CN112152246A - Energy management optimization method based on double-source trackless vehicle scheduling system - Google Patents
Energy management optimization method based on double-source trackless vehicle scheduling system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/63—Monitoring or controlling charging stations in response to network capacity
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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
- B60L58/12—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]
- B60L58/13—Maintaining the SoC within a determined range
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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
- B60L58/12—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]
- B60L58/14—Preventing excessive discharging
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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
- B60L2200/00—Type of vehicles
- B60L2200/18—Buses
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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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Abstract
The invention relates to an energy management optimization method based on a double-source trackless vehicle dispatching system, which is characterized in that vehicle real-time information is collected through a vehicle-mounted module, energy consumption and passing time of a vehicle on a driving route based on standard sections are measured and calculated by means of a machine learning method, and distributable corrected charging power of the vehicle is obtained by combining the rated charging power of a wire network of each standard section and an energy balance equation. The invention can reasonably distribute the charging power of the network, effectively restrain the SOC descending amplitude of the vehicle-mounted battery of the single vehicle and prolong the service life of the battery.
Description
Technical Field
The invention relates to energy management of double-source trackless vehicles, in particular to an energy management optimization method based on a double-source trackless vehicle dispatching system.
Background
The double-source trackless vehicle adopts a wire net as a power supply and is provided with a vehicle-mounted battery, so that the double-source trackless vehicle is mostly applied to a bus line. In the road section with the overhead wire network, the vehicle is connected with the wire network through the electric pole to run, charge and store electric energy, and in the road section without the overhead wire network, the electric quantity stored in the vehicle-mounted battery is utilized to drive the vehicle to run.
At present, energy management methods of double-source trackless vehicles roughly include the following types:
1. the method has the defects that only the protection of the vehicle-mounted battery is considered, and the problem of how to distribute charging power when the charging power of the network is insufficient is not considered.
2. The method has two problems that firstly, the method has no foresight property and cannot give consideration to the problem that the off-line distance of each vehicle on certain driving routes is long, and the problem can cause the state of charge (SOC) of the vehicles to be reduced below an expected value when the vehicles are on off-line road sections; secondly, the SOC fluctuation range of the on-vehicle battery on all the vehicles increases, resulting in a reduction in the life of the on-vehicle battery.
3. The method has the defects that the experimental method consumes resources and has harsh conditions, and the expected effect is difficult to ensure in an actual operation route.
Disclosure of Invention
The invention aims to provide an energy management optimization method based on a double-source trackless vehicle dispatching system, which is used for obtaining energy consumption and passing time of a vehicle based on standard sections on a driving route through machine learning according to the driving route information and vehicle real-time information, and obtaining available charging power of the vehicle by combining with rated charging power of each standard section and utilizing an energy balance equation.
The invention is realized by the following steps:
an energy management optimization method based on a double-source trackless vehicle dispatching system is disclosed, wherein the double-source trackless vehicle dispatching system comprises a plurality of running routes, a vehicle dispatching platform and a vehicle-mounted module;
the driving route consists of a plurality of standard sections, each standard section sequentially comprises an on-line section and an off-line section, the network section of each standard section is provided with the rated charging power of a line network, and a vehicle is charged from the line network and stores electric energy when in the network section; the vehicle dispatching platform and the vehicle-mounted module are in two-way communication, the vehicle-mounted module collects vehicle real-time information and sends the vehicle real-time information to the vehicle dispatching platform, the vehicle real-time information comprises battery information, position information, energy consumption and passing time, and the vehicle dispatching platform sends charging power information to the vehicle-mounted module;
the energy management optimization method comprises the following steps:
step one, calculating by a vehicle dispatching platform according to the driving route information and the vehicle real-time information to obtain: each driving route is based on the average energy consumption of each standard section of each driving route and the average passing time of each on-network section and off-network section of each driving route;
