Electric vehicle orderly charging control method and system

Abstract

The present invention discloses a method and system for controlling the orderly charging of electric vehicles. Power loss modeling is performed on the PFC circuit and DC / DC conversion circuit of a DC charging pile, respectively. With respect to the loss model of the PFC circuit, the Pareto optimization method is used to obtain the minimum loss, thereby obtaining the first optimal charging efficiency of the PFC circuit under a certain input power. With respect to the loss model of the DC / DC conversion circuit, the Pareto optimization method is used to obtain the minimum loss, thereby obtaining the second optimal charging efficiency of the dual-active bridge DC / DC conversion circuit under a certain input power. Based on the first optimal charging efficiency and the second optimal charging efficiency, the optimal charging efficiency of the charging pile is solved. Based on the optimal charging efficiency of the charging pile, the particle swarm optimization algorithm is used to solve the pre-constructed target optimization function to obtain the minimum load fluctuation, thereby achieving the goal of orderly charging of large-scale electric vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a method and system for controlling orderly charging of electric vehicles taking into account the optimal efficiency of charging piles. Background Art

[0002] In recent years, the large-scale integration of distributed energy resources and electric vehicles has posed significant challenges to the economic and reliable operation of distribution networks. The disorderly charging of a large number of electric vehicles has further exacerbated the imbalance in distribution network loads. To address this issue, it is necessary to implement and optimize a coordinated charging strategy based on actual user needs, the characteristics of electric vehicles and charging stations, and the goal of economic and reliable operation of the distribution network.

[0003] As a nonlinear optimization problem, the implementation of organized charging can generally be solved using evolutionary algorithms, traditional optimization algorithms, and machine learning algorithms. One approach uses differential evolution to determine the charging time and duration of electric vehicles, thereby simultaneously meeting the constraints and requirements of both the grid and users. Another approach uses a genetic algorithm to solve an organized charging model for electric vehicles, with the objective function of minimizing the peak-to-valley difference of the grid. This approach exhibits good peak-to-valley shaving and convergence speed. Using a genetic algorithm to optimize time-of-use pricing schedules based on time-of-use electricity pricing can also achieve the dual goals of optimizing user charging costs and ensuring economic operation of the grid. Some researchers have proposed an organized charging strategy for electric vehicles based on an elite genetic algorithm. This strategy achieves the optimization goal of reducing the peak-to-valley difference rate while adopting time-of-use pricing. Furthermore, the particle swarm optimization algorithm is also widely used in organized charging strategies. For example, the particle swarm optimization algorithm, with the goals of both system load peak shaving and minimizing charging costs, can achieve optimized charging of electric vehicles on a large scale. For large-scale charging stations and residential charging scenarios, an orderly charging model can be implemented to smooth load curves and reduce charging costs, based on demand response and time-of-use electricity pricing mechanisms and using an adaptive mutation particle swarm optimization algorithm. Furthermore, an improved k-clustering algorithm uses a hierarchical clustering method to cluster electric vehicles with similar behavioral characteristics. This modeling objective functions the utilization rate of charging stations and the range of electric vehicles, enabling orderly charging of electric vehicles. However, current orderly charging models rarely consider the impact of charging pile efficiency on model accuracy. Summary of the Invention

[0004] The object of the present invention is to provide an orderly charging control method and system for electric vehicles to solve the problem that the orderly charging model in the prior art does not consider the influence of the charging efficiency of the charging pile on the model accuracy.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In one aspect, a method for controlling orderly charging of an electric vehicle comprises:

[0007] The power factor correction (PFC) circuit and DC / DC conversion circuit of the DC charging pile are modeled for power loss, and the PFC circuit loss model and the DC / DC conversion circuit loss model are obtained.

[0008] Performing a multi-objective optimization solution on the PFC circuit loss model to obtain a minimum loss of the PFC circuit at the input power, and calculating a first optimal charging efficiency of the PFC circuit at the input power based on the minimum loss;

[0009] Performing a multi-objective optimization solution on the DC / DC conversion circuit loss model to obtain a minimum loss of the DC / DC conversion circuit at the input power, and calculating a second optimal charging efficiency of the DC / DC conversion circuit at the input power based on the minimum loss;

[0010] Calculating the optimal charging efficiency of the charging pile based on the first optimal charging efficiency and the second optimal charging efficiency;

[0011] Obtaining an orderly charging control model with minimizing grid load fluctuation as the objective function and grid dispatch capacity, electric vehicle battery power, charging pile charging power, and user behavior as constraints; the charging pile charging power is associated with the charging pile charging efficiency;

[0012] Based on the optimal charging efficiency of the charging pile, the orderly charging control model is solved to obtain the charging period and power of the electric vehicle.

