CN114640120A - Electric automobile charging and discharging scheduling method and device - Google Patents

Electric automobile charging and discharging scheduling method and device Download PDF

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CN114640120A
CN114640120A CN202210415751.4A CN202210415751A CN114640120A CN 114640120 A CN114640120 A CN 114640120A CN 202210415751 A CN202210415751 A CN 202210415751A CN 114640120 A CN114640120 A CN 114640120A
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scheduling
battery
model
electric vehicle
discharging
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侯慧
王逸凡
吴细秀
石英
谢长君
黄亮
熊斌宇
陆宁
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Shenzhen Research Institute Of Whut Co ltd
Shenzhen Research Institute Of Wuhan University Of Technology
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Shenzhen Research Institute Of Whut Co ltd
Shenzhen Research Institute Of Wuhan University Of Technology
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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
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention relates to a method and a device for scheduling charge and discharge of an electric automobile, wherein the method comprises the following steps: acquiring battery parameters and mileage parameters of the electric automobile; establishing a battery loss cost model and a psychological effect quantification model according to the battery parameters and the mileage parameters; and establishing a long-time-scale electric vehicle charging and discharging scheduling model according to the battery loss cost model and the psychological effect quantification model. The invention comprehensively considers the long-time scale and the electric automobile user psychological effect quantification, and establishes a double-layer real-time scheduling model which comprises an upper-layer scheduling model and a lower-layer real-time optimization model. And guiding the lower layer to optimize in real time through the scheduling result of the upper layer scheduling model, and completing the optimization process of the whole scheduling period.

Description

Electric automobile charging and discharging scheduling method and device
Technical Field
The invention belongs to the technical field of electric vehicle dispatching, and particularly relates to a method and a device for dispatching charging and discharging of an electric vehicle.
Background
With the increase in environmental pollution, users who have consciously chosen to use electric vehicles have increased year by year. The huge electric automobile group makes the electric automobile charging demand increasingly growing.
The scheduling problem of the short time scale of the multi-attention electric vehicle is researched at present, such as 1 hour on the way of going out or a plurality of hours of residence in a residential area. In the short process of the electric automobile on the way, the electric automobile generally considers the factors of economy, timeliness, distance length and the like to select a proper charging station so as to realize the space transfer of the charging load. In the residence process of the residential area or the commercial area of the electric automobile, a dispatcher such as a load aggregator can dispatch the electric automobile to transfer on a short time scale. Although the short timescale optimization described above works significantly, it is generally assumed that electric vehicles are scheduled on every scheduling day. In the actual charging process, the daily power consumption of the electric automobile is not severe and may not need to be supplemented. However, there is currently little research on longer time scales. In part, even if a multi-day one-charging scene of the electric automobile under the expanded time scale is researched, only the influence of the charging process of the electric automobile is considered, and the influence of the discharging of the electric automobile on the long-time scale scheduling is ignored. Therefore, how to perform efficient scheduling of electric vehicles on a long time scale is an urgent problem to be solved.
Disclosure of Invention
In view of the above, there is a need to provide a method and an apparatus for scheduling charging and discharging of an electric vehicle, so as to overcome the problem in the prior art that an effective method for scheduling an electric vehicle for a long time is not available.
In order to solve the technical problem, the invention provides a method for scheduling charging and discharging of an electric vehicle, which comprises the following steps:
acquiring battery parameters and mileage parameters of the electric automobile;
establishing a battery loss cost model and a psychological effect quantification model according to the battery parameters and the mileage parameters;
and establishing a long-time-scale electric vehicle charging and discharging scheduling model according to the battery loss cost model and the psychological effect quantification model.
Further, the establishing a battery loss cost model and a psychological effect quantification model according to the battery parameter and the mileage parameter includes:
establishing a battery loss cost model for determining the loss cost of the power battery in the primary discharging process according to the battery parameters;
and establishing the psychological effect quantification model for reflecting the relation between the SOC level of the battery and the psychological anxiety of the electric automobile user according to the battery parameters and the mileage parameters.
Further, the battery parameters include a first initial capacity of the power battery of the electric vehicle before the start of scheduling, a first SOC value of the electric vehicle before the start of discharging, a second SOC value of the electric vehicle after the end of discharging, a maximum SOC value of the power battery, a discharging depth of the power battery when the discharging is ended, and a replacement cost for replacing the primary power battery, and the battery loss cost model for determining the loss cost of the power battery in the primary discharging process is established according to the battery parameters, including:
determining the battery degradation capacity in the primary discharging process according to the first initial capacity, the first SOC value, the second SOC value, the maximum SOC value and the discharging depth;
and determining the loss cost of the power battery generated in the primary discharging process according to the first initial capacity, the battery degradation capacity and the replacement cost.
Further, the battery parameter includes a finished SOC value after charging is finished, the mileage parameter includes trip mileage, and the creating of the psychological effect quantification model for reflecting a relationship between a battery SOC level and psychological anxiety of an electric vehicle user according to the battery parameter and the mileage parameter includes:
determining a stopping probability density function according to the finished SOC value and the trip mileage;
determining the electric automobile stopping probability of a preset travel SOC value according to the stopping probability density function;
and determining a psychological effect value of the electric automobile user according to the stopping probability of the electric automobile.