step two, a certain driving route comprises any two continuous standard segments, namely a first standard segment and a second standard segment, when a certain vehicle drives into the first standard segment, the vehicle dispatching platform calculates and obtains the time of the vehicle driving into the second standard segment and the total number of all vehicles in the network segment of the second standard segment; calculating the average charging power of the vehicle in the network segment of the second standard segment according to the rated charging power of the wire network of the second standard segment and the total number of the vehicles, obtaining the initial SOC value of the vehicle when the vehicle drives into the second standard segment according to the preset target SOC value of the vehicle when the vehicle drives out of the second standard segment, and taking the initial SOC value as the target SOC value of the vehicle when the vehicle drives out of the first standard segment, wherein the calculation formula of the expected charging power p1 of the vehicle in the first standard segment is as follows:
in the formula, SOC1iniFor an initial SOC value at the time of driving into the first standard range, SOC2iniFor the initial SOC value at the time of driving into the second standard range, SOC2tarFor the target SOC value of the second standard segment, L1 and L2 are the travel distances of the first standard segment and the second standard segment, respectively, e1 and e2 are the average energy consumptions of the vehicle in the first standard segment and the second standard segment, respectively,
average charging power of the vehicle on the network segment in the second standard segment, t1onAnd t2onRespectively the average passing time of the vehicle in the network segment of the first standard segment and the average passing time of the vehicle in the network segment of the second standard segment, and C is the battery capacity of the vehicle;
step three, calculating according to the step two to obtain the expected charging power of all other vehicles in the network segment of the first standard segment: p2, P3, …, pn, thereby obtaining a desired charging power vector P ═ P1, P2, P3, …, pn ];
step four, summing the expected charging power vector P to obtain a net power limit correction coefficient f and a corrected charging power vector P' of the first standard section, wherein the calculation formula is as follows:
P′＝P×f
in the formula, PlimRated charging power is set for the wire mesh of the first standard section;
and step five, calculating and obtaining the corrected charging power of all vehicles on all driving lines according to the step two to the step four.
The vehicle real-time information includes time characteristics including a work day and a morning and evening peak.
The driving route information comprises exclusive bus lane information.
In the first step, the vehicle dispatching platform carries out calculation based on a machine learning method.
The machine learning method is XGboost.
In the second step, SOC2tar＞40％。
The charging power information comprises a net power limit correction coefficient and a corrected charging power; and step five, when f is less than 1, limiting the driving power output of the whole vehicle by the vehicle.
The invention relates to an energy management optimization method based on a double-source trackless vehicle dispatching system, which is characterized in that vehicle real-time information is collected through a vehicle-mounted module, energy consumption and passing time of a vehicle on a driving route based on standard sections are measured by means of a machine learning method, and distributable corrected charging power of the vehicle is obtained by combining the rated charging power of a line network of each standard section and an energy balance equation. Firstly, the invention can improve the service efficiency of the double-source trackless vehicle net, the charging power distributed to each vehicle is more reasonable through the calculated net power limit correction coefficient, the insufficient energy storage of the vehicle-mounted battery can be prevented, the vehicle is prevented from being anchored midway, the SOC fluctuation interval of the vehicle is fully considered, and the cycle life times are prevented from being influenced by the over discharge of the battery. Secondly, on the basis of the data of the real-time information of the vehicle, the energy consumption, the passing time and the like of the vehicle are predicted by means of a machine learning method, so that the adaptability to the actual running condition of the double-source trackless vehicle is stronger, and the energy management is more accurate. In addition, when the rated charging power of the wire network can meet the requirement, the charging power distributed to each vehicle is slightly higher than the expected charging power, so that the electric energy of the wire network is fully utilized, and when the rated charging power of the wire network cannot meet the requirement, namely when the requirement of the vehicle in the network segment on the charging power of the wire network greatly exceeds the load of the wire network, the electric quantity consumption of a vehicle-mounted battery is saved by limiting the driving power output of the whole vehicle, the influence of insufficient distributed charging power in the network segment at present is weakened, and the power notch can be responded by instantaneously increasing the output current of the wire network.
Compared with the prior art, the invention has the following beneficial effects: the charging power of the network can be reasonably distributed, the SOC descending amplitude of the vehicle-mounted battery of the single vehicle can be effectively inhibited, and the service life of the battery can be prolonged.