[0013] Furthermore, the PFC circuit loss model and the DC / DC conversion circuit loss model both include relevant losses generated by all semiconductor devices, magnetic devices, and capacitive devices in the circuit.

[0014] Furthermore, the PFC circuit is a PFC circuit with a Vienna topology, and the DC / DC conversion circuit is a DC / DC circuit with a dual active bridge (DAB) topology.

[0015] Furthermore, the PFC circuit loss model of the Vienna topology is:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] In formula (1), P cnM is the conduction loss of MOSFET, I rmsM is the effective value of the MOSFET current, R on is the on-state resistance of MOSFET; in formula (2), P swM is the switching loss, V o is the output voltage of the PFC circuit, I o is the load current, I ch is the current flowing through the MOSFET channel, t ron t is the time required for the MOSFET drive voltage to rise from the turn-on threshold to the Miller platform. fon is the time required for the MOSFET drain-source voltage to drop to 0, t roff The time required for the MOSFET drain-source voltage to rise from 0 to the steady-state stress value, t foff is the time required for the MOSFET channel current to drop from the steady-state stress value to 0; in formula (3), P cnD is the conduction loss of the diode, I avD is the average value of the diode current, V FD is the forward voltage drop of the diode, I rmsD is the effective value of the diode current, R D is the dynamic resistance of the diode; in formula (4), P cnL is the copper loss of the inductor, I rmsL is the effective value of the inductor current, R L is the DC resistance of the inductor winding; in formula (5), P swL is the iron loss of the inductor, V e is the effective volume of the core, α, β, k are Steimetz parameters, f in is the power frequency, B p is the peak flux density, N is the number of segments after digital quantization of a switching cycle, ΔB i and T i are the change in magnetic flux density in the i-th section and the duration of the i-th section, respectively, and θ is the flux linkage angle.

[0022] Furthermore, the loss model of the DC / DC conversion circuit of the dual active bridge topology is:

[0023]

[0024] P sw =φ1{φ2[k(1-D1)+2D2-1]+φ3[k(D1-2D2+1)-φ4D1+φ5(D1-1)]} (7)

[0025]

[0026]

[0027]

[0028]

[0029] In formula (6), P cn is the conduction loss of the semiconductor device, D1 is the inward phase shift ratio, and D2 is the outward phase shift ratio; in formula (7), P sw is the switching loss, k is the proportional coefficient; in formula (8), P mag is the total loss of the magnetic core device; in formula (9), n t is the transformer turns ratio, VO is the output voltage of the charging pile, T S Switching cycle of the dual active bridge DC / DC converter circuit; L is the inductance, V F is the forward conduction voltage drop of the diode, V sat is the saturation voltage drop of IGBT; in formula (10), V B is the input voltage of the dual active bridge DC / DC converter circuit, t on and t off are the turn-on time and turn-off time of IGBT respectively; in formula (11), n t is the transformer turns ratio, V2 is the output voltage of the dual active bridge DC / DC converter circuit, R tr is the transformer winding resistance, R au is the winding resistance of the auxiliary inductor, f s is the switching frequency, μ0 is the vacuum permeability, g is the air gap size, N W is the number of winding turns, V e is the effective volume of the core, and m is the iron loss coefficient.

[0030] Furthermore, a Pareto genetic algorithm is used to perform multi-objective optimization on the PFC circuit loss model.

[0031] Furthermore, a Pareto genetic algorithm is used to perform multi-objective optimization on the DC / DC conversion circuit loss model.

[0032] Furthermore, the optimal charging efficiency of the charging pile is calculated according to the following formula:

[0033] η=η1η2 (12)

[0034] In formula (12), η is the optimal charging efficiency of the charging pile, η1 is the first optimal charging efficiency of the PFC circuit under the input power, and η2 is the second optimal charging efficiency of the DC / DC conversion circuit under the input power.