Further, the determining the electric vehicle stop probability of the preset SOC value according to the stop probability density function includes:
determining a first mileage capable of being driven by the electric automobile as an integral lower limit according to the trip preset SOC value, the minimum SOC value, the capacity of a power battery of the electric automobile and the unit mileage energy consumption of the electric automobile;
determining a second mileage which can be driven by the electric automobile and corresponds to the maximum SOC value as an integral upper limit according to the maximum SOC value, the minimum SOC value, the capacity of the power battery of the electric automobile and the unit mileage energy consumption of the electric automobile;
determining a first probability that the travel mileage is greater than the maximum SOC (system on chip) travelable mileage according to the probability density of the travel mileage of the electric automobile;
and integrating according to the lower integration limit, the upper integration limit, the first probability and the stopping probability density function to determine the stopping probability of the electric automobile.
Further, the determining the psychological effect value of the electric vehicle user according to the electric vehicle stopping probability comprises:
integrating the probability density of the trip mileage of the electric vehicle according to the lower integral limit, the upper integral limit and the first probability respectively to determine a minimum electric vehicle stopping probability and a maximum electric vehicle stopping probability;
and determining the psychological effect value according to the minimum electric automobile stopping probability and the maximum electric automobile stopping probability.
Further, the establishing a long-time-scale electric vehicle charging and discharging scheduling model according to the battery loss cost model and the psychological effect quantification model includes:
constructing an upper-layer scheduling model according to the minimum power battery loss cost and the psychological effect value;
and constructing a lower-layer real-time optimization model according to the SOC value and the actual charging and discharging power, and realizing the following of the upper-layer scheduling model.
Further, the constructing an upper layer scheduling model according to the minimum power battery loss cost and the minimum psychological effect value includes:
constructing a first scheduling target with minimized scheduling cost according to the minimum power battery loss cost;
constructing a second scheduling target with the minimum psychological effect according to the minimum psychological effect value;
and constructing at least one upper layer constraint condition of the upper layer scheduling model according to the first factor parameter, wherein the at least one upper layer constraint condition comprises a variable constraint condition, an SOC state constraint condition, a driving energy consumption constraint condition, a time constraint condition and a psychological effect constraint condition.
Further, according to the SOC value and the actual charge-discharge power, a lower real-time optimization model is constructed to realize the following of the upper scheduling model, including:
according to the SOC value, a third scheduling target which enables the SOC error to be minimum when the scheduling days are over is constructed;
constructing a fourth scheduling target which enables the real-time scheduling cost on the day l to be minimum according to the actual charging and discharging power;
and constructing at least one lower layer constraint condition of the lower layer scheduling model according to the second factor parameter, wherein the at least one lower layer constraint condition comprises an actual driving energy consumption constraint condition and an actual time constraint condition.
The invention also provides a charging and discharging scheduling device for the electric automobile, which comprises:
the acquiring unit is used for acquiring battery parameters and mileage parameters of the electric automobile;
the processing unit is used for establishing a battery loss cost model and a psychological effect quantification model according to the battery parameters and the mileage parameters;
and the scheduling unit is used for establishing a long-time-scale electric vehicle charging and discharging scheduling model according to the battery loss cost model and the psychological effect quantification model.
Compared with the prior art, the invention has the beneficial effects that: firstly, effectively acquiring a battery parameter and a mileage parameter; then, according to the battery parameters and the mileage parameters, comprehensively considering the battery loss cost and the psychological effect of the user, modeling from two aspects, and establishing a battery loss cost model and a psychological effect quantification model; and finally, establishing a double-layer long-time-scale electric automobile charging and discharging scheduling model based on the battery loss cost model and the psychological effect quantification model. In conclusion, the invention comprehensively considers the long-time scale and the electric automobile user psychological effect quantification, and establishes a double-layer real-time scheduling model which comprises an upper-layer scheduling model and a lower-layer real-time optimization model. And guiding the lower layer to optimize in real time through the scheduling result of the upper layer scheduling model, and completing the optimization process of the whole scheduling period.
Drawings
Fig. 1 is a schematic flow chart illustrating an embodiment of a method for scheduling charging and discharging of an electric vehicle according to the present invention;
FIG. 2 is a flowchart illustrating an embodiment of step S102 in FIG. 1 according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of step S202 in FIG. 2 according to the present invention;
FIG. 4 is a flowchart illustrating an embodiment of step S103 in FIG. 1 according to the present invention;
fig. 5 is a schematic structural diagram of an embodiment of a charging and discharging scheduling device for an electric vehicle according to the present invention;
fig. 6 is a schematic structural diagram of an embodiment of an electronic device provided in the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
In the description of the present invention, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to imply that the number of technical features indicated is significant. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Further, "plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise.
