Refined equivalent modeling method for MMC battery energy storage

Abstract

This invention discloses a refined equivalent modeling method for MMC battery energy storage. Voltage and current sensors installed on the bridge arm respectively obtain the bridge arm voltage and current, as well as the battery voltage and current in the submodule. A mathematical model is established in conjunction with the submodule's switching function. A Kalman filter algorithm is then used to calculate the conduction voltage drop of the IGBT and anti-parallel diode in the bridge arm, as well as the battery model parameters. After obtaining these model parameters, the entire bridge arm is equated using the Thevenin equivalence theorem. This method enables refined equivalent modeling of MMC battery energy storage, reduces the number of electrical nodes and computational complexity in the simulation circuit, and improves simulation speed and accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery energy storage, and in particular relates to a refined equivalent modeling method for MMC type battery energy storage. Background Art

[0002] Currently, battery energy storage systems are developing toward higher voltages and larger capacities. Traditional topologies are no longer able to meet these demands, necessitating the adoption of multilevel converters. The modular multilevel converter (MMC) is a novel multilevel topology that, compared to traditional diode-clamped, flying capacitor, and cascaded H-bridge topologies, offers modularity, energy decentralization, and enhanced fault tolerance. However, currently, there are few equivalent modeling methods for MMC-type energy storage systems. Therefore, research is needed to investigate equivalent modeling methods for MMC-type energy storage systems.

[0003] The shortcomings of existing technologies include:

[0004] (1) Existing methods simplify the IGBT and anti-parallel diode into a single binary resistor in the on state and the blocking state. The binary resistor has a fixed resistance value and cannot reflect its actual on-state voltage drop, which has certain limitations.

[0005] (2) Existing methods ignore the changes in battery parameters in submodules. Summary of the Invention

[0006] This invention discloses a refined equivalent modeling method for MMC battery energy storage. Voltage and current sensors installed on the bridge arm respectively obtain the bridge arm voltage and current, as well as the battery voltage and current in the submodule. A mathematical model is established based on the submodule's switching function. A Kalman filter algorithm is then used to calculate the conduction voltage drop of the IGBT and anti-parallel diode in the bridge arm, as well as the battery model parameters. Finally, after obtaining these model parameters, the entire bridge arm is equivalent using the Thevenin equivalence theorem. This method enables refined equivalent modeling of MMC battery energy storage, reduces the number of electrical nodes in the simulation circuit, significantly reduces the amount of computation, and improves simulation speed and accuracy.

[0007] The present invention specifically adopts the following technical solutions:

[0008] A refined equivalent modeling method for MMC battery energy storage is characterized by: obtaining the bridge arm voltage and current and the battery voltage and current in the submodule respectively through a voltage sensor and a current sensor installed on the bridge arm; establishing a mathematical model in combination with the switching function of the submodule, and then calculating the conduction voltage drop of the IGBT and anti-parallel diode in the bridge arm and the battery model parameters using a Kalman filter algorithm; after obtaining the model parameters, the entire bridge arm is equivalent using the Thevenin equivalence theorem to achieve refined equivalent modeling of MMC battery energy storage.

[0009] Furthermore, it specifically includes the following steps:

[0010] Step S1: Initialize SOC, battery terminal voltage, bridge arm current, and IGBT switch state;

[0011] Step S2: Determine whether the battery is bypassed. If it is bypassed, the corresponding submodule battery observation array H batx (t i )=0, then execute step S3; if it is not bypassed, directly execute step S3;

[0012] Step S3: Record the current SOC, battery terminal voltage, bridge arm current, and IGBT switch status;

[0013] Step S4: Calculate the error covariance matrix and the Kalman gain formula;

[0014] Step S5: Update the state quantity and error covariance matrix;

[0015] Step S6: Obtaining IGBT on-state voltage drop and battery parameters;

[0016] Step S7: Perform Thevenin equivalent modeling on the MMC battery energy storage module.

[0017] Furthermore, n+2 sensors are configured on each bridge arm of the MMC battery energy storage to obtain relevant electrical quantities: including n+1 voltage sensors, that is, a voltage sensor is added to each of the n submodule battery packs to measure the battery voltage U batx , and a voltage sensor on the bridge arm to measure the bridge arm voltage U S Only one current sensor is needed, i.e. the bridge arm current i in n submodules pj .

