CN117841774A - Electric vehicle charging method based on battery health state and time-of-use electricity price - Google Patents
Electric vehicle charging method based on battery health state and time-of-use electricity price Download PDFInfo
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Abstract
The invention relates to an electric automobile charging method based on battery health state and time-of-use electricity price, which comprises the following steps: step 1, obtaining a fitting relation between the daily driving mileage of a user and the charging time length of an electric vehicle; step 2, obtaining fitting relations between the battery health state and the charging times under different discharging depths; step 3, a time-sharing electricity price method considering the peak-valley difference of the power grid load and the charging cost of the user is provided, and the relationship between the charging price of the electric automobile and the power grid load is analyzed; and 4, establishing an electric vehicle charging model taking the minimum peak-valley difference of the power grid load and the minimum charging cost as targets according to the battery health state, and calculating to obtain an optimal solution, thereby obtaining the electric vehicle optimal charging method. The invention can reduce peak-valley difference of power grid load and reduce charging cost of users while considering user side requirements and maintenance requirements on battery health state.
Description
Technical Field
The invention belongs to the technical field of automatic control of power systems, and relates to an electric vehicle charging method, in particular to an electric vehicle charging method based on a battery health state and a time-of-use electricity price.
Background
In recent years, with the exhaustion of fossil resources and the increasing prominence of environmental problems, new energy is paid attention to, and the full-scale development of the electric automobile industry is one of effective ways for relieving the shortage of traditional energy and reducing environmental pollution. The electric automobile has the characteristics of environment friendliness, energy conservation and low carbon emission, and can effectively realize bidirectional flow of energy and information.
However, with the popularization of electric vehicles, the charging load of the electric vehicle with high permeability, which is brought by disordered access of the large-scale electric vehicle to the power grid, will tend to have a certain influence on the power grid, including increasing the power grid peak Gu Chalv, affecting the power quality, shortening the service life of the transformer, etc., and will further bring negative influence on the safe and stable operation of the power grid under the condition of lacking an active and effective scheduling strategy.
However, most of the prior art approaches focus on aspects of multi-objective solution of intelligent algorithm, coordinated scheduling with ordered charging of distributed energy sources, and the like, and fail to fully consider charging behaviors at a user side. Therefore, consideration of the vehicle battery health state and the time-of-use electricity price is introduced into the electric vehicle charging strategy, and a feasible path is provided for reducing the peak-valley difference of the power grid, the fluctuation of the power grid and the safety of the power grid.
No prior art documents identical or similar to the present invention were found upon retrieval.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides an electric vehicle charging method based on battery health status and time-of-use electricity price, which can fully consider factors such as time-of-use electricity price, influence of the battery health status of the electric vehicle on the degree of user willingness and participation in power grid dispatching, peak-valley difference of power grid load, charging cost of a user side and the like.
The invention solves the practical problems by adopting the following technical scheme:
an electric vehicle charging method based on battery health state and time-of-use electricity price comprises the following steps:
step 1, collecting data of a power grid base load and an electric vehicle charging electricity price, analyzing a user vehicle habit, and obtaining a fitting relation between a daily driving distance of a user and an electric vehicle charging time length;
step 2, calculating the battery health state of the user vehicle, and establishing the relationship between the battery health state and the cycle times of the remaining battery to obtain the fitting relationship between the battery health state and the charging times under different discharging depths;
step 3, based on the related data collected in the step 1 and the fitting relation between the daily driving mileage of the user and the charging time of the electric vehicle, a time-sharing electricity price method considering the peak-valley difference of the power grid load and the charging cost of the user is provided, and the relation between the charging price of the electric vehicle and the power grid load is analyzed;
and 4, comprehensively considering the time-of-use electricity price method in the step 3, combining the fitting relation between the battery health state and the charging times in the step 2, establishing an electric vehicle charging model taking the minimum peak-valley difference of the power grid load and the minimum charging cost as targets and calculating to obtain an optimal solution, thereby obtaining the electric vehicle optimal charging method.
Moreover, the specific steps of the step 1 include:
(1) Collecting data of basic load of a power grid and charging electricity price of an electric automobile;
(2) The habit of the user is collected and analyzed, the daily driving mileage of the electric automobile is fitted through a function, the daily driving mileage is subjected to log-normal distribution, and the probability density function is as follows:
wherein x is 1 A disordered charge start time; mu (mu) e 、σ e The expected and standard deviations at chaotic charging, respectively; mu according to the statistical data e ＝17.6，σ e ＝3.4。
(3) Collecting data statistics, daily mileage x 2 The probability density distribution function of the electric vehicle has a correlation with the charging time length of the electric vehicle, satisfies the lognormal distribution, and is as follows:
wherein mu is T 、σ T The expected and standard deviations of the probability density functions, respectively; mu (mu) T ＝3.2，σ T =0.88; w is battery power; battery capacity q=30kw·h of the electric vehicle.
