A method and device for power distribution of a hybrid energy storage system considering SOC constraints

Abstract

The application discloses a power distribution method and device of a hybrid energy storage system considering SOC constraints, and the method comprises the following steps: acquiring state information; the state information comprises system power demand, super capacitor state of charge and battery output power at the last moment; a power distribution cost function with super capacitor output power as an optimization variable is constructed according to the state information; the power distribution cost function comprises a battery dynamic inertia term, a super capacitor direction-aware state of charge constraint term, a super capacitor power regulation term, a state of charge prediction term and a super capacitor zero-power attraction term; the power distribution cost function is solved explicitly to obtain a super capacitor power reference value; and a battery power reference value is obtained according to the super capacitor power reference value.

Description

Technical Field

[0001] This invention relates to the field of power coordination control technology, and in particular to a power allocation method and apparatus for a hybrid energy storage system that takes into account SOC constraints. Background Technology

[0002] With the development of applications such as new energy power generation, grid frequency regulation, DC microgrids, and high-power pulse loads, energy storage systems need to simultaneously meet the requirements of energy support and rapid power response. While single-battery energy storage systems offer the advantage of high energy density, they are susceptible to significant charge / discharge power variations under frequent power fluctuations and high-power transient impacts, leading to increased battery polarization, thermal stress, and accelerated lifespan degradation. Supercapacitors, on the other hand, possess high power density, fast response speed, and long cycle life, making them suitable for handling short-term, high-frequency power fluctuations. Therefore, hybrid energy storage systems composed of batteries and supercapacitors can balance energy density and power density, demonstrating high application value in scenarios such as power smoothing, frequency regulation support, and transient power compensation.

[0003] In hybrid energy storage systems, the rational allocation of external power demand to batteries and supercapacitors is a key issue affecting system performance. Existing power allocation methods mainly include low-pass filtering, rule-based control, fuzzy control, and model predictive control. Low-pass filtering typically decomposes the power signal based on its frequency components, assigning low-frequency power to batteries and high-frequency power to supercapacitors. However, its filtering parameters are fixed, making it difficult to adaptively adjust based on the operating boundaries of the energy storage unit's SOC (State of Charge). While rule-based and fuzzy control methods are simple in structure, their control effectiveness relies on empirical parameters, making it difficult to simultaneously ensure smooth battery power delivery, supercapacitor SOC safety, and real-time system response performance. Model predictive control can optimize power allocation by establishing a predictive model and optimizing the objective function. However, traditional model predictive control usually requires online solving of quadratic programming or nonlinear optimization problems, resulting in high computational complexity, which is not conducive to implementation in millisecond-level control cycles or embedded controllers. Furthermore, while existing power allocation methods based on quadratic cost functions can typically suppress sudden changes in battery power, after the dynamic power support ends, the supercapacitor's output power tends to have a small residual value, making it difficult to converge quickly to zero. This results in the supercapacitor remaining in an unnecessary operating state for an extended period, affecting the functional division between the battery and the supercapacitor. Simultaneously, the supercapacitor's energy capacity is limited. When its SOC approaches its upper or lower limit, continuing to allocate power according to fixed rules or ordinary optimization weights may lead to overcharging or over-discharging risks. Existing methods for handling the supercapacitor's SOC boundary often employ simple limiting or fixed penalty terms, making it difficult to dynamically adjust the safety constraint strength according to the charging and discharging directions.

[0004] Therefore, a new technical solution is urgently needed to address the technical problem of how to rationally allocate external power demand to hybrid energy storage batteries and supercapacitors. Summary of the Invention

[0005] This invention provides a power allocation method and apparatus for a hybrid energy storage system considering SOC constraints, in order to solve the technical problem of how to rationally allocate external power demand to batteries and supercapacitors in hybrid energy storage.

[0006] To achieve the above objectives, the present invention provides a power allocation method for a hybrid energy storage system considering SOC constraints, comprising: Obtain state information; state information includes system power demand, supercapacitor state of charge and battery output power at the previous moment; construct a power allocation cost function with supercapacitor output power as the optimization variable based on the state information; The power allocation cost function includes a battery dynamic inertia term, a supercapacitor orientation sensing state of charge constraint term, a supercapacitor power adjustment term, a state of charge prediction term, and a supercapacitor zero power attraction term. The power allocation cost function is explicitly solved to obtain the supercapacitor power reference value. The battery power reference value is then obtained based on the supercapacitor power reference value.

