CN116562405A - Intelligent method, device and computer system for reserving charging of extended-range plug-in hybrid electric vehicle - Google Patents
Intelligent method, device and computer system for reserving charging of extended-range plug-in hybrid electric vehicle Download PDFInfo
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Abstract
The utility model discloses an intelligent method for reserving charging of a range-extending plug-in hybrid electric vehicle, which comprises the following steps: first round recommendation: selecting a specific method, namely selecting a first recommended charging station, predicting the idle speed, the idle charging speed and the total idle number of the charging stations at the reserved charging time point, and selecting a first round of final recommended reserved charging stations; two rounds of recommendation: the map information is combined with power consumption calculation logic in a pure electric mode to calculate the residual SOC reaching the first-round final recommended reservation charging station, and the second-round recommended reservation charging station is recommended; final recommendation: and during reservation time, recommending a final charging station by using the SOC and the real-time charging station fast and slow charging idle information according to the map real-time road condition information and the pure electric mode power consumption calculation logic. The utility model also discloses an intelligent device and a computer system for the reservation charging of the extended-range plug-in hybrid electric vehicle. The utility model has the function of arranging the optimal reservation charging station and route according to the reservation condition of the user, and can be widely applied to the range-extending plug-in hybrid electric vehicle.
Description
Technical Field
The utility model relates to a range-extending plug-in hybrid electric vehicle, in particular to an intelligent method, a device and a computer system for reserving charging of the range-extending plug-in hybrid electric vehicle.
Background
The range-extending plug-in hybrid electric vehicle can effectively meet the national requirements on energy conservation and emission reduction and eliminate the mileage anxiety of customers, has the traditional internal combustion engine and the pure electric vehicle motor, can reduce the dependence and the demand on petroleum fuel, and improves the fuel economy and the emission performance of the vehicle. The extended range plug-in hybrid typically has the following several drive modes: 1. in the pure electric mode, the automobile is driven by the driving motor to run by supplying electric energy to the driving motor by means of the high-voltage battery; 2. in the series mode, the engine starts to run at the moment, and on one hand, the engine drives the generator to charge the high-voltage battery; on the other hand, the engine drives the generator to provide electric energy for the driving motor so as to drive the automobile to run; therefore, when the battery power is low, the automobile can be driven by using a series mode, and the problem of anxiety of mileage of a user is eliminated.
For new energy automobiles, the industry notices their charging needs and attempts to propose related solutions. For example, an application of the utility model with publication number CN104779680A and the utility model name of electric automobile reserved charging control method and device is disclosed. Wherein the method comprises the following steps: acquiring reservation charging information and preset optimization control parameters; acquiring vehicle state information of a vehicle pre-bound with reservation charging information; determining a reserved charging scheme set of the vehicle according to the reserved charging information and the vehicle state information; and screening the reserved charging scheme set by using the optimized control parameters to obtain a recommended charging scheme. The method solves the technical problems of charging equipment resource waste, poor user charging experience and the like caused by the fact that the reserved charging of the electric vehicle cannot be optimized.
However, this utility model also has the following disadvantages: 1. the utility model is aimed at a pure electric vehicle, and the utility model aims at a range-extending plug-in hybrid electric vehicle, which has larger difference in the whole vehicle mode; 2. the specific driving habit of the driver is not considered, and the actual driving mode and habit user behavior analysis of the driver are not performed; 3. the method is mainly aimed at a power grid side or a charging pile side, and specific vehicle control parameters are not specifically described; 4. the influence of the actual road condition on the reserved charging is not considered.
The utility model of a reserved automobile charging system is disclosed by the utility model of a reserved automobile charging system which is declared by Jiangsu gold jar automobile engineering institute of China, jiangsu gold jar automobile industry, inc., with the publication number of CN207683369U, and the utility model name of the reserved automobile charging system, and the reserved automobile charging system comprises a remote control terminal, a TBOX, an on-board charger (OBC), a Battery Management System (BMS) and a whole automobile controller (VCU), wherein the remote control terminal is connected with the TBOX, the TBOX is respectively connected with the on-board charger (OBC) and the Battery Management System (BMS), and the Battery Management System (BMS) is connected with the whole automobile controller (VCU). The utility model has the advantages that: the BMS and TBOX are controlled to be closed and started and stopped by adopting direct network management, so that the reserved charging function is simplified to be realized; the mobile phone is used for remotely setting the reservation time for charging, so that the charging process is convenient to simplify. As with the previous utility model, it has the disadvantages: 1. specific new energy vehicle types are not distinguished, and the whole vehicle mode and control are not described in detail; 2. the past charging habit of the user and the situation of the charging station are not considered, and the actual road situation is not considered.
