CN114707239A - Electric energy resource allocation planning method and device and electronic equipment - Google Patents
Electric energy resource allocation planning method and device and electronic equipment Download PDFInfo
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Abstract
The application provides an electric energy resource allocation planning method, an electric energy resource allocation planning device and electronic equipment. The method comprises the steps of obtaining historical voltage sag data, selecting voltage characteristic dimension items of correlation analysis and carrying out discretization processing, determining a strong correlation rule based on the voltage characteristic dimension items, setting parameter thresholds based on vehicle requirements and the strong correlation rule, determining a plurality of vehicle required power thresholds based on the parameter thresholds, judging a required power interval according to vehicle required power when determining that the vehicle is located in a range extender starting area based on the current state of the vehicle, and planning power generation based on the required power interval. Therefore, the resources of the range extender and the battery are reasonably distributed by considering the voltage sag factor, the efficiency of the range extender is improved while the protection of the battery is ensured, and the method is suitable for planning the electric energy resources in the voltage sag scene.
Description
Technical Field
The invention relates to the technical field of electric automobiles, in particular to a method and a device for planning electric energy resource allocation and electronic equipment.
Background
With the increasing aggravation of energy environmental problems and the support of related policies, new energy automobiles are greatly developed. The range-extended electric automobile has the advantages of long endurance, pure electric drive and the like, can be used as a transition selection from a traditional fuel automobile to a pure electric automobile, has two energy sources of a power battery and a range extender, and directly determines the performance of the whole automobile through an energy management strategy.
The voltage sag is a serious power quality problem frequently occurring in the power system, and according to statistics, the economic loss caused by the voltage sag accounts for 70% -90% of the economic loss caused by all the power quality problems. The voltage sag is a "short-time voltage sag," which is often caused by a short circuit, a lightning strike, a device failure, and the like. Once a voltage sag accident occurs, a motor is shut down, data is lost and the like if the voltage sag accident occurs, and equipment is damaged if the voltage sag accident occurs, so that normal life and operation of a power utilization user are seriously influenced. Investigations have shown that a voltage sag accident can cause huge monetary losses. In the prior art, the damage caused by voltage sag is less considered in the planning problem of the electric vehicle system.
Disclosure of Invention
The invention aims to provide an electric energy resource allocation planning method, an electric energy resource allocation planning device and an electronic device, which can realize optimal planning of electric energy resources applicable to a scene with voltage sag.
Embodiments of the invention may be implemented as follows:
in a first aspect, the present invention provides a method for planning electric energy resource allocation, where the method includes:
constructing a finished automobile simulation model;
constructing an objective function based on the energy consumption of the range extender and the ohmic loss of the battery, and performing iterative optimization on the objective function to obtain an optimal control variable, wherein the optimal control variable comprises the required power of the whole vehicle;
acquiring historical voltage sag data, selecting voltage characteristic dimension items of correlation analysis from the historical voltage sag data, and performing discretization processing;
determining a strong association rule according to each discretized voltage characteristic dimension item, setting a parameter threshold value based on the vehicle demand and the strong association rule, and determining a plurality of vehicle demand power threshold values based on the parameter threshold value;
and when the vehicle is determined to be in the range extender starting area based on the current state of the vehicle, judging the located required power interval according to the vehicle required power, and planning the generating power based on the located required power interval.
In an optional embodiment, the step of constructing the finished automobile simulation model includes:
a whole vehicle simulation model consisting of a driver model, a whole vehicle controller model, a whole vehicle longitudinal dynamics model, a power battery model, a range extender model and a driving motor model is built on a simulation platform;
and verifying the built finished automobile simulation model based on the obtained road test data of the automobile, and determining model parameters in the finished automobile simulation model.
In an optional embodiment, the step of performing iterative optimization on the objective function to obtain an optimal control variable includes:
dividing the decision problem into a plurality of stages according to a time sequence, performing reverse calculation to an initial state based on the objective function, recording an optimal control variable obtained by calculation of each stage, and completing the transition from the initial state to an end state.
In an optional embodiment, the step of performing inverse calculation to an initial state based on the objective function and recording the optimal control variable calculated in each stage includes:
discretizing the state variable and the control variable in the objective function according to a time domain to generate a state space discrete grid;
deducing from the back to the front of the termination iteration step, solving the optimal accumulated objective function value from each state space discrete grid to the state space discrete grid in the termination state under each iteration step, and recording the control variable when each state space discrete grid under each iteration step leads the optimal accumulated objective function value to be minimum until the first iteration step is deduced;
and (4) starting forward recovery from the front to the back in the first iteration step to obtain an optimal control track and an optimal control variable.
