相关数据用于构成历史行程信息的相关步骤，云端数据系统可以收集上述数据，并进行统计分析，以得到历史行程信息。并且，如图5中所示，云端数据系统会将历史行程信息同步至云端预测系统。
云端预测系统是执行本公开所提供的车辆充电意图确定方法的主要载体，其中包含了4个功能模块，分别为用于预测行程生成的模块(后文将以M1指代)、用于目标行程确定的模块(后文将以M2指代)、用于充电意图置信度确定的模块(后文将以M3指代)以及用于车辆信息获取的模块(后文将以M4指代)。M1～M4可以通过如下方式确定用户在目标行程中的充电意图：
M1基于历史行程信息，生成预测行程；
M1将预测行程对应的预测行程信息发送至M2，同时，还将预测行程信息发送至用户的手机端，以使用户确认是否接受该行程；
M2在接收到用户针对预测行程信息的确认指令之后，会根据预测行程信息，确定目标行程，进而获得目标行程信息；
M2将目标行程信息发送至M3；
M3根据目标行程信息以及从M4获取到的参数，确定用户在目标行程中的充电意图置信度，以确定用户在目标行程中是否存在充电意图，并将确定结果发送至车辆端；
M4通过车辆端获取M3在数据处理过程中所需的相关参数。
需要说明的是，上述系统中各个组成部分执行操作的具体方式已经在前文方法部分的实施例中进行了详细描述，此处将不做详细阐述说明。
图6是根据本公开的一种实施方式提供的车辆充电意图确定装置的框图，如图6所示，所述装置40包括：
第一获取模块41，用于获取目标行程的行程信息，作为目标行程信息；
第一确定模块42，用于根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度，所述充电意图置信度是通过至少一个预设维度的充电概率确定的，其中，所述预设维度包括充电位置维度、充电时间维度和电池状态维度；
第二确定模块43，用于根据所述充电意图置信度，确定所述用户在所述目标行程中是否存在充电意图。
在一个实施方式中，所述目标行程信息包括所述目标行程的目标终点位置；
所述第一确定模块42包括：
第一确定子模块，用于确定能够与所述目标终点位置相匹配的充电点，作为目标充电点，其中，充电点为所述车辆在历史充电行程中进行充电的位置，历史充电行程为所述车辆进行过充电的行程；
第二确定子模块，用于根据所述目标充电点和所述目标行程信息，确定所述车辆在至少一个预设维度的充电概率；
第三确定子模块，用于根据所述至少一个预设维度的充电概率，确定所述充电意图置信度。
在一个实施方式中，所述目标行程信息还包括所述目标行程的目标起点位置以及所述目标行程的目标行车路线；
若所述预设维度包括所述充电位置维度，所述车辆在所述充电位置维度的充电概率通过如下模块获得：
第四确定子模块，用于确定所述车辆在所述目标充电点进行充电的概率，作为第一概率；
第五确定子模块，用于确定所述目标终点位置为充电点的概率，作为第二概率；
第六确定子模块，用于根据所述第一概率与所述第二概率的乘积，确定所述车辆在所述充电位置维度的充电概率。
在一个实施方式中，所述第四确定子模块用于：
获取所述车辆在历史充电行程中在所述目标充电点进行过充电的第一充电次数以及所述历史充电行程中以所述目标充电点为终点的匹配行程的行程数量；
根据所述第一充电次数与所述行程数量的比值，确定所述第一概率。
在一个实施方式中，所述第五确定子模块用于：
确定所述目标行车路线与每一指定充电行程的行车路线的相似度，其中，指定充电行程为各历史充电行程中行程起点与所述目标起点位置相匹配的历史充电行程；
根据各指定充电行程对应的相似度，确定所述第二概率。
在一个实施方式中，所述目标行程信息还包括所述目标行程的目标起始时间点；
若所述预设维度包括所述充电时间维度，所述车辆在所述充电时间维度的充电概率通过如下模块获得：
第七确定子模块，用于确定所述目标起始时间点所处的充电起始时间区间，作为目标时间区间，其中，充电起始时间区间是基于各个历史充电行程的起始时间点预先定义的；