[0007] Preferably, the battery dynamic inertia term includes: The battery dynamic inertia term is used to suppress sudden changes in battery output power and guide the supercapacitor to handle rapidly changing power; the change in battery output power is used as the basis for this. As a characterization of battery dynamic stress, the battery dynamic inertia term Represented as: ; ; in, This represents the battery output power at the current moment. This represents the battery output power at the previous moment; This is the battery dynamic inertia weighting coefficient.

[0008] Preferably, the supercapacitor orientation sensing state of charge constraint terms include: Dynamically select the state-of-charge safety boundary based on the power regulation direction to prevent overcharging or over-discharging of the supercapacitor, including: When the system power demand requires the supercapacitor to discharge, the distance between the supercapacitor's state of charge and its upper limit is used as the safety distance; when the system power demand requires the supercapacitor to charge and absorb power, the distance between the upper limit of the supercapacitor's state of charge and its upper limit is used as the safety distance. Dynamic state-of-charge constraint weights are constructed based on safety distances. Then the supercapacitor direction sensing state of charge constraint term Represented as: ; ; in, This represents the current output power of the supercapacitor. Based on the weighting coefficient, This is the state-of-charge constraint gain coefficient; For reference safety distance; To maintain a safe distance; To prevent positive numbers with a denominator of zero; N This is a constraint index.

[0009] Preferably, the supercapacitor power regulation includes: The supercapacitor power regulation term is used to constrain the output power amplitude of the supercapacitor, and is expressed as: ; in, This is for the supercapacitor power regulation item; This is the power adjustment weighting coefficient for supercapacitors.

[0010] Preferably, the state of charge prediction term includes: Constructing a one-step state-of-charge prediction model for supercapacitors: ; A state-of-charge prediction term is constructed based on the one-step state-of-charge prediction model for supercapacitors. : ; in, This represents the state of charge of the supercapacitor at the next moment. This represents the current state of charge of the supercapacitor. To control the cycle; This refers to the rated energy capacity of the supercapacitor. Weighting coefficients for one-step state of charge prediction; This is a reference value for the state of charge of a supercapacitor.

[0011] Preferably, the zero-power attraction term of the supercapacitor includes: The zero-power attraction term for supercapacitors is used to enhance the convergence trend of supercapacitor power near zero, causing the supercapacitor to tend towards a zero-power state after completing dynamic power support, thus actively exiting the regulation process. This is expressed as: ; in, This is the zero-power attraction term for supercapacitors; The zero-power attraction weighting coefficient for supercapacitors.

[0012] Preferably, the power allocation cost function includes: The power allocation cost function is composed of the sum of the battery dynamic inertia term, the supercapacitor orientation-sensing state-of-charge constraint term, the supercapacitor power adjustment term, the state-of-charge prediction term, and the supercapacitor zero-power attraction term, including: ; in, The power allocation cost function.

[0013] Preferably, the power allocation cost function is explicitly solved to obtain the supercapacitor power reference value, including: The battery dynamic inertia term, the supercapacitor orientation sensing state of charge constraint term, the supercapacitor power regulation term, and the state of charge prediction term are all summarized as follows: The quadratic expression; based on the quadratic expression and the zero-power attraction term of the supercapacitor, the power allocation cost function is rearranged into the first expression, including: Define the power gap at the current moment Intermediate quantity The difference between the current state of charge of the supercapacitor and its reference state of charge. , represented as: ; The first expression is then represented as: ; ; For the first expression, ignoring the absolute value term, we obtain the unconstrained analytical solution for the quadratic part of the supercapacitor power. : ; Based on the unconstrained analytical solution of the quadratic part and the zero-power attraction term of the supercapacitor, a soft thresholding process is performed to obtain the supercapacitor power reference value. : ; ; in, For the current system power requirements; A The coefficient of the quadratic term; B The coefficient of the linear term; D To and Irrelevant constant terms; This is the soft threshold coefficient.