[0018] Furthermore, in step S4, the measurement equations of the IGBT and battery state parameters of all sub-modules are listed separately:

[0019] U s (t i )-S s (t i )U bat (t i )=Φ s (t i )X s (t i )+v s (t i ) (31)

[0020] U batx (t i )=H batx (t i )Bbatx (t i )+v batx (t i ) (32)

[0021] Among them, Φ s (t i ), H batx (t i ) are the bridge arm observation array and submodule battery observation array of MMC battery energy storage, v s (t i ),v batx (t i ) are all measurement noise signals; set the state quantity X s (t0) and B batx (t0) and the covariance matrix P s (t0) and P batx The initial value of (t0), I s is the identity matrix of order 8n, I batx is the unit matrix of order 1, and the initial value P is s (t0) = M s I s ,P batx (t0) = M batx I batx ,M s 、M batx is any constant greater than 0; Q s , Q batx is the process noise, set to a value;

[0022] t i At the moment, the Kalman gain matrices of IGBT and battery parameters are:

[0023] K s (t i )=P s (t i-1 )Φ s (t i ) T (Φ s (t i )P s (t i-1 )Φ(t i ) T +R s (t i )) -1 (33)

[0024] K batx (t i )=P batx (t i-1 )H batx(t i ) T (H batx (t i )P batx (t i-1 )H batx (t i ) T +R batx (t i )) -1 (34)

[0025] Among them, K s (t i ), K batx (t i ) are 8n-dimensional column vectors and 1-order diagonal matrices, R s (t i )R batx (t i ) are the variance matrices of IGBT and battery observation noise, respectively.

[0026] Furthermore, in step S5, the updated system state estimation values are:

[0027] X s (t i )=X s (t i-1 )+K s (t i )(U s (t i )-S s (t i )U bat (t i )-Φ s (t i-1 )X s (t i-1 )) (35)

[0028] B batx (t i )=B batx (t i-1 )+K batx (t i )(i pj (t i )-Η batx (t i-1 )B batx (t i-1 )) (36)

[0029] The corrected error covariance matrix is:

[0030]

[0031]

[0032] The state parameter estimation results of all sub-module IGBTs and anti-parallel diodes on the bridge arm at the next moment are obtained through equations (31), (33), (35), and (37). The state parameter estimation results of all sub-module batteries on the bridge arm at the next moment are obtained through the steps of equations (32), (34), (36), and (38). The above steps are continuously iterated until convergence, and the IGBT anti-parallel diode and battery parameters are continuously updated to realize online parameter monitoring of each sub-module IGBT and battery of MMC battery energy storage.

[0033] Furthermore, in step S7, the obtained Thevenin equivalent model is as follows:

[0034]

[0035] Where k = a, b, c are the three phases of AC; r = p, n are the upper and lower bridge arms; i is the i-th submodule in each sub-bridge arm; R smeq_rk ,u smeq_rk (t) are the equivalent resistance and equivalent voltage source of each bridge arm respectively; R smeq_rk_i ,u smeq_rk_i are the equivalent resistance and equivalent voltage source of each sub-module respectively.

[0036] The present invention and its preferred solution establish energy storage submodule and bridge arm state models, adopt the Kalman filtering method to obtain the model parameters in all submodules, and use the Thevenin theorem to equate the bridge arms, so as to achieve the purpose of fast and accurate simulation of MMC battery energy storage circuits. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0038] Figure 1 This is a schematic diagram of the topological structure of the MMC battery energy storage system according to an embodiment of the present invention.

[0039] Figure 2 This is a topological diagram of the battery circuit according to an embodiment of the present invention.

[0040] Figure 3 Schematic diagram of three working states of the submodule of the MMC battery energy storage according to an embodiment of the present invention.

[0041] Figure 4 Schematic diagram of the equivalent circuit of the IGBT and the anti-parallel diode according to an embodiment of the present invention.

[0042] Figure 5 This is a simplified circuit diagram of a submodule according to an embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram of a Thevenin simplified circuit according to an embodiment of the present invention.

[0044] Figure 7 This is a schematic diagram of the MMC battery energy storage equivalent circuit according to an embodiment of the present invention.