[0035] Furthermore, the objective function of the orderly charging control model is:

[0036]

[0037]

[0038] Where f1 is the grid load fluctuation rate, L Bi is the basic load of the power grid in period i; L EVi is the charging load of all electric vehicles in the area where the charging pile is located during time period i; L AV is the average load of the power grid within 24 hours;

[0039] The constraints are:

[0040]

[0041] L ijmin ≤L ij ≤L ijmax (16)

[0042] L ijmax =min(L ijcmax ,L ijbmax ,L ijlmax ) (17)

[0043] 80%E EVi ≤E id ≤100%E EVi (18)

[0044] E ij =ηL ij Δt+E ijini (19)

[0045]

[0046] In formula (15), N EV is the number of electric vehicles connected to all charging piles in the area at time t, L i (t) is the charging load of the i-th electric vehicle at time t, L dis (t) is the upper limit of the capacity allocated by the power grid to the charging pile area at time t; In formula (16), L ij is the charging power of the i-th electric vehicle in period j, L ijmin and L ijmax are the maximum and minimum charging power of the i-th electric vehicle in period j respectively; in formula (17), L ijcmax is the upper limit of the output power of the charging pile for charging the i-th electric vehicle in period j, L ijbmaxL is the upper limit of charging power that the battery of the i-th electric vehicle can bear during period j, ijlmax is the upper limit of the power that the cable can bear for charging the i-th electric vehicle during period j; in formula (18), E EVi is the rated capacity of the battery of the i-th electric vehicle; E id is the battery capacity of the i-th electric vehicle at the end of charging; in formula (19), E ij is the battery capacity of the i-th electric vehicle in the j-th period, η is the charging efficiency, E ijini is the initial capacity of the battery of the i-th electric vehicle at the beginning of period j, Δt is the time step of the charging process; in formula (20), U i is the user behavior of the i-th electric vehicle, 0 indicates participating in orderly charging, and 1 indicates not participating in orderly charging.

[0047] In another aspect, an orderly charging control system for an electric vehicle includes:

[0048] The first loss model establishment module is used to model the power loss of the PFC circuit of the DC charging pile to obtain a PFC circuit loss model;

[0049] The second loss model establishment module is used to model the power loss of the DC / DC conversion circuit of the DC charging pile to obtain a DC / DC conversion circuit loss model;

[0050] a first optimal efficiency solving module, performing a multi-objective optimization solution on the PFC circuit loss model to obtain a minimum loss of the PFC circuit at the input power, and calculating a first optimal charging efficiency of the PFC circuit at the input power based on the minimum loss;

[0051] a second optimal efficiency solving module, performing a multi-objective optimization solution on the DC / DC conversion circuit loss model to obtain a minimum loss of the DC / DC conversion circuit under the input power, and calculating a second optimal charging efficiency of the DC / DC conversion circuit under the input power based on the minimum loss;

[0052] a charging pile optimal efficiency solving module, solving the optimal charging efficiency of the charging pile according to the first optimal charging efficiency and the second optimal charging efficiency;

[0053] An acquisition module is used to acquire an orderly charging control model constructed with minimizing grid load fluctuation as the objective function and grid dispatching capacity, electric vehicle battery power, charging pile charging power, and user behavior as constraints; the charging pile charging power is associated with the charging pile charging efficiency;

[0054] The objective function solving module is used to solve the orderly charging control model based on the optimal charging efficiency of the charging pile to obtain the charging period and power of the electric vehicle.

[0055] Compared with the prior art, the beneficial technical effects achieved by the present invention are:

[0056] The present invention fully considers the impact of the charging efficiency of the charging pile on the accuracy of the orderly charging model. By modeling the power loss of the PFC circuit part and the DC / DC conversion circuit part of the DC charging pile respectively, the Pareto optimization method is used to obtain the minimum loss of the PFC circuit and the DC / DC conversion circuit, thereby obtaining the optimal charging efficiency of the charging pile under a certain input power. Based on the optimal charging efficiency of the charging pile, the particle swarm optimization algorithm is used to solve the target optimization function with grid operation, battery power, charging power and user behavior as constraints to obtain the minimum load fluctuation, thereby achieving the goal of orderly charging of large-scale electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of an orderly charging control method for an electric vehicle according to an embodiment of the present invention;

[0058] Figure 2 The figure is a schematic structural diagram of an orderly charging control system for an electric vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further described below in conjunction with specific examples. The following examples are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0060] Combine Figure 1 As shown, a method for controlling orderly charging of an electric vehicle comprises the following steps:

[0061] Step S1, respectively performing power loss modeling on the PFC circuit and DC / DC conversion circuit of the DC charging pile to obtain a PFC circuit loss model and a DC / DC conversion circuit loss model;

[0062] In this embodiment, the PFC circuit adopts a PFC circuit with a Vienna topology, and the DC / DC conversion circuit adopts a DC / DC conversion circuit with a dual active bridge topology.

