Charge state balance control method applied to island light storage and charging integrated charging station comprising multiple distributed energy storage units

Abstract

The invention provides a state-of-charge balance control method applied to an island light storage and charging integrated charging station comprising a plurality of distributed energy storage units, and aims to solve the problem of state-of-charge balance of energy storage batteries in the charging and discharging process of the light storage and charging integrated charging station comprising the plurality of energy storage units. The invention provides a preset performance control method based on multiple agents, and the method combines charge state balance control with a finite time preset performance function, adaptively adjusts the power output of each energy storage unit, and aims to realize that the charge state difference between the energy storage units is quickly converged to a preset performance boundary within a set time. And over-charging and over-discharging of a single energy storage module are prevented. In addition, the adopted control algorithm collects the real-time state information of the distributed energy storage system, so that the communication line pressure when the local node exchanges information with the neighbor node is greatly reduced. A simulation experiment verifies that the strategy provided by the invention can realize rapid charge state equalization and accurate output current distribution.

Description

Technical Field

[0001] This invention belongs to the field of microgrid control technology, and more specifically, relates to a state-of-charge control method for an islanded photovoltaic-storage-charging integrated charging station containing multiple distributed energy storage units. Background Technology

[0002] In recent years, facing the dual pressures of ever-increasing demand for renewable energy and environmental protection, the automotive industry has received widespread attention for electric vehicles, which replace traditional fossil fuels with low-carbon, environmentally friendly, clean, and efficient electricity. As a crucial supporting infrastructure, the construction and energy management of charging stations play a vital role in the development of electric vehicles. Integrated photovoltaic-storage-charging stations use solar energy as their primary energy source, efficiently utilizing the electricity converted from photovoltaic cells through maximum power point tracking to maintain the normal operation of the charging station's load. They also charge the energy storage batteries via electrical busbars, ensuring the release of energy from the batteries at night or when sunlight is insufficient, enabling island-like operation of the charging station. This is an effective way to solve the power supply problems for electric vehicles in remote areas such as mountainous regions and islands. However, due to the randomness and fluctuation of the energy source and load of charging stations, the state of charge of the energy storage batteries exhibits an inconsistent trend. Continuous operation of the energy storage batteries under inconsistent charge states may lead to overcharging and over-discharging, significantly reducing the lifespan of the energy storage batteries and ultimately disrupting the stable operation of the charging station.

[0003] In practical applications, the construction of integrated photovoltaic-storage-charging stations is mostly carried out within the context of distribution network access. This is because there is relatively little research on new energy charging stations that build isolated microgrids in remote areas and achieve energy self-sufficiency. Generally, this involves connecting multiple distributed energy storage units in parallel to a bus, each charging and discharging to the bus. During this process, the continuous operation of energy storage units under inconsistent states of charge can lead to overcharging and over-discharging, significantly reducing the lifespan of the energy storage batteries. Furthermore, this inconsistency may prematurely cause some energy storage units to go offline, disrupting the stable operation of the DC microgrid. Therefore, state-of-charge (SOC) balance control is crucial for managing charging stations containing numerous energy storage units. A multi-intelligent collaborative control method is adopted, which, by introducing a preset performance function and error transformation, rapidly converges the difference in SOC between the various energy storage batteries to the target value, while simultaneously improving the transient and steady-state performance of the system.

[0004] This invention aims to address the technical problems of slow convergence speed, insufficient control accuracy, and inadequate system reliability in existing distributed energy storage systems for photovoltaic-storage-charging microgrids. This invention provides a novel distributed control strategy that can achieve rapid and accurate consistency of the state of charge (SOC) of each energy storage unit within a finite time, while effectively reducing the communication and computational burden on the system. Summary of the Invention

[0005] To address the problems of large fluctuations in controller output amplitude and long convergence time in existing technologies, which make it difficult to meet the requirements for safe and efficient operation of charging stations, this application aims to propose a state-of-charge (SOC) balancing control method for isolated photovoltaic-energy storage-charging integrated charging stations containing multiple distributed energy storage units, including:

[0006] S1: Construct a dynamic model of the state of charge of an integrated photovoltaic-storage-charging station;

[0007] S2: Treat each distributed energy storage unit in the microgrid as an intelligent agent, and construct a second-order multi-agent system model with the state-of-charge value and output current of each unit as state variables.