[0045] Figure 8 Schematic diagram of the overall process of an embodiment of the present invention. DETAILED DESCRIPTION

[0046] To make the features and advantages of this patent more clearly understood, the following embodiments are specifically described in detail as follows:

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components generally described and shown in the drawings herein can be designed in combination with different configurations. Therefore, the following detailed description of the selected embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but only represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present invention.

[0048] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0049] 1.MMC battery energy storage topology

[0050] The main circuit topology of the MMC battery energy storage in this embodiment is as follows Figure 1 As shown, it consists of two components: the main circuit and the submodule. It can also be thought of as consisting of three phase units, each of which consists of two upper and lower bridge arms connected in series via a filter inductor L0. Each bridge arm is composed of N identical energy storage sub-modules (E-SMs) connected in cascade. The inductance of each phase bridge arm suppresses circulating current during normal system operation and reduces short-circuit current generated when the converter fails. This access method offers high energy transfer efficiency.

[0051] 2.E-SM battery model

[0052] The simplified battery model in the submodule is as follows Figure 2 As shown, it consists of a resistor and an open-circuit voltage source, and the open-circuit voltage source U ocvx It is related to the current SOC state of the battery and is a function of SOC.

[0053] The voltage and current of the battery satisfy the following formula:

[0054] U batx =i pj R batx +U ocvx (t i ) (1)

[0055] According to the known OCV-SOC curve, in the normal working range of the battery, its OCV-SOC curve can be approximately regarded as a straight line, so let U ocvx (t i )=a x0 (t i )+a x1 (t i )SOC x (t i ).

[0056] According to the definition of SOC:

[0057]

[0058] So we can get:

[0059]

[0060] Written in matrix form:

[0061]

[0062] 3.E-SM working status model

[0063] The topology of MMC battery energy storage reveals that the output voltage of the upper and lower bridge arms is the sum of the voltages of all submodules within them, while the AC side voltage is equal to half the difference between the voltages of the lower and upper arms. Controlling the output voltages of the upper and lower arms allows the AC side voltage to be controlled. The key to controlling the bridge arm voltage lies in controlling the submodules.

[0064] The on / off of the two fully controlled power devices will determine the submodule's input and output. The sum of the output voltages of the submodules that are input constitutes the voltage of each bridge arm, so the bridge arm cannot provide a negative voltage. According to the state of the switch tube and the direction of the current, the submodule of the MMC battery energy storage has three working states, such as Figure 3 As shown, they are 3(a) locked state, Figure 3 (b) input status, and Figure 3 (c) Bypass state.

[0065] 4. Bridge arm state model

[0066] The refined equivalent modeling method of the MMC type battery energy storage in this embodiment involves the modeling of the conduction voltage drop of the IGBT and diode in the submodule and its energy storage module. It is necessary to configure n+2 sensors on each bridge arm of the MMC type battery energy storage to obtain relevant electrical quantities: including n+1 voltage sensors, that is, each of the n submodule battery packs is equipped with a voltage sensor to measure the battery voltage U batx , and a voltage sensor on the bridge arm to measure the bridge arm voltage U S Only one current sensor is needed, i.e. the bridge arm current i in n submodules pj The above electrical quantities can be used to calculate and estimate the parameters of the MMC battery energy storage submodule IGBT and battery.

[0067] In the submodule, the conduction voltage drop of the IGBT and diode can be approximately linearly expressed as follows:

[0068] U T =U T0 +i T ·r T (5)

[0069] U D =U D0 +i D ·r D (6)

[0070] Among them, U T 、U D are the port voltage values when the IGBT and the anti-parallel diode are in the on state, and the conduction direction of the switching device is taken as the reference direction of the port voltage; i T is the IGBT collector current, i D is the current on the anti-parallel diode, U T0 、U D0 is the on-state voltage; r T 、r D is the on-state resistance.

[0071] In order to analyze the working status of the submodule, the upper arm of the MMC battery energy storage is taken as an example, and S x 、S x1 、S x2 、S x3 and S x4 is the working status of each component in the xth submodule: S x is the switch signal of T1, the turn-on signal is 1 and the turn-off signal is 0; S x1 、S x2 、S x3 and S x4The conduction states of T1, T2, D1, and D2 in the submodules are 1 and 0, respectively. The submodule input or bypass state and the corresponding submodule port voltage U smx By S x and the bridge arm current i pj The direction of the gate is determined by the direction of the gate, as shown in Table 1. x =1, when the bridge arm current i pj <0, the battery in the submodule is in the input state and T1 is turned on, the port voltage of the submodule is U batx -U T1x , when the bridge arm current i pj >0, the battery in the submodule is in the input state and D1 is turned on, the port voltage of the submodule is U batx +U D1x ; T1 gate plus shutdown signal S x = 0, when the bridge arm current i pj >0, the battery in the submodule is in bypass state and T2 is turned on, the port voltage of the submodule is U T2x , when the bridge arm current i pj <0, the battery in the submodule is in bypass state and D2 is turned on, the port voltage of the submodule is -U D2x .