The specific method of the step 2 is as follows:
defining the state of health of the battery as a percentage of the current maximum available capacity of the battery to the nominal capacity; classifying the battery states according to the relationship between the battery state and the cycle times of the remaining battery and the statistical data, and classifying the health state grades according to the fitting relationship between the battery state and the charge times under different discharge depths and the actual conditions, wherein the fitting relationship is as follows:
SOH DOD ＝α DOD k 3 +β DOD k 2 +γ DOD k+1
in SOH DOD The battery state of health under different battery charge and discharge depths DOD; k is the number of cycles; alpha DOD 、β DOD And gamma DOD Fitting coefficients at different depths of discharge.
The specific method of the step 3 is as follows:
dividing a day into n time periods according to an equal interval delta t, and obtaining the electricity price of each time period according to the basic load condition of a power grid, wherein the relationship between the charging price of the electric automobile and the load of the power grid is as follows:
wherein n is the number of divided time periods; l represents the average value of the daily power grid base load; p (P) 0 To optimize the electricity price before. The charging load of the electric vehicle has transferability, and in the guide of the electricity price, in order to reduce the own charging cost, the user may charge in a period in which the electricity price is low.
Moreover, the specific steps of the step 4 include:
(1) The method comprises the steps of establishing an electric automobile charging model taking the minimum peak-valley difference of a power grid and the minimum charging cost as targets according to the state of health of a battery, wherein the electric automobile charging model comprises the following steps:
wherein Q is the number of electric vehicles.
The total objective function is a nonlinear multi-objective function obtained by weighting the three objective functions, and the nonlinear multi-objective function is as follows:
F＝af 1 +bf 2 +cf 3
wherein a, b and c are f respectively 1 、f 2 、f 3 Linear weighting of the function.
(2) And carrying out sectional linear processing on the data of the actual time-sharing electricity price, the electric vehicle charge state and the charging power to obtain the optimal charging method of the electric vehicle based on the battery health state and the time-sharing electricity price.
The invention has the advantages and beneficial effects that:
the invention provides an electric vehicle charging method based on battery health state and time-of-use electricity price, which takes the battery health state of an electric vehicle as one of decision targets, comprehensively considers two aspects of a user and a power grid, takes the minimum peak-valley difference of the power grid and the minimum charging cost of the user of the electric vehicle as optimization targets, and establishes the electric vehicle optimized charging method. The method can reduce peak-valley difference of the power grid load and simultaneously reduce charging cost of the user while considering the requirements of the user side and the maintenance requirements on the battery health state.
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Fig. 1 is a process flow chart of an electric vehicle charging method based on battery state of health and time-of-use electricity prices according to the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
An electric vehicle charging method based on battery health state and time-of-use electricity price is shown in fig. 1, and comprises the following steps:
step 1, collecting data such as power grid base load, electric vehicle charging electricity price and the like, analyzing the habit of a user, and obtaining a fitting relation between daily driving mileage of the user and electric vehicle charging time;
the specific steps of the step 1 comprise:
(1) Collecting data such as basic load of a power grid, charging electricity price of an electric automobile and the like;
(2) The habit of the user is collected and analyzed, the daily driving mileage of the electric automobile is fitted through a function, the daily driving mileage is subjected to log-normal distribution, and the probability density function is as follows:
wherein x is 1 A disordered charge start time; mu (mu) e 、σ e The expected and standard deviations at chaotic charging, respectively; mu according to the statistical data e ＝17.6，σ e ＝3.4。
(3) Collecting data statistics, daily mileage x 2 The probability density distribution function of the electric vehicle has a correlation with the charging time length of the electric vehicle, satisfies the lognormal distribution, and is as follows:
wherein mu is T 、σ T The expected and standard deviations of the probability density functions, respectively; mu (mu) T ＝3.2，σ T =0.88; w is battery power; battery capacity q=30kw·h of the electric vehicle.
Step 2, calculating the battery health state of the user vehicle, and establishing the relationship between the battery health state and the cycle times of the remaining battery to obtain the fitting relationship between the battery health state and the charging times under different discharging depths;
the specific method of the step 2 is as follows:
defining the state of health of the battery as a percentage of the current maximum available capacity of the battery to the nominal capacity; classifying the battery states according to the relationship between the battery state and the cycle times of the remaining battery and the statistical data, and classifying the health state grades according to the fitting relationship between the battery state and the charge times under different discharge depths and the actual conditions, wherein the fitting relationship is as follows:
SOH DOD ＝α DOD k 3 +β DOD k 2 +γ DOD k+1
in SOH DOD The battery state of health under different battery charge and discharge depths DOD; k is the number of cycles; alpha DOD 、β DOD And gamma DOD For different depth of dischargeFitting coefficients below.