Drawings
FIG. 1 is a schematic diagram of information interaction between a vehicle dispatching platform and a vehicle-mounted module of the energy management optimization method based on a dual-source trackless vehicle dispatching system;
fig. 2 is a schematic diagram of a vehicle of the present invention in two standard segments of a travel route.
In the figure, 1 vehicle dispatching platform, 2 vehicles.
Detailed Description
The present invention will be further described with reference to the following specific examples.
Referring to fig. 1, the energy management optimization method based on the double-source trackless vehicle dispatching system comprises a plurality of driving routes, a vehicle dispatching platform 1 and a vehicle-mounted module. The driving route is composed of a plurality of standard sections, each standard section sequentially comprises an on-line section and an off-line section, the rated charging power of the line network is set in the on-line section of each standard section, and the vehicle 2 is charged from the line network and stores electric energy when in the on-line section. The vehicle dispatching platform and a vehicle-mounted module arranged on the vehicle 2 are in two-way communication, the vehicle-mounted module is used for collecting vehicle real-time information and sending the vehicle real-time information to the vehicle dispatching platform, and the vehicle dispatching platform sends charging power information to the vehicle-mounted module. Specifically, the vehicle real-time information includes battery information, position information, energy consumption, and transit time, wherein the battery information includes SOC, SOH, remaining capacity, battery capacity, and the like. Specifically, the charging power information includes a net power limit correction coefficient and a corrected charging power.
The energy management optimization method based on the double-source trackless vehicle dispatching system comprises the following steps:
step one, calculating by a vehicle dispatching platform according to the driving route information and the vehicle real-time information to obtain: each driving route is based on the average energy consumption of each standard section of each driving route and the average passing time of each driving route on the network section and the off-line section. The vehicle real-time information is collected every day, the vehicle real-time information comprises time characteristics, such as whether the vehicle is a working day or whether the vehicle is a peak in the morning and evening, and the time interval of the information collection is defined according to the time characteristics of the information, such as the time interval of the peak in the morning and evening is 15 minutes, the time interval of the peak in the morning and evening is 30 minutes, and the shorter the time interval is, the faster the road condition changes. The driving route information comprises information such as whether each driving route has a dedicated bus lane.
In this embodiment, the vehicle scheduling platform performs calculation based on a machine learning method, and the machine learning method is preferably XGBoost. The XGboost is an efficient nonlinear machine learning classification algorithm, is mainly based on a decision tree model and a gradient propulsion system, has the characteristics of difficulty in overfitting, high flexibility, high convergence speed, high accuracy and the like, can process sparse features, and supports multi-thread parallel processing.
Step two, referring to fig. 2, a certain driving route includes any two consecutive standard segments, namely a first standard segment and a second standard segment, when a certain vehicle drives into the first standard segment, the vehicle dispatching platform calculates to obtain the time when the vehicle drives into the second standard segment and the total number of all vehicles in the network segment of the second standard segment; calculating the average charging power of the vehicle in the network segment of the second standard segment according to the wire network rated charging power and the total number of the vehicles in the second standard segment (namely, the value is the initial estimation of the charging power of each vehicle in the network segment of the second standard segment), and obtaining the initial SOC value of the vehicle when the vehicle enters the second standard segment according to the preset target SOC value of the vehicle when the vehicle exits the second standard segment, and using the initial SOC value as the target SOC value of the vehicle exiting the first standard segment, so that the calculation formula of the expected charging power p1 of the vehicle in the first standard segment is as follows:
in the formula, SOC1iniFor an initial SOC value at the time of driving into the first standard range, SOC2iniFor the initial SOC value at the time of driving into the second standard range, SOC2tarFor the target SOC value of the second standard segment, L1 and L2 are the travel distances of the first standard segment and the second standard segment, respectively, e1 and e2 are the average energy consumptions of the vehicle in the first standard segment and the second standard segment, respectively,
average charging power in the network segment for each vehicle in the second standard segment, t1onAnd t2onThe average passing time of the vehicle in the network segment of the first standard segment and the average passing time of the vehicle in the network segment of the second standard segment are respectively, and C is the battery capacity of the vehicle.