[0008] S3: For each agent, define the neighborhood consistency error between its own state of charge and the state of charge of neighboring agents in the communication network. Crucially, design a finite-time preset performance function for this neighborhood error, and use this function for error transformation;

[0009] S4: Design a preset performance controller. Adjust the duty cycle of the converter according to the calculated control input signal to dynamically adjust the charging and discharging current of each energy storage unit, thereby changing its state of charge value so that they are all accurately consistent with the set value. Finally, perform convergence analysis on the system.

[0010] Step S1 includes:

[0011] The charge of the energy storage battery is estimated using the Coulomb counting method, and the battery state of charge is calculated using the ampere-hour integral method; further differentiation yields its dynamic model:

[0012] In equation (1): i = 1, 2, 3, ..., N are the module numbers of the energy storage units, S i Q i These represent the state of charge (SOC) value of the i-th energy storage unit module and the capacity of the energy storage battery, respectively; I bi This refers to the charging and discharging current of the energy storage battery; I is the current during battery discharge. bi >0, I during battery charging bi <0.

[0013] Step S2 includes:

[0014] Establish a dynamic model of the output current of the energy storage unit:

[0015]

[0016] Based on this design, control input controller u i :

[0017]

[0018] In (2) and (3), I oi is the current from the converter to the bus, R i is the line impedance, and U busi is the bus voltage.

[0019] Let the state variable x i1 = S i and x i2 = -I bi / Q i Then, for the i-th energy storage unit, a second-order multi-agent system model with the state of charge and current as variables can be constructed as follows:

[0020]

[0021] where ρ i is a real number that can be positive or negative, determined by system parameters such as line impedance and bus voltage, and is used to characterize the charging and discharging conditions of the energy storage battery in agent i.

[0022] The step S3 includes:

[0023] A preset performance function is proposed:

[0024]

[0025] β i and 0 < c i < 1 are the designed positive constants; m is a design parameter related to the error convergence rate, and 0 < T i < ∞ is the time when the system is set to reach stability. The consistency of the i-th energy storage unit is defined as:

[0026] x j1 is the state variable of the j-th energy storage unit, S avg is the set reference value of the state of charge, and the following form of error conversion is defined:

[0027]

[0028] where F t is the defined error conversion function, and the converted error is ξ(t). The function F t is smooth and strictly increasing, satisfying -l(t) < Ft < l(t). Select The error transformation function F t (ξ(t), l(t)) is defined as:

[0029]

[0030] The error is transformed using an error transformation function to obtain the transformed neighborhood error:

[0031]

[0032] Differentiating ξ(t) yields:

[0033]

[0034] Step S4 includes:

[0035] To avoid the differential explosion phenomenon during the iterative differentiation process of the virtual controller, a finite-time differentiator is used to solve this problem. This paper designs a finite-time differentiator in the following form:

[0036]

[0037] Where π iq,1 and π iq,1 Let k1 and k2 represent the state variables of the differentiator, respectively, where k1 and k2 > 0 are the parameters of the differentiator. S41, Construct the Lyapunov function:

[0038] Differentiation yields:

[0039]

[0040] in Design a virtual controller α i1 :

[0041]

[0042] Where c i1 Given a positive constant z, design z = z + z. i2 =x i2 -α i1 Substituting this into equation (13) along with the virtual controller, we get:

[0043]

[0044] S42, another Lyapunov function is constructed:

[0045] Differentiation yields:

[0046]

[0047] in For an unknown positive constant M i The estimated value.

[0048] S43, the controller design is as follows:

[0049]

[0050] Where c i2 Let ε(t) be a positive function with constant constants, satisfying the following conditions:

[0051] S44, convergence analysis is performed using the properties of the boundedness theorem;

[0052] Substituting the controller into equation (17) yields:

[0053]

[0054] According to the principle of the finite-time differentiator, it can be known that... Let the parameter update rate Substituting into equation (19), we get:

[0055]

[0056] Scaling equation (19) and integrating both sides, we get:

[0057]

[0058] in It is bounded, and according to Barbalat's lemma, we can deduce that... According to ξ i1 From the formula, we can see that when At that time, it can be deduced Therefore, it can be concluded that This ensures that the state of charge of the energy storage batteries in each energy storage unit of the charging station is consistent and tracked to the set reference value.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] 1. The preset performance tracking control method based on the event triggering mechanism proposed in this invention not only ensures that error tracking meets the preset performance indicators, but also has a faster convergence speed.