Disclosure of Invention
The utility model aims to overcome the defects of the background technology, and provides an intelligent method, device and computer system for reserving charging of a range-extending plug-in hybrid electric vehicle, which have the function of reasonably arranging optimal reserved charging stations and route information according to reservation conditions of users.
The utility model provides an intelligent method for reserving charging of a range-extending plug-in hybrid electric vehicle, which is used for recommending a reserved charging station in multiple rounds and comprises the following steps: first round recommendation: selecting a specific method to select first recommended charging stations according to the reserved charging record number, predicting the idle speed and the idle number of the charging stations at reserved charging time points according to historical idle data of the charging stations, and selecting first recommended reserved charging stations according to an idle number threshold value; two rounds of recommendation: according to map information, calculating the residual SOC reaching the first-round final recommended reservation charging station by combining with power consumption calculation logic in a pure electric mode, and recommending a second-round recommended reservation charging station according to the residual SOC; final recommendation: and when the reservation time point is reached, calculating the SOC reaching the second round of recommended reservation charging station according to the map real-time road condition information and the pure electric mode power consumption calculation logic, and recommending the final charging station according to the SOC and the real-time charging station fast and slow charging idle information.
In the above technical solution, the specific process of the first round of recommending step is as follows: reservation of departure information: inputting a departure place and departure time of reservation charging through a car machine or a mobile phone; first reservation recording acquisition: if the reserved charging records of the user in the system are smaller than n, the system calculates y charging stations closest to the x km near the departure point as recommended charging stations, and acquires charging station information; second reservation recording acquisition: if the reservation charging records of the user in the system are larger than n, firstly sorting the using times of charging stations actually used by the user, selecting z charging stations with the largest using times, calculating the distance between a departure place and the selected charging stations, if the distance is smaller than or equal to a distance threshold value, reserving the distance as recommended charging stations, and acquiring charging station information; removing if the distance is greater than the distance threshold; and (3) information extraction: and extracting the idle charging gun quantity information of the recommended charging station for the past m days from the charging station historical data system.
In the above technical solution, the information extraction step includes the following steps: data collection: dividing the number data of the recommended charging stations for m days, namely charging guns capable of charging idle quickly and slowly into an input set and a target set, wherein the first m is that 1 Data of the day is taken as an input data set, and the last m 2 Day data as target data set, where m=m 1 +m 2 And m is 1 >m 2 The method comprises the steps of carrying out a first treatment on the surface of the And (5) filling the fast and slow filling values: if a certain station m 1 If null value exists in the data of the fast and slow charges of the day, m is adopted 1 Filling the fast filling empty value by the fast filling idle number average value mu 1 in the day, and filling the slow filling empty value by the slow filling idle number average value mu 2; normalization: for m 1 And m 2 Respectively carrying out normalization treatment on the fast and slow charge data of the day; time series analysis: time series analysis is carried out by adopting a time series machine learning model, and root mean square error R is adopted 2 Evaluating the time series model, selecting R 2 And predicting the speed and the speed of a charging station at the reserved time point of the driver by using the model with the minimum value as a first round of final charging station recommendation.
In the above technical scheme, the normalization step is a min-max method, and the normalized fast charge idle number is: (fast-fast) min )/(fast max –fast min ) The slow charge idle number after normalization is the same as the above: (slow-slow) fast )/(slow max –slow min ) Target set m for same reason 2 The fast charge average mu 3 and the slow charge average mu 4 of (1) to fill in the null of the target data set while taking the maximum fastTarget of the target set max And fastTarget min The target set is denormalized.
In the above technical solution, in the step of analyzing the time sequence, the process of predicting the number of fast and slow charging idles of a charging station at the reserved time point of the driver is as follows: if a station finally predicts that the fast charge idle quantity is > fast0 and the slow charge idle quantity is > slow0, and the total idle quantity of the sum of the fast charge idle quantity and the slow charge idle quantity is > total0, the station is recommended as a first-round final charging station.
In the above technical solution, in the time-series analysis step, the time-series machine learning model includes, but is not limited to, an lstm\rnn model.
