Optimal dispatching method for electric vehicles considering user satisfaction

Abstract

The present invention discloses an electric vehicle optimization scheduling method considering user satisfaction. The present invention relates to the technical field of electric vehicles and solves the problem that electric vehicles have different power consumption for different driving sections, so the original mileage evaluation method is not accurate. The present invention analyzes relevant historical completion data of electric vehicles, identifies corresponding unit power consumption data from the corresponding analysis process, performs correlation analysis on the identified corresponding data columns, locks characteristic parameters from the corresponding data columns, and adopts this variable range to determine the characteristic parameters. The determined characteristic parameters can be made more accurate, and the numerical effect performance is optimal, which is convenient for subsequent specific optimization scheduling analysis of power. For the subsequent data optimization scheduling analysis process, it is necessary to perform numerical analysis based on the corresponding remaining power, and perform power station scheduling based on the specific numerical analysis process, so as to improve the specific scheduling experience.

Description

Technical Field

[0001] The invention relates to the technical field of electric vehicles, and in particular to an electric vehicle optimization scheduling method taking user satisfaction into consideration. Background Art

[0002] The battery is the core component of an electric vehicle, just like the fuel tank of a traditional fuel vehicle. Currently, common electric vehicle batteries include lithium-ion batteries, including different types such as lithium iron phosphate and ternary lithium. Lithium iron phosphate batteries are safer and have a long cycle life. Ternary lithium batteries have a high energy density and can provide a longer driving range.

[0003] The patent application with the publication number CN108229733A provides an electric vehicle charging scheduling method that takes into account user satisfaction evaluation. By analyzing the factors affecting the travel demand of electric vehicle users, a user travel demand model is established; in combination with the actual travel scenarios of electric vehicle users, a corresponding classification method is designed, and electric vehicle users are classified; for each type of user, combined with the relevant travel data of electric vehicles, the charging power demand forecast value of each type of user is calculated; finally, the corresponding objective function and constraint conditions are designed to optimize the charging power scheduling of each type of user. The present invention is simple to implement, with a small amount of calculation, and can better reflect the charging needs of different users and perform classified scheduling, which is conducive to the flexible scheduling of electric vehicle charging and the popularization of electric vehicle use.

[0004] When conducting power analysis on electric vehicles, mileage assessment is generally performed based on the relevant power consumption per unit time to evaluate the specific mileage margin and make relevant displays. However, the original analysis method of this type does not cover a wide range of areas, because electric vehicles have different power consumption for different driving sections. Therefore, the original mileage assessment method is not accurate and cannot effectively improve the user experience. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an electric vehicle optimization scheduling method taking user satisfaction into consideration, which solves the problem that electric vehicles have different power consumption for different driving sections, so the original mileage evaluation method is not accurate.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electric vehicle optimization scheduling method considering user satisfaction comprises the following steps:

[0007] Step 1: From the historical completion data of the current vehicle, confirm the unit power consumption data generated by the current vehicle on different road sections, and make relevant records to generate the power consumption data column related to the corresponding road section. The specific sub-steps are:

[0008] S11, identifying the specific mileage data and total power consumption data of the vehicle on a flat road, uphill or downhill from the historical completion data of the current vehicle, wherein the flat road, uphill or downhill are specifically confirmed from the vehicle navigation, and the unit power consumption data of the corresponding road section is locked from the confirmed specific data, and for the corresponding road section: the unit power consumption data = the total power consumption data / the specific mileage data of the corresponding road section;

[0009] S12, sorting out several groups of unit power consumption data belonging to the same road section type, and confirming the power consumption data column of this road section type, wherein the same road section type includes flat road, uphill and downhill sections;

[0010] Step 2: Identify the driving habits of the driver corresponding to the current vehicle from the historical completion data generated by the driver corresponding to the current vehicle, and confirm the power consumption data column related to the driving habits of the corresponding driver. The specific sub-steps are:

[0011] S21, from the historical completion data generated by the current vehicle, identifying the driving data of the corresponding vehicle on the road section where the vehicle speed increases from 0 m / s to 60 m / s, confirming the total power consumption and total mileage of the driving data on this road section, and then determining the unit power consumption data associated with the driving data on different road sections, wherein the unit power consumption data = total power consumption / total mileage;

[0012] S22, confirming the different units of power consumption data associated with each set of road section driving data in turn, and confirming the power consumption data column related to the corresponding driving habits of the driver;

[0013] Step 3: Based on the different power consumption data columns confirmed about the current vehicle, numerical processing is performed on several groups of unit power consumption data associated in the power consumption data columns, and the associated data column with the maximum numerical feature is selected from the corresponding power consumption data columns, and the feature of the associated data column is confirmed, and the feature parameters of this power consumption data column are locked. The specific sub-steps are:

[0014] S31, based on the confirmed power consumption data column, sorting the confirmed groups of unit power consumption data in the power consumption data column in ascending order of value, and confirming the sorting sequence;

[0015] S32, identifying associated data columns from the sorted sequence, determining a set of value ranges, wherein the endpoint values ​​of the value ranges can be changed in real time, and the minimum value of the value ranges corresponds to the minimum value of the sorted sequence, and confirming range data columns belonging to the corresponding value ranges from the sorted sequence, wherein each different value range corresponds to a different range data column, and selecting a set of range data columns with the largest number of values ​​from the confirmed range data columns as associated data columns, and if there are multiple associated data columns, randomly selecting a set from the multiple associated data columns as the selected associated data columns;

[0016] S33, performing mean processing on several groups of unit power consumption data associated in the associated data column, locking the characteristic parameters of this power consumption data column, and marking the confirmed characteristic parameters as T k , where k represents different categories, including driving habits and similar road types, and similar road types include flat roads, uphill roads, and downhill roads;

[0017] Step 4: Confirm the remaining power of the current vehicle and the corresponding destination, and perform power estimation analysis to assess whether the current vehicle can reach the corresponding destination with sufficient power. The specific sub-steps are:

[0018] S41, confirming the remaining power of the current vehicle and marking it as SY, and confirming the road section to be traveled from the vehicle navigation based on the current location of the vehicle and the corresponding destination;

[0019] S42, identifying the number of intersections, uphill sections, downhill sections, and flat sections from the road section to be traveled, and marking the corresponding number of intersections as G k , the distance of the corresponding road section is marked as L k , where k represents different categories. Based on the characteristic parameters associated with the corresponding categories, the total power consumption associated with the corresponding categories is determined. The total power consumption = corresponding category × characteristic parameters. The total power consumption associated with several categories is summed up to determine the characteristic power DL.

[0020] S43, checking the characteristic power DL with the remaining power SY: if DL≥SY, generating an associated dispatch signal, which means that the remaining power of the current vehicle is insufficient to reach the corresponding destination, and the associated dispatch of the charging station is required; if DL<SY, no processing is required;

[0021] Step 5: Based on the current location of the vehicle, search for charging stations in the surrounding area of ​​the current location, where the surrounding area is a preset range. The charging stations searched in sequence are used as corresponding destinations, and analyzed in the same way as step 4, and the corresponding charging stations that meet the requirements are displayed.

[0022] The present invention provides an electric vehicle optimization scheduling method taking into account user satisfaction. Compared with the prior art, it has the following beneficial effects:

[0023] The present invention analyzes the relevant historical completion data of the electric vehicle, identifies the corresponding unit power consumption data from the corresponding analysis process, performs correlation analysis on the identified corresponding data column, and locks the characteristic parameters from the corresponding data column. The method of determining the characteristic parameters in such a variable range can make the determined characteristic parameters more accurate, and the numerical effect thereof is optimal, which is convenient for the subsequent specific optimization and scheduling analysis of the power consumption.