[0061] 2. Compared with the performance function with a constant decay rate used in traditional preset performance control, this invention proposes a performance function with a time-varying decay rate, which effectively solves the over-control problem that occurs when the initial value of the tracking error is large, and the convergence speed is faster when the error is large. Attached Figure Description

[0062] Figure 1 A step diagram illustrating the state-of-charge (SOC) balancing control method for an islanded photovoltaic-energy storage-charging integrated charging station containing multiple distributed energy storage units.

[0063] Figure 2 This is a schematic diagram of the structural model of the integrated photovoltaic, energy storage and charging station of the present invention.

[0064] Figure 3 This is a schematic diagram of the circuit model of the bidirectional DC / DC converter of the present invention.

[0065] Figure 4 This is a flowchart of the distributed control method of the present invention.

[0066] Figure 5 The figures show the state-of-charge curves and neighborhood error curves of each energy storage unit obtained from simulation under low-charge conditions in this invention.

[0067] Figure 6 The figures show the state-of-charge curves and neighborhood error curves of each energy storage unit obtained from simulation under high-charge conditions in this invention. Detailed Implementation

[0068] The present invention will be further described in detail below with reference to the embodiments and figures, but the embodiments of the present invention are not limited thereto.

[0069] like Figure 2 Structural model diagram of an integrated photovoltaic, energy storage and charging station

[0070] A method for state-of-charge balancing control applied to an islanded photovoltaic-energy storage-charging integrated charging station containing multiple distributed energy storage units, characterized in that it includes:

[0071] S1: Construct a dynamic model of the state of charge of an integrated photovoltaic-storage-charging station;

[0072] S2: Treat each distributed energy storage unit in the microgrid as an intelligent agent and construct a second-order multi-agent system model with the state of charge value and output current of each unit as state variables.

[0073] S3: For each agent, define the neighborhood consistency error between its own state of charge and the state of charge of neighboring agents in the communication network. Crucially, design a finite-time preset performance function for this neighborhood error, and use this function for error transformation;

[0074] S4: Design a preset performance controller. Adjust the duty cycle of the converter according to the calculated control input signal to dynamically adjust the charging and discharging current of each energy storage unit, thereby changing its state of charge value so that they are all accurately consistent with the set value. Finally, perform convergence analysis on the system.

[0075] Step S1 includes:

[0076] The charge of the energy storage battery is estimated using the Coulomb counting method, and the battery state of charge is calculated using the ampere-hour integral method; further differentiation yields its dynamic model:

[0077]

[0078] In equation (1): i = 1, 2, 3, ..., N are the module numbers of the energy storage units, S i Q i These represent the state of charge (SOC) value of the i-th energy storage unit module and the capacity of the energy storage battery, respectively; I bi This refers to the charging and discharging current of the energy storage battery; I is the current during battery discharge. bi >0, I during battery charging bi <0.

[0079] Step S2 includes:

[0080] Establish a dynamic model of the output current of the energy storage unit:

[0081]

[0082] Based on this design, control input controller u i :

[0083]

[0084] In (2) and (3), I oi R is the current input from the converter to the bus. i U is the line impedance. busi This is the bus voltage.

[0085] Let the state variable x i1 =Si,x i2 = -I bi / Q i For the i-th energy storage unit, a second-order multi-agent system model can be constructed as follows, with state of charge and current as variables:

[0086]

[0087] Where ρ is i A real number that can be positive or negative, determined by system parameters such as line impedance and bus voltage, is used to characterize the charging and discharging status of the energy storage battery in agent i.