[0063] The Vienna PFC circuit model uses a three-phase AC-DC converter with power factor correction. The circuit also has a boost capability, meaning the output voltage is higher than the input voltage.

[0064] The dual active bridge DC / DC conversion circuit model has the feature of electrical isolation, and optionally has a boost function, a buck function, or both a boost and a buck function.

[0065] The Vienna PFC circuit model and dual-active-bridge DC / DC converter circuit model can be built using power electronics circuit modeling tools such as MATLAB Simulink or PLECS. Based on the specific circuit topology, components with specific parameters are selected to build simulation circuits, allowing the behavior of the Vienna PFC and dual-active-bridge DC / DC circuits to be analyzed.

[0066] Then, based on the specific circuit topology, circuit behavior and component parameters of the established Vienna PFC circuit model and dual active bridge DC / DC conversion circuit model, the Vienna PFC circuit loss model and dual active bridge DC / DC conversion circuit loss model were established respectively based on mathematical analytical analysis and circuit simulation software analysis.

[0067] The established power loss model primarily includes the losses associated with all semiconductor, magnetic, and capacitive devices in the circuit. Semiconductor losses primarily include conduction losses, switching losses, and drive losses. Magnetic losses primarily include winding conduction losses, core hysteresis losses, and eddy current losses. Capacitive losses primarily include conduction losses of the capacitor's equivalent series resistance.

[0068] In this embodiment, based on the circuit topology, circuit behavior, and component parameters of the Vienna PFC circuit model, a Vienna PFC circuit loss model is established as follows:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] In formula (1), P cnM is the conduction loss of MOSFET, I rmsM is the effective value of the MOSFET current, R on is the on-state resistance of MOSFET; in formula (2), P swM is the switching loss, V o is the output voltage of the PFC circuit, I o is the load current, I ch is the current flowing through the MOSFET channel, t ron t is the time required for the MOSFET drive voltage to rise from the turn-on threshold to the Miller platform.fon is the time required for the MOSFET drain-source voltage to drop to 0, t roff The time required for the MOSFET drain-source voltage to rise from 0 to the steady-state stress value, t foff is the time required for the MOSFET channel current to drop from the steady-state stress value to 0; in formula (3), P cnD is the conduction loss of the diode, I avD is the average value of the diode current, V FD is the forward voltage drop of the diode, I rmsD is the effective value of the diode current, R D is the dynamic resistance of the diode; in formula (4), P cnL is the copper loss of the inductor, I rmsL is the effective value of the inductor current, R L is the DC resistance of the inductor winding; in formula (5), P swL is the iron loss of the inductor, V e is the effective volume of the core, α, β, k are Steimetz parameters, f in is the power frequency, B p is the peak flux density, N is the number of segments after digital quantization of a switching cycle, ΔB i and T i are the change in magnetic flux density in the i-th section and the duration of the i-th section, respectively, and θ is the flux linkage angle.

[0075] According to the specific circuit topology, circuit behavior and component parameters of the dual active bridge DC / DC conversion circuit model, the loss model of the dual active bridge DC / DC conversion circuit is established as follows:

[0076]

[0077] P sw =φ1{φ2[k(1-D1)+2D2-1]+φ3[k(D1-2D2+1)-φ4D1+φ5(D1-1)]} (7)

[0078]

[0079]

[0080]

[0081]

[0082] In formula (6), P cn is the conduction loss of the semiconductor device, D1 is the inward phase shift ratio, and D2 is the outward phase shift ratio; in formula (7), P swis the switching loss, k is the proportional coefficient; in formula (8), P mag is the total loss of the magnetic core device; in formula (9), n t is the transformer turns ratio, V O is the output voltage of the charging pile, T S Switching cycle of the dual active bridge DC / DC converter circuit; L is the inductance, V F is the forward conduction voltage drop of the diode, V sat is the saturation voltage drop of IGBT; in formula (10), V B is the input voltage of the dual active bridge DC / DC converter circuit, t on and t off are the turn-on time and turn-off time of IGBT respectively; in formula (11), n t is the transformer turns ratio, V2 is the output voltage of the dual active bridge DC / DC converter circuit, R tr is the transformer winding resistance, R au is the winding resistance of the auxiliary inductor, f s is the switching frequency, μ0 is the vacuum permeability, g is the air gap size, N W is the number of winding turns, V e is the effective volume of the core, and m is the iron loss coefficient.