In the above technical solution, the specific process of the two-round recommendation step is as follows: map information acquisition: acquiring map information from a departure point to each charging station according to the departure point input by a user and the charging stations recommended by the first round; speed segment map conversion: converting the road condition information into a speed section graph according to the road condition information of the actual map; residual SOC calculation: according to the actual SOC of the vehicle, a pure electric mode driving mileage calibration meter is combined to obtain actual electric quantity consumption, so that the residual SOC1 of the vehicle after the vehicle completely passes through the first section of road can be calculated; sequentially calculating the residual SOCn from the 2 nd section path to the 3 rd section path … … to the n-th end; and selecting a pure electric mode: if the SOC is smaller than 0 after a certain section of road, indicating that the pure electric mode cannot reach the recommended charging station, finally selecting the remaining SOCn >0, namely, the charging station which can be reached by the pure electric mode, as a second round of recommended charging station, and providing the reachable route information; and (3) series mode selection: if the final SOCn < = 0, the user is prompted to use a serial mode to reach the part of the charging station that was recommended for the second round in the first round of recommended charging stations.
In the above technical solution, the specific process of the final recommendation step is as follows: pure mode SOC calculation: when the user reservation time point is reached, acquiring actual map information of a route of the second-round recommended charging station from a map, recalculating the residual SOC when the pure electric mode reaches the second-round recommended charging station according to a two-round recommended step mode, and prompting the user to reach the station to use the serial mode if the calculated residual SOC is less than 0; comparing the ranking recommendations: if the residual SOC is greater than 0, the quick charge free quantity, the slow charge free quantity and the total free quantity of the recommended charging stations are inquired in a charging station information system as recommended charging stations, if the quick charge free quantity is greater than fastLast, the slow charge free quantity is greater than slow Last and the total free quantity is greater than total Last, the recommended charging stations can be used as final recommended charging stations, the final recommended charging stations are ordered from large to small according to the residual SOC, the recommended charging stations are used as a final recommended charging station list, and corresponding driving routes are recommended.
The utility model also provides an intelligent device for reserving charging of the extended-range plug-in hybrid electric vehicle, which stores a computer program, and the computer program can execute the intelligent method for reserving charging of the extended-range plug-in hybrid electric vehicle.
The utility model also provides a computer system, which comprises the intelligent device for reserving charging of the extended-range plug-in hybrid electric vehicle.
The intelligent method, the intelligent device and the intelligent computer system for the reserved charging of the extended-range plug-in hybrid electric vehicle have the following beneficial effects:
the intelligent reservation charging system and the method for the extended range plug-in hybrid electric vehicle are mainly aimed at the extended range plug-in hybrid electric vehicle, consider the actual road condition, the historical behavior of the user, the vehicle and the actual condition of the charging station, are based on the advantages of cheapness, reliability and good expandability of the cloud platform, and establish a reservation charging system and a reservation charging method for the extended range plug-in hybrid electric vehicle, which consider the characteristics of the vehicle, the historical charging behavior of the user, the road condition information of the vehicle going to the charging station, the charging station information and the like, and can provide the most economically available reservation charging station and driving route for the extended range plug-in hybrid electric vehicle user.
Drawings
FIG. 1 is a schematic diagram of a power architecture of a range-extending plug-in hybrid vehicle of the present utility model;
FIG. 2 is a schematic diagram of the whole flow of the intelligent method for reserving charging for the extended range plug-in hybrid electric vehicle;
FIG. 3 is a schematic diagram of a specific flow chart of a first-round recommendation step in the intelligent method for reserving charging of the extended-range plug-in hybrid electric vehicle;
FIG. 4 is a schematic diagram of a specific flow of two-wheel recommendation steps in the intelligent method for reserving charging for a range-extended plug-in hybrid electric vehicle;
FIG. 5 is a graph of actual road conditions and speed in the 2.2 nd substep of the two-wheel recommendation step in the intelligent method for reserving charging of the extended-range plug-in hybrid electric vehicle;
FIG. 6 is a schematic diagram of a final recommended procedure in the intelligent method for reservation charging of a range-extended plug-in hybrid electric vehicle according to the present utility model;
FIG. 7 is a schematic diagram of the intelligent device for reserving charging for the extended range plug-in hybrid electric vehicle;
FIG. 8 is a schematic diagram of a computer system according to the present utility model.
Detailed Description
The present utility model will be described in further detail with reference to the drawings and examples, which should not be construed as limiting the utility model.