Reference throughout this specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the described embodiments can be combined with other embodiments.
The invention provides a method and a device for scheduling charging and discharging of an electric vehicle, which are used for modeling in multiple aspects by combining a psychological effect and a long-time scale, constructing a double-layer long-time scale electric vehicle charging and discharging scheduling model and providing a new idea for further ensuring the effective scheduling of the electric vehicle in the long-time scale.
Before the description of the embodiments, the related words are paraphrased:
electric vehicle charging and discharging scheduling: the popularization of the electric vehicle gradually brings new problems, the problems that the charging time of a large number of people using the electric vehicle is not standard, the voltage requirement is high, and the like bring great damage to the whole electric power transmission system, and the electric vehicles on the market are different from appearance to internal storage batteries. When the electric automobile is connected with a line, not only the charging condition but also the discharging condition possibly occur. Therefore, when the overall charging efficiency is improved, the overall power transmission network is protected, and the overall electric vehicle users need to be uniformly scheduled and managed;
psychological effects of range anxiety: when driving an electric vehicle, the driver is worried about the mental pain or anxiety caused by sudden power failure. After the internal combustion engine is developed for more than one hundred years, the heat efficiency is improved, and the endurance mileage of the fuel vehicle is controlled within a certain range and exceeds the psychological expectation of people. The technology of the electric vehicle can say that the electric vehicle just starts, the range of the endurance mileage of the vehicle is large, and the difference exists between the range and the psychological expectation of consumers, so that mileage anxiety is easily caused.
Based on the description of the technical terms, in the prior art, on one hand, there is only a few research on longer time scale, and only the influence of the charging process of the electric vehicle is considered, but the influence of the discharging of the electric vehicle on the long-time scale scheduling is ignored; on the other hand, research has been focused on considering the comprehensive benefits of electric vehicles, but the research is less related to quantifying the psychological effect of range anxiety (hereinafter, referred to as psychological effect) of electric vehicles before traveling. Therefore, the invention aims to provide an effective scheduling method for the long-time scale of the electric automobile by combining the psychological effect of mileage anxiety.
Specific examples are described in detail below:
an embodiment of the present invention provides a method for scheduling charging and discharging of an electric vehicle, and referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of the method for scheduling charging and discharging of an electric vehicle provided by the present invention, and includes steps S101 to S103, where:
in step S101, a battery parameter and a mileage parameter of the electric vehicle are acquired;
in step S102, a battery loss cost model and a psychological effect quantification model are established according to the battery parameter and the mileage parameter;
in step S103, a long-time-scale electric vehicle charging and discharging scheduling model is established according to the battery loss cost model and the psychological effect quantization model.
In the embodiment of the invention, firstly, the battery parameter and the mileage parameter are effectively obtained; then, according to the battery parameters and the mileage parameters, comprehensively considering the battery loss cost and the psychological effect of the user, modeling from two aspects, and establishing a battery loss cost model and a psychological effect quantification model; and finally, establishing a double-layer long-time-scale electric automobile charging and discharging scheduling model based on the battery loss cost model and the psychological effect quantification model.
As a preferred embodiment, referring to fig. 2, fig. 2 is a schematic flowchart of an embodiment of step S102 in fig. 1 provided by the present invention, and includes steps S201 to S202, where:
in step S201, according to the battery parameters, establishing a battery loss cost model for determining a loss cost of the power battery in a primary discharging process;
in step S202, the psychological effect quantification model for reflecting the relationship between the battery SOC level and the psychological anxiety of the user of the electric vehicle is established according to the battery parameter and the mileage parameter.
In the embodiment of the invention, the charge and discharge battery loss cost and the psychological effect quantification method are effectively provided.
In a preferred embodiment, the battery parameters include a first initial capacity of the power battery of the electric vehicle before the start of the scheduling, a first SOC value of the electric vehicle before the start of discharging, a second SOC value of the electric vehicle after the end of discharging, a maximum SOC value of the power battery, a discharging depth of the power battery when the discharging is ended, and a replacement cost for replacing the power battery once, and the step S201 specifically includes:
determining the battery degradation capacity in the primary discharging process according to the first initial capacity, the first SOC value, the second SOC value, the maximum SOC value and the discharging depth;
and determining the loss cost of the power battery generated in the primary discharging process according to the first initial capacity, the battery degradation capacity and the replacement cost.
In the embodiment of the invention, the fine loss of the power battery in the primary discharging process is quantified, and the cost of the power battery in unit capacity can be calculated by using the replacement cost of the power battery, so that the loss cost of the power battery in the primary discharging process can be calculated.
In a specific embodiment of the present invention, the method for determining the loss cost of the charge and discharge battery specifically includes the following steps:
it is to be noted that, it is generally considered that the discharge behavior of the electric vehicle causes a certain loss to the power battery, and the power battery may need to be replaced again when the discharge loss accumulates for a long time or at a high frequency. It is generally considered that the power battery of the electric automobile should be replaced after the capacity of the power battery is degraded to a certain threshold value. Therefore, the fine loss of the power battery in the primary discharging process can be quantified, the cost of the power battery in unit capacity can be calculated by using the replacement cost of the power battery, and the loss cost of the power battery in the primary discharging process can be calculated. The specific calculation procedure is as follows.