[0014] Preferably, the battery power reference value obtained from the supercapacitor power reference value includes: Subtract the supercapacitor power reference value from the current system power demand to obtain the battery power reference value. : ; After obtaining the supercapacitor power reference value and the battery power reference value, a limiting process is performed, including: When the supercapacitor power reference value exceeds the supercapacitor's allowable power range, the supercapacitor power reference value will be limited to the allowable range. When the battery power reference value exceeds the battery's allowable power range, the battery power reference value is limited to the allowable range, and the remaining power is compensated by the supercapacitor.

[0015] The present invention also provides a power distribution device for a hybrid energy storage system that takes into account SOC constraints, the device being used to implement the method of the present invention.

[0016] The present invention has the following beneficial effects: This invention presents a power allocation method for hybrid energy storage systems considering State of Charge (SOC) constraints. It suppresses sudden power abrupt changes in battery output through a battery dynamic inertia term, allowing the battery to primarily bear relatively smooth power components, thus reducing the impact of high-frequency charging and discharging. A supercapacitor-oriented state of charge (SOC) constraint term dynamically selects the SOC safety boundary based on the charging and discharging direction, improving the supercapacitor's SOC boundary protection capability and reducing overcharging and over-discharging risks. A supercapacitor power regulation term limits excessive supercapacitor output. A SOC prediction term incorporates the impact of the current power allocation on the supercapacitor's SOC at the next moment into the optimization objective, giving the power allocation process model predictive control characteristics, which helps maintain the supercapacitor's SOC within a reasonable range. A supercapacitor zero-power attraction term enables the supercapacitor to approach a zero-power state after dynamic power support ends, reducing small residual power output and allowing the supercapacitor to actively exit the regulation process. The supercapacitor power reference value is obtained through explicit analytical solution, avoiding the computational burden of traditional online quadratic programming problems solved by MPC, thus improving the method's real-time performance and engineering feasibility. The method of the present invention can simultaneously take into account battery power smoothing, supercapacitor state of charge safety protection, supercapacitor power reasonable adjustment, dynamic support and active withdrawal, and real-time power distribution, and is suitable for hybrid energy storage systems composed of batteries and supercapacitors.

[0017] The power distribution device for a hybrid energy storage system considering SOC constraints of the present invention, when used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0018] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0019] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the structure of a cascaded H-bridge energy storage converter according to a preferred embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of a hybrid energy storage system according to a preferred embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the method flow of a preferred embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram illustrating the power distribution effect between the battery and the supercapacitor when the active power command Pref jumps from 1MW to 1.5MW and the supercapacitor SOC is 0.8 when using the method of the present invention in a preferred embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram illustrating the power distribution effect between the battery and the supercapacitor when the active power command Pref jumps from 1MW to 1.5MW and the supercapacitor SOC is 0.2 when using the method of the present invention in a preferred embodiment of the present invention.

[0024] Figure 6 This is a schematic diagram illustrating the power distribution effect between the battery and the supercapacitor when the active power command Pref jumps from 1MW to 0.5MW and the supercapacitor SOC is 0.8 when using the method of the present invention in a preferred embodiment of the present invention.

[0025] Figure 7 This is a schematic diagram illustrating the power distribution effect between the battery and the supercapacitor when the active power command Pref jumps from 1MW to 0.5MW and the supercapacitor SOC is 0.2 when using the method of the present invention in a preferred embodiment of the present invention.

[0026] Figure 8 This is a schematic diagram illustrating the power distribution effect between the battery and the supercapacitor when the active power command Pref jumps from 1MW to 1.5MW and the supercapacitor SOC is 0.8 using a fixed-parameter low-pass filter power distribution method according to a preferred embodiment of the present invention.

[0027] Figure 9 This is a schematic diagram illustrating the power distribution effect between the battery and the supercapacitor when the active power command Pref jumps from 1MW to 1.5MW and the supercapacitor SOC is 0.2, using a fixed-parameter low-pass filter power distribution method according to a preferred embodiment of the present invention.