In an alternative embodiment, the objective function is constructed as follows:
wherein L represents an instantaneous transition cost; SOCrefIs the target value of the SOC at the time of termination; SOC (N) is the actual value of SOC at the time of termination; alpha is a weight factor of the deviation of the SOC of the battery from a preset value;
instantaneous fuel consumption; qfuelThe heat value of the automobile; r is0The equivalent internal resistance of the power battery is obtained; i isbCharging and discharging current for the power battery; beta is a weight factor; pREIs the output power.
In an optional implementation manner, the step of acquiring historical voltage sag data, selecting a voltage characteristic dimension item for correlation analysis from the historical voltage sag data, and performing discretization processing includes:
acquiring historical voltage sag data and associated historical climate data;
clustering the historical voltage sag data by taking historical climate data as a clustering index;
and selecting voltage sag characteristic dimension items of correlation analysis from the clustered historical voltage sag data and carrying out discretization processing.
In an optional embodiment, the step of determining a strong association rule according to each discretized voltage feature dimension item includes:
establishing a dimension matrix according to each discretized voltage characteristic dimension item, and taking each value in the dimension matrix as a candidate item set;
accumulating the statistical values in the row where each value is located to obtain the minimum support degree, and removing the candidate item sets with the support degrees which do not meet the requirement of the minimum support degree in the candidate item sets to obtain frequent item sets;
deleting rows which do not contain the frequent item sets in the dimensional matrix to obtain an updated dimensional matrix, connecting the frequent item sets in the updated dimensional matrix to obtain an updated candidate item set, then executing the step of removing the candidate item sets with the support degrees which do not meet the requirement of the minimum support degree in the candidate item sets to obtain the frequent item sets, and stopping operation until the updated frequent item sets cannot be generated to obtain a final frequent item set;
and obtaining a plurality of association rules based on the final frequent item set, and determining the association rules of which the confidence degrees are not less than the minimum confidence degrees as strong association rules.
In an optional embodiment, the step of setting the parameter threshold based on the vehicle demand and the strong association rule includes:
when the strong association rule represents that the number of association factors in a plurality of factors is smaller than or equal to a set number, setting a parameter threshold of a plurality of parameters considered based on the requirement of the whole vehicle as a preset minimum threshold, wherein the plurality of factors comprise voltage level, occurrence area, occurrence season, week and time, and the plurality of parameters considered based on the requirement of the whole vehicle comprise power, vehicle speed and battery SOC;
and when the number of the associated factors in the multiple factors represented by the strong association rule is greater than the set number, setting the parameter threshold values of the multiple parameters as a preset highest threshold value.
In a second aspect, the present invention provides an electric energy resource allocation planning apparatus, including:
the building module is used for building a finished automobile simulation model;
the optimization module is used for constructing an objective function based on the energy consumption of the range extender and the ohmic loss of the battery, and performing iterative optimization on the objective function to obtain an optimal control variable, wherein the optimal control variable comprises the required power of the whole vehicle;
the selection module is used for acquiring historical voltage sag data, selecting voltage characteristic dimension items of correlation analysis from the historical voltage sag data and carrying out discretization processing;
the determining module is used for determining a strong association rule according to each discretized voltage characteristic dimension item, setting a parameter threshold value based on the finished automobile demand and the strong association rule, and determining a plurality of finished automobile demand power threshold values based on the parameter threshold value;
and the planning module is used for judging the located required power interval according to the whole vehicle required power and planning the generating power based on the located required power interval when the vehicle is determined to be in the range extender starting area based on the current state of the vehicle.
In a third aspect, the present invention provides an electronic device comprising one or more storage media and one or more processors in communication with the storage media, the one or more storage media storing processor-executable machine-executable instructions that, when executed by the electronic device, are executed by the processors to perform the method steps of any one of the preceding embodiments.
The beneficial effects of the embodiment of the invention include, for example:
the application provides an electric energy resource allocation planning method, an electric energy resource allocation planning device and electronic equipment. In addition, historical voltage sag data are obtained, voltage characteristic dimension items of correlation analysis are selected and discretized, a strong correlation rule is determined based on the voltage characteristic dimension items, parameter thresholds are set based on vehicle requirements and the strong correlation rule, a plurality of vehicle required power thresholds are determined based on the parameter thresholds, when the vehicle is determined to be in a range extender starting area based on the current state of the vehicle, required power intervals are judged according to vehicle required power, and power generation power is planned based on the required power intervals. In the scheme, the resources of the range extender and the battery are reasonably distributed by considering the voltage sag factor, the efficiency of the range extender is improved while the battery is protected, and the method and the device are suitable for planning the electric energy resources in the voltage sag scene.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of an electric energy resource allocation planning method according to an embodiment of the present application;
FIG. 2 is a flowchart of sub-steps included in step S110 of FIG. 1;
FIG. 3 is a flowchart of sub-steps included in step S120 of FIG. 1;
FIG. 4 is a flowchart of sub-steps included in step S130 of FIG. 1;
FIG. 5 is a flowchart of sub-steps included in step S140 of FIG. 1;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present disclosure;
fig. 7 is a functional block diagram of an electric energy resource allocation planning apparatus according to an embodiment of the present application.