The degraded capacity of a battery during one discharge can be expressed as:
ΔE＝kwE0|Sini-Send|＝kwE0|Dini-Dend|
in the formula, delta E is the capacity loss of the power battery of the electric automobile caused by one-time discharge, and the unit is kWh; k is a radical of formulawFor a calculation of the coefficient, kw0.00015; e0The initial capacity of the power battery of the electric automobile before the dispatching is started is represented by kWh; siniThe SOC of the electric automobile before the discharge starts; sendThe SOC of the electric automobile after the discharge is finished; diniIs the initial discharge depth of the power battery, Dini＝Smax-Sini；SmaxThe maximum SOC of the power battery is obtained; dendThe discharge depth at the end of the power battery discharge, Dend＝Smax-Send。
It is generally considered that the capacity of the power battery is degraded to 80% of the initial capacity, i.e., the power battery should be replaced. Thus, the actual available degradation capacity is 0.2E0. Assuming that the influence of interest rate is not considered for the moment, there is a simple linear relationship between battery degradation capacity and battery replacement cost, and therefore power battery capacityThe relationship between the amount loss and the cost of power battery loss can be expressed as:
Figure BDA0003605860380000081
in the formula, CcycThe unit is element of the power battery loss cost generated in the primary discharge process; cexcThe cost of replacing the primary power battery.
Therefore, the cost of power battery loss generated in one discharge process can be expressed as:
Ccyc＝Cexc·ΔE/0.2E0
as a preferred embodiment, with reference to fig. 3, fig. 3 is a schematic flowchart of an embodiment of step S202 in fig. 2 provided by the present invention, and includes steps S301 to S303, where:
in step S301, determining a stopping probability density function according to the finished SOC value and the trip mileage;
in step S302, determining an electric vehicle stop probability of a preset trip SOC value according to the stop probability density function;
in step S303, a psychological effect value of the electric vehicle user is determined according to the electric vehicle stopping probability.
In the embodiment of the invention, the psychological effect value of the electric vehicle user is determined by utilizing the relation between the specific SOC level and the psychological anxiety of the electric vehicle user.
As a preferred embodiment, the step S302 specifically includes:
determining a first mileage capable of being driven by the electric automobile as an integral lower limit according to the trip preset SOC value, the minimum SOC value, the capacity of a power battery of the electric automobile and the unit mileage energy consumption of the electric automobile;
determining a second mileage which can be driven by the electric automobile and corresponds to the maximum SOC value as an integral upper limit according to the maximum SOC value, the minimum SOC value, the capacity of the power battery of the electric automobile and the unit mileage energy consumption of the electric automobile;
determining a first probability that the trip mileage is greater than the maximum SOC (system on chip) driving mileage according to the probability density of the trip mileage of the electric automobile;
and integrating according to the lower integral limit, the upper integral limit, the first probability and the stopping probability density function to determine the stopping probability of the electric automobile.
In the embodiment of the invention, the maximum SOC value, the minimum SOC value, the power battery capacity of the electric automobile and the unit mileage energy consumption of the electric automobile are utilized to determine the relevant upper and lower integral limits, and then the stopping probability density function is integrated.
As a preferred embodiment, the step S303 specifically includes:
integrating the probability density of the trip mileage of the electric vehicle according to the integral lower limit, the integral upper limit and the first probability respectively to determine a minimum electric vehicle stopping probability and a maximum electric vehicle stopping probability;
and determining the psychological effect value according to the minimum electric automobile stopping probability and the maximum electric automobile stopping probability.
In the embodiment of the invention, the psychological effect value is effectively obtained according to the minimum stopping probability and the maximum stopping probability of the electric automobile.
In a specific embodiment of the present invention, the method for quantifying the user psychological effect of the electric vehicle specifically comprises the following steps:
the driving range of the electric vehicle is limited, and anxiety and worry caused during driving are called range anxiety. The main direction for solving the mileage anxiety is to enlarge the capacity of the power battery; another solution is to maintain a higher level of SOC while traveling in order to maximize travel range. If the SOC of the electric automobile at the trip time is low, the psychological anxiety of the electric automobile user is obviously higher during the trip process, and is lower otherwise. However, the relation between the specific SOC level and the psychological anxiety of the user of the electric automobile needs to be further explored;
the travel mileage rule of the electric automobile is as follows:
Figure BDA0003605860380000101
in the formula, x is trip mileage and the unit is km; (x) is the probability density when the trip mileage is x; mu.sDIs the mean of the lognormal distribution; sigmaDIs the variance of the log normal distribution.
The travel mileage distribution of the electric automobile describes the relationship between the travel mileage value of the electric automobile and the corresponding probability. After charging of the electric vehicle user is completed in one day, the SOC is a determined value, but the trip mileage on the next day is a random value, so that the following two cases can be distinguished according to the relationship between the SOC and the trip mileage on the next day when charging of the same day is completed.
