A soc equalization control method for energy storage and electric vehicles in a direct current microgrid

Abstract

This invention relates to a SOC equalization control method for energy storage and electric vehicles in a DC microgrid. The method includes: (1) collecting current, voltage, and state of charge (SOC) parameters of the energy storage unit and the electric vehicle, and performing low-pass filtering; (2) calculating the SOC deviation and generating a dynamic nonlinear sensitivity factor based on the maximum deviation; (3) generating a voltage regulation amount including bus voltage secondary recovery compensation based on the deviation and sensitivity factor, eliminating steady-state voltage drop and driving SOC equalization; (4) introducing a hysteresis comparison mechanism to determine the net power flow direction, and adaptively switching the charging, discharging, and exit modes of the electric vehicle based on the SOC threshold and power state; (5) performing coordinated power allocation according to the determined mode; and (6) performing multi-constraint processing and closed-loop control. This invention improves the system's operational stability and SOC equalization performance by constructing a coordinated mechanism that considers power hysteresis sensing and electric vehicle SOC constraints.

Description

Technical Field

[0001] This invention relates to the field of DC microgrid control technology, specifically to a SOC equalization control method for the coordinated operation of energy storage and electric vehicles in a DC microgrid. Background Technology

[0002] As the penetration rate of renewable energy in DC microgrids continues to increase, distributed energy storage units (DESUs) play a crucial role in mitigating power fluctuations from the source and load and maintaining stable bus voltage. However, due to differences in initial state of charge (SOC), feeder impedance mismatch, and varying operating conditions, parallel energy storage units are prone to SOC inconsistencies, which can lead to overcharging or over-discharging of some units, seriously threatening the system's operational lifespan and overall stability.

[0003] Existing SOC balancing strategies are mostly limited to internal regulation within energy storage units, achieving power redistribution by adjusting the droop coefficient or introducing error compensation. These methods are effective when the system power is sufficient, but when the overall SOC of the energy storage system is low or faces a persistent power deficit, relying solely on the energy storage's own regulation is insufficient to simultaneously address voltage support and SOC recovery. Furthermore, electric vehicles (EVs), as mobile flexible resources with energy storage characteristics, possess significant potential for collaborative operation in isolated microgrids. However, current research lacks a control scheme that can suppress energy measurement noise, smoothly switch modes, and achieve adaptive switching between charging, discharging, and decommissioning modes for EVs while simultaneously coordinating with energy storage SOC balancing.

[0004] Therefore, there is an urgent need to propose a collaborative control method that takes into account hysteresis sensing and dynamic sensitivity adjustment, so as to achieve adaptive allocation of multi-source energy and efficient SOC balance while ensuring the power quality of the system. Summary of the Invention

[0005] This invention aims to provide a SOC equalization control method for the coordinated operation of energy storage and electric vehicles in a DC microgrid, in order to solve the technical problems of equalization speed lag, mode switching oscillation and insufficient support capacity for low SOC in existing solutions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A SOC (State of Charge) equalization control method for coordinated energy storage and electric vehicles in a DC microgrid includes the following steps:

[0008] S1. State Awareness and Preprocessing: Collect the SOC, current and voltage parameters of distributed energy storage units and electric vehicles, perform low-pass filtering, and calculate the SOC consistency error between energy storage units.

[0009] S2. Dynamic voltage regulation generation: Based on the SOC consistency error, a dynamic nonlinear sensitivity factor is generated in real time, and the voltage regulation including the bus voltage secondary recovery compensation term is calculated accordingly.

[0010] S3. Power State Hysteresis Determination: A hysteresis comparison mechanism is introduced to determine the net power flow between the photovoltaic output current and the load current. Combined with the SOC threshold and system power state constraints, the mode of electric vehicle participating in discharge support, participating in charging, and exiting operation is adaptively determined.

[0011] S4. Cooperative power distribution regulation: Based on the electric vehicle's operating mode and the real-time SOC status of the energy storage unit, a reference voltage command is generated to achieve adaptive power distribution and SOC balance regulation of multiple sources.

[0012] S5. Operational Constraints and Closed-Loop Execution: Implement safety constraints on the voltage, current, and SOC of each unit in the system, and execute closed-loop control to maintain stable system operation and promote high-precision convergence of SOC.

[0013] Furthermore, the technical features of the present invention also include:

[0014] Power status determination: By setting a current hysteresis threshold, when the net current exceeds the positive / negative threshold, the system is determined to be in a state of power surplus or deficit, effectively suppressing frequent mode switching caused by measurement noise.

[0015] EV mode adaptive switching:

[0016] When the system is in short supply and the average SOC of the energy storage is below the threshold, while the SOC of the EV is above the lower limit, the EV enters the discharge support mode.