Icon: 110-a processor; 120-a memory; 130-multimedia components; 140-I/O interface; 150-a communication component; 200-electric energy resource allocation planning device; 210-a building block; 220-an optimization module; 230-selecting module; 240-a determination module; 250-planning module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that the features in the embodiments of the present invention may be combined with each other without conflict.
Referring to fig. 1, a flowchart of an electric energy resource allocation planning method according to an embodiment of the present application is shown, where method steps defined by a flow related to the electric energy resource allocation planning method can be implemented by an electronic device with a data analysis processing function. The specific process shown in fig. 1 will be described in detail below.
And S110, constructing a finished automobile simulation model.
And S120, constructing an objective function based on the energy consumption of the range extender and the ohmic loss of the battery, and performing iterative optimization on the objective function to obtain an optimal control variable, wherein the optimal control variable comprises the required power of the whole vehicle.
Step S130, obtaining historical voltage sag data, selecting voltage characteristic dimension items of correlation analysis from the historical voltage sag data, and performing discretization processing.
Step S140, determining a strong association rule according to each discretized voltage characteristic dimension item, setting a parameter threshold value based on the vehicle demand and the strong association rule, and determining a plurality of vehicle demand power threshold values based on the parameter threshold value.
And S150, when the vehicle is determined to be in a range extender starting area based on the current state of the vehicle, judging a located required power interval according to the required power of the whole vehicle, and planning the power generation power based on the located required power interval.
In the embodiment, the whole vehicle model can be reasonably adjusted according to the calculation requirement of the dynamic programming algorithm, the range extender model is simplified into a component working at a specific power point, and the whole vehicle model and the driving motor model are converted into the calculation of the required power of the driving motor.
In detail, referring to fig. 2, in this embodiment, constructing a finished automobile simulation model may be implemented in the following manner:
and S111, building a finished automobile simulation model consisting of a driver model, a finished automobile controller model, a finished automobile longitudinal dynamics model, a power battery model, a range extender model and a driving motor model on the simulation platform.
And S112, verifying the built finished automobile simulation model based on the acquired road test data of the automobile, and determining model parameters in the finished automobile simulation model.
In this embodiment, each model is built on the MATLAB/Simulink simulation platform in a manner of mainly testing data and secondarily performing physical modeling. When the finished automobile simulation model is verified, rolling resistance coefficients, transmission system efficiency parameter values and the like in a finished automobile longitudinal dynamic model are mainly determined by parameter identification through real automobile road test data.
In this embodiment, the sum of the energy consumption of the range extender and the european mode loss of the battery may be set as a target function, the end value of the SOC (State of Charge) of the battery is limited, the State variable is selected as the SOC of the power battery, the decision variable is selected as the output power of the range extender, and after discretizing the time domain and the State variable, the optimal solution is performed by using a dynamic programming algorithm under a specific driving cycle.
In detail, in this step, the decision problem may be divided into a plurality of stages in time sequence, inverse calculation is performed to an initial state based on an objective function, an optimal control variable calculated in each stage is recorded, and transition from the initial state to an end state is completed.
Referring to fig. 3, in this embodiment, the above steps may be implemented in the following manner:
and step S121, discretizing the state variables and the control variables in the objective function according to a time domain to generate a state space discrete grid.
And step S122, deducing from the end iteration step to the front, solving the optimal accumulated objective function value from each state space discrete grid in each iteration step to the state space discrete grid in the end state, and recording the control variable when each state space discrete grid in each iteration step leads the optimal accumulated objective function value to be minimum until deducing to the first iteration step.
And S123, starting forward recovery from the front to the back in the first iteration step to obtain an optimal control track and an optimal control variable.
In this embodiment, the target function concerns the fuel consumption of the extended range electric vehicle in the operation process, and the instantaneous transfer cost is composed of the loss power of the extended range device and the ohmic loss power of the battery, and is as follows:
the final objective function is calculated as:
wherein L represents an instantaneous transition cost; SOCrefIs the target value of the SOC at the time of termination; SOC (N) is the actual value of SOC at the time of termination; alpha is a weight factor of the deviation of the SOC of the battery from a preset value;
instantaneous fuel consumption; qfuelThe heat value of the automobile; r0The equivalent internal resistance of the power battery is obtained; i isbCharging and discharging current for the power battery; beta is a weight factor; pREIs the output power.