[0028] Figure 10This is a schematic diagram illustrating the power distribution effect between the battery and the supercapacitor when the active power command Pref jumps from 1MW to 0.5MW and the supercapacitor SOC is 0.8, using a fixed-parameter low-pass filter power distribution method according to a preferred embodiment of the present invention.

[0029] Figure 11 This is a schematic diagram illustrating the power distribution effect between the battery and the supercapacitor when the active power command Pref jumps from 1MW to 0.5MW and the supercapacitor SOC is 0.2, using a fixed-parameter low-pass filter power distribution method according to a preferred embodiment of the present invention. Detailed Implementation

[0030] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0031] In a preferred embodiment of the present invention, the hybrid energy storage system includes a battery energy storage unit and a supercapacitor energy storage unit. The battery energy storage unit mainly handles relatively stable energy-type power demands, while the supercapacitor energy storage unit mainly handles rapidly changing power-type demands.

[0032] Figure 1 This is a structural diagram of the cascaded H-bridge energy storage converter involved in the method of the present invention. It is a three-phase cascaded H-bridge energy storage converter with a star connection, wherein each phase consists of n sub-modules, and the sub-modules are cascaded with each other through inductors. L Connect to the power grid for grid integration. and For the output voltage of each phase, Let O be the output current of each phase, and let O be the midpoint of the cascaded H-bridge energy storage converter. Figure 2 for Figure 1 The diagram shows the structure of the neutron module, specifically the hybrid energy storage system structure on the DC side of the H-bridge module. The H-bridge DC side consists of batteries and supercapacitors. The batteries are connected to the H-bridge DC side after being filtered by an inductor, and the supercapacitors are connected to the H-bridge DC side through a bidirectional DC / DC converter. Figure 2 In the middle, V bat Battery voltage; This refers to the battery current. L bat For battery filter inductance; R bat V is the battery's internal resistance. sc This is the voltage of the supercapacitor; C sc This refers to the capacitance value of the supercapacitor. V is the supercapacitor current; dc This refers to the DC-side voltage of the H-bridge in the submodule. C For the DC-side filter capacitor of the H-bridge in the submodule; This refers to the DC-side current of the H-bridge in the submodule. This is the output voltage of the submodule.

[0033] See Figure 3 In a preferred embodiment of the present invention, a power allocation method for a hybrid energy storage system considering SOC constraints is provided, comprising: S1. Obtain status information; status information includes system power demand, supercapacitor state of charge, and battery output power at the previous moment.

[0034] At the current sampling time k Power requirements of the data acquisition system State of charge of supercapacitors Battery output power at the previous moment The current output power of the battery is determined by the power balance relationship: ; in, This represents the battery output power at the current moment. Let S be the current output power of the supercapacitor. Based on the power balance relationship described above, the battery power reference value can be obtained by solving for the supercapacitor power reference value. Therefore, proceed to S2.

[0035] S2. Construct a power allocation cost function with the supercapacitor output power as the optimization variable based on the state information.

[0036] The power allocation cost function includes the battery dynamic inertia term, the supercapacitor orientation sensing state of charge constraint term, the supercapacitor power adjustment term, the state of charge prediction term, and the supercapacitor zero power attraction term.

[0037] In a preferred embodiment of the present invention, the battery dynamic inertia term includes: The battery dynamic inertia term is used to suppress sudden changes in battery output power, making the battery output power smoother and guiding the supercapacitor to handle rapidly changing power; to reduce the instantaneous power surge experienced by the battery when power demand changes rapidly, the change in battery output power is used as the basis for... As a characterization of battery dynamic stress, the battery dynamic inertia term Represented as: ; ; in, This represents the battery output power at the current moment. This represents the battery output power at the previous moment; This is the battery dynamic inertia weighting coefficient.

[0038] In a preferred embodiment of the present invention, the supercapacitor orientation sensing state of charge constraint term includes: Dynamically select the state-of-charge safety boundary based on the power regulation direction to prevent overcharging or over-discharging of the supercapacitor, including: When the system power demand requires supercapacitor discharge support, the distance between the supercapacitor's state of charge and its state of charge limit is used as the safety distance: ; When the system power demand requires the supercapacitor to absorb power through charging, the distance between the upper limit of the state of charge and the state of charge of the supercapacitor is used as the safe distance. ; in, To maintain a safe distance; and These are the lower limit of the state of charge and the upper limit of the state of charge, respectively.