[0017] When the system has excess capacity and the EV's SOC is below the upper limit, the EV enters charging mode.

[0018] When the EV does not meet the operating conditions or reaches the SOC boundary, the EV can smoothly exit the system through the integral reset mechanism.

[0019] Safety constraint mechanism: A unified SOC operating range is set. When the boundary is reached, the voltage regulation is forcibly corrected to stop charging and discharging, and the output current is limited.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] (1) By introducing a dynamic nonlinear sensitivity factor, the impact suppression in the early stage of equilibrium and the rapid convergence in the late stage are taken into account, thereby improving the SOC equilibrium efficiency.

[0022] (2) By utilizing the hysteresis sensing mechanism and the adaptive access / exit strategy of electric vehicles, the system's response robustness under source load fluctuations is enhanced, and zero-crossing oscillations are eliminated.

[0023] (3) By compensating for the droop voltage drop through the voltage secondary recovery term, the high steady-state accuracy of the bus voltage is ensured while achieving coordinated energy distribution.

[0024] (4) It realizes deep collaboration between energy storage units and mobile EV batteries, which significantly improves the power supply reliability and engineering practical value of isolated DC microgrids. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the system structure provided in an embodiment of the present invention;

[0026] Figure 2 This is a flowchart of the overall SOC equalization control provided in an embodiment of the present invention;

[0027] Figure 3 This is a logic diagram for determining the operating mode of an electric vehicle considering hysteresis perception, provided in an embodiment of the present invention.

[0028] Figure 4 The dynamic response curves of SOC and current of each unit under the V2G support scenario in working condition 1;

[0029] Figure 5 The curves show the changes in SOC and current of each unit under the two-condition power adaptive redistribution scenario. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0031] This invention proposes a SOC (State of Charge) balancing control method for coordinated energy storage and electric vehicles in a DC microgrid, applicable to DC microgrid systems including DESUs, EVs, photovoltaics, and local loads. Each unit is connected to the DC bus via a power converter, and power distribution and SOC balancing are achieved through the coordinated control method described in this invention.

[0032] I. Overall Control Method Flow

[0033] like Figure 2 As shown, the control method of the present invention specifically includes the following steps:

[0034] S1: Status Acquisition and Preprocessing

[0035] Real-time operating status parameters of each energy storage unit and electric vehicle are collected, including the state of charge (SOC) of each energy storage unit. i Electric vehicle state of charge (SOC) EV Output current I of each unit i and DC bus voltage U bus .

[0036] Signal filtering: The sampled signal is smoothed using a first-order low-pass filter. The calculation formula is as follows:

[0037]

[0038] Among them, X filt X is the filtered output value at the current time. meas X represents the sensor's raw measurement value at the current moment; filt_prev This is the feedback of the output result of the filter in the previous calculation cycle; α is the filter coefficient, which ranges from 0 to 1 and is used to reduce measurement noise.

[0039] Error calculation: Based on the filtered data, calculate the SOC consistency error between energy storage units.

[0040] S2: Dynamic nonlinear sensitivity factor and voltage regulation generation

[0041] The average SOC of the energy storage units participating in operation is calculated using the following expression:

[0042]

[0043] Among them, SOC i Let N represent the state of charge of the i-th energy storage unit, and N be the number of energy storage units.

[0044] Furthermore, based on the difference between the SOC of each energy storage unit and the average SOC, the SOC deviation is calculated:

[0045]

[0046] Wherein, ΔSOC i Let be the SOC deviation of the i-th energy storage unit.

[0047] A dynamic nonlinear sensitivity factor is introduced, which is based on the system's maximum SOC deviation Δ|SOC| max Adaptive adjustment, its expression is:

[0048]

[0049] Where, k minand k max , where are the upper and lower limits of the adjustment coefficient, and τ is the decay constant.

[0050] Furthermore, in the process of generating the voltage regulation, an arctangent function is introduced to construct a nonlinear regulation relationship:

[0051]

[0052] Where n is the convergence adjustment factor; U rec The bus voltage secondary recovery compensation amount is calculated by the voltage PI regulator based on the deviation between the rated voltage and the measured voltage, and is used to compensate for the steady-state voltage deviation caused by droop control.

[0053] The arctangent function exhibits output saturation characteristics when the SOC deviation is large, thereby avoiding current surges caused by excessive voltage regulation; when the SOC deviation is small, it changes approximately linearly, improving regulation sensitivity and thus accelerating the SOC convergence process.