The state variables reflect the state of the entire system, are usually easier to observe, and are not aftereffect. For the extended range electric vehicle, the fluctuation range of the power battery SOC is relatively large during the running process of the vehicle, and the battery SOC value can reflect the state of the whole vehicle, so the state variable is selected as the battery SOC, as follows:
x(k)＝SOC(k)
the decision variables are the control quantities applied when the state variables of two adjacent stages are transferred. During the operation of the range-extended electric automobile, the output power of the range extender determines the migration of the state variable, so the output power P of the range extender is selectedREAs a decision variable.
According to the determined state quantity power battery SOC and the control quantity range extender output power, a system state transition equation can be obtained, and the equation is shown as the following formula:
SOC(k+1)＝f[SOC(k),PRE(k)]＝SOC(k)+ΔSOC
the required power of the whole vehicle, namely the required electric power of the driving motor, can be obtained by one-dimensional longitudinal dynamics of the vehicle and the efficiency of the driving motor, as shown in the following formula, wherein the efficiency of the driving motor can be obtained by searching the efficiency MAP of the driving motor through the torque and the rotating speed of the end of the driving motor:
Preqin order to drive the motor to demand electric power, W (positive value is driving, negative value is braking energy recovery); etaTIs the overall efficiency of the drive train,%; etamotRepresents the efficiency of the drive motor; f is a rolling resistance coefficient; alpha is the ramp angle, DEG; m is vehicle mass, kg; delta is a rotating mass conversion coefficient; a is the windward area and the square meter; cDIs the air resistance coefficient; mu is vehicle speed, km/h; v is the vehicle speed, m/s.
Voltage sags have a significant impact on electric car systems, and therefore need to be taken into account when planning. Referring to fig. 4, in step S130, the acquired historical voltage sag data may be processed in the following manner:
step S131, historical voltage sag data and associated historical climate data are obtained.
And S132, clustering the historical voltage sag data by taking the historical climate data as a clustering index.
And step S133, selecting voltage sag characteristic dimension items of correlation analysis from the clustered historical voltage sag data and carrying out discretization processing.
In this embodiment, the historical voltage sag data and the historical climate data may be associated according to time, that is, the historical voltage sag data and the historical climate data at the same time may be associated.
When the historical voltage sag data are clustered, clustering can be performed by taking historical climate data of different temperatures, humidity, wind power and the like as clustering indexes. Historical voltage sag data in categories of temperature, humidity, wind, etc. can thus be obtained.
In the historical voltage sag data of each cluster, any one or more of load properties, voltage levels, occurrence areas, occurrence seasons, weeks, time and the like can be selected as sag characteristic dimension items. And after the voltage sag dimension item is determined, discretization processing is carried out, and a strong association rule is determined based on the discretization processed voltage characteristic dimension item.
In this embodiment, referring to fig. 5, when determining a strong association rule, the following method may be implemented:
step S141, a dimension matrix is established according to each discretized voltage characteristic dimension item, and each value in the dimension matrix is used as a candidate.
And S142, accumulating the statistical values of the lines where the values are located to obtain the minimum support degree, and eliminating the candidate item sets with the support degrees which do not meet the requirement of the minimum support degree in the candidate item sets to obtain frequent item sets.
And S143, deleting rows which do not contain the frequent item sets in the dimensional matrix to obtain an updated dimensional matrix, connecting the frequent item sets in the updated dimensional matrix to obtain an updated candidate item set, then executing the step of removing the candidate item sets with the support degrees which do not meet the requirement of the minimum support degree in the candidate item set to obtain the frequent item set, and stopping operation until the updated frequent item set cannot be generated to obtain a final frequent item set.
Step S144, obtaining a plurality of association rules based on the final frequent item set, and determining the association rule with the confidence coefficient not less than the minimum confidence coefficient as a strong association rule.
In the association rule, each sample data is called a transaction, the transaction database D is composed of n transactions, each transaction is specified by a plurality of attributes and is marked as an item, a plurality of items form an item set, the probability of the item set is called a support degree, and the item set exceeding the minimum support degree is called a frequent item set. If the items are all contained in D, X → Y is called the association rule, the probability that the union of X, Y is contained in D is called the support degree, and the support degree represents the importance degree and the occurrence number of the association rule. The support degree of the association rule X → Y is that when the confidence degree of the rule X → Y is not less than the confidence degree of the preset set of the minimum support degrees, the rule is a strong association rule.