[0088] Step S3 includes:

[0089] A pre-defined performance function is proposed:

[0090]

[0091] β i and 0 < c i <1 is the designed positive constant; m is the design parameter related to the error convergence rate, 0 < Ti <∞ is the time when the system reaches stability. The consistency of the i-th energy storage unit is defined as:

[0092] x j1 The state variable of the j-th energy storage unit, S avg is the set reference value of the state of charge. Define the following form of error transformation:

[0093]

[0094] where F t is the defined error transformation function, and the transformed error is ξ(t). The function F t is smooth and strictly increasing, satisfying -l(t) < Ft < l(t). Select The error transformation function F t (ξ(t), l(t)) is defined as:

[0095]

[0096] Use the error transformation function to perform error transformation to obtain the transformed neighborhood error:

[0097] Deriving ξ(t) gives:

[0098]

[0099] The step S4 includes:

[0100] To avoid the phenomenon of differential explosion during the repeated derivation of the virtual controller, a finite-time differentiator in is used to solve this problem. In this paper, the finite-time differentiator is designed in the following form:

[0101]

[0102] where π iq,1 and π iq,1 respectively represent the state variables of the differentiator, and k1, k2 > 0 are the parameters of the differentiator.

[0103] S41, construct the Lyapunov function:

[0104]

[0105] Deriving gives:

[0106]

[0107] Among them, design the virtual controller:

[0108]

[0109] Where is a positive constant, and by substituting it back into formula (13) along with the virtual controller, we can obtain:

[0110]

[0111] S42, another Lyapunov function is constructed:

[0112]

[0113] Differentiation yields:

[0114]

[0115] in For an unknown positive constant M i The estimated value.

[0116] S43, the controller design is as follows:

[0117]

[0118] Where c i2 Let ε(t) be a positive function with constant constants, satisfying the following conditions:

[0119] S44, convergence analysis is performed using the properties of the boundedness theorem;

[0120] Substituting the controller into equation (17) yields:

[0121]

[0122] According to the principle of the finite-time differentiator, it can be known that... Let the parameter update rate Substituting into equation (19), we get:

[0123]

[0124] Scaling equation (19) and integrating both sides, we get:

[0125]

[0126] in It is bounded, and according to Barbalat's lemma, we can deduce that... According to ξ i1 From the formula, we can see that when At that time, it can be deduced Therefore, it can be concluded that This ensures that the state of charge of the energy storage batteries in each energy storage unit of the charging station is consistent and tracked to the set reference value.

[0127] Figure 3 This is a schematic diagram of the circuit model of the bidirectional DC / DC converter of the energy storage unit in an embodiment of the present invention.

[0128] Each energy storage unit's power switch can operate in a complementary conduction mode, with alternating conduction achieved by controlling the output PWM signal through an internal controller. When Qi1 is off and Qi2 is on, as shown by the blue path, the energy storage unit can charge or discharge from the DC bus; when Qi1 is on and Qi2 is off, as shown by the red path, the energy storage system forms an inner loop for charging or discharging, and there is no power exchange with the DC bus system.

[0129] Figure 4 This is a distributed control flowchart in an embodiment of the present invention.

[0130] First, a performance conversion is preset for consistency errors; second, a finite-time differentiator is added to simplify the calculation; finally, a virtual controller and a distributed controller are designed.

[0131] Figure 5 This is a simulation curve of the low charge state operating condition in this invention. Figure 6 This is a simulation curve of the high-charge state in this invention.

[0132] The text describes the state-of-charge (POC) curves of each energy storage unit and the neighborhood error control process under low and high POC conditions. It shows that the proposed method exhibits good convergence under both low and high POC conditions, quickly converging to the set reference value, demonstrating the effectiveness of the control method. Furthermore, the convergence speed is even faster when the error is large, meaning that energy storage units with higher POC bear more power supply responsibility. Therefore, energy loss on the resistance line is reduced, and the reduced charging and discharging between batteries increases battery life.

Claims

1. A method for state-of-charge balancing control applied to an islanded photovoltaic-energy storage-charging integrated charging station containing multiple distributed energy storage units, characterized in that, include: S1: Construct a dynamic model of the state of charge of an integrated photovoltaic-storage-charging station; S2: Treat each distributed energy storage unit in the microgrid as an intelligent agent and construct a second-order multi-agent system model with the state of charge value and output current of each unit as state variables. S3: For each agent, define the neighborhood consistency error between its own state of charge and the state of charge of neighboring agents in the communication network. Crucially, design a finite-time preset performance function for this neighborhood error, and use this function for error transformation; S4: Design a preset performance controller. Adjust the duty cycle of the converter according to the calculated control input signal to dynamically adjust the charging and discharging current of each energy storage unit, thereby changing its state of charge value so that they are all accurately consistent with the set value. Finally, perform convergence analysis on the system.