Figure BDA0003605860380000102
Wherein P (S, x) is a stopping probability density function when the trip mileage is x and the SOC level is S; f. ofS(x) Is the stopping probability density function when the SOC cannot satisfy the trip mileage.
According to the distribution, when the travel SOC is S, the probability that the electric automobile user stops in the same day can be obtained as follows:
Figure BDA0003605860380000103
LS＝(S-Smin)·C/w
Figure BDA0003605860380000104
Figure BDA0003605860380000105
in the formula, PSWhen the travel SOC is S, the stopping probability of the electric automobile is obtained; l isSThe mileage that the electric automobile can run when the travel SOC is SThe unit is km;
Figure BDA0003605860380000106
the mileage that the electric automobile can run when the travel SOC is the full SOC is given in km; epsilon is the probability that the trip mileage is larger than the available driving mileage at full SOC; smax、SminThe maximum SOC and the minimum SOC of the electric automobile are respectively; c is the capacity of the power battery of the electric automobile, and the unit is kWh; w is the unit mileage energy consumption of the electric automobile, and the unit is km/kWh; m is a sufficiently large number;
weber-fisher's law is a law that states the relationship between psychological and physical quantities. According to weber-fisher's law, the sensory intensity of a human to an external stimulus can be described as being proportional to the logarithm of the external stimulus. The mathematical model is represented as follows:
K＝α·lgR
wherein K is sensory intensity; alpha is an organoleptic coefficient; r is external stimulation intensity;
therefore, the worry that the electric vehicle user cannot finish traveling can be quantified as:
KS＝α·lgPS
in the formula, KSThe method is the sensory intensity of the electric vehicle user who cannot finish worrying about going out, namely the psychological effect of the electric vehicle user. Normalized to this, it can be expressed as:
Figure BDA0003605860380000111
Figure BDA0003605860380000112
Figure BDA0003605860380000113
in the formula (I), the compound is shown in the specification,
Figure BDA0003605860380000114
is an electric motorThe minimum stopping probability of the vehicle;
Figure BDA0003605860380000115
the maximum stopping probability of the electric automobile.
As a preferred embodiment, referring to fig. 4, fig. 4 is a schematic flowchart of an embodiment of step S103 in fig. 1 provided by the present invention, and includes steps S401 to S403, where:
in step S401, an upper layer scheduling model is constructed according to the minimum power battery loss cost and the psychological effect value;
in step S402, a lower real-time optimization model is constructed according to the SOC value and the actual charging and discharging power, so as to follow the upper scheduling model.
In the embodiment of the invention, a double-layer model is effectively established.
As a preferred embodiment, the step S401 specifically includes:
constructing a first scheduling target with minimized scheduling cost according to the minimum power battery loss cost;
constructing a second scheduling target with the minimum psychological effect according to the minimum psychological effect value;
and constructing at least one upper layer constraint condition of the upper layer scheduling model according to the first factor parameter, wherein the at least one upper layer constraint condition comprises a variable constraint condition, an SOC state constraint condition, a driving energy consumption constraint condition, a time constraint condition and a psychological effect constraint condition.
In the embodiment of the invention, the upper-layer scheduling model is effectively modeled, and relevant constraint conditions are constructed.
In a specific embodiment of the invention, from the perspective of an electric vehicle user, a scheduling objective of an upper-layer scheduling model is an electric vehicle comprehensive benefit, and two aspects of objectives are mainly considered. The first is the dispatching cost of the electric automobile user in the dispatching process, wherein the dispatching cost comprises the electricity purchasing cost of the electric automobile for charging and discharging dispatching and the loss cost of the power battery of the electric automobile. Another objective is that the psychological effect of the user of the electric vehicle on anxiety of the trip mileage a day after the dispatch period is minimal at the end of the dispatch period. The two scheduling objectives are represented as follows:
Figure BDA0003605860380000121
Figure BDA0003605860380000122
Figure BDA0003605860380000123
in the formula, f1A scheduling cost minimization objective; f. of2A goal of minimizing psychological effects; l is the total scheduling days;
Figure BDA0003605860380000124
the starting scheduling time of the day I;
Figure BDA0003605860380000125
the departure scheduling time of the day l; pl(t) the charging and discharging power of the l day at the t period, wherein the unit is kW; Δ t is the scheduling period length; r (t) is the electricity price in units of yuan/kWh in the period t;
Figure BDA0003605860380000126
the power battery loss cost of the ith section of the discharge stage on the ith day is unit; n is a radical oflThe number of discharge stages on day l; kS,lThe psychological effect of the electric automobile user on the first day; r isch(t) charging electricity prices for a period of t; r isdis(t) is the discharge power rate for a period of t.
The constraint conditions of the upper layer scheduling model mainly comprise:
1. constraint of variables;
Pl(t)∈[0,Pch,Pdis]
in the formula, PchCharging power for the electric vehicle; pdisThe power is discharged for the electric automobile.