In this embodiment, a dimension matrix a may be established according to the discrete data items of each feature dimension item, and the dimension matrix a is used for performing association analysis, and each value in the dimension matrix a is used as a candidate item set 1. And then, accumulating the statistical values in the row where each value is located by scanning the dimension matrix A to obtain the minimum support degree. And eliminating the item set with the support degree not meeting the requirement of the minimum support degree, namely the item set with the support degree smaller than the minimum support degree, in the candidate item set 1 to obtain a frequent item set 1.
And deleting rows which do not contain the frequent item set 1 in the dimension matrix A to obtain a dimension matrix A2, connecting item sets in the frequent item set 1 to obtain a candidate item set 2, and removing the item sets with the support degree smaller than the minimum support degree in the candidate item set 2 to obtain the frequent item set 2.
In this way, multiple times of loop execution are performed, a dimension matrix a (k +1) is obtained by deleting rows in the dimension matrix Ak which do not contain the frequent item set k, a candidate item set k is obtained according to the frequent item set k through self-connection, and an item set smaller than the minimum support degree in the candidate item set (k +1) is removed, so that a frequent item set (k +1) is obtained.
And when the new frequent item set cannot be generated, finishing the operation to obtain the final frequent item set. And obtaining a plurality of association rules X → Y by utilizing the non-empty subsets according to the final frequent item set, wherein the item set Y is the cause of voltage sag, and the item set X is the plurality of dimensionalities, namely the load property, the voltage level, the occurrence area, the occurrence season, the week and the time.
And calculating the confidence coefficient of the association rule, and comparing the confidence coefficient with the set minimum confidence coefficient, wherein the association rule which is not less than the minimum confidence coefficient is a strong association rule.
After determining the strong association rule, in this embodiment, the parameter threshold may be set in the following manner, and the multiple vehicle required power thresholds are determined based on the parameter threshold:
when the number of the association factors in the strong association rule representation multiple factors is smaller than or equal to the set number, the parameter threshold values of the multiple parameters considered based on the whole vehicle demand are set as the preset minimum threshold values, the multiple factors comprise voltage levels, occurrence areas, occurrence seasons, weeks and time, and the multiple parameters considered based on the whole vehicle demand comprise power, vehicle speed and battery SOC.
And when the number of the associated factors in the multiple factors represented by the strong association rule is greater than the set number, setting the parameter threshold values of the multiple parameters as a preset highest threshold value.
In this embodiment, the requirements of the entire vehicle are considered comprehensively, and the requirements at least include the power, the vehicle speed, and the state quantity of the battery SOC during the vehicle running in the strong association rule, and the corresponding parameter thresholds are set in combination with the strong association rule.
In this embodiment, if the strong association rule indicates that the association factors of the voltage level, the occurrence area, the occurrence season, the week, and the time are 2 or 2, the corresponding parameter threshold is set as the lowest threshold. If the strong association rule indicates that the association factors in the voltage level, the occurrence area, the occurrence season, the week and the time are 3 or more, the corresponding parameter threshold is set as the highest threshold. Wherein the lowest threshold is less than the highest threshold, and the lowest threshold and the highest threshold are set based on experience.
Under different vehicle power demand intervals, the range extender generates power with different powers, and along with the increase of the vehicle power demand, the output power of the range extender can be approximately divided into a plurality of working intervals which can be defined as P3, P4, P5, P6 and P7 and are respectively corresponding vehicle power demand thresholds when the power point of the range extender changes.
When Preq < P3, the range extender generates power at 15 kW; when P3 is not more than Preq < P4, the range extender generates power at 20 kW; when P4 is not more than Preq < P5, the range extender generates power at 25 kW; when P5 is not more than Preq < P6, the range extender generates power at 30 kW; when P6 is not more than Preq < P7, the range extender generates electricity at 35 kW; when P7 is less than or equal to Preq, the range extender generates power at 40 kW.
By the above method, the parameter thresholds P3, P4, P5, P6 and P7 can be determined, and the power generation rule after the range extender is started can be determined.
In addition, whether the vehicle is in a range extender starting area or not can be judged according to the SOC value of the power battery and the required power of the whole vehicle, wherein the required power of the whole vehicle can be obtained through optimization based on the objective function. If the range extender is in the range extender starting area, judging the current required power interval according to the required power of the whole vehicle, namely one of the required power intervals, and generating power according to the rule of the required power interval.
In this embodiment, the vehicle state parameters and the control rules of the start, stop, and output power of the range extender can be extracted from the optimization result of the dynamic programming algorithm, so as to implement the design of the strategy.
The electric energy resource allocation planning method provided by this embodiment takes an extended range electric vehicle as a research object, solves the energy allocation thereof by using a dynamic planning algorithm in combination with voltage sag cluster analysis for a specific driving cycle and a specific vehicle end state, and designs a regularized energy management strategy based on a global optimization result.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment. As shown in fig. 6, the electronic device may include: a processor 110, a memory 120, a multimedia component 130, an I/O interface 140, and a communications component 150.