[0072] Table 1 Relationship between submodule switch status and submodule voltage

[0073]

[0074] The three phases of an MMC battery energy storage system are completely symmetrical, and the switching conditions of the upper and lower bridge arms are identical. Therefore, the principle of submodule IGBT identification is explained using phase A as an example. Combining the submodule conduction and cutoff conditions in Table 1, the port voltage of the xth submodule on the bridge arm can be expressed as follows:

[0075] U smx =S x U Cx -S x1 U T1x +S x2 U T2x +S x3 U D1x -S x4 U D2x (7)

[0076] A bridge arm is obtained by cascading n sub-modules, so the bridge arm voltage can be obtained by summing the voltages of all sub-modules on the bridge arm:

[0077]

[0078]

[0079] Among them, U s (t i ) is t i The bridge arm voltage at the moment is shifted to Equation (4):

[0080] From equations (1) and (2), we can see that the conduction voltage drop of the IGBT and diode on the x-th submodule is:

[0081] U T1x (t i )=U T1x0 (t i )-i pj (t i )·r T1x (t i ) (10)

[0082] U T2x (t i )=U T2x0 (t i )+i pj (t i )·r T2x (t i ) (11)

[0083] U D1x (t i )=U D1x0 (t i )+i pj (t i )·r D1x (t i ) (12)

[0084] U D2x (t i )=U D2x0 (t i )-i pj (t i )·r D2x (t i ) (13)

[0085] Among them, U T1x0 (t i ), U T2x0 (t i ), U D1x0 (t i ), U D2x0 (t i ) represents the on-state voltage bias of the corresponding component of the submodule, r T1x (t i ), r T2x (t i ), rD1x (t i ), r D2x (t i ) represents the on-state resistance. Substitute equations (10) to (13) into equation (9) and write them in matrix form:

[0086]

[0087] Written in matrix form:

[0088] U s (t i )-S s (t i )U bat (t i )=-S s1 (t i )U T1s0 (t i )+S s1 (t i )i pj (t i )r T1s (t i )+S s2 (t i )U T2s0 (t i )

[0089] +S s2 (t i )i pj (t i )r T2s (t i )+S s3 (t i )U D1s0 (t i )+S s3 (t i )i pj (t i )r D1s (t i ) (15)

[0090] -S s4 (t i )U D2s0 (t i )+S s4 (t i )i pj (t i )r D2s (t i )

[0091] Among them, U T1s0 (ti ), U T2s0 (t i ), U D1s0 (t i ), U D2s0 (t i ) are t i At this moment, the on-state voltage bias of the IGBTs T1, T2 and the anti-parallel diodes D1, D2 in the sub-module on the bridge arm is T1s (t i ), r T2s (t i ), r D1s (t i ), r D2s (t i ) are t i At the moment, the on-state resistances of the IGBTs of the bridge arms, i.e., T1, T2, and the anti-parallel diodes D1, D2, are all n-dimensional column vectors; s1 、S s2 、S s3 、S s4 is the actual conduction state of the IGBT in the anti-parallel diode, which is an n-dimensional row vector. For simplicity, let

[0092] X s (t i )=[U T1s0 (t i ) r T1s (t i ) U T2s0 (t i ) r T2s (t i ) U D1s0 (t i ) r D1s (t i ) U D2s0 (t i ) r D2s (t i )] T (16)

[0093] Φ s (t i )=[-S s1 (t i ) S s1 (t i )i pj (t i ) S s2 (t i ) S s2 (t i )i pj (ti ) S s3 (t i ) S s3 (t i )i pj (t i )-S s4 (t i ) S s4 (t i )i pj (t i )] (17)

[0094] Among them, X s (t i ) are the state parameters of the IGBT and anti-parallel diode of the submodule on the bridge arm. The current flowing through the IGBT and anti-parallel diode of each submodule can be expressed as:

[0095] i T1s (t i )=S sl (t i )·i pj (t i ) (18)

[0096] i T2s (t i )=S s2 (t i )·i pj (t i ) (19)

[0097] i D1s (t i )=S s3 (t i )·i pj (t i ) (20)

[0098] i D2s (t i )=S s4 (t i )·i pj (t i ) (twenty one)

[0099] Therefore, formula (17) can be expressed as:

[0100] Φ s (t i )=[-S s1 (t i )i T1s (t i )S s2 (t i )iT2s (t i )S s3 (t i )i D1s (t i )-S s4 (t i )i D2s (t i )] (twenty two)

[0102] Therefore, the relationship between the bridge arm voltage and the state parameters of the IGBTs and anti-parallel diodes of all sub-modules on the bridge arm is:

[0103] U S (t i )-S s (t i )U bat (t i )=Φ s (t i )X s (t i ) (twenty three)

[0104] Among them, Φ s (t i ) is an 8n-dimensional row vector, X s (t i ) is an 8n-dimensional column vector.

[0105] 5. Kalman filter algorithm

[0106] The IGBT online identification parameter algorithm adopted in the present invention is the Kalman filter algorithm. The Kalman filter algorithm is an optimized autoregressive data processing algorithm that can process data signals containing noise and eliminate the errors caused by sensors during online estimation. It is widely used in the online estimation of parameters of power systems.

[0107] For a system, it should first be able to be described by the following linear differential equation:

[0108] X k =AX k-1 +q k-1 (twenty four)

[0109] Z k =HX k +r k (25)

[0110] Among them, X k , Z k ,q k 、r kare the state variables, observation variables, process noise, and measurement noise of the system at time k. A is the one-step transition matrix from time k-1 to time k, and H is the observation matrix, which is determined by the system itself.

[0111] The steps of the Kalman filter algorithm can be described by the following formula:

[0112] X k,k-1 =AX k-1 (26)

[0113] P k,k-1 =AP k-1 A T +Q (27)

[0114] K k =P k,k-1 H T (HP k,k-1 H T +R) -1 (28)

[0115] X k =X k,k-1 +K k (Z k -HX k,k-1 ) (29)

[0116] P k =(IK k H)P k,k-1 (30)

[0117] Among them, X k-1 、P k-1 is the system state quantity and error covariance at the previous moment; X k,k-1 、P k,k-1 They are the system state quantity and error covariance at the current moment predicted based on the previous moment; process and measurement noise are generally directly assumed to be Gaussian white noise, and Q and R are their covariance matrices; I is the unit matrix; K k 、P k is the Kalman gain and error covariance matrix at time k after the update; X k This is the system state obtained after the kth reduction calculation. Repeating the above algorithm steps multiple times can gradually obtain a system state that is closer to the actual state. The values of X, P, Q, and R should be pre-set before the first calculation. After a certain number of iterations, the state X will eventually approach the actual state value, achieving the goal of state parameter estimation.

[0118] 6. Parameter identification method of MMC battery energy storage module

[0119] It should be noted that in actual operation, the batteries of each submodule will not always be in operation and may be bypassed. However, the above derivation is based on the premise that the battery is in charge or discharge working state. Therefore, the calculation must be performed over a continuous period of time when the submodule is in operation. Before each iteration, the switch signal applied to the T1 tube of each submodule at two consecutive moments should be used to determine whether the submodule is in operation. If it is bypassed, the corresponding H batx (t i )=0.

[0120] Based on equations (4) and (23), the measurement equations of the IGBT and battery state parameters on all sub-modules can be written separately:

[0121] U s (t i )-S s (t i )U bat (t i )=Φ s (t i )X s (t i )+v s (t i ) (31)

[0122] U batx (t i )=H batx (t i )B batx (t i )+v batx (t i ) (32)

[0123] Among them, v s (t i ),v batx (t i ) are all measurement noise signals. Set the state quantity X s (t0) and B batx (t0) and the covariance matrix P s (t0) and P batx The initial value of (t0), I s is the identity matrix of order 8n, I batx is the unit matrix of order 1, and the initial value P is s (t0) = M s I s ,P batx (t0) = M batx I batx ,M s 、M batx is any constant greater than 0; Qs , Q batx is the process noise, set to a value.