[0083] Step S2, performing a multi-objective optimization solution on the PFC circuit loss model to obtain a minimum loss of the PFC circuit at the input power, and calculating a first optimal charging efficiency of the PFC circuit at the input power based on the minimum loss;

[0084] In this embodiment, the Pareto optimization method is used to find the minimum loss for the Vienna PFC circuit loss model established in step S1. Based on the input power and the minimum loss, the first optimal charging efficiency of the Vienna PFC circuit at the input power is solved.

[0085] By mapping the design variable space to the performance variable space, multiple sets of circuit parameters are substituted into the calculation to obtain the Pareto frontier for multiple optimization objectives. The design variables can be the Vienna PFC circuit control method, modulation strategy, and parameter values ​​of each circuit component. The performance variables can be the Vienna PFC circuit's efficiency, cost, size, weight, and electromagnetic characteristics.

[0086] In a specific implementation, a behavioral model of the Vienna PFC circuit for a charging pile is created using software tools such as SIMULINK or PLECS. After assigning specific design variable values ​​to this model, the corresponding circuit components are selected from a device database and their relevant parameters, such as the on-state resistance of the MOSFET, the DC resistance of the inductor winding, and the size of the diode, are extracted. These parameters are then input into the device model, and the converter behavioral model is run to evaluate specific performance metrics, such as loss and size. By inputting the relevant parameters of all optional components into the model one by one, the Pareto front of loss and size for the Vienna PFC circuit for a specific design can be obtained.

[0087] In practice, the charging pile uploads its parameters to the control center. Based on the Pareto frontier curve data, the control center determines the optimal efficiency of the charging pile's Vienna power factor correction circuit and sends it to the charging pile. The charging pile adjusts its own control parameters to ensure the Vienna power factor correction circuit operates at optimal efficiency.

[0088] Step S3, performing a multi-objective optimization solution on the DC / DC conversion circuit loss model to obtain a minimum loss of the DC / DC conversion circuit at the input power, and calculating a second optimal charging efficiency of the DC / DC conversion circuit at the input power based on the minimum loss;

[0089] In this embodiment, for the dual active bridge DC / DC conversion circuit loss model, the Pareto optimization method is also used to obtain the minimum loss, thereby obtaining the second optimal charging efficiency of the dual active bridge DC / DC conversion circuit under a certain input power.

[0090] Step S4, solving the optimal charging efficiency of the charging pile according to the first optimal charging efficiency and the second optimal charging efficiency;

[0091] In this embodiment, the optimal charging efficiency of the charging pile is calculated according to the following formula:

[0092] η=η1η2 (12)

[0093] Where η is the optimal charging efficiency of the charging pile, η1 is the first optimal charging efficiency of the PFC circuit under the input power, and η2 is the second optimal charging efficiency of the DC / DC conversion circuit under the input power.

[0094] Step S5, obtaining an orderly charging control model that takes minimizing grid load fluctuation as the objective function and takes grid dispatching capacity, electric vehicle battery power, charging pile charging power, and user behavior as constraints;

[0095] Among them, the objective function of the orderly charging control model is:

[0096]

[0097]

[0098] Where f1 is the grid load fluctuation rate, L Bi is the basic load of the power grid in period i; L EVi is the charging load of all electric vehicles in the jurisdiction during period i; L AV is the average load of the power grid within 24 hours;

[0099] The constraints are:

[0100] 1) Grid dispatch capacity constraints:

[0101]

[0102] Among them, N EV is the number of electric vehicles connected to all charging piles in the area at time t, L i (t) is the charging load of the i-th electric vehicle at time t, L dis (t) is the upper limit of the capacity allocated by the power grid to the charging station at time t;