第一获取子模块，用于获取所述车辆在历史充电行程中在所述目标时间区间进行充电的第二充电次数以及所述车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
第八确定子模块，用于根据所述第二充电次数与所述第一充电次数的比值，确定所述车辆在所述充电时间维度的充电概率。
在一个实施方式中，所述目标行程信息还包括所述目标行程开始时车辆电池的目标起始SOC值；
若所述预设维度包括所述电池状态维度，所述车辆在所述电池状态维度的充电概率通过如下模块获得：
第九确定子模块，用于确定所述目标起始SOC值所处的SOC区间，作为目标SOC区间，其中，SOC区间是基于各个历史充电行程的起始SOC值预先定义的；
第二获取子模块，用于获取所述车辆在历史充电行程中在所述目标SOC区间进行充电的第三充电次数以及所述车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
第十确定子模块，用于根据所述第三充电次数与所述第一充电次数的比值，确定所述车辆在所述电池状态维度的充电概率。
在一个实施方式中，所述第三确定子模块用于根据所述至少一个预设维度的充电概率的乘积，确定所述充电意图置信度。
在一个实施方式中，所述第二确定模块43包括：
第十一确定子模块，用于若所述充电意图置信度大于或者等于置信度阈值，确定所述用户在所述目标行程中存在充电意图；
第十二确定子模块，用于若所述充电意图置信度小于所述置信度阈值，确定所述用户在所述目标行程中不存在充电意图。
在一个实施方式中，所述目标行程信息包括所述目标行程的目标终点位置、目标起始时间点以及所述目标行程开始时车辆电池的目标起始SOC值；
所述第一确定模块用于在根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度之前，确定所述目标行程信息满足预设条件；
其中，所述预设条件包括以下中的至少一者：
在预先定义的充电点中，在所述目标终点位置周边的预设范围内存在至少一个充电点；
在预先定义的充电起始时间区间中，存在包含所述目标起始时间点的充电起始时间区间；
在预先定义的SOC区间中，存在包含所述目标起始SOC值的SOC区间。
在一个实施方式中，所述目标行程通过如下模块确定：
第二获取模块，用于获取车辆位置信息；
第三获取模块，用于获取所述车辆的历史行程信息，所述历史行程信息包括每一历史充电行程的历史起始时间点、历史起始SOC值和历史充电点，以及，还包括所述车辆以每一历史充电点为行程终点的历史行程数量和所述车辆在每一历史充电点进行充电的历史充电次数；
生成模块，用于根据所述历史行程信息和所述车辆位置信息，生成预测行程；
第四确定模块，用于根据所述预测行程，确定所述目标行程。
在一个实施方式中，所述第四确定模块包括：
输出子模块，用于输出用于表征所述预测行程的预测行程信息；
第十三确定子模块，用于若接收到用户针对所述预测行程信息的确认指令，将所述预测行程确定为所述目标行程。
在一个实施方式中，所述目标行程通过如下模块确定：
接收模块，用于接收用户针对目标行程的设置指令，所述设置指令用于指示所述目标行程的行程信息；
第五确定模块，用于将所述设置指令所指示的行程确定为所述目标行程。
在一个实施方式中，所述装置40还包括：
温度控制模块，用于在确定所述用户在所述目标行程中存在充电意图的情况下，在所述车辆行驶于所述目标行程的过程中，控制所述车辆的电池温度处于目标温度，其中，所述目标温度为有利于车辆电池充电的温度。
关于上述实施例中的装置，其中各个模块执行操作的具体方式已经在有关该方法的实施例中进行了详细描述，此处将不做详细阐述说明。
本公开还提供一种车辆，所述车辆用于执行本公开任意实施例所提供的车辆充电意图确定方法。
以上结合附图详细描述了本公开的优选实施方式，但是，本公开并不限于上述实施方式中的具体细节，在本公开的技术构思范围内，可以对本公开的技术方案进行多种简单变型，这些简单变型均属于本公开的保护范围。