[0124] Therefore, t i At the moment, the Kalman gain matrices of IGBT and battery parameters are:

[0125] K s (t i )=P s (t i-1 )Φ s (t i ) T (Φ s (t i )P s (t i-1 )Φ(t i ) T +R s (t i )) -1 (33)

[0126] K batx (t i )=P batx (t i-1 )H batx (t i ) T (H batx (t i )P batx (t i-1 )H batx (t i ) T +R batx (t i )) -1 (34)

[0127] Among them, K s (t i ), K batx (t i ) are 8n-dimensional column vectors and 1-order diagonal matrices, R s (t i )R batx (t i ) are the variance matrices of IGBT and battery observation noise respectively. The updated system state estimates are:

[0128] X s (t i )=X s (t i-1 )+K s (t i )(U s (ti )-S s (t i )U bat (t i )-Φ s (t i-1 )X s (t i-1 )) (35)

[0129] B batx (t i )=B batx (t i-1 )+K batx (t i )(i pj (t i )-Η batx (t i-1 )B batx (t i-1 )) (36)

[0130] The corrected error covariance matrix is:

[0131]

[0132]

[0133] The state parameter estimation results of all the sub-module IGBTs and anti-parallel diodes on the bridge arm at the next moment can be obtained through equations (31), (33), (35), and (37). The state parameter estimation results of all the sub-module batteries on the bridge arm at the next moment can be obtained through steps similar to equations (32), (34), (36), and (38). By continuously iterating according to the above steps until convergence, the IGBT anti-parallel diode and battery parameters can be continuously updated to realize online parameter monitoring of the IGBTs and batteries of each sub-module of the MMC battery energy storage.

[0134] 7. Refined Modeling and Simulation Methods for MMC Battery Energy Storage

[0135] During the normal operation of the MMC battery energy storage module, the on-state voltage drop of the IGBT on the anti-parallel diode can be regarded as a voltage source, and when it is in the blocking state, it can be regarded as an infinite resistance. In summary, the IGBT and its anti-parallel diode can be regarded as a series connection of a voltage source and a resistor, as shown in the following example: Figure 4 As shown:

[0136]

[0137]

[0138] Based on the above analysis of IGBT on-state voltage drop and battery parameter identification, the equivalent circuit based on the MMC battery energy storage submodule can be derived, as shown in the following figure: Figure 5 shown.

[0139] Using the Thevenin equivalent theorem, the submodule is equivalent to a voltage source and a resistor in series. The final equivalent circuit is as follows: Figure 6 shown.

[0140] in:

[0141]

[0142] According to the derivation of the equivalent formula of the submodule above, the Thevenin equivalent circuit of n series submodules in each bridge arm can be derived as shown in formula (42). Therefore, the equivalent circuit of MMC battery energy storage is as follows: Figure 7 shown.

[0143]

[0144] Where k (k = a, b, c) is the AC three-phase; r (r = p, n) is the upper and lower bridge arms; i is the i-th submodule in each sub-bridge arm; R smeq_rk ,u smeq_rk (t) are the equivalent resistance and equivalent voltage source of each bridge arm respectively; R smeq_rk_i ,u smeq_rk_i are the equivalent resistance and equivalent voltage source of each sub-module respectively.

[0145] In summary, the embodiment of the present invention uses the Kalman filter algorithm to obtain the on-state voltage drop of the IGBT and its anti-parallel diode and the battery open-circuit voltage and resistance parameters. The MMC battery energy storage can be finely modeled according to the identified parameters, which fills the gap in MMC battery energy storage modeling and improves the accuracy of the modeling.

[0146] like Figure 8 As shown, based on the above design, the specific embodiment process of the present invention can be obtained as follows:

[0147] Step S1: Initialize SOC, battery terminal voltage, bridge arm current, and IGBT switch state;

[0148] Step S2: Determine whether the battery is bypassed. If it is bypassed, set the corresponding H batx (t i )=0 and then execute step S3,

[0149] If it is not bypassed, directly execute step S3;

[0150] Step S3: Record the current SOC, battery terminal voltage, bridge arm current, and IGBT switch status;

[0151] Step S4: Calculate the error covariance matrix and the Kalman gain formula;

[0152] Step S5: Update the state quantity and error covariance matrix;

[0153] Step S6: Calculate and obtain the IGBT on-state voltage drop and battery parameters;

[0154] Step S7: Perform Thevenin equivalent modeling on the MMC battery energy storage module.