The processor 110 is configured to control the overall operation of the electronic device, so as to complete all or part of the steps of the above power resource allocation planning method. The memory 120 is used to store various types of data to support operations at the electronic device, and such data may include, for example, instructions for any processing software or method operating on the electronic device, as well as processing software-related data.
The Memory 120 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
The multimedia component 130 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. The I/O interface 140 provides an interface between the processor 110 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 150 is used for wired or wireless communication between the electronic device and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding Communication component 150 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic Device may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors 110, or other electronic components for performing the above-mentioned power resource allocation planning method.
In another exemplary embodiment, a computer readable storage medium, such as a memory 120, is also provided that includes program instructions executable by the processor 110 of the electronic device to perform the power resource allocation planning method described above.
Referring to fig. 7, an electric energy resource allocation planning apparatus 200 is further provided in the embodiment of the present application, where the electric energy resource allocation planning apparatus 200 may be understood as the electronic device or the processor 110 of the electronic device, or may be understood as a software functional module that is independent from the electronic device or the processor 110 and implements the electric energy resource allocation planning method under the control of the electronic device.
As shown in fig. 7, the power resource allocation planning apparatus 200 may include a construction module 210, an optimization module 220, a selection module 230, a determination module 240, and a planning module 250. The functions of the functional modules of the power resource allocation planning apparatus 200 are described in detail below.
A building module 210, configured to build a finished automobile simulation model;
it is understood that the building block 210 can be used to execute the above step S110, and for the detailed implementation of the building block 210, reference can be made to the above description related to the step S110.
The optimization module 220 is configured to construct an objective function based on the energy consumption of the range extender and the ohmic loss of the battery, and perform iterative optimization on the objective function to obtain an optimal control variable, where the optimal control variable includes a required power of the entire vehicle;
it is understood that the optimization module 220 can be used to perform the step S120, and the detailed implementation of the optimization module 220 can refer to the above description about the step S120.
The selecting module 230 is configured to obtain historical voltage sag data, select a voltage characteristic dimension item for correlation analysis from the historical voltage sag data, and perform discretization processing;
it is understood that the selecting module 230 can be used to perform the step S130, and the detailed implementation manner of the selecting module 230 can refer to the content related to the step S130.
The determining module 240 is configured to determine a strong association rule according to each discretized voltage feature dimension item, set a parameter threshold value based on the vehicle demand and the strong association rule, and determine a plurality of vehicle demand power threshold values based on the parameter threshold value;
it is understood that the determining module 240 may be configured to perform the step S140, and for detailed implementation of the determining module 240, reference may be made to the content related to the step S140.
And the planning module 250 is used for judging the required power interval according to the whole vehicle required power and planning the generating power based on the required power interval when the vehicle is determined to be in the range extender starting area based on the current state of the vehicle.
It is understood that the planning module 250 can be used to perform the step S150, and the detailed implementation of the planning module 250 can refer to the content related to the step S150.
In a possible implementation manner, the building module 210 may specifically be configured to:
a whole vehicle simulation model consisting of a driver model, a whole vehicle controller model, a whole vehicle longitudinal dynamics model, a power battery model, a range extender model and a driving motor model is built on a simulation platform;
and verifying the built finished automobile simulation model based on the obtained road test data of the automobile, and determining model parameters in the finished automobile simulation model.
In a possible implementation manner, the optimization module 220 may specifically be configured to:
dividing the decision problem into a plurality of stages according to a time sequence, performing reverse calculation to an initial state based on the objective function, recording an optimal control variable obtained by calculation of each stage, and completing the transition from the initial state to an end state.
In a possible implementation manner, the optimization module 220 may specifically be configured to:
discretizing the state variable and the control variable in the objective function according to a time domain to generate a state space discrete grid;
deducing from the back to the front of the termination iteration step, solving the optimal accumulated objective function value from each state space discrete grid to the state space discrete grid in the termination state under each iteration step, and recording the control variable when each state space discrete grid under each iteration step leads the optimal accumulated objective function value to be minimum until the first iteration step is deduced;
and (4) starting forward recovery from the front to the back in the first iteration step to obtain an optimal control track and an optimal control variable.
In one possible implementation, the objective function is constructed as follows:
wherein L represents an instantaneous transition cost; SOCrefIs the target value of the SOC at the time of termination; SOC (N) is the actual value of SOC at the time of termination; alpha is a weight factor of the deviation of the battery SOC and a preset value;
instantaneous fuel consumption; qfuelThe heat value of the automobile; r0The equivalent internal resistance of the power battery is obtained; i isbCharging and discharging current for the power battery; beta is a weight factor; pREIs the output power.