另外需要说明的是，在上述具体实施方式中所描述的各个具体技术特征，在不矛盾的情况下，可以通过任何合适的方式进行组合。为了避免不必要的重复，本公开对各种可能的组合方式不再另行说明。
此外，本公开的各种不同的实施方式之间也可以进行任意组合，只要其不违背本公开的思想，其同样应当视为本公开所公开的内容。

Claims (16)

  1. 一种车辆充电意图确定方法，包括：
    获取目标行程的行程信息，作为目标行程信息；
    根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度，所述充电意图置信度是通过至少一个预设维度的充电概率确定的，其中，所述预设维度包括充电位置维度、充电时间维度和电池状态维度；
    根据所述充电意图置信度，确定所述用户在所述目标行程中是否存在充电意图。
  2. 根据权利要求1所述的方法，其中所述目标行程信息包括所述目标行程的目标终点位置；
    所述根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度，包括：
    确定能够与所述目标终点位置相匹配的充电点，作为目标充电点，其中，充电点为所述车辆在历史充电行程中进行充电的位置，历史充电行程为所述车辆进行过充电的行程；
    根据所述目标充电点和所述目标行程信息，确定所述车辆在至少一个预设维度的充电概率；
    根据所述至少一个预设维度的充电概率，确定所述充电意图置信度。
  3. 根据权利要求2所述的方法，其中所述目标行程信息还包括所述目标行程的目标起点位置以及所述目标行程的目标行车路线；
    若所述预设维度包括所述充电位置维度，所述车辆在所述充电位置维度的充电概率通过如下方式获得：
    确定所述车辆在所述目标充电点进行充电的概率，作为第一概率；
    确定所述目标终点位置为充电点的概率，作为第二概率；
    根据所述第一概率与所述第二概率的乘积，确定所述车辆在所述充电位置维度的充电概率。
  4. 根据权利要求3所述的方法，其中所述确定所述车辆在所述目标充电点进行充电的概率，作为第一概率，包括：
    获取所述车辆在历史充电行程中在所述目标充电点进行过充电的第一充电次数以及所述历史充电行程中以所述目标充电点为终点的匹配行程的行程数量；
    根据所述第一充电次数与所述行程数量的比值，确定所述第一概率。
  5. 根据权利要求3所述的方法，其中所述确定所述目标终点位置为充电点的概率，作为第二概率，包括：
    确定所述目标行车路线与指定充电行程的行车路线的相似度，其中，指定充电行程为各历史充电行程中行程起点与所述目标起点位置相匹配的历史充电行程；
    根据各指定充电行程对应的相似度，确定所述第二概率。
  6. 根据权利要求2所述的方法，其中所述目标行程信息还包括所述目标行程的目标起始时间点；
    若所述预设维度包括所述充电时间维度，所述车辆在所述充电时间维度的充电概率通过如下方式获得：
    确定所述目标起始时间点所处的充电起始时间区间，作为目标时间区间，其中，充电起始时间区间是基于各个历史充电行程的起始时间点预先定义的；
    获取所述车辆在历史充电行程中在所述目标时间区间进行充电的第二充电次数以及所述车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
    根据所述第二充电次数与所述第一充电次数的比值，确定所述车辆在所述充电时间维度的充电概率。
  7. 根据权利要求2所述的方法，其中所述目标行程信息还包括所述目标行程开始时车辆电池的目标起始SOC值；
    若所述预设维度包括所述电池状态维度，所述车辆在所述电池状态维度的充电概率通过如下方式获得：
    确定所述目标起始SOC值所处的SOC区间，作为目标SOC区间，其中，SOC区间是基于各个历史充电行程的起始SOC值预先定义的；