[0103] 2) Charging power constraints of charging piles:

[0104] L ij min ≤L ij ≤L ij max (16)

[0105] L ij max =min(L ijc max ,L ijb max ,L ijl max ) (17)

[0106] Among them, Lij is the charging power of the i-th electric vehicle in period j; L ij min and L ij max are the maximum and minimum values ​​of charging power, L ijc max is the upper limit of the charging pile output power, L ijb max The upper limit of the battery's charging power, L ijl max Provide a power ceiling for charging cables;

[0107] 3) Battery power limit:

[0108] 80%E EVi ≤E id ≤100%E EVi (18)

[0109] E ij =ηL ij Δt+Eijini (19)

[0110] Among them, E EVi is the rated capacity of the battery of the i-th electric vehicle; E id The battery capacity of the i-th electric vehicle at the end of charging, E ij is the battery capacity of the i-th electric vehicle in the j-th period, η is the charging efficiency, E ijini is the initial capacity of the battery of the ith electric vehicle at the beginning of period j, and Δt is the time step of the charging process;

[0111] 4) User behavior constraints:

[0112]

[0113] Among them, U i is the user behavior of the i-th electric vehicle. The user behavior of the i-th electric vehicle mainly includes participating in orderly charging and not participating in orderly charging. 0 indicates participating in orderly charging, and 1 indicates not participating in orderly charging.

[0114] Step S6: Solve the orderly charging control model based on the optimal charging efficiency of the charging pile to obtain the charging period and power of the electric vehicle.

[0115] The optimal charging efficiency η of the charging pile obtained in step S4 is substituted into formula (19). Then, the orderly charging control model of step S5 is solved by the particle swarm optimization algorithm to obtain the minimum grid load fluctuation rate, thereby obtaining the charging period and power of the electric vehicle.

[0116] In another embodiment, an electric vehicle orderly charging control system, such as Figure 2 Shown, including:

[0117] The first loss model establishment module is used to model the power loss of the PFC circuit of the DC charging pile to obtain a PFC circuit loss model;

[0118] The second loss model establishment module is used to model the power loss of the DC / DC conversion circuit of the DC charging pile to obtain a DC / DC conversion circuit loss model;

[0119] a first optimal efficiency solving module, performing a multi-objective optimization solution on the PFC circuit loss model to obtain a minimum loss of the PFC circuit at the input power, and calculating a first optimal charging efficiency of the PFC circuit at the input power based on the minimum loss;

[0120] a second optimal efficiency solving module, performing a multi-objective optimization solution on the DC / DC conversion circuit loss model to obtain a minimum loss of the DC / DC conversion circuit under the input power, and calculating a second optimal charging efficiency of the DC / DC conversion circuit under the input power based on the minimum loss;

[0121] a charging pile optimal efficiency solving module, solving the optimal charging efficiency of the charging pile according to the first optimal charging efficiency and the second optimal charging efficiency;

[0122] An acquisition module is used to acquire an orderly charging control model constructed with minimizing grid load fluctuation as the objective function and grid dispatching capacity, electric vehicle battery power, charging pile charging power, and user behavior as constraints; the charging pile charging power is associated with the charging pile charging efficiency;

[0123] The objective function solving module is used to solve the orderly charging control model based on the optimal charging efficiency of the charging pile to obtain the charging period and power of the electric vehicle.