[0024] For the subsequent data optimization scheduling analysis process, it is necessary to perform numerical analysis based on the corresponding remaining power, and perform power station scheduling based on the specific numerical analysis process, so as to improve the specific scheduling experience and improve the satisfaction of relevant users. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process of the present invention;

[0026] Figure 2 It is a schematic diagram for determining the characteristic parameters of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] First embodiment

[0029] See also Figure 1 , the present application provides an electric vehicle optimization scheduling method considering user satisfaction, comprising the following steps:

[0030] Step 1: From the historical completion data of the current vehicle, confirm the unit power consumption data generated by the current vehicle on different road sections, and make relevant records to generate a power consumption data column related to the corresponding road section, wherein the specific sub-steps for generating are:

[0031] S11, identifying the specific mileage data and total power consumption data of the vehicle on a flat road, uphill or downhill from the historical completion data of the current vehicle, wherein the flat road, uphill or downhill are specifically confirmed from the vehicle navigation, and the unit power consumption data of the corresponding road section is locked from the confirmed specific data, and for the corresponding road section: the unit power consumption data = the total power consumption data / the specific mileage data of the corresponding road section;

[0032] S12, sorting out several groups of unit power consumption data belonging to the same type of road section, and confirming the power consumption data column of this type of road section, wherein the same type of road section includes different road sections such as flat road, uphill and downhill, that is, different road sections confirm different characteristic parameters, so as to facilitate the subsequent mileage evaluation based on such characteristic parameters, assess whether the corresponding electric vehicle needs to be optimized and dispatched in time, make a reasonable estimate of the mileage, and timely conduct relevant analysis of the distance to the charging station, and make relevant dispatches for the vehicle;

[0033] Step 2: Identify the driving habits of the driver corresponding to the current vehicle from the historical completion data generated by the driver corresponding to the current vehicle, and confirm the power consumption data column related to the driving habits of the corresponding driver. Different people have different driving habits. Some drivers like to step on the accelerator hard for high acceleration, so that the vehicle accelerates quickly. This state will consume a lot of electric energy. If the accelerator is lightly stepped on, a small amount of electric energy will be consumed. Among them, the specific sub-steps of confirming the power consumption data column related to the driving habits of the corresponding driver are:

[0034] S21, from the historical completion data generated by the current vehicle, identifying the driving data of the corresponding vehicle on the road section where the vehicle speed increases from 0 m / s to 60 m / s, confirming the total power consumption and total mileage of the driving data on this road section, and then determining the unit power consumption data associated with the driving data on different road sections, wherein the unit power consumption data = total power consumption / total mileage;

[0035] S22, confirming the different unit power consumption data associated with each set of road section driving data in turn, and confirming the power consumption data column related to the corresponding driving habit of the driver. Specifically, the corresponding acceleration stage and acceleration data can be directly confirmed from the acceleration data of the vehicle. Different unit power consumption data can be determined for each different acceleration stage, so that several groups of different unit power consumption data can be confirmed to determine the corresponding power consumption data column;

[0036] Step 3: Based on the different power consumption data columns confirmed about the current vehicle, numerically process several groups of unit power consumption data associated in the power consumption data columns, select the associated data column with the maximum numerical feature from the corresponding power consumption data columns, and confirm the features of the associated data columns, and lock the feature parameters of this power consumption data column. Specifically, there are several different values ​​in each different data column, and the different values ​​belong to the same category, so there must be similar features. Based on the corresponding similar features, the corresponding feature parameters can be locked for subsequent scheduling process analysis, so as to ensure the specific satisfaction of the user. Among them, the specific sub-steps of locking the feature parameters of this power consumption data column are:

[0037] S31, Combination Figure 2 , based on the confirmed power consumption data column, sorting the confirmed groups of unit power consumption data in the power consumption data column in ascending order of value, and confirming the sorting sequence;

[0038] S32, identifying associated data columns from the sorted sequence, determining a set of numerical ranges, the endpoint values ​​of which can be changed in real time, and the minimum value of which corresponds to the minimum value of the sorted sequence. For example, if the proposed corresponding numerical range is 3, and the minimum value of which is 0.3, then the confirmed corresponding numerical range is [0.3, 3.3]. When the numerical range gradually changes, [0.3, 3.3] gradually increases, that is, it will change to: [0.4, 3.4], ..., and change in sequence thereafter. Confirm the range data columns belonging to the corresponding numerical range from the sorted sequence, and each different numerical range corresponds to a different range data column. From the confirmed multiple range data columns, select a group of range data columns with the largest number of values ​​as the associated data columns. If there are multiple associated data columns (that is, there are multiple range data columns with the largest number of values), randomly select a group from the multiple associated data columns as the selected associated data column.