Step 3, based on the related data collected in the step 1 and the fitting relation between the daily driving mileage of the user and the charging time of the electric vehicle, a time-sharing electricity price method considering the peak-valley difference of the power grid load and the charging cost of the user is provided, and the relation between the charging price of the electric vehicle and the power grid load is analyzed;
the specific method of the step 3 is as follows:
the conventional time-of-use electricity price division divides one day into 3 periods according to electricity consumption conditions. In the invention, in order to reduce the peak-valley difference of the power grid load and the charging cost of a user, a day is divided into n time periods according to equal intervals delta t, and the electricity price of each time period is obtained according to the basic load condition of the power grid, so that the relationship between the charging price of the electric automobile and the power grid load is as follows:
wherein n is the number of divided time periods; l represents the average value of the daily power grid base load; p (P) 0 To optimize the electricity price before. The charging load of the electric vehicle has transferability, and in the guide of the electricity price, in order to reduce the own charging cost, the user may charge in a period in which the electricity price is low.
And 4, comprehensively considering the time-of-use electricity price method in the step 3, combining the fitting relation between the battery state of health and the charging times in the step 2, establishing an electric vehicle charging model taking the minimum peak-valley difference of the power grid load and the minimum charging cost as targets and considering the battery state of health, and calculating to obtain an optimal solution through a corresponding algorithm to obtain the electric vehicle optimal charging method.
The specific steps of the step 4 include:
(1) In order to reduce load fluctuation caused by the electric vehicle accessing to a power grid and reduce peak-valley difference of power grid load, an electric vehicle charging model taking the minimum peak-valley difference of power grid load and the minimum charging cost of battery health state as targets is established, wherein the electric vehicle charging model comprises the following components:
wherein Q is the number of electric vehicles.
The total objective function is a nonlinear multi-objective function obtained by weighting the three objective functions, and the nonlinear multi-objective function is as follows:
F＝af 1 +bf 2 +cf 3
wherein a, b and c are f respectively 1 、f 2 、f 3 Linear weighting of the function.
(2) And carrying out sectional linear processing on the data such as the actual time-sharing electricity price, the electric vehicle charge state, the charging power and the like, and obtaining an optimal charging method of the electric vehicle based on the battery health state and the time-sharing electricity price.
The invention is further illustrated by the following specific examples:
taking a certain community as an example for simulation verification. 600 electric vehicles with the same model and different battery health states exist in the community, the battery capacity is 43 kW.h, the vehicle-mounted charging power is 7kW, and E 100 The community base load parameters are shown in the following table for 14 kW.h.
Table 1 electric vehicle base load parameters
| Time/h | 0:00 | 3:00 | 6:00 | 9:00 | 12:00 | 15:00 | 18:00 | 21:00 | 24:00 |
| power/kW | 961 | 352 | 447 | 3873 | 3942 | 4599 | 3526 | 2464 | 1334 |
In this example, the probability distribution diagram of the electric vehicle return time is shown in table 2, and the state of charge of the power battery at the electric vehicle return time, that is, the state of charge at the time when the electric vehicle starts to charge, is counted as most of electric vehicles with 40% -50% of the initial electric quantity of the electric vehicle.
Table 2 probability distribution parameters of the return time of electric vehicles
| Time/h | 0:00 | 3:00 | 6:00 | 9:00 | 12:00 | 15:00 | 18:00 | 21:00 | 24:00 |
| Duty cycle/% | 1.88 | 0.82 | 0.09 | 1.30 | 1.89 | 2.32 | 12.09 | 14.71 | 2.27 |
According to the electricity price method provided by the password, on the basis of unordered charging electricity price, the charging electricity price of each period is calculated on the basis of grid base load, and the time-sharing electricity price is shown in a table 3.
TABLE 3 charging price distribution parameters
As can be seen from Table 3, the optimized charging electricity price is consistent with the basic load of the power grid, the electricity price is formulated higher when the load is larger, and the electricity price is relatively lower in the period of small load, so that the user is stimulated to participate in ordered charging, and the purpose of stabilizing the load is achieved. Simulation results under the ordered charging control strategy of the electric vehicle with the lowest charging cost of the electric vehicle user and the smallest peak-to-valley difference of the power grid load as objective functions are shown in table 4.