[0124] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0128] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for controlling orderly charging of electric vehicles, characterized in that: include: The power loss modeling of the PFC circuit and DC / DC conversion circuit of the DC charging pile is carried out respectively to obtain the PFC circuit loss model and the DC / DC conversion circuit loss model; Performing a multi-objective optimization solution on the PFC circuit loss model to obtain a minimum loss of the PFC circuit at the input power, and calculating a first optimal charging efficiency of the PFC circuit at the input power based on the minimum loss; Performing a multi-objective optimization solution on the DC / DC conversion circuit loss model to obtain a minimum loss of the DC / DC conversion circuit at the input power, and calculating a second optimal charging efficiency of the DC / DC conversion circuit at the input power based on the minimum loss; Calculating the optimal charging efficiency of the charging pile based on the first optimal charging efficiency and the second optimal charging efficiency; Obtaining an orderly charging control model with minimizing grid load fluctuation as the objective function and grid dispatch capacity, electric vehicle battery power, charging pile charging power, and user behavior as constraints; the charging pile charging power is associated with the charging pile charging efficiency; Based on the optimal charging efficiency of the charging pile, the orderly charging control model is solved to obtain the charging period and power of the electric vehicle; The PFC circuit loss model is: （1） （2） （3） （4） （5） In formula (1), is the conduction loss of MOSFET, is the effective value of the MOSFET current, is the on-state resistance of MOSFET; in formula (2), is the switching loss, is the output voltage of the PFC circuit, is the load current, is the current flowing through the MOSFET channel, The time required for the MOSFET drive voltage to rise from the turn-on threshold to the Miller platform, is the time required for the MOSFET drain-source voltage to drop to 0, is the time required for the MOSFET drain-source voltage to rise from 0 to the steady-state stress value, The time required for the MOSFET channel current to drop from the steady-state stress value to 0; In formula (3), is the conduction loss of the diode, is the average diode current, is the diode forward voltage drop, is the effective value of the diode current, is the dynamic resistance of the diode; in formula (4), is the copper loss of the inductor, is the effective value of the inductor current, is the DC resistance of the inductor winding; in formula (5), is the iron loss of the inductor, is the effective volume of the core, 、 、 is the Steimetz parameter, is the power frequency, is the peak flux density, N is the number of segments after digital quantization of a switching cycle, and Respectively The change in magnetic flux density in each section and the The duration of a segment, is the magnetic linkage angle; The DC / DC conversion circuit loss model is: （6） （7） （8） （9） （10） （11） In formula (6), is the conduction loss of the semiconductor device, Compared with the inward shift, is compared with the outward shift; in formula (7), is the switching loss, is the proportional coefficient; in formula (8), is the total loss of the magnetic core device; in formula (9), is the transformer turns ratio, is the output voltage of the charging pile, Switching cycle of dual active bridge DC / DC converter circuit; is the inductance, is the forward conduction voltage drop of the diode, is the saturation voltage drop of IGBT; in formula (10), is the input voltage of the dual active bridge DC / DC conversion circuit, and are the turn-on time and turn-off time of IGBT respectively; in formula (11), is the transformer turns ratio, is the output voltage of the dual active bridge DC / DC conversion circuit, is the transformer winding resistance, is the winding resistance of the auxiliary inductor, is the switching frequency, is the vacuum permeability, is the air gap size, is the number of winding turns, is the effective volume of the core, is the iron loss coefficient.

2. The method for controlling orderly charging of an electric vehicle according to claim 1, characterized in that: The PFC circuit loss model and the DC / DC conversion circuit loss model both include the related losses generated by all semiconductor devices, magnetic devices, and capacitive devices in the circuit.

3. The method for controlling orderly charging of an electric vehicle according to claim 1, characterized in that: The PFC circuit is a PFC circuit with Vienna topology, and the DC / DC conversion circuit is a DC / DC circuit with dual active bridge topology.

4. The method for controlling orderly charging of an electric vehicle according to claim 1, characterized in that: The Pareto genetic algorithm is used to perform multi-objective optimization on the PFC circuit loss model.

5. The method for controlling orderly charging of an electric vehicle according to claim 1, characterized in that: The Pareto genetic algorithm is used to perform multi-objective optimization on the DC / DC conversion circuit loss model.

6. The method for controlling orderly charging of an electric vehicle according to claim 1, characterized in that: The optimal charging efficiency of the charging pile is calculated according to the following formula: （12） In formula (12), To achieve the best charging efficiency for charging piles, It is the first optimal charging efficiency of the PFC circuit under input power, It is the second best charging efficiency of the DC / DC conversion circuit under input power.