The utility model relates to a range-extending plug-in hybrid electric vehicle power structure shown in figure 1, wherein an engine is a gasoline engine. The solid line connections in the figure are mechanical connections and the dotted line connections are electrical connections; there are two drive modes, an electric mode and a series mode; 1. when the vehicle is in an electric mode, the engine does not work at the moment, the vehicle supplies electric energy for the driving motor by means of the high-voltage battery, and the vehicle is driven by means of the driving motor; 2. when the vehicle is in the series mode, the engine starts to work, on one hand, the engine drives the generator to charge the high-voltage battery, and on the other hand, the engine drives the generator to provide electric energy for the driving motor, and the driving motor drives the vehicle to run.
Referring to fig. 2, the whole idea of the intelligent method for reserving charging of the extended-range plug-in hybrid electric vehicle is as follows:
1. first round recommendation: selecting a specific method to select first recommended charging stations according to the reserved charging record number, predicting the idle speed and the idle number of the charging stations at reserved charging time points according to historical idle data of the charging stations, and selecting first recommended reserved charging stations according to an idle number threshold value;
referring to fig. 3, the first-round reservation charging recommended charging station logic diagram is as follows:
1.1. the driver inputs the departure place and departure time of the reserved charging through a car machine or a mobile phone;
1.2. if the user reserves less than n (such as 500, configurable) charging records in the system, the system calculates y (such as 40) charging stations closest to the closest x km (such as 20 km) in the vicinity of the departure point as recommended charging stations, and acquires charging station information;
1.3. if the reserved charging records of the user are larger than n (such as 500 and configurable) in the system, firstly sorting the using times of charging stations actually used by the user, selecting z (such as 50 and configurable) charging stations with the largest using times, calculating the distance between a departure place and the selected charging stations, if the distance is smaller than or equal to a distance threshold (which can be set such as 20 km), reserving the distance as a recommended charging station, and acquiring charging station information; if the distance is greater than the distance threshold value, removing;
1.4. extracting idle charging gun quantity information of the recommended charging station for the past 30 days from a charging station historical data system;
1.4.1. the sampling frequency of the idle charging station information in the charging station information system is t hours (settable, default 1 h), and the following processing is carried out on the quantity data of the recommended charging station fast and slow charging idle charging guns for 30 days: dividing the data of the number of the recommended charging stations for quickly and slowly charging idle charging guns for 30 days into an input set and a target set, wherein the data of the first 27 days are used as the input data set, and the data of the last 3 days are used as the target data set;
1.4.2. if the fast and slow charge data of a certain station for 27 days have null values, filling the fast and slow charge null values respectively by adopting a fast and slow charge idle number average value mu 1 (corresponding to a fast charge average value) and mu 2 (corresponding to a slow charge average value) of 27 days;
1.4.3. normalization processing (min-max method or z-score method) is respectively carried out on the fast and slow charge data of 27 days:
1.4.3.1. taking the min-max method as an example, the normalized fast charge idle number is: (fast-fastmin)/(fastmax-fastmin), and similarly, the normalized slow charge idle number is: (slow-slow)/(slow max-slow min), similarly filling the null value of the target data set (corresponding to the last 3 days of data) with the fast and slow charge average values mu 3 and mu 4 of the target set (3 days of data), and normalizing the target set by adopting the maximum value fastTargetmax and fastTargetmin of the target set;
1.4.4. time series analysis is carried out by adopting a time series machine learning model (not limited to LSTM\RNN and other models), and root mean square error R is adopted 2 Evaluating the time series model, selecting R 2 And predicting a reserved time point (accurate to h) of a driver by using the model with the minimum value to ensure that a certain charging station charges idle quickly and slowly.
1.4.4.1. If a site eventually predicts a fast charge idle number > fast0 (settable, e.g., 2) and a slow charge idle number > slow0 (settable, e.g., 2) and a total idle number (sum of fast and slow charge idle numbers) > total0 (settable, e.g., 2), then it is recommended as a first round final charging station.