[0054] The dynamic nonlinear sensitivity factor and the arctangent nonlinear function together constitute a hierarchical adjustment structure, wherein the dynamic sensitivity factor is used for overall adjustment intensity control, and the arctangent function is used for local nonlinear adjustment.

[0055] S3: System power state and mode determination considering hysteresis sensing

[0056] like Figure 3 As shown, a hysteresis comparison mechanism is introduced to determine the system power state in order to suppress frequent oscillations near the power zero-crossing point:

[0057] Power Status Determination: Define System Net Current:

[0058]

[0059] When I net >I hys When this occurs, it is determined to be a state of excess power.

[0060] When I net <-I hys When this occurs, it is determined to be a power deficit state. Among them, I... hys The hysteresis threshold is set to 0.5A.

[0061] EV operating mode determined:

[0062] Discharge mode: When the system is in a power deficit state and the average SOC of the energy storage is lower than the set threshold SOC. th Meanwhile, SOC EV SOC min At that time, the EV participates in the discharge support.

[0063] Charging mode: When the system has excess power and the SOC is high. EV <SOC max At that time, EVs participate in charging and energy consumption.

[0064] Exit Mode: When the EV reaches the SOC boundary, the system power state changes, or the EV is disconnected, the EV exits system operation. An integral reset operation is performed immediately upon exit to ensure a smooth voltage switching process.

[0065] S4: Cooperative Power Allocation and Reference Value Synthesis

[0066] The reference value U of the output voltage of each energy storage unit is calculated based on the droop law. i :

[0067]

[0068] Among them, U ref R is the rated voltage of the DC bus. i The basic droop coefficient. Through ΔU i Real-time adjustment allows cells with higher SOC to bear more discharge load or less charging load, thereby achieving SOC balance.

[0069] S5: Run constraint processing

[0070] Implement triple security protection for the system:

[0071] SOC constraint: Set the operating range to 30%~80%, and forcibly stop charging and discharging when the boundary is reached.

[0072] Current constraint: Limit the output current I i ≤I limit To prevent battery overcurrent.

[0073] Voltage constraint: for U i Implement voltage limiting measures to ensure that the bus voltage fluctuates within a safe range.

[0074] S6: Closed-loop execution, which sends the processed reference value to the underlying PWM controller to drive the power converter, thereby realizing the closed-loop feedback control of the system. Example Description

[0075] To shorten simulation time and verify the effectiveness of the control strategy, the embodiment uses small-capacity energy storage parameters for simulation verification.

[0076] This embodiment constructs a DC microgrid model containing two sets of energy storage units and one set of electric vehicles. The rated bus voltage is 750V, the energy storage capacity is 5Ah, and the electric vehicle capacity is 2Ah.

[0077] Operating Scenario 1: Emergency Support and Exit Verification of EVs under Extreme Shortages

[0078] Initial state: Energy storage SOC is 25% and 20% respectively, in low battery warning state; EV SOC is 80%.

[0079] Process Description: The system net current is negative. The EV detects the weakness of the energy storage system and enters discharge mode (V2G), outputting current to support the bus voltage.

[0080] Equalization effect: such as Figure 4 As shown, under coordinated control, the energy storage unit 2 with a lower SOC obtains a larger charging ratio, and the two SOC curves quickly converge.

[0081] Exit Response: After running for 200 seconds, the EV exited the system. Thanks to the introduction of the integral reset pre-synchronization mechanism, the bus voltage recovered quickly after only a slight fluctuation, and the energy storage unit smoothly took over the remaining load, verifying the robustness of plug-and-play.

[0082] Operating Condition 2: Collaborative Charging Response under Power Surplus

[0083] Initial state: Energy storage SOC is 75% and 70% respectively; EV SOC is 40%.

[0084] Process description: The photovoltaic output exceeds the load. The EV switches to charging mode to absorb the surplus power.

[0085] Equalization effect: such as Figure 5 As shown, the energy storage unit adaptively adjusts its charging power according to its own SOC difference, achieving high-precision SOC balance and bus voltage stability throughout the entire cycle.

[0086] In summary, this invention significantly improves the stability and collaborative efficiency of islanded DC microgrids under complex operating conditions by constructing a collaborative mechanism that considers hysteresis sensing, dynamic sensitivity adjustment, and multi-dimensional SOC constraints.

[0087] In other embodiments of the present invention, the number of energy storage units, the electric vehicle access method, and the system operating parameters can all be adjusted according to actual application requirements, and the present invention does not limit them.