[0039] Dynamic state-of-charge constraint weights are constructed based on safety distances. Then the supercapacitor direction sensing state of charge constraint term Represented as: ; ; in, This represents the current output power of the supercapacitor. Based on the weighting coefficient, The state-of-charge constraint gain coefficient; For reference safety distance; To maintain a safe distance; To prevent positive numbers with a denominator of zero; N This is a constraint index.

[0040] In a preferred embodiment of the present invention, when the state of charge of the supercapacitor gradually approaches the corresponding boundary, the safety distance... Reduce the dynamic state of charge constraint weights The increase strengthens the constraint that the supercapacitor continues to move towards the corresponding charge state boundary, thereby achieving direction-aware SOC safety protection.

[0041] In a preferred embodiment of the present invention, the supercapacitor power regulation term includes: The supercapacitor power regulation term is used to constrain the output power amplitude of the supercapacitor, and is expressed as: ; in, This is for the supercapacitor power regulation item; This is the power adjustment weighting coefficient for supercapacitors.

[0042] In a preferred embodiment of the present invention, the supercapacitor power regulation term enables a reasonable power division relationship between the battery and the supercapacitor, avoiding the supercapacitor from bearing excessive power under unnecessary operating conditions.

[0043] In a preferred embodiment of the present invention, the state of charge prediction term includes: A one-step state-of-charge (SOC) prediction model for supercapacitors is constructed, which predicts the SOC of the supercapacitor at the next time step based on the current SOC and the current output power of the supercapacitor. ; A state-of-charge prediction term is constructed based on the one-step state-of-charge prediction model for supercapacitors. : ; in, This represents the state of charge of the supercapacitor at the next moment. This represents the current state of charge of the supercapacitor. To control the cycle; This refers to the rated energy capacity of the supercapacitor. Weighting coefficients for one-step state of charge prediction; This is a reference value for the state of charge of supercapacitors.

[0044] In a preferred embodiment of the present invention, based on the state of charge prediction term, the controller can consider the impact of the current power allocation result on the state of charge of the supercapacitor at the next moment, so that the state of charge of the supercapacitor tends to a reasonable operating range.

[0045] In a preferred embodiment of the present invention, the zero-power attraction term of the supercapacitor includes: The zero-power attraction term for supercapacitors is used to enhance the convergence trend of supercapacitor power near zero, causing the supercapacitor to tend towards a zero-power state after completing dynamic power support, thus actively exiting the regulation process. This is expressed as: ; in, This is the zero-power attraction term for supercapacitors; The zero-power attraction weighting coefficient for supercapacitors.

[0046] In a preferred embodiment of the present invention, the zero-power attraction term of the supercapacitor can solve the problem that the output power of the supercapacitor is difficult to converge completely to zero under the traditional quadratic cost function.

[0047] In a preferred embodiment of the present invention, the power allocation cost function includes: The power allocation cost function is composed of the sum of the battery dynamic inertia term, the supercapacitor orientation-sensing state-of-charge constraint term, the supercapacitor power adjustment term, the state-of-charge prediction term, and the supercapacitor zero-power attraction term, including: ; in, The power allocation cost function.

[0048] S3. Explicitly solve the power allocation cost function to obtain the supercapacitor power reference value. S3 specifically includes: The battery dynamic inertia term, the supercapacitor orientation sensing state of charge constraint term, the supercapacitor power regulation term, and the state of charge prediction term are all summarized as follows: The quadratic expression; the zero-power attraction term of the supercapacitor is about The absolute value term. Therefore, based on the quadratic expression and the zero-power attraction term of the supercapacitor, the power allocation cost function is rearranged into the first expression, including: Define the power gap at the current moment Intermediate quantity The difference between the current state of charge of the supercapacitor and its reference state of charge. , is represented as: ; The first expression is then represented as: ; ; For the first expression, ignoring the absolute value term, we obtain the unconstrained analytical solution for the quadratic part of the supercapacitor power. : ; Based on the unconstrained analytical solution of the quadratic part and the zero-power attraction term of the supercapacitor, a soft thresholding process is performed to obtain the supercapacitor power reference value. : ; ; in, For the current system power requirements; A The coefficient of the quadratic term; B The coefficient of the linear term; D To and Irrelevant constant terms; This is the soft threshold coefficient.