2. Two rounds of recommendation: according to map information, calculating the residual SOC reaching the first-round final recommended reservation charging station by combining with power consumption calculation logic in a pure electric mode, and recommending a second-round recommended reservation charging station according to the residual SOC;
referring to fig. 4, the second round of reservation charging stations are recommended using the following method:
firstly, technical principle analysis for calculating the residual SOC of the automobile is as follows: in the pure electric mode, the calculation logic of the actual pure electric residual SOC is disclosed in detail in FIG. 4, and Table 1 is a basic calibration table of the electric consumption of the battery in the pure electric mode, wherein the abscissa is the residual electric quantity SOC of the high-voltage battery, the ordinate is the constant speed, and the data in the table represent the electric consumption percentage per kilometer from the abscissa to 0% when the speed is equal to the speed on the ordinate; table 2 shows the ambient temperature correction factor, correcting the power consumption of table 1; table 3 shows the power correction coefficients, different table 1 is corrected according to the past history behavior of the user, and the specific history behavior analysis is as follows: 1) When the running distance of the vehicle is less than or equal to 1000km, a default power correction table is adopted as a power correction table, as shown in the numerical value in table 3; 2) When the running distance of the vehicle is greater than 1000km, according to the historical behavior of the user, calculating the power of the user at a certain speed (10 km/h, 20km/h, 30km/h, 40km/h, 50km/h, 60km/h, 70km/h, 80km/h, 100km/h, 120km/h, 140km/h, 160km/h and the like) according to the historical behavior of the user, calculating the average value as the driving required power of the vehicle at the speed, dividing the driving required power by the required power of the vehicle at a constant speed, and using the driving required power as the power correction coefficient of the vehicle at the constant speed, wherein the required power at the constant speed can be obtained through an actual road test, and if the speed is not found in the historical behavior data, adopting the default value of table 3.
Table 1 pure electric mode driving mileage basic calibration meter
| Ambient temperature (. Degree. C.) | -30 | -25 | -20 | -10 | 0 | 10 | 20 | 30 | 40 | 50 | 60 |
| Correction coefficient of electric quantity consumption | 2.5 | 2.0 | 1.5 | 1.3 | 1.2 | 1.1 | 1 | 1 | 1 | 1 | 1 |
Table 2 ambient temperature correction table
| Vehicle speed (km/h) | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 100 | 120 | 140 | 160 |
| Power correction factor | 1 | 1.2 | 1.3 | 1.4 | 1.55 | 1.68 | 1.81 | 1.94 | 2.07 | 2.2 | 2.33 | 2.46 |
Table 3 power correction table
2.1. According to the departure point input by the user and the charging stations recommended by the first round, acquiring map information (multiple driving routes can be obtained) from the departure point to each charging station;
2.2. taking a certain recommended charging station as an example, converting road condition information into a speed section chart according to actual map road condition information (such as vehicle speed and the like), and as shown in fig. 5, in the chart, the highest vehicle speed of a first section is v1, if the vehicle driving distance is less than 50km, the vehicle driving speed of the section is set as v1, and if the vehicle driving distance is more than or equal to 50km, the average vehicle speed of a speed section passing through the section according to the history of customers is the vehicle driving speed of the section;
2.3. according to the actual SOC of the vehicle, a pure electric mode driving mileage calibration meter is combined to obtain actual electric quantity consumption, so that the residual SOC1 of the vehicle after the vehicle completely passes through the first section of road can be calculated; the residual SOCn after passing through the nth end of the 2 nd section path and the 3 rd section path … … can be calculated in sequence;
2.4. if the SOC is smaller than 0 after a certain section of road m, indicating that the pure electric mode cannot reach the recommended charging station, finally selecting the charging station with the residual SOCn >0 (namely that the pure electric mode can reach) as a second round of recommended charging station, and providing reachable route information;
2.5. if the final SOCn < = 0, the user is prompted that a series mode (with the engine on) is needed to reach the first round recommended charging station to be used as part of the second round recommended charging station.
3. Final recommendation: and when the reservation time point is reached, calculating the SOC reaching the second round of recommended reservation charging station according to the map real-time road condition information and the pure electric mode power consumption calculation logic, and recommending the final charging station according to the SOC and the real-time charging station fast and slow charging idle information.
When reaching the user reservation time point, acquiring actual map information of a route reaching a second round of recommended charging stations from a map, recalculating the residual SOC when the pure electric mode reaches the second round of recommended charging stations in a step 4 mode, and prompting the user to reach the station to use the serial mode if the calculated residual SOC is less than 0;
3.2. if the remaining SOC >0, then the charging station is recommended. And inquiring the fast charge free number, the slow charge free number and the total free number of the recommended charging stations in the charging station information system, if the fast charge free number is larger than fastLast (settable, for example, 2) and the slow charge free number is larger than slow last (settable, for example, 2) and the total free number is larger than total last (settable, for example, 2), sequencing the final recommended charging stations from large to small according to the residual SOC, serving as a final recommended charging station list, and recommending a corresponding driving route.