    获取所述车辆在历史充电行程中在所述目标SOC区间进行充电的第三充电次数以及所述车辆在历史充电行程中在目标充电点进行过充电的第一充电次数；
    根据所述第三充电次数与所述第一充电次数的比值，确定所述车辆在所述电池状态维度的充电概率。
  8. 根据权利要求2所述的方法，其中所述根据所述至少一个预设维度的充电概率，确定所述充电意图置信度，包括：
    根据所述至少一个预设维度的充电概率的乘积，确定所述充电意图置信度。
  9. 根据权利要求1所述的方法，其特征在于，所述根据所述充电意图置信度，确定所述用户在所述目标行程中是否存在充电意图，包括：
    若所述充电意图置信度大于或者等于置信度阈值，确定所述用户在所述目标行程中存在充电意图；
    若所述充电意图置信度小于所述置信度阈值，确定所述用户在所述目标行程中不存在充电意图。
  10. 根据权利要求1所述的方法，其中所述目标行程信息包括所述目标行程的目标终点位置、目标起始时间点以及所述目标行程开始时车辆电池的目标起始SOC值；
    在所述根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度的步骤之前，所述方法还包括：
    确定所述目标行程信息满足预设条件；
    其中，所述预设条件包括以下中的至少一者：
    在预先定义的充电点中，在所述目标终点位置周边的预设范围内存在至少一个充电点；
    在预先定义的充电起始时间区间中，存在包含所述目标起始时间点的充电起始时间区间；
    在预先定义的SOC区间中，存在包含所述目标起始SOC值的SOC区间。
  11. 根据权利要求1所述的方法，其中所述目标行程通过如下方式确定：
    获取车辆位置信息；
    获取所述车辆的历史行程信息，所述历史行程信息包括每一历史充电行程的历史起始时间点、历史起始SOC值和历史充电点，以及，还包括所述车辆以每一历史充电点为行程终点的历史行程数量和所述车辆在每一历史充电点进行充电的历史充电次数；
    根据所述历史行程信息和所述车辆位置信息，生成预测行程；
    根据所述预测行程，确定所述目标行程。
  12. 根据权利要求11所述的方法，其中所述根据所述预测行程，确定所述目标行程，包括：
    输出用于表征所述预测行程的预测行程信息；
    若接收到用户针对所述预测行程信息的确认指令，将所述预测行程确定为所述目标行程。
  13. 根据权利要求1所述的方法，其中所述目标行程通过如下方式确定：
    接收用户针对目标行程的设置指令，所述设置指令用于指示所述目标行程的行程信息；
    将所述设置指令所指示的行程确定为所述目标行程。
  14. 根据权利要求1所述的方法，其中所述方法还包括：
    在确定所述用户在所述目标行程中存在充电意图的情况下，在所述车辆行驶于所述目标行程的过程中，控制所述车辆的电池温度处于目标温度，其中，所述目标温度为有利于车辆电池充电的温度。
  15. 一种车辆充电意图确定装置，包括：
    第一获取模块，用于获取待判断充电意图的目标行程的行程信息，作为目标行程信息；
    第一确定模块，用于根据所述目标行程信息，确定用户在所述目标行程中的充电意图置信度，所述充电意图置信度是通过至少一个预设维度的充电概率确定的，其中，所述预设维度包括充电位置维度、充电时间维度和电池状态维度；
    第二确定模块，用于根据所述充电意图置信度，确定所述用户在所述目标行程中是否存在充电意图。
  16. 一种车辆，其中所述车辆用于执行权利要求1-14中任一项所述的方法。
PCT/CN2021/132492 2020-11-24 2021-11-23 车辆充电意图确定方法、装置及车辆 WO2022111480A1 (zh)

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Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 04/10/2023)