2. SOC state constraint: charge and discharge SOC change constraint;
Figure BDA0003605860380000131
Figure BDA0003605860380000132
in the formula,. DELTA.Sl(t) is the SOC variation from the starting scheduling time of the first day to the period t; k is a charge-discharge coefficient; etacCharging coefficient for the electric vehicle; etadThe discharge coefficient of the electric automobile.
Constraint of SOC equation for different periods:
Figure BDA0003605860380000133
Figure BDA0003605860380000134
Figure BDA0003605860380000135
in the formula (I), the compound is shown in the specification,
Figure BDA0003605860380000136
starting initial SOC before starting scheduling for the l day; siniIs the initial SOC before the start of the scheduling period; delta SjThe SOC charge-discharge variation quantity of the j day;
Figure BDA0003605860380000137
the j-th driving energy consumption SOC; sl(t) SOC level at time t on day i; s. theminThe minimum SOC of the electric vehicle; smaxThe maximum SOC of the electric automobile.
3. And (3) constraint of running energy consumption:
Figure BDA0003605860380000138
this constraint means that the initial SOC must be able to afford the trip energy consumption on day i before the start of scheduling on day i.
4. And (3) time constraint:
the electric vehicle does not perform the charging and discharging operation regardless of the time period.
Figure BDA0003605860380000139
The charging and discharging scheduling time periods of two consecutive days are not overlapped.
Figure BDA00036058603800001310
Where τ is a sufficiently small positive number.
5. And (3) psychological effect constraint:
KS,l≤Kmax
in the formula, KmaxThe maximum mileage anxiety psychological effect acceptable for the electric automobile user.
As a preferred embodiment, the step S402 specifically includes:
according to the SOC value, a third scheduling target which enables the SOC error to be minimum when the scheduling days are over is constructed;
constructing a fourth scheduling target which enables the real-time scheduling cost on the day l to be minimum according to the actual charging and discharging power;
and constructing at least one lower layer constraint condition of the lower layer scheduling model according to the second factor parameter, wherein the at least one lower layer constraint condition comprises an actual driving energy consumption constraint condition and an actual time constraint condition.
In the embodiment of the invention, the lower-layer scheduling model is effectively modeled, and relevant constraint conditions are constructed.
In a specific embodiment of the invention, the primary objective of the lower layer real-time optimization model is to follow the long-time-scale electric vehicle charging and discharging scheduling process planned by the upper layer scheduling model, and the charging and discharging scheduling process directly following the electric vehicle at different time intervals is obviously too complex. Considering that the influence of the scheduling result of the electric vehicle on the day to be scheduled is mainly the SOC level at the end of the scheduling of the day, a suitable method is to follow the SOC level at the end of the scheduling of the day. The first goal of the underlying real-time optimization model is therefore to minimize the SOC error at the end of the scheduled day.
Figure BDA0003605860380000141
In the formula (f)1 lMinimizing the SOC error for the end of day;
Figure BDA0003605860380000142
the SOC result of the upper layer scheduling model at the end of the day I is obtained;
Figure BDA0003605860380000143
and (5) optimizing the actual SOC level of the lower layer real-time optimization model at the end of the optimization on the day I.
Due to different lengths of the dispatching time of the electric automobile, different dispatching plans may cause the final SOC to be the same. Therefore, it is not enough to just guarantee the SOC following the upper layer scheduling model, and two targets of the upper layer scheduling model also need to be followed. However, considering that psychological factors are mainly related to the SOC at the end, the constraint is already performed in the first target of the lower layer, so that only the second target needs to be established to constrain the scheduling cost of the real-time optimization model of the lower layer.
Figure BDA0003605860380000144
In the formula (I), the compound is shown in the specification,
Figure BDA0003605860380000145
the real-time scheduling cost is minimized for the day; pl,r(t) actual charge and discharge power for a t-period on the l-th day;
Figure BDA0003605860380000151
the actual starting scheduling time of the day l;
Figure BDA0003605860380000152
the actual departure scheduling time of the day l;
Figure BDA0003605860380000153
the actual power battery loss cost of the ith section of the discharge stage on the ith day; n is a radical ofl,rThe number of actual discharge stages on day l.
The lower layer real-time optimization model constraint conditions comprise:
1. and (5) actual running energy consumption constraint.
Figure BDA0003605860380000154
In the formula (I), the compound is shown in the specification,
Figure BDA0003605860380000155
actual initial SOC before starting scheduling for the l day;
Figure BDA0003605860380000156
the actual driving energy consumption SOC on the first day.
2. The actual time constraint.
Figure BDA0003605860380000157
Figure BDA0003605860380000158
An embodiment of the present invention further provides a charge and discharge scheduling device for an electric vehicle, and with reference to fig. 5, fig. 5 is a schematic structural diagram of an embodiment of the charge and discharge scheduling device for an electric vehicle according to the present invention, where the charge and discharge scheduling device 500 for an electric vehicle includes:
an obtaining unit 501, configured to obtain a battery parameter and a mileage parameter of an electric vehicle;
a processing unit 502, configured to establish a battery loss cost model and a psychological effect quantification model according to the battery parameter and the mileage parameter;
and the scheduling unit 503 is configured to establish a long-time-scale electric vehicle charging and discharging scheduling model according to the battery loss cost model and the psychological effect quantization model.