In a possible implementation manner, the selecting module 230 may specifically be configured to:
acquiring historical voltage sag data and associated historical climate data;
clustering the historical voltage sag data by taking historical climate data as a clustering index;
and selecting voltage sag characteristic dimension items of correlation analysis from the clustered historical voltage sag data and carrying out discretization processing.
In a possible implementation manner, the determining module 240 may specifically be configured to:
establishing a dimension matrix according to each discretized voltage characteristic dimension item, and taking each value in the dimension matrix as a candidate item set;
accumulating the statistical values in the row where each value is located to obtain the minimum support degree, and removing the candidate item sets with the support degrees which do not meet the requirement of the minimum support degree in the candidate item sets to obtain frequent item sets;
deleting rows which do not contain the frequent item sets in the dimensional matrix to obtain an updated dimensional matrix, connecting the frequent item sets in the updated dimensional matrix to obtain an updated candidate item set, then executing the step of removing the candidate item sets with the support degrees which do not meet the requirement of the minimum support degree in the candidate item sets to obtain the frequent item sets, and stopping operation until the updated frequent item sets cannot be generated to obtain a final frequent item set;
and obtaining a plurality of association rules based on the final frequent item set, and determining the association rules of which the confidence degrees are not less than the minimum confidence degrees as strong association rules.
In a possible implementation manner, the determining module 240 may be further specifically configured to:
when the strong association rule represents that the number of association factors in a plurality of factors is smaller than or equal to a set number, setting a parameter threshold of a plurality of parameters considered based on the requirement of the whole vehicle as a preset minimum threshold, wherein the plurality of factors comprise voltage level, occurrence area, occurrence season, week and time, and the plurality of parameters considered based on the requirement of the whole vehicle comprise power, vehicle speed and battery SOC;
and when the number of the associated factors in the multiple factors represented by the strong association rule is greater than the set number, setting the parameter threshold values of the multiple parameters as a preset highest threshold value.
The description of the processing flow of each module in the device and the interaction flow between the modules may refer to the related description in the above method embodiments, and will not be described in detail here.
In summary, according to the method, the device and the electronic device for planning electric energy resource allocation provided by the embodiment of the application, the target function is constructed by constructing the whole vehicle simulation model and based on the energy consumption of the range extender and the ohmic loss of the battery, and the target function is optimized to obtain the optimal control variable. In addition, historical voltage sag data are obtained, voltage characteristic dimension items of correlation analysis are selected and discretized, a strong correlation rule is determined based on the voltage characteristic dimension items, parameter thresholds are set based on vehicle requirements and the strong correlation rule, a plurality of vehicle required power thresholds are determined based on the parameter thresholds, when the vehicle is determined to be in a range extender starting area based on the current state of the vehicle, required power intervals are judged according to vehicle required power, and power generation power is planned based on the required power intervals. In the scheme, the resources of the range extender and the battery are reasonably distributed by considering the voltage sag factor, the efficiency of the range extender is improved while the battery is protected, and the method and the device are suitable for planning the electric energy resources in the voltage sag scene.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for planning power resource allocation, the method comprising:
constructing a finished automobile simulation model;
constructing an objective function based on the energy consumption of the range extender and the ohmic loss of the battery, and performing iterative optimization on the objective function to obtain an optimal control variable, wherein the optimal control variable comprises the required power of the whole vehicle;
acquiring historical voltage sag data, selecting voltage characteristic dimension items of correlation analysis from the historical voltage sag data, and performing discretization processing;
determining a strong association rule according to each discretized voltage characteristic dimension item, setting a parameter threshold value based on the vehicle demand and the strong association rule, and determining a plurality of vehicle demand power threshold values based on the parameter threshold value;
and when the vehicle is determined to be in the range extender starting area based on the current state of the vehicle, judging the located required power interval according to the vehicle required power, and planning the generating power based on the located required power interval.
2. The power resource allocation planning method according to claim 1, wherein the step of constructing the entire vehicle simulation model includes:
a whole vehicle simulation model consisting of a driver model, a whole vehicle controller model, a whole vehicle longitudinal dynamics model, a power battery model, a range extender model and a driving motor model is built on a simulation platform;
and verifying the built finished automobile simulation model based on the obtained road test data of the automobile, and determining model parameters in the finished automobile simulation model.
3. The method according to claim 1, wherein the step of iteratively optimizing the objective function to obtain the optimal control variable comprises:
dividing the decision problem into a plurality of stages according to a time sequence, performing reverse calculation to an initial state based on the objective function, recording an optimal control variable obtained by calculation of each stage, and completing the transition from the initial state to an end state.