In the present embodiment, preferably, SOC2tar＞40％。
And calculating based on two continuous standard sections in the step two, thereby obtaining the expected charging power of the vehicle in the first standard section, mainly considering that the distances of off-line sections of each standard section are different, if only the expected charging power in one standard section is considered for calculation, if the distance of the off-line section of the next standard section is long, the charging capacity of the vehicle-mounted battery in the off-line section of the next standard section is not enough, so that the SOC of the vehicle-mounted battery is reduced too much, and the battery is damaged, and if two continuous standard sections are considered together, the electric quantity can be supplemented in advance, so that the electric quantity consumption of the vehicle-mounted battery is avoided being too much.
In addition, if the SOC2 is when the vehicle is driven into the last standard segment of the travel routeini＝SOC2tarAnd a target SOC value, preferably SOC2, for exiting the standard segment is to be satisfiedini＞40％。
Step three, calculating according to the step two to obtain the expected charging power of all other vehicles in the network segment of the first standard segment: p2, P3, …, pn, from which the desired charging power vector P is obtained [ P1, P2, P3, …, pn ].
Step four, summing the expected charging power vector P to obtain a net power limit correction coefficient f and a corrected charging power vector P' of the first standard section, wherein the calculation formula is as follows:
P′＝P×f
in the formula, PlimThe rated charging power is for the first standard section net.
Therefore, when f is larger than or equal to 1, namely the rated charging power of the line network can meet the charging requirement, the charging power distributed to each vehicle is slightly higher than the expected charging power, and the electric energy of the line network is fully utilized; when f is less than 1, the rated charging power of the wire network cannot meet the charging requirement, so that all vehicles in the network segment in the current standard section cannot be charged to the target SOC, and the power gap can be met by limiting the driving power output of the whole vehicle or instantaneously increasing the output current of the wire network.
Moreover, through the energy balance equation calculation method in the second step to the fourth step, when individual vehicles need larger charging power, the charging power gaps can be equally distributed to all vehicles in the network segment in the current standard segment, the SOC reduction range of the single vehicle can be effectively inhibited, and the service life of the vehicle-mounted battery can be prolonged.
And step five, calculating and obtaining the corrected charging power of all vehicles on all driving lines according to the step two to the step four.
In this embodiment, preferably, when f is less than 1, the vehicle dispatching platform sends the charging power information to a vehicle-mounted module on the vehicle, and the vehicle adjusts and limits the driving power output of the entire vehicle according to the wire mesh power limit correction factor, so as to sacrifice part of the dynamic property. Additionally, wire grids can typically withstand charging in excess of rated charging power for a short period of time, and this capability can be used with caution to replenish the vehicle.
According to the specific steps of the energy management optimization method, when a new vehicle enters a certain standard section, the correction coefficient f of the wire network power limit value and the correction charging power vector P' of the standard section are obtained through recalculation according to the second step, the second step and the fourth step; when a vehicle drives out of a certain standard section, recalculating according to the second step to the fourth step to obtain a net power limit correction coefficient f and a corrected charging power vector P' of the standard section; if no vehicle enters or exits the standard segment, no calculation is performed.
In addition, the actual SOC value of the vehicle when driving out of a certain standard segment should not be smaller than the target SOC value of the standard segment. If the actual SOC of an individual vehicle is low and not expected even after the maximum available charging power is allocated in actual operation, it indicates that the vehicle is equipped with a low on-board battery capacity, and the on-board battery capacity of the vehicle needs to be adjusted due to non-grid and platform reasons.