For a more specific implementation manner of each unit of the electric vehicle charging and discharging scheduling device, reference may be made to the description of the electric vehicle charging and discharging scheduling method, and similar beneficial effects are obtained, and details are not repeated herein.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method for scheduling charging and discharging of an electric vehicle is implemented.
Generally, computer instructions for carrying out the methods of the present invention may be carried using any combination of one or more computer-readable storage media. Non-transitory computer readable storage media may include any computer readable medium except for the signal itself, which is temporarily propagating.
A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages, and in particular may employ Python languages suitable for neural network computing and TensorFlow, PyTorch-based platform frameworks. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Fig. 6 is a schematic structural diagram of an embodiment of the electronic device provided in the present invention, and when viewed in conjunction with fig. 6, the electronic device 600 includes a processor 601, a memory 602, and a computer program stored in the memory 602 and operable on the processor 601, and when the processor 601 executes the computer program, the OTA upgrade method and/or the OTA maintenance method described above are implemented.
In a preferred embodiment, the electronic device 600 further includes a display 603 for displaying a data processing result after the processor 601 executes the OTA upgrade method.
Illustratively, the computer program may be partitioned into one or more modules/units, which are stored in the memory 602 and executed by the processor 601 to implement the present invention. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of a computer program in the electronic device 600. For example, the computer program may be divided into the obtaining unit 501, the processing unit 502 and the scheduling unit 503 in the above embodiments, and specific functions of each unit are as described in the above embodiments, which are not described herein again.
The electronic device 600 may be a desktop computer, a notebook, a palm computer, or a smart phone with an adjustable camera module.
The processor 601 may be an integrated circuit chip having signal processing capability. The Processor 601 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 602 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 602 is configured to store a program, and the processor 601 executes the program after receiving an execution instruction, and the method defined by the flow disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 601, or implemented by the processor 601.
The display 603 may be an LCD display screen or an LED display screen. Such as a display screen on a cell phone.
It is understood that the structure shown in fig. 6 is only a schematic diagram of the structure of the electronic device 600, and the electronic device 600 may further include more or less components than those shown in fig. 6. The components shown in fig. 6 may be implemented in hardware, software, or a combination thereof.
The computer-readable storage medium and the electronic device provided by the above embodiments of the present invention may be implemented by referring to the content specifically described for implementing the OTA upgrading method and/or the OTA maintenance method described above according to the present invention, and have similar beneficial effects to the OTA upgrading method and/or the OTA maintenance method described above, and will not be described in detail herein.
The invention discloses a method and a device for scheduling charge and discharge of an electric automobile, wherein firstly, battery parameters and mileage parameters are effectively obtained; then, according to the battery parameters and the mileage parameters, comprehensively considering the battery loss cost and the psychological effect of the user, modeling from two aspects, and establishing a battery loss cost model and a psychological effect quantification model; and finally, establishing a double-layer long-time-scale electric automobile charging and discharging scheduling model based on the battery loss cost model and the psychological effect quantification model. In conclusion, the invention comprehensively considers the long-time scale and the electric automobile user psychological effect quantification, and establishes a double-layer real-time scheduling model which comprises an upper-layer scheduling model and a lower-layer real-time optimization model. And guiding the lower layer to optimize in real time through the scheduling result of the upper layer scheduling model, and completing the optimization process of the whole scheduling period.
According to the technical scheme, the electric vehicle user can carry out charging and discharging scheduling according to a macroscopic time scale and comprehensively considering the psychological effect of the user on mileage anxiety, the benefit degradation of the macroscopic time scale caused by short-time local optimization of the electric vehicle can be effectively avoided, the user satisfaction degree of the electric vehicle is improved, and therefore the response effect of the electric vehicle user on the scheduling is improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (10)

1. A method for scheduling charging and discharging of an electric automobile is characterized by comprising the following steps:
acquiring battery parameters and mileage parameters of the electric automobile;
establishing a battery loss cost model and a psychological effect quantification model according to the battery parameters and the mileage parameters;
and establishing a long-time-scale electric vehicle charging and discharging scheduling model according to the battery loss cost model and the psychological effect quantification model.
2. The electric vehicle charging and discharging scheduling method according to claim 1, wherein the establishing a battery loss cost model and a psychological effect quantification model according to the battery parameter and the mileage parameter comprises:
establishing a battery loss cost model for determining the loss cost of the power battery in the primary discharging process according to the battery parameters;
and establishing the psychological effect quantification model for reflecting the relation between the SOC level of the battery and the psychological anxiety of the electric automobile user according to the battery parameters and the mileage parameters.