7. The method for controlling orderly charging of an electric vehicle according to claim 1, characterized in that: The objective function of the orderly charging control model is: （13） （14） Where, is the grid load fluctuation rate, for The basic load of the power grid during the period; for The charging load of all electric vehicles in the area where the charging pile is located during the time period; is the average load of the power grid within 24 hours; The constraints are: （15） （16） （17） （18） （19） （20） In formula (15), is the number of electric vehicles connected to all charging piles in the area at time t, for Moment The charging load of electric vehicles, for The upper limit of the capacity allocated by the power grid to the charging pile area at the moment; In formula (16), for Time period Charging power of electric vehicles, and They are Time period The maximum and minimum values ​​of the charging power of electric vehicles; In formula (17), for Time period for the The output power limit of the charging pile for charging electric vehicles is for Time period The upper limit of charging power that electric vehicle batteries can withstand. for Time period for the The power limit of the cable used to charge an electric vehicle; In formula (18), For the The rated capacity of the electric vehicle battery; For the The battery capacity of an electric vehicle at the end of charging; in formula (19), For the Electric vehicles in Battery capacity during the period, For charging efficiency, for At the beginning of the period The starting capacity of an electric vehicle battery, is the time step of the charging process; in formula (20), For the The user behavior of electric vehicles is represented by 0, which indicates participation in orderly charging and 1 indicates non-participation in orderly charging.

8. An orderly charging control system for electric vehicles, characterized in that: include: The first loss model establishment module is used to model the power loss of the PFC circuit of the DC charging pile to obtain a PFC circuit loss model; The second loss model establishment module is used to model the power loss of the DC / DC conversion circuit of the DC charging pile to obtain a DC / DC conversion circuit loss model; a first optimal efficiency solving module, performing a multi-objective optimization solution on the PFC circuit loss model to obtain a minimum loss of the PFC circuit at the input power, and calculating a first optimal charging efficiency of the PFC circuit at the input power based on the minimum loss; a second optimal efficiency solving module, performing a multi-objective optimization solution on the DC / DC conversion circuit loss model to obtain a minimum loss of the DC / DC conversion circuit under the input power, and calculating a second optimal charging efficiency of the DC / DC conversion circuit under the input power based on the minimum loss; a charging pile optimal efficiency solving module, solving the optimal charging efficiency of the charging pile according to the first optimal charging efficiency and the second optimal charging efficiency; An acquisition module is used to acquire an orderly charging control model constructed with minimizing grid load fluctuation as the objective function and grid dispatching capacity, electric vehicle battery power, charging pile charging power, and user behavior as constraints; the charging pile charging power is associated with the charging pile charging efficiency; An objective function solving module, configured to solve the orderly charging control model based on the optimal charging efficiency of the charging pile to obtain the charging period and power of the electric vehicle; The PFC circuit loss model is: （1） （2） （3） （4） （5） In formula (1), is the conduction loss of MOSFET, is the effective value of the MOSFET current, is the on-state resistance of MOSFET; in formula (2), is the switching loss, is the PFC circuit output voltage, is the load current, is the current flowing through the MOSFET channel, The time required for the MOSFET drive voltage to rise from the turn-on threshold to the Miller platform, is the time required for the MOSFET drain-source voltage to drop to 0, is the time required for the MOSFET drain-source voltage to rise from 0 to the steady-state stress value, The time required for the MOSFET channel current to drop from the steady-state stress value to 0; In formula (3), is the conduction loss of the diode, is the average diode current, is the diode forward voltage drop, is the effective value of the diode current, is the dynamic resistance of the diode; in formula (4), is the copper loss of the inductor, is the effective value of the inductor current, is the DC resistance of the inductor winding; in formula (5), is the iron loss of the inductor, is the effective volume of the core, 、 、 is the Steimetz parameter, is the power frequency, is the peak flux density, N is the number of segments after digital quantization of a switching cycle, and Respectively The change in magnetic flux density in each section and the The duration of a segment, is the magnetic linkage angle; The DC / DC conversion circuit loss model is: （6） （7） （8） （9） （10） （11） In formula (6), is the conduction loss of the semiconductor device, Compared with the inward shift, is compared with the outward shift; in formula (7), is the switching loss, is the proportional coefficient; in formula (8), is the total loss of the magnetic core device; in formula (9), is the transformer turns ratio, is the output voltage of the charging pile, Switching cycle of dual active bridge DC / DC converter circuit; is the inductance, is the forward conduction voltage drop of the diode, is the saturation voltage drop of IGBT; in formula (10), is the input voltage of the dual active bridge DC / DC conversion circuit, and are the turn-on time and turn-off time of IGBT respectively; in formula (11), is the transformer turns ratio, is the output voltage of the dual active bridge DC / DC conversion circuit, is the transformer winding resistance, is the winding resistance of the auxiliary inductor, is the switching frequency, is the vacuum permeability, is the air gap size, is the number of winding turns, is the effective volume of the core, is the iron loss coefficient.

Images