[0049] Based on the above processing, when When the amplitude is less than the threshold, the supercapacitor power reference value is directly set to zero; when When the amplitude exceeds the threshold, the amplitude contracts while maintaining the power direction. This achieves the zero-power attraction effect of the supercapacitor.

[0050] In a preferred embodiment of the present invention, the power allocation cost function has a structure combining quadratic terms and absolute value terms. By merging the quadratic terms, an explicit analytical solution for the supercapacitor power can be obtained; then, combined with the soft thresholding corresponding to the supercapacitor zero-power attraction term, a reference value for the supercapacitor power is obtained. Compared with traditional model predictive control methods that require online invocation of quadratic programming solvers, the method of the present invention can reduce online computational complexity, improve the real-time performance of the controller, and is suitable for millisecond-level control cycles and embedded controller implementations.

[0051] S4. Obtain the battery power reference value based on the supercapacitor power reference value. S4 specifically includes: Subtract the supercapacitor power reference value from the current system power demand to obtain the battery power reference value. : ; After obtaining the supercapacitor power reference value and the battery power reference value, a limiting process is performed, including: When the supercapacitor power reference value exceeds the supercapacitor's allowable power range, the supercapacitor power reference value is limited to the allowable range; when the battery power reference value exceeds the battery's allowable power range, the battery power reference value is limited to the allowable range, and the supercapacitor compensates for the remaining power to ensure that the power allocation result meets the energy storage unit's operating constraints.

[0052] After limiting processing, the final supercapacitor power reference value and battery power reference value are output to drive the hybrid energy storage system to complete real-time power distribution.

[0053] This invention presents a power allocation method for hybrid energy storage systems considering State of Charge (SOC) constraints. It suppresses sudden power abrupt changes in battery output through a battery dynamic inertia term, allowing the battery to primarily bear relatively smooth power components, thus reducing the impact of high-frequency charging and discharging. A supercapacitor-oriented state of charge (SOC) constraint term dynamically selects the SOC safety boundary based on the charging and discharging direction, improving the supercapacitor's SOC boundary protection capability and reducing overcharging and over-discharging risks. A supercapacitor power regulation term limits excessive supercapacitor output. A SOC prediction term incorporates the impact of the current power allocation on the supercapacitor's SOC at the next moment into the optimization objective, giving the power allocation process model predictive control characteristics, which helps maintain the supercapacitor's SOC within a reasonable range. A supercapacitor zero-power attraction term enables the supercapacitor to approach a zero-power state after dynamic power support ends, reducing small residual power output and allowing the supercapacitor to actively exit the regulation process. The supercapacitor power reference value is obtained through explicit analytical solution, avoiding the computational burden of traditional online quadratic programming problems solved by MPC, thus improving the method's real-time performance and engineering feasibility. The method of this invention can simultaneously take into account battery power smoothing, supercapacitor state of charge safety protection, supercapacitor power reasonable adjustment, dynamic support and active withdrawal, and real-time power distribution, and is suitable for hybrid energy storage systems composed of batteries and supercapacitors.

[0054] In a preferred embodiment of the present invention, a power distribution device for a hybrid energy storage system considering SOC constraints is also provided, the device being used to implement the method of the present invention.

[0055] The power distribution device for a hybrid energy storage system considering SOC constraints of the present invention, when used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0056] Verification section: The method of this invention was verified by simulation in the MATLAB / Simulink environment, and a fixed-parameter low-pass filter power allocation method was selected as a comparison method. Under each corresponding operating condition, except for the power allocation method, the two methods used the same active power command, simulation step size, energy storage unit power limit, and initial operating state.