3. The electric vehicle charge-discharge scheduling method of claim 2, wherein the battery parameters include a first initial capacity of the power battery of the electric vehicle before the start of the scheduling, a first SOC value of the electric vehicle before the start of the discharging, a second SOC value of the electric vehicle after the end of the discharging, a maximum SOC value of the power battery, a depth of discharging when the discharging of the power battery is finished, and a replacement cost of replacing the power battery, and the establishing the battery loss cost model for determining the loss cost of the power battery during the discharging process comprises:
determining the battery degradation capacity in the primary discharging process according to the first initial capacity, the first SOC value, the second SOC value, the maximum SOC value and the discharging depth;
and determining the loss cost of the power battery generated in the primary discharging process according to the first initial capacity, the battery degradation capacity and the replacement cost.
4. The electric vehicle charge-discharge scheduling method of claim 2, wherein the battery parameter comprises a finished SOC value after charging is finished, the mileage parameter comprises trip mileage, and the establishing of the psychological effect quantification model for reflecting the relationship between the battery SOC level and the psychological anxiety of the electric vehicle user according to the battery parameter and the mileage parameter comprises:
determining a stopping probability density function according to the finished SOC value and the trip mileage;
determining the electric automobile stop probability of a preset SOC value during traveling according to the stop probability density function;
and determining a psychological effect value of the electric automobile user according to the stopping probability of the electric automobile.
5. The electric vehicle charging and discharging scheduling method according to claim 4, wherein the determining the electric vehicle stopping probability of the preset SOC value according to the stopping probability density function comprises:
determining a first mileage capable of being driven by the electric automobile as an integral lower limit according to the trip preset SOC value, the minimum SOC value, the capacity of a power battery of the electric automobile and the unit mileage energy consumption of the electric automobile;
determining a second mileage which can be driven by the electric automobile and corresponds to the maximum SOC value as an upper limit of an integral according to the maximum SOC value, the minimum SOC value, the capacity of a power battery of the electric automobile and the unit mileage energy consumption of the electric automobile;
determining a first probability that the travel mileage is greater than the maximum SOC (system on chip) travelable mileage according to the probability density of the travel mileage of the electric automobile;
and integrating according to the lower integral limit, the upper integral limit, the first probability and the stopping probability density function to determine the stopping probability of the electric automobile.
6. The electric vehicle charging and discharging scheduling method according to claim 5, wherein the determining the psychological effect value of the electric vehicle user according to the electric vehicle stopping probability comprises:
integrating the probability density of the trip mileage of the electric vehicle according to the lower integral limit, the upper integral limit and the first probability respectively to determine a minimum electric vehicle stopping probability and a maximum electric vehicle stopping probability;
and determining the psychological effect value according to the minimum electric vehicle stopping probability and the maximum electric vehicle stopping probability.
7. The electric vehicle charging and discharging scheduling method according to claim 6, wherein the establishing of the long-time-scale electric vehicle charging and discharging scheduling model according to the battery loss cost model and the psychological effect quantification model comprises:
constructing an upper-layer scheduling model according to the minimum power battery loss cost and the psychological effect value;
and constructing a lower-layer real-time optimization model according to the SOC value and the actual charging and discharging power, and realizing the following of the upper-layer scheduling model.
8. The electric vehicle charging and discharging scheduling method according to claim 7, wherein the constructing an upper layer scheduling model according to the minimum power battery loss cost and the psychological effect value comprises:
constructing a first scheduling target with minimized scheduling cost according to the minimum power battery loss cost;
constructing a second scheduling target with the minimum psychological effect according to the minimum psychological effect value;
and constructing at least one upper layer constraint condition of the upper layer scheduling model according to the first factor parameter, wherein the at least one upper layer constraint condition comprises a variable constraint condition, an SOC state constraint condition, a driving energy consumption constraint condition, a time constraint condition and a psychological effect constraint condition.
9. The electric vehicle charging and discharging scheduling method of claim 7, wherein the constructing of the lower layer real-time optimization model according to the SOC value and the actual charging and discharging power to realize the following of the upper layer scheduling model comprises:
according to the SOC value, a third scheduling target which enables the SOC error to be minimum when the scheduling days are over is constructed;
constructing a fourth scheduling target which enables the real-time scheduling cost on the day l to be minimum according to the actual charging and discharging power;
and constructing at least one lower layer constraint condition of the lower layer real-time optimization model according to the second factor parameter, wherein the at least one lower layer constraint condition comprises an actual running energy consumption constraint condition and an actual time constraint condition.
10. The utility model provides an electric automobile charge-discharge scheduling device which characterized in that includes:
the acquiring unit is used for acquiring battery parameters and mileage parameters of the electric automobile;
the processing unit is used for establishing a battery loss cost model and a psychological effect quantification model according to the battery parameters and the mileage parameters;
and the scheduling unit is used for establishing a long-time-scale electric vehicle charging and discharging scheduling model according to the battery loss cost model and the psychological effect quantification model.
CN202210415751.4A 2022-04-20 2022-04-20 Electric automobile charging and discharging scheduling method and device Pending CN114640120A (en)

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