The energy management optimization method based on the double-source trackless vehicle scheduling system is applied to a public transport line, on the basis of data of a driving line and real-time information of vehicles, energy consumption of each vehicle in different time periods of each day can be calculated by means of a machine learning method, passing time of the vehicle in a network segment and passing time of the vehicle out of the network segment based on a standard segment are integrated, historical data analysis of the front standard segment and the rear standard segment is integrated, charging power actually distributed to each vehicle by a network is calculated through an energy balance equation, a vehicle-mounted battery can be effectively protected, and electric energy of the network can be fully utilized.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An energy management optimization method based on a double-source trackless vehicle dispatching system is disclosed, wherein the double-source trackless vehicle dispatching system comprises a plurality of running routes, a vehicle dispatching platform and a vehicle-mounted module; the method is characterized in that:
the driving route consists of a plurality of standard sections, each standard section sequentially comprises an on-line section and an off-line section, the network section of each standard section is provided with the rated charging power of a line network, and a vehicle is charged from the line network and stores electric energy when in the network section; the vehicle dispatching platform and the vehicle-mounted module are in two-way communication, the vehicle-mounted module collects vehicle real-time information and sends the vehicle real-time information to the vehicle dispatching platform, the vehicle real-time information comprises battery information, position information, energy consumption and passing time, and the vehicle dispatching platform sends charging power information to the vehicle-mounted module;
the energy management optimization method comprises the following steps:
step one, calculating by a vehicle dispatching platform according to the driving route information and the vehicle real-time information to obtain: each driving route is based on the average energy consumption of each standard section of each driving route and the average passing time of each on-network section and off-network section of each driving route;
step two, a certain driving route comprises any two continuous standard segments, namely a first standard segment and a second standard segment, when a certain vehicle drives into the first standard segment, the vehicle dispatching platform calculates and obtains the time of the vehicle driving into the second standard segment and the total number of all vehicles in the network segment of the second standard segment; calculating the average charging power of the vehicle in the network segment of the second standard segment according to the rated charging power of the wire network of the second standard segment and the total number of the vehicles, obtaining the initial SOC value of the vehicle when the vehicle drives into the second standard segment according to the preset target SOC value of the vehicle when the vehicle drives out of the second standard segment, and taking the initial SOC value as the target SOC value of the vehicle when the vehicle drives out of the first standard segment, wherein the calculation formula of the expected charging power p1 of the vehicle in the first standard segment is as follows:
in the formula, SOC1iniFor an initial SOC value at the time of driving into the first standard range, SOC2iniFor the initial SOC value at the time of driving into the second standard range, SOC2tarFor the target SOC value of the second standard segment, L1 and L2 are the travel distances of the first standard segment and the second standard segment, respectively, e1 and e2 are the average energy consumptions of the vehicle in the first standard segment and the second standard segment, respectively,
average charging power of the vehicle on the network segment in the second standard segment, t1onAnd t2onRespectively the average passing time of the vehicle in the network segment of the first standard segment and the average passing time of the vehicle in the network segment of the second standard segment, and C is the battery capacity of the vehicle;
step three, calculating according to the step two to obtain the expected charging power of all other vehicles in the network segment of the first standard segment: p2, P3, …, pn, thereby obtaining a desired charging power vector P ═ P1, P2, P3, …, pn ];
step four, summing the expected charging power vector P to obtain a net power limit correction coefficient f and a corrected charging power vector P' of the first standard section, wherein the calculation formula is as follows:
P′＝P×f
in the formula, PlimRated charging power is set for the wire mesh of the first standard section;
and step five, calculating and obtaining the corrected charging power of all vehicles on all driving lines according to the step two to the step four.
2. The energy management optimization method based on the dual-source trackless vehicle dispatching system according to claim 1, characterized in that: the vehicle real-time information includes time characteristics including a work day and a morning and evening peak.
3. The energy management optimization method based on the dual-source trackless vehicle dispatching system according to claim 1, characterized in that: the driving route information comprises exclusive bus lane information.
4. The energy management optimization method based on the dual-source trackless vehicle dispatching system according to any one of claims 1 to 3, characterized in that: in the first step, the vehicle dispatching platform carries out calculation based on a machine learning method.
5. The energy management optimization method based on the dual-source trackless vehicle dispatching system according to claim 4, wherein: the machine learning method is XGboost.
6. The energy management optimization method based on the dual-source trackless vehicle dispatching system according to claim 1, characterized in that: in the second step, SOC2tar＞40％。
7. The energy management optimization method based on the dual-source trackless vehicle dispatching system according to claim 1, characterized in that: the charging power information comprises a net power limit correction coefficient and a corrected charging power; and step five, when f is less than 1, limiting the driving power output of the whole vehicle by the vehicle.
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