Claims

1. A SOC (State of Charge) equalization control method for coordinated energy storage and electric vehicles in a DC microgrid, characterized in that, Includes the following steps: (1) Collect the state of charge (SOC), output current and DC bus voltage of each energy storage unit and electric vehicle (EV), and perform low-pass filtering on the sampled signals; (2) Calculate the average SOC of the energy storage unit based on the SOC of each energy storage unit, and calculate the SOC deviation between the SOC of each energy storage unit and the average SOC. At the same time, generate a dynamic nonlinear sensitivity factor in real time based on the maximum SOC deviation. (3) Generate the corresponding voltage regulation amount based on the SOC deviation and dynamic nonlinear sensitivity factor. The voltage regulation amount includes the bus voltage secondary recovery compensation term. The voltage regulation amount is superimposed with the DC bus rated voltage to obtain the voltage reference value of each energy storage unit. (4) Calculate the output voltage of each energy storage unit based on the voltage reference value, the output current of the energy storage unit and the droop coefficient, and realize the power adaptive allocation adjustment based on SOC deviation and dynamic sensitivity. (5) Introduce a hysteresis comparison mechanism to determine the relationship between photovoltaic output current and load current in order to determine the system power state; (6) Based on the system power state, the average SOC of the energy storage unit and the SOC of the EV, the operating mode of the EV is adaptively determined. The operating mode includes three states: participating in discharge support, participating in charging and exiting operation. The switching of the operating mode is realized based on the joint constraints of the SOC threshold and the power state. (7) Adjust the output power of the energy storage unit and the EV in a coordinated manner according to the EV operating mode and the SOC state of the energy storage unit; (8) Constrain the SOC, voltage and current of the energy storage unit and EV, and perform closed-loop control based on the constrained control quantity to make the SOC of the energy storage unit gradually become consistent and maintain the stable operation of the system.

2. The SOC equalization control method according to claim 1, characterized in that: The method for calculating the average SOC of the energy storage unit in step (2) is as follows: Among them, SOC i Let N represent the state of charge of the i-th energy storage unit, and N be the number of energy storage units.

3. The method according to claim 1, characterized in that: In step (2), the dynamic nonlinear sensitivity factor is adaptively adjusted according to the change of the maximum SOC deviation of the system, so as to realize the change of adjustment intensity under different SOC deviation stages.

4. The SOC equalization control method according to claim 1, characterized in that: The voltage adjustment amount ΔU in the step (3) i The expression is: wherein k is a dynamic nonlinear sensitivity factor, f (·) is a nonlinear function, ΔU rec is a bus voltage secondary recovery compensation quantity for eliminating the steady-state voltage drop of droop control; the dynamic nonlinear sensitivity factor and the nonlinear function jointly constitute a hierarchical regulation structure; wherein the voltage regulation quantity amplitude is limited when the SOC deviation is large, and the regulation sensitivity is improved when the SOC deviation is small.

5. The SOC equalization control method according to claim 1, characterized in that: The reference value for the output voltage of the energy storage unit in step (4) is: Among them, U i U is the reference value for the output voltage of the i-th energy storage unit. ref I is the reference value for the DC bus voltage. i R is the output current of the energy storage unit. i This is the droop coefficient.

6. The SOC equalization control method according to claim 1, characterized in that: In step (5), a current buffer is set through a hysteresis comparison mechanism to filter out sensor measurement noise and small fluctuations near the power zero point, thereby preventing frequent switching of EV operating modes.

7. The SOC equalization control method according to claim 1, characterized in that: The condition for EV to participate in discharge in step (6) is: when the photovoltaic output current I pv Less than the load current I load The system is in a power deficit state, and the average SOC of the energy storage units is lower than the set threshold SOC. th Meanwhile, the SOC of the EV is greater than the minimum SOC limit. min At that time, the EV is controlled to participate in the discharge.

8. The SOC equalization control method according to claim 1, characterized in that: The conditions for the EV to participate in charging in step (6) are as follows: When the photovoltaic output current I pv Greater than the load current I load The system is in a state of power surplus, and the EV's SOC is lower than the maximum SOC limit. max At that time, control the EV to participate in charging.

9. The SOC equalization control method according to claim 1, characterized in that: In step (6), when the EV exits the operating state, the integral saturation is eliminated by forcibly resetting the controller integral term, so as to realize the energy storage unit's disturbanceless takeover of the system power deficit and the EV smoothly exits.

10. The SOC equalization control method according to claim 1, characterized in that: The constraint processing of the energy storage unit and EV in step (8) includes: Set upper and lower limits for the State of Charge (SOC) of the energy storage unit and the EV to ensure that they meet the following requirements: And its output current is limited to meet the following requirements: The participation and withdrawal of EVs do not change the continuity of the SOC equalization control process of the energy storage unit, thus realizing the coordinated operation of the energy storage unit and the electric EV.

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