2. The state-of-charge balancing control method according to claim 1, applied to an islanded photovoltaic-energy storage-charging integrated charging station containing multiple distributed energy storage units, is characterized in that... Step S1 includes: The charge of the energy storage battery is estimated using the Coulomb counting method, and the battery state of charge is calculated using the ampere-hour integral method; further differentiation yields its dynamic model: In equation (1): i = 1, 2, 3, ..., N are the module numbers of the energy storage units, S i Q i These represent the state of charge (SOC) value of the i-th energy storage unit module and the capacity of the energy storage battery, respectively; I bi This refers to the charging and discharging current of the energy storage battery; I is the current during battery discharge. bi >0, I during battery charging bi <0.

3. The state-of-charge balancing control method according to claim 1, applied to an islanded photovoltaic-energy storage-charging integrated charging station containing multiple distributed energy storage units, is characterized in that... Step S2 includes: Establish a dynamic model of the output current of the energy storage unit: Based on this design, control input controller u i : In (2) and (3), I oi R is the current input from the converter to the bus. i U is the line impedance. busi This is the bus voltage. Let the state variable x i1 =Si,x i2 =-I bi / Q i For the i-th energy storage unit, a second-order multi-agent system model can be constructed as follows, with state of charge and current as variables: Where ρ is i A real number that can be positive or negative, determined by system parameters such as line impedance and bus voltage, is used to characterize the charging and discharging status of the energy storage battery in agent i.

4. The state-of-charge balancing control method according to claim 1 for an islanded photovoltaic-energy storage-charging integrated charging station containing multiple distributed energy storage units, characterized in that, Step S3 includes: A pre-defined performance function is proposed: β i and 0 <c i <1 is the designed positive constant; m is the design parameter related to the error convergence rate, 0 <T i <∞ represents the time it takes for the system to reach stability. The consistency of the i-th energy storage unit is defined as: x j1 The state variable of the j-th energy storage unit, S avg For the given state of charge reference value, the following error transformation is defined: where F t is a defined error conversion function, and the converted error is ξ(t). The function F t is smooth and strictly increasing, satisfying -l(t) < Ft < l(t). Select λ, h ∈ R + , and the error transformation function F t (ξ(t), l(t)) is defined as: The error is transformed using an error transformation function to obtain the transformed neighborhood error: Differentiating ξ(t) yields:

5. The state-of-charge balancing control method according to claim 1 for an islanded photovoltaic-energy storage-charging integrated charging station containing multiple distributed energy storage units, characterized in that, Step S4 includes: To avoid the differential explosion phenomenon during the iterative differentiation process of the virtual controller, a finite-time differentiator is used to solve this problem. This paper designs a finite-time differentiator in the following form: Where π iq,1 and π iq,1 These represent the state variables of the differentiator, and k1 and k2>0 are the parameters of the differentiator. S41, Construct the Lyapunov function: Differentiation yields: Among them, the design of the virtual controller: Where is a positive constant, and by substituting it back into formula (13) along with the virtual controller, we can obtain: S42, another Lyapunov function is constructed: Differentiation yields: in For an unknown positive constant M i The estimated value. S43, the controller design is as follows: Where c i2 Let ε(t) be a positive function with constant constants, satisfying the following conditions: S44, convergence analysis is performed using the properties of the boundedness theorem; Substituting the controller into equation (17) yields: According to the principle of the finite-time differentiator, it can be known that... Let the parameter update rate Substituting into equation (19), we get: Scaling equation (19) and integrating both sides, we get: in It is bounded, and according to Barbalat's lemma, we can deduce that... According to ξ i1 From the formula, we can see that when At that time, it can be deduced Therefore, it can be concluded that This ensures that the state of charge of the energy storage batteries in each energy storage unit of the charging station is consistent and tracked to the set reference value.

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