Referring to fig. 7, the extended-range plug-in hybrid electric vehicle reservation charging intelligent device of the utility model carries out multi-wheel recommendation on reservation charging stations, and comprises the following contents:
the first round recommendation module: selecting a specific method to select first recommended charging stations according to the reserved charging record number, predicting the idle speed and the idle number of the charging stations at reserved charging time points according to historical idle data of the charging stations, and selecting first recommended reserved charging stations according to an idle number threshold value;
two-round recommendation module: according to map information, calculating the residual SOC reaching the first-round final recommended reservation charging station by combining with power consumption calculation logic in a pure electric mode, and recommending a second-round recommended reservation charging station according to the residual SOC;
and a final recommendation module: and when the reservation time point is reached, calculating the SOC reaching the second round of recommended reservation charging station according to the map real-time road condition information and the pure electric mode power consumption calculation logic, and recommending the final charging station according to the SOC and the real-time charging station fast and slow charging idle information.
Referring to fig. 8, the computer system of the utility model comprises a range-extending plug-in hybrid electric vehicle reservation charging intelligent device.
The technical principle and key points of the utility model are as follows:
the intelligent method comprises the steps of performing multi-round recommendation on reserved charging stations, selecting a specific method to select first recommended charging stations according to reserved charging record numbers, predicting the idle speed charging idle numbers and the total idle numbers of the charging stations at reserved charging time points according to historical idle data of the charging stations, and selecting first-round final recommended reserved charging stations according to idle number threshold values; according to map information, calculating the residual SOC reaching the first-round final recommended reservation charging station by combining with power consumption calculation logic in a pure electric mode, and recommending a second-round recommended reservation charging station according to the residual SOC; and when the reservation time point is reached, calculating the SOC reaching the second round of recommended reservation charging station according to the map real-time road condition information and the pure electric mode power consumption calculation logic, and recommending the final charging station according to the SOC and the real-time charging station fast and slow charging idle information. Wherein, the liquid crystal display device comprises a liquid crystal display device,
1. as described above, the charging station specific method is recommended for the first time, in order that when the number of reserved charging records is smaller than a certain threshold (e.g. 500), the system calculates y (settable, such as 40) charging stations closest to the x km (settable, such as 20 km) near the departure point and acquires charging station information; when the distance between the reserved charging departure point and the selected charging station is smaller than or equal to a distance threshold (which can be set, such as 20 km), the reserved charging stations are reserved and used as first-round recommended charging stations; and if the distance is larger than the distance threshold value, removing.
2. As described above, the charging station idle speed, the charging idle number and the total idle number of the reserved charging time point are predicted from the historical idle data of the charging station, the time point is accurate to the hour, and the method adopted for predicting the idle number is as followsThe time sequence prediction uses a time sequence model which is not limited to a machine learning model such as LSTM/RNN, and adopts a model accuracy evaluation standard of root mean square error (R) 2 ) And selecting a final machine learning model according to the evaluation standard to predict the idle charge speed of the charging station in the reserved time, and selecting a final first-round recommended charging station according to the predicted idle charge speed.
3. The pure electric mode power consumption calculation logic considers the basic battery consumption, the ambient temperature influence and the client history actual power consumption influence, wherein the basic battery consumption considers the influence of the vehicle speed and the high-voltage battery SOC, and the client history actual power represents the client history actual demand driving power under the specific vehicle speed.
4. As described above, the map information may include a plurality of paths to reach the first-round final recommended reservation charging station, the path information is converted into a speed segment map, and the SOC to reach the first-round final recommended reservation charging station is calculated based on the speed segment map and the power consumption calculation logic.
5. When the reservation time point is reached, the residual SOC reaching the second-round recommended reservation charging station is calculated again according to the map real-time road condition information and the pure electric mode power consumption calculation logic, if the SOC is smaller than or equal to 0, a client is prompted to use a serial mode to reach a reservation charging station, if the residual SOC is larger than 0, real-time charging idle information of the second-round recommended reservation charging station is required to be obtained, if the residual SOC is larger than a charging idle threshold value, the second-round recommended reservation charging station is sequenced from large to small according to the residual SOC to be used as a final recommended charging station, and a corresponding driving route is recommended.