[0039] S33, performing mean processing on several groups of unit power consumption data associated in the associated data column, locking the characteristic parameters of this power consumption data column, and marking the confirmed characteristic parameters as T k , where k represents different categories, including driving habits and similar road types, and similar road types include flat roads, uphill roads, and downhill roads;

[0040] Specifically, there are a large number of unit power consumption data in the associated data column. After the corresponding unit power consumption data is numerically processed, relevant data with strong characteristics are determined and data confirmation is performed therefrom to achieve a better numerical confirmation effect, thereby ensuring the overall accuracy and precision of the corresponding unit power consumption data and facilitating subsequent mileage confirmation;

[0041] Step 4: Confirm the remaining power of the current vehicle and the corresponding destination, and perform power estimation analysis to assess whether the current vehicle can reach the corresponding destination with sufficient power, so as to improve user satisfaction. The specific sub-steps of the assessment are:

[0042] S41, confirming the remaining power of the current vehicle and marking it as SY, and confirming the road section to be traveled from the vehicle navigation based on the current location of the vehicle and the corresponding destination;

[0043] S42, identifying the number of intersections, uphill sections, downhill sections, and flat sections from the road section to be traveled, and marking the corresponding number of intersections as G k , the distance of the corresponding road section is marked as L k, where k represents different categories. Based on the characteristic parameters associated with the corresponding categories, the total power consumption associated with the corresponding categories is determined. The total power consumption = corresponding category × characteristic parameters. The total power consumption associated with several categories is summed up to determine the characteristic power DL.

[0044] S43, check the characteristic power DL with the remaining power SY: if DL≥SY, generate an associated dispatch signal, which means that the remaining power of the current vehicle is insufficient to reach the corresponding destination, and associated dispatch of the charging station is required; if DL<SY, no processing is required.

[0045] Step 5. Based on the current location of the vehicle, search for charging stations in the surrounding area of ​​the current location. The surrounding area is a preset range, which is prepared in advance by relevant personnel based on experience. Generally, it is within a range of 50 kilometers. The charging stations searched in sequence are used as the corresponding destinations, and analyzed in the same way as step 4. The corresponding charging stations that meet the requirements are displayed for the driver to view and choose, so as to improve the overall usage experience of the corresponding driver.

[0046] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0047] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An electric vehicle optimization scheduling method considering user satisfaction is characterized by: The following steps are involved: Step 1: confirm the unit power consumption data generated by the current vehicle on different road sections from the historical completion data of the current vehicle, and make relevant records to generate a power consumption data column related to the corresponding road section; Step 2: Identify the driving habits of the driver corresponding to the current vehicle from the historical completion data generated by the driver corresponding to the current vehicle, and confirm the power consumption data column related to the driving habits of the corresponding driver; Step 3: Based on the different power consumption data columns confirmed about the current vehicle, numerically process several groups of unit power consumption data associated in the power consumption data columns, select the associated data column with the maximum numerical feature from the corresponding power consumption data columns, and perform feature confirmation on the associated data column to lock the feature parameters of the power consumption data column; Step 4: Confirm the remaining power of the current vehicle and the corresponding destination, and perform a power estimation analysis to assess whether the current vehicle can reach the corresponding destination with sufficient power.