[0057] The low-pass filter power allocation method uses a first-order low-pass filter to extract the low-frequency component of the system's active power command and uses it as a battery power reference value. Its transfer function... for: ; The reference power values ​​for the battery and supercapacitor are as follows: ; ; in, This is a reference value for battery power in the low-pass filter power allocation method under the complex frequency domain; The supercapacitor power reference value is used for the low-pass filter power allocation method in the complex frequency domain. This is the active power command in the complex frequency domain; s For the Laplace operator; β The time constant of the low-pass filter is set to 0.12s. To avoid the initial state of the low-pass filter affecting the step response comparison, the system is kept in a stable operating state of 1MW before 2.5s.

[0058] This experiment sets up the following two active power step conditions: Operating Condition 1: The active power command Pref jumps from 1MW to 1.5MW in 2.5s. The initial state of charge of the supercapacitor is set to 0.8 and 0.2, respectively. The power allocation results between the battery and supercapacitor obtained using the method of this invention are shown in [reference 1]. Figure 4 and Figure 5 The power allocation results obtained using the fixed-parameter low-pass filter power allocation method are shown in the following figures. Figure 8 and Figure 9 .

[0059] Operating Condition 2: The active power command Pref jumps from 1MW to 0.5MW in 2.5s. The initial state of charge of the supercapacitor is set to 0.8 and 0.2, respectively. The power allocation results between the battery and supercapacitor obtained using the method of this invention are shown in [reference 1]. Figure 6 and Figure 7 The power allocation results obtained using the fixed-parameter low-pass filter power allocation method are shown in the following figures. Figure 10 and Figure 11 .

[0060] exist Figures 4 to 11 In this context, Pref is the active power reference value (i.e., active power command) of the energy storage converter, PBref is the active power reference value of the battery after power allocation, and PCref is the active power reference value of the supercapacitor after power allocation. The mathematical relationship between the three is Pref = PBref + PCref. In this embodiment, a positive active power value represents discharging, and a negative active power value represents charging.

[0061] Depend on Figure 4 and Figure 8 , Figure 5 and Figure 9 , Figure 6 and Figure 10 as well as Figure 7 and Figure 11The corresponding comparison shows that in the fixed parameter low-pass filter power allocation method used in this experiment, the power allocation between the battery and the supercapacitor is mainly determined by the input power command and the low-pass filter parameters. Therefore, the power allocation results obtained under different initial state of charge conditions of the supercapacitor are basically the same.

[0062] The method of the present invention can dynamically adjust the power participation of the supercapacitor based on the supercapacitor's state of charge, the battery's output power at the previous moment, and the predicted state of charge. This achieves smooth battery power while enhancing the protection of the supercapacitor's state of charge boundary and causing the supercapacitor to tend to a zero power state after the dynamic support ends.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A power allocation method for a hybrid energy storage system considering SOC constraints, characterized in that, include: Acquire status information; the status information includes system power demand, supercapacitor state of charge, and battery output power at the previous moment. Based on the state information, a power allocation cost function is constructed with the supercapacitor output power as the optimization variable; The power allocation cost function includes a battery dynamic inertia term, a supercapacitor orientation-sensing state of charge constraint term, a supercapacitor power adjustment term, a state of charge prediction term, and a supercapacitor zero-power attraction term. The power allocation cost function is explicitly solved to obtain a supercapacitor power reference value. Based on the supercapacitor power reference value, a battery power reference value is obtained.

2. The power allocation method for a hybrid energy storage system considering SOC constraints according to claim 1, characterized in that, Battery dynamic inertia terms include: The battery dynamic inertia term is used to suppress sudden changes in battery output power and guide the supercapacitor to handle rapidly changing power; the change in battery output power is used to... As a characterization of battery dynamic stress, the battery dynamic inertia term Represented as: ； ； in, This represents the battery output power at the current moment. This represents the battery output power at the previous moment; This is the battery dynamic inertia weighting coefficient.

3. The power allocation method for a hybrid energy storage system considering SOC constraints according to claim 2, characterized in that, The constraints for supercapacitor orientation sensing state of charge include: Dynamically select the state-of-charge safety boundary based on the power regulation direction to prevent overcharging or over-discharging of the supercapacitor, including: When the system power demand requires supercapacitor discharge support, the distance between the supercapacitor's state of charge and its upper limit is used as the safety distance; when the system power demand requires supercapacitor charging to absorb power, the distance between the upper limit of the supercapacitor's state of charge and its upper limit is used as the safety distance. Dynamic state-of-charge constraint weights are constructed based on the aforementioned safety distance. Then the supercapacitor direction sensing state of charge constraint term Represented as: ； ； in, This represents the current output power of the supercapacitor. Based on the weighting coefficient, The state-of-charge constraint gain coefficient; For reference safety distance; To maintain a safe distance; To prevent positive numbers with a denominator of zero; N This is a constraint index.