The technical scheme of the utility model has the following beneficial effects:
aiming at the problem of reserved charging of the extended-range plug-in hybrid electric vehicle, the utility model designs an intelligent reserved charging system and method of the extended-range plug-in hybrid electric vehicle, which take a vehicle residual SOC calculation method into consideration, take historical driving behavior habits of a user into consideration, synthesize historical and real-time use information of a charging station, and intelligently recommend an optimal reserved charging station and a running route for a driver by combining map information.
Abbreviation and key term definitions
VCU: a vehicle controller;
PDCU Power Domain controller
VIN: vehicle identification code
SOC: battery power
It will be apparent to those skilled in the art that various modifications and variations can be made to the present utility model without departing from the spirit or scope of the utility model. Thus, it is intended that the present utility model also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
What is not described in detail in this specification is prior art known to those skilled in the art.
Claims (10)
1. An intelligent method for reserving charging of a range-extending plug-in hybrid electric vehicle is characterized by comprising the following steps of: the multi-round recommendation is carried out on the reserved charging stations, and the method comprises the following steps of:
first round recommendation: selecting a specific method to select first recommended charging stations according to the reserved charging record number, predicting the idle speed and the idle number of the charging stations at reserved charging time points according to historical idle data of the charging stations, and selecting first recommended reserved charging stations according to an idle number threshold value;
two rounds of recommendation: according to map information, calculating the residual SOC reaching the first-round final recommended reservation charging station by combining with power consumption calculation logic in a pure electric mode, and recommending a second-round recommended reservation charging station according to the residual SOC;
final recommendation: and when the reservation time point is reached, calculating the SOC reaching the second round of recommended reservation charging station according to the map real-time road condition information and the pure electric mode power consumption calculation logic, and recommending the final charging station according to the SOC and the real-time charging station fast and slow charging idle information.
2. The extended range plug-in hybrid electric vehicle reservation charging intelligent method of claim 1, characterized by comprising the steps of: the specific process of the first round of recommendation step is as follows:
reservation of departure information: inputting a departure place and departure time of reservation charging through a car machine or a mobile phone;
first reservation recording acquisition: if the reserved charging records of the user in the system are smaller than n, the system calculates y charging stations closest to the x km near the departure point as recommended charging stations, and acquires charging station information;
second reservation recording acquisition: if the reservation charging records of the user in the system are larger than n, firstly sorting the using times of charging stations actually used by the user, selecting z charging stations with the largest using times, calculating the distance between a departure place and the selected charging stations, if the distance is smaller than or equal to a distance threshold value, reserving the distance as recommended charging stations, and acquiring charging station information; removing if the distance is greater than the distance threshold;
and (3) information extraction: and extracting the idle charging gun quantity information of the recommended charging station for the past m days from the charging station historical data system.
3. The extended range plug-in hybrid electric vehicle reservation charging intelligent method of claim 2, characterized by comprising the steps of: the information extraction step comprises the following steps:
data collection: dividing the number data of the recommended charging stations for m days, namely charging guns capable of charging idle quickly and slowly into an input set and a target set, wherein the first m is that 1 Data of the day is taken as an input data set, and the last m 2 Day data as target data set, where m=m 1 +m 2 And m is 1 >m 2 ；
And (5) filling the fast and slow filling values: if a certain station m 1 If null value exists in the data of the fast and slow charges of the day, m is adopted 1 Filling the fast filling empty value by the fast filling idle number average value mu 1 in the day, and filling the slow filling empty value by the slow filling idle number average value mu 2;
normalization: for m 1 And m 2 Respectively carrying out normalization treatment on the fast and slow charge data of the day;
time series analysis: using time-series machinesThe learning model performs time sequence analysis by adopting root mean square error R 2 Evaluating the time series model, selecting R 2 And predicting the speed and the speed of a charging station at the reserved time point of the driver by using the model with the minimum value as a first round of final charging station recommendation.
4. The extended range plug-in hybrid electric vehicle reservation charging intelligent method according to claim 3, characterized by comprising the following steps: the normalization step is a min-max method, and the normalized fast charge idle number is as follows: (fast-fast) min )/(fast max –fast min ) The slow charge idle number after normalization is the same as the above: (slow-slow) fast )/(slow max –slow min ) Target set m for same reason 2 The fast charge average mu 3 and the slow charge average mu 4 of (1) to fill in the null of the target data set while taking the maximum fastTarget of the target set max And fastTarget min The target set is denormalized.