[0155] The logic program design scheme in the above scheme provided in this embodiment can be stored in a computer-readable storage medium in a coded form and implemented in the form of a computer program, and the basic parameter information required for calculation is input through computer hardware, and the calculation results are output.

[0156] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] The present invention is described with reference to methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in the process or processes.

[0158] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device that implements the functions specified in one or more processes.

[0159] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes.

[0160] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

[0161] This patent is not limited to the above-mentioned best implementation method. Anyone can derive other forms of MMC battery energy storage refined equivalent modeling methods based on the inspiration of this patent. All equivalent changes and modifications made within the scope of the patent application of this invention shall fall within the scope of this patent.

Claims

1. A refined equivalent modeling method for MMC battery energy storage, characterized by: Voltage and current sensors installed on the bridge arm are used to obtain the bridge arm voltage and current, as well as the battery voltage and current in the submodule. A mathematical model is established based on the submodule's switching function. The Kalman filter algorithm is then used to calculate the conduction voltage drop of the IGBT and anti-parallel diode in the bridge arm, as well as the battery model parameters. Once the model parameters are obtained, the entire bridge arm is equated using the Thevenin equivalence theorem, achieving refined equivalent modeling of MMC battery energy storage. The specific steps include: Step S1: Initialize SOC, battery terminal voltage, bridge arm current, and IGBT switch state; Step S2: Determine whether the battery is bypassed. If it is bypassed, the corresponding submodule battery observation array =0, then execute step S3. If it is not bypassed, then execute step S3 directly; Step S3: Record the current SOC, battery terminal voltage, bridge arm current, and IGBT switch status; Step S4: Calculate the error covariance matrix and the Kalman gain formula; Step S5: Update the state quantity and error covariance matrix; Step S6: Obtaining IGBT on-state voltage drop and battery parameters; Step S7: Performing Thevenin equivalent modeling on the MMC battery energy storage module; In step S4, the measurement equations of the IGBT and battery state parameters of all sub-modules are listed separately: (31) (32) Among them, U s () is the bridge arm voltage, 、 They are the bridge arm observation array and submodule battery observation array of MMC battery energy storage. 、 All are measurement noise signals; State parameters of the IGBT and anti-parallel diode of the sub-module on the bridge arm; set the state quantity and and the covariance matrix and The initial value of is the identity matrix of order 8n, is the unit matrix of order 1, and the initial value is , , 、 is any constant greater than 0; 、 is the process noise, set to a value; At the moment, the Kalman gain matrices of IGBT and battery parameters are: (33) (34) in, 、 They are 8n-dimensional column vectors and 1-order diagonal matrices, both of which are Kalman gain matrices. are the variance matrices of IGBT and battery observation noise, respectively; and are all the error covariance matrices of the previous moment; In step S5, the updated system state estimation values are: (35) (36) The corrected error covariance matrix is: (37) (38) The state parameter estimation results of all the sub-module IGBTs and anti-parallel diodes on the bridge arm at the next moment are obtained through equations (31), (33), (35), and (37). The state parameter estimation results of all the sub-module batteries on the bridge arm at the next moment are obtained through the steps of equations (32), (34), (36), and (38). The above steps are continuously iterated until convergence, and the IGBT anti-parallel diode and battery parameters are continuously updated to realize online parameter monitoring of the IGBTs and batteries of each sub-module of the MMC battery energy storage.

2. The MMC battery energy storage refined equivalent modeling method according to claim 1 is characterized in that: n+2 sensors are configured on each bridge arm of the MMC battery energy storage to obtain relevant electrical quantities: including n+1 voltage sensors, that is, a voltage sensor is added to each of the n submodule battery packs to measure the battery voltage. , and a voltage sensor on the bridge arm to measure the bridge arm voltage Only one current sensor is needed, i.e. the bridge arm current in n submodules .

3. The MMC battery energy storage refined equivalent modeling method according to claim 1, characterized in that: In step S7, the obtained Thevenin equivalent model is as follows: (42) in, It is three-phase AC; For the upper and lower bridge arms; For each sub-bridge arm submodules; , are the equivalent resistance and equivalent voltage source of each bridge arm respectively; , are the equivalent resistance and equivalent voltage source of each sub-module respectively.

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