4. The power allocation method for a hybrid energy storage system considering SOC constraints according to claim 3, characterized in that, The supercapacitor power regulation items include: The supercapacitor power regulation term is used to constrain the output power amplitude of the supercapacitor, and is expressed as follows: ； in, This is for the supercapacitor power regulation item; This is the power adjustment weighting coefficient for supercapacitors.

5. The power allocation method for a hybrid energy storage system considering SOC constraints according to claim 4, characterized in that, The state of charge prediction terms include: Constructing a one-step state-of-charge prediction model for supercapacitors: ； The state of charge prediction term is constructed based on the one-step state of charge prediction model for the supercapacitor. : ； in, This represents the state of charge of the supercapacitor at the next moment. This represents the current state of charge of the supercapacitor. To control the cycle; This refers to the rated energy capacity of the supercapacitor. Weighting coefficients for one-step state of charge prediction; This is a reference value for the state of charge of supercapacitors.

6. The power allocation method for a hybrid energy storage system considering SOC constraints according to claim 5, characterized in that, Zero-power attraction terms for supercapacitors include: The zero-power attraction term of the supercapacitor is used to enhance the convergence trend of the supercapacitor power near zero, so that the supercapacitor tends to a zero-power state after completing dynamic power support, thereby actively exiting the adjustment process, as expressed as: ； in, This is the zero-power attraction term for supercapacitors; The zero-power attraction weighting coefficient for supercapacitors.

7. The power allocation method for a hybrid energy storage system considering SOC constraints according to claim 6, characterized in that, The power allocation cost function includes: The power allocation cost function is composed of the sum of the battery dynamic inertia term, the supercapacitor orientation-sensing state of charge constraint term, the supercapacitor power adjustment term, the state of charge prediction term, and the supercapacitor zero-power attraction term, including: ； in, The power allocation cost function.

8. The power allocation method for a hybrid energy storage system considering SOC constraints according to claim 7, characterized in that, Explicitly solving the power allocation cost function yields the following supercapacitor power reference values: The battery dynamic inertia term, the supercapacitor orientation-sensing state of charge constraint term, the supercapacitor power regulation term, and the state of charge prediction term are all summarized as follows: The quadratic expression; based on the quadratic expression and the zero-power attraction term of the supercapacitor, the power allocation cost function is rearranged into a first expression, including: Define the power gap at the current moment Intermediate quantity The difference between the current state of charge of the supercapacitor and its reference state of charge. , is represented as: ； The first expression is then represented as: ； ； For the first expression, ignoring the absolute value term, we obtain the unconstrained analytical solution for the quadratic part of the supercapacitor power. : ； Based on the unconstrained analytical solution of the quadratic part and the zero-power attraction term of the supercapacitor, a soft thresholding process is performed to obtain the supercapacitor power reference value. : ； ； in, For the current system power requirements; A The coefficient of the quadratic term; B The coefficient of the linear term; D To and Irrelevant constant terms; This is the soft threshold coefficient.

9. The power allocation method for a hybrid energy storage system considering SOC constraints according to claim 8, characterized in that, The battery power reference value obtained based on the supercapacitor power reference value includes: Subtracting the supercapacitor power reference value from the current system power demand yields the battery power reference value. : ； After obtaining the supercapacitor power reference value and the battery power reference value, a limiting process is performed, including: When the supercapacitor power reference value exceeds the supercapacitor's allowable power range, the supercapacitor power reference value is limited to the allowable range; When the battery power reference value exceeds the battery's allowable power range, the battery power reference value is limited to the allowable range, and the remaining power is compensated by the supercapacitor.

10. A power distribution device for a hybrid energy storage system considering SOC constraints, characterized in that, The apparatus is used to implement the method according to any one of claims 1 to 9.

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