5. The extended range plug-in hybrid electric vehicle reservation charging intelligent method of claim 4, characterized by comprising the following steps: in the time sequence analysis step, the process of predicting the idle charge speed of a charging station at the reserved time point of the driver is as follows:
if a station finally predicts that the fast charge idle quantity is > fast0 and the slow charge idle quantity is > slow0, and the total idle quantity of the sum of the fast charge idle quantity and the slow charge idle quantity is > total0, the station is recommended as a first-round final charging station.
6. The extended range plug-in hybrid electric vehicle reservation charging intelligent method of claim 5, characterized by comprising the following steps: in the time series analysis step, the time series machine learning model includes, but is not limited to, an lstm\rnn model.
7. The extended range plug-in hybrid electric vehicle reservation charging intelligent method of claim 6, characterized by comprising the steps of: the specific process of the two-round recommendation step is as follows:
map information acquisition: acquiring map information from a departure point to each charging station according to the departure point input by a user and the charging stations recommended by the first round;
speed segment map conversion: converting the road condition information into a speed section graph according to the road condition information of the actual map;
residual SOC calculation: according to the actual SOC of the vehicle, a pure electric mode driving mileage calibration meter is combined to obtain actual electric quantity consumption, so that the residual SOC1 of the vehicle after the vehicle completely passes through the first section of road can be calculated; sequentially calculating the residual SOCn from the 2 nd section path to the 3 rd section path … … to the n-th end;
and selecting a pure electric mode: if the SOC is smaller than 0 after a certain section of road, indicating that the pure electric mode cannot reach the recommended charging station, finally selecting the remaining SOCn >0, namely, the charging station which can be reached by the pure electric mode, as a second round of recommended charging station, and providing the reachable route information;
and (3) series mode selection: if the final SOCn < = 0, the user is prompted to use a serial mode to reach the part of the charging station that was recommended for the second round in the first round of recommended charging stations.
8. The extended range plug-in hybrid electric vehicle reservation charging intelligent method of claim 7, characterized by comprising the steps of: the specific process of the final recommending step is as follows:
pure mode SOC calculation: when the user reservation time point is reached, acquiring actual map information of a route of the second-round recommended charging station from a map, recalculating the residual SOC when the pure electric mode reaches the second-round recommended charging station according to a two-round recommended step mode, and prompting the user to reach the station to use the serial mode if the calculated residual SOC is less than 0;
comparing the ranking recommendations: if the residual SOC is greater than 0, the quick charge free quantity, the slow charge free quantity and the total free quantity of the recommended charging stations are inquired in a charging station information system as recommended charging stations, if the quick charge free quantity is greater than fastLast, the slow charge free quantity is greater than slow Last and the total free quantity is greater than total Last, the recommended charging stations can be used as final recommended charging stations, the final recommended charging stations are ordered from large to small according to the residual SOC, the recommended charging stations are used as a final recommended charging station list, and corresponding driving routes are recommended.
9. An intelligent device for reservation charging of a range-extending plug-in hybrid electric vehicle is stored with a computer program, which is characterized in that: the computer program can execute the intelligent method for reserving charging of the extended-range plug-in hybrid electric vehicle according to the claims 1-8.
10. A computer system, characterized in that: the computer system comprises the extended-range plug-in hybrid electric vehicle reservation charging intelligent device of claim 9.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117172409A (en) * | 2023-09-05 | 2023-12-05 | 河海大学 | Intelligent charging method of electric automobile based on photovoltaic energy |
| CN117728589A (en) * | 2024-02-08 | 2024-03-19 | 山西同鑫达电气工程有限公司 | Power utilization monitoring method, device, equipment and medium for distribution box |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117172409A (en) * | 2023-09-05 | 2023-12-05 | 河海大学 | Intelligent charging method of electric automobile based on photovoltaic energy |
| CN117172409B (en) * | 2023-09-05 | 2024-05-03 | 河海大学 | Intelligent charging method of electric automobile based on photovoltaic energy |
| CN117728589A (en) * | 2024-02-08 | 2024-03-19 | 山西同鑫达电气工程有限公司 | Power utilization monitoring method, device, equipment and medium for distribution box |
| CN117728589B (en) * | 2024-02-08 | 2024-04-26 | 山西同鑫达电气工程有限公司 | Power utilization monitoring method, device, equipment and medium for distribution box |
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