TABLE 4 ordered charge load parameters
| Time/h | 0:00 | 3:00 | 6:00 | 9:00 | 12:00 | 15:00 | 18:00 | 21:00 | 24:00 |
| power/kW | 3000 | 2893 | 2964 | 3548 | 4238 | 4787 | 4203 | 3088 | 3159 |
As can be seen from Table 4, the target ordered charging strategy based on time-of-use electricity price provided by the method does not cause the phenomenon of peak-to-peak addition, meanwhile, the method transfers the charging load of the electric automobile to a period of low power grid load, the total load peak value of the power grid is not changed, but the valley value of the load is increased, the total load peak-valley difference is obviously reduced, the stable operation of the power grid is ensured, and the effectiveness of the provided ordered charging method is verified.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (5)
1. An electric automobile charging method based on battery health state and time-of-use electricity price is characterized in that: the method comprises the following steps:
step 1, collecting data of a power grid base load and an electric vehicle charging electricity price, analyzing a user vehicle habit, and obtaining a fitting relation between a daily driving distance of a user and an electric vehicle charging time length;
step 2, calculating the battery health state of the user vehicle, and establishing the relationship between the battery health state and the cycle times of the remaining battery to obtain the fitting relationship between the battery health state and the charging times under different discharging depths;
step 3, based on the related data collected in the step 1 and the fitting relation between the daily driving mileage of the user and the charging time of the electric vehicle, a time-sharing electricity price method considering the peak-valley difference of the power grid load and the charging cost of the user is provided, and the relation between the charging price of the electric vehicle and the power grid load is analyzed;
and 4, comprehensively considering the time-of-use electricity price method in the step 3, combining the fitting relation between the battery health state and the charging times in the step 2, establishing an electric vehicle charging model taking the minimum peak-valley difference of the power grid load and the minimum charging cost as targets and calculating to obtain an optimal solution, thereby obtaining the electric vehicle optimal charging method.
2. The electric vehicle charging method based on the battery state of health and the time-of-use electricity price according to claim 1, wherein the method comprises the following steps: the specific steps of the step 1 comprise:
(1) Collecting data of basic load of a power grid and charging electricity price of an electric automobile;
(2) The habit of the user is collected and analyzed, the daily driving mileage of the electric automobile is fitted through a function, the daily driving mileage is subjected to log-normal distribution, and the probability density function is as follows:
wherein x is 1 A disordered charge start time; mu (mu) e 、σ e The expected and standard deviations at chaotic charging, respectively; mu according to the statistical data e ＝17.6，σ e ＝3.4；
(3) Collecting data statistics, daily mileage x 2 The probability density distribution function of the electric vehicle has a correlation with the charging time length of the electric vehicle, satisfies the lognormal distribution, and is as follows:
wherein mu is T 、σ T The expected and standard deviations of the probability density functions, respectively; mu (mu) T ＝3.2，σ T =0.88; w is battery power; battery capacity q=30kw·h of the electric vehicle.
3. The electric vehicle charging method based on the battery state of health and the time-of-use electricity price according to claim 1, wherein the method comprises the following steps: the specific method of the step 2 is as follows:
defining the state of health of the battery as a percentage of the current maximum available capacity of the battery to the nominal capacity; classifying the battery states according to the relationship between the battery state and the cycle times of the remaining battery and the statistical data, and classifying the health state grades according to the fitting relationship between the battery state and the charge times under different discharge depths and the actual conditions, wherein the fitting relationship is as follows:
SOH DOD ＝α DOD k 3 +β DOD k 2 +γ DOD k+1
in SOH DOD The battery state of health under different battery charge and discharge depths DOD; k is the number of cycles; alpha DOD 、β DOD And gamma DOD Fitting coefficients at different depths of discharge.
4. The electric vehicle charging method based on the battery state of health and the time-of-use electricity price according to claim 1, wherein the method comprises the following steps: the specific method of the step 3 is as follows:
dividing a day into n time periods according to an equal interval delta t, and obtaining the electricity price of each time period according to the basic load condition of a power grid, wherein the relationship between the charging price of the electric automobile and the load of the power grid is as follows:
wherein n is the number of divided time periods; l represents the average value of the daily power grid base load; p (P) 0 To optimize the electricity price before; the charging load of the electric vehicle has transferability, and in the guide of the electricity price, in order to reduce the own charging cost, the user may charge in a period in which the electricity price is low.
5. The electric vehicle charging method based on the battery state of health and the time-of-use electricity price according to claim 1, wherein the method comprises the following steps: the specific steps of the step 4 include:
(1) The method comprises the steps of establishing an electric automobile charging model taking the minimum peak-valley difference of a power grid and the minimum charging cost as targets according to the state of health of a battery, wherein the electric automobile charging model comprises the following steps:
wherein Q is the number of electric vehicles;
the total objective function is a nonlinear multi-objective function obtained by weighting the three objective functions, and the nonlinear multi-objective function is as follows:
F＝af 1 +bf 2 +cf 3
wherein a, b and c are f respectively 1 、f 2 、f 3 Linear weighting of the function;
(2) And carrying out sectional linear processing on the data of the actual time-sharing electricity price, the electric vehicle charge state and the charging power to obtain the optimal charging method of the electric vehicle based on the battery health state and the time-sharing electricity price.
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