2. The electric vehicle optimization scheduling method considering user satisfaction according to claim 1 is characterized in that: In step 1, the specific sub-steps of generating the power consumption data column related to the corresponding road section are: S11, identifying the specific mileage data and total power consumption data of the vehicle on a flat road, uphill or downhill from the historical completion data of the current vehicle, wherein the flat road, uphill or downhill are specifically confirmed from the vehicle navigation, and the unit power consumption data of the corresponding road section is locked from the confirmed specific data, and for the corresponding road section: the unit power consumption data = the total power consumption data / the specific mileage data of the corresponding road section; S12, sorting out several groups of unit power consumption data belonging to the same road section type, and confirming the power consumption data column of this road section type, wherein the same road section type includes flat road, uphill and downhill sections.

3. The electric vehicle optimization scheduling method considering user satisfaction according to claim 1 is characterized in that: In the step 2, the specific sub-steps of confirming the power consumption data column corresponding to the driver's driving habits are: S21, from the historical completion data generated by the current vehicle, identifying the driving data of the corresponding vehicle on the road section where the vehicle speed increases from 0 m / s to 60 m / s, confirming the total power consumption and total mileage of the driving data on this road section, and then determining the unit power consumption data associated with the driving data on different road sections, wherein the unit power consumption data = total power consumption / total mileage; S22, confirming the different units of power consumption data associated with each group of road section driving data in turn, and confirming the power consumption data column related to the corresponding driving habit of the driver.

4. The electric vehicle optimization scheduling method considering user satisfaction according to claim 1 is characterized in that: In step 3, the specific sub-steps of locking the characteristic parameters of the power consumption data series are: S31, based on the confirmed power consumption data column, sorting the confirmed groups of unit power consumption data in the power consumption data column in ascending order of value, and confirming the sorting sequence; S32, identifying associated data columns from the sorted sequence, determining a set of value ranges, wherein the endpoint values ​​of the value ranges can be changed in real time, and the minimum value of the value ranges corresponds to the minimum value of the sorted sequence, and confirming range data columns belonging to the corresponding value ranges from the sorted sequence, wherein each different value range corresponds to a different range data column, and selecting a set of range data columns with the largest number of values ​​from the confirmed range data columns as associated data columns; S33, performing mean processing on several groups of unit power consumption data associated in the associated data column, locking the characteristic parameters of this power consumption data column, and marking the confirmed characteristic parameters as T k , where k represents different categories, including driving habits and similar road types, and similar road types include flat roads, uphill roads, and downhill roads.

5. The electric vehicle optimization scheduling method considering user satisfaction according to claim 4 is characterized in that: In the step S32, if there are multiple associated data columns, one group is randomly selected from the multiple associated data columns as the selected associated data columns.

6. The electric vehicle optimization scheduling method considering user satisfaction according to claim 1 is characterized in that: In step 4, the specific sub-steps for evaluating whether the current vehicle can reach the corresponding destination with sufficient power are: S41, confirming the remaining power of the current vehicle and marking it as SY, and confirming the road section to be traveled from the vehicle navigation based on the current location of the vehicle and the corresponding destination; S42, identifying the number of intersections, uphill sections, downhill sections, and flat sections from the road section to be traveled, and marking the corresponding number of intersections as G k , the distance of the corresponding road section is marked as L k , where k represents different categories. Based on the characteristic parameters associated with the corresponding categories, the total power consumption associated with the corresponding categories is determined. The total power consumption = corresponding category × characteristic parameters. The total power consumption associated with several categories is summed up to determine the characteristic power DL. S43, comparing the characteristic power DL with the remaining power SY: if DL≥SY, generating an associated dispatch signal, which means that the remaining power of the current vehicle is insufficient to reach the corresponding destination, and associated dispatch of the charging station is required.

7. The electric vehicle optimization scheduling method considering user satisfaction according to claim 6 is characterized in that: In the step S43, if DL<SY, no processing is required.

8. The electric vehicle optimization scheduling method considering user satisfaction according to claim 6 is characterized in that: The following steps are also included: Step 5: Based on the current location of the vehicle, search for charging stations in the surrounding area of ​​the current location, where the surrounding area is a preset range. The charging stations searched in sequence are used as corresponding destinations, and analyzed in the same way as step 4, and the corresponding charging stations that meet the requirements are displayed.

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