4. The method for planning electric energy resource allocation according to claim 3, wherein the step of performing inverse calculation to an initial state based on the objective function and recording the optimal control variable calculated in each stage comprises:
discretizing the state variable and the control variable in the objective function according to a time domain to generate a state space discrete grid;
deducing from the back to the front of the termination iteration step, solving the optimal accumulation objective function value from each state space discrete grid to the state space discrete grid in the termination state under each iteration step, and recording the control variable when each state space discrete grid under each iteration step leads the optimal accumulation objective function value to be minimum until deducing to the first iteration step;
and (4) starting forward recovery from the front to the back in the first iteration step to obtain an optimal control track and an optimal control variable.
5. The power resource allocation planning method according to claim 3, wherein the objective function is constructed as follows:
wherein L represents an instantaneous transition cost; SOC (system on chip)refIs the target value of the SOC at the time of termination; SOC (N) is the actual value of SOC at the time of termination; alpha is a weight factor of the deviation of the SOC of the battery from a preset value;
instantaneous fuel consumption; qfuelThe heat value of the automobile; r0The equivalent internal resistance of the power battery is obtained; i isbCharging and discharging current for the power battery; beta is a weight factor; pREIs the output power.
6. The method for planning distribution of electric energy resources according to claim 1, wherein the step of obtaining historical voltage sag data, selecting a voltage characteristic dimension item for correlation analysis from the historical voltage sag data, and performing discretization processing includes:
acquiring historical voltage sag data and associated historical climate data;
clustering the historical voltage sag data by taking historical climate data as a clustering index;
and selecting voltage sag characteristic dimension items of correlation analysis from the clustered historical voltage sag data and carrying out discretization processing.
7. The method for planning distribution of electric energy resources according to claim 1, wherein the step of determining the strong association rule according to each discretized voltage characteristic dimension item includes:
establishing a dimension matrix according to each discretized voltage characteristic dimension item, and taking each value in the dimension matrix as a candidate item set;
accumulating the statistical values in the row where each value is located to obtain the minimum support degree, and removing the candidate item sets with the support degrees which do not meet the requirement of the minimum support degree in the candidate item sets to obtain frequent item sets;
deleting rows which do not contain the frequent item sets in the dimensional matrix to obtain an updated dimensional matrix, connecting the frequent item sets in the updated dimensional matrix to obtain an updated candidate item set, then executing the step of removing the candidate item sets with the support degrees which do not meet the requirement of the minimum support degree in the candidate item sets to obtain the frequent item sets, and stopping operation until the updated frequent item sets cannot be generated to obtain a final frequent item set;
and obtaining a plurality of association rules based on the final frequent item set, and determining the association rules of which the confidence degrees are not less than the minimum confidence degrees as strong association rules.
8. The power resource allocation planning method according to claim 1, wherein the step of setting the parameter threshold based on the vehicle demand and the strong association rule comprises:
when the strong association rule represents that the number of association factors in a plurality of factors is smaller than or equal to a set number, setting a parameter threshold of a plurality of parameters considered based on the requirement of the whole vehicle as a preset minimum threshold, wherein the plurality of factors comprise voltage level, occurrence area, occurrence season, week and time, and the plurality of parameters considered based on the requirement of the whole vehicle comprise power, vehicle speed and battery SOC;
and when the number of the associated factors in the multiple factors represented by the strong association rule is greater than the set number, setting the parameter threshold values of the multiple parameters as a preset highest threshold value.
9. An apparatus for planning the allocation of electric energy resources, the apparatus comprising:
the building module is used for building a finished automobile simulation model;
the optimization module is used for constructing an objective function based on the energy consumption of the range extender and the ohmic loss of the battery, and performing iterative optimization on the objective function to obtain an optimal control variable, wherein the optimal control variable comprises the required power of the whole vehicle;
the selection module is used for acquiring historical voltage sag data, selecting voltage characteristic dimension items of correlation analysis from the historical voltage sag data and carrying out discretization processing;
the determining module is used for determining a strong association rule according to each discretized voltage characteristic dimension item, setting a parameter threshold value based on the finished automobile demand and the strong association rule, and determining a plurality of finished automobile demand power threshold values based on the parameter threshold value;
and the planning module is used for judging the located required power interval according to the whole vehicle required power and planning the generating power based on the located required power interval when the vehicle is determined to be in the range extender starting area based on the current state of the vehicle.
10. An electronic device comprising one or more storage media and one or more processors in communication with the storage media, the one or more storage media storing processor-executable machine-executable instructions that, when executed by the electronic device, are executed by the processors to perform the method steps of any of claims 1-8.
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