A dynamic power adaptive virtual inertia control method for a fuel cell hybrid system

Abstract

The present invention discloses a dynamic power adaptive virtual inertia control method for a fuel cell hybrid system. First, a dynamic power virtual inertia control method is proposed based on the output characteristics of the fuel cell in the hybrid system, so that the fuel cell autonomously bears low-frequency power, while the energy storage device passively provides high-frequency power. Second, the virtual inertia coefficient is adaptively adjusted based on the load dynamic power change rate to achieve the purpose of adaptively smoothing the fuel cell output power according to the load power change rate. Then, based on the dynamic power adaptive virtual inertia control, a dynamic power adaptive distribution method is proposed. This method realizes system dynamic power distribution by adaptively adjusting the charging factor and damping factor. The present invention effectively smooths the high-speed fluctuations of the fuel cell output power, improves the fuel cell output characteristics and operating performance, and can adaptively distribute the system dynamic power.

Description

Technical Field

[0001] The present invention belongs to the technical field of fuel cell hybrid power systems, and in particular relates to a dynamic power adaptive virtual inertia control method for a fuel cell hybrid system. Background Art

[0002] With the development of technology, fuel cells have been applied in many fields, such as automobiles, rail trains, ships, airplanes, microgrids, etc. Fuel cells have the advantages of clean emissions, high efficiency and low noise, and are considered to be an effective means to reduce fossil fuel consumption and pollution emissions. The promotion and application of fuel cells are still restricted by factors such as short life and high cost. The life of a fuel cell is usually affected by four key factors, namely start-stop, power variation, degradation during high-power operation and low-power operation. Excluding unnecessary start-stop, power variation is the main degradation factor affecting the normal operation of fuel cells. Therefore, reducing high-speed power fluctuations has become an effective way to improve the operating performance of fuel cells and extend their operating life.

[0003] Because fuel cells are devices with slow dynamic characteristics, drastic fluctuations in output power can affect their operating performance. Therefore, some scholars have proposed combining energy storage devices and fuel cells into a hybrid system. The energy storage device (supercapacitor or battery, etc.) can recover regenerative energy and generate instantaneous power to compensate for the shortcomings of the slow dynamic characteristics of the fuel cell. Some studies have shown that the smaller the fluctuation in output power, the better the fuel cell's operating performance is maintained and the slower the performance degradation. Conversely, the greater the power fluctuation, the faster the fuel cell's performance degrades. Therefore, suppressing high-speed fluctuations in output power has become an effective method to improve the operating performance of fuel cells and extend their operating life.

[0004] Currently, suppressing fuel cell output power fluctuations is mostly achieved through energy management strategies based on optimization theory. The fuel cell degradation level is considered as part of the optimization objective function, and a reference value for the fuel cell output power is calculated using optimization theory or algorithms to suppress fuel cell power fluctuations and reduce operating pressure. However, as the upper layer of the system, energy management strategies based on optimization theory often require a very large amount of computation, resulting in increased control step sizes and an inability to quickly and effectively control the fuel cell output power in real time. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a dynamic power adaptive virtual inertia control method for a fuel cell hybrid system, which effectively smooths the high-speed fluctuations of the fuel cell output power, improves the fuel cell output characteristics and operating performance, and can adaptively distribute the system dynamic power.

[0006] To achieve the above-mentioned object, the technical solution adopted by the present invention is: a method for dynamic power adaptive virtual inertia control of a fuel cell hybrid system, comprising the steps of:

[0007] Step 1: The fuel cell hybrid system consists of a fuel cell unit, an energy storage device unit, and a traction load. The fuel cell output power P of the entire system is obtained. fc , energy storage device output power P es , traction load power P load The balance between the three: fc +P es =P load ;

[0008] Step 2: Based on the inertial support capability of the energy storage device, there is a transient power fluctuation ΔP in the traction load load When the virtual inertia power ΔP of the energy storage device is obtained vir , establish a balanced relationship:

[0009] P fc +P es +ΔP vir =P load +ΔP load ;

[0010] Step 3: Propose dynamic power virtual inertia control for the fuel cell unit, so that the energy storage device can passively provide virtual inertia power ΔP for the system in transient state. vir The dynamic power virtual inertia control: fuel cell output power reference value P fc_ref Subtract the fuel cell output power P fc The deviation of the transient power ΔP is obtained through the first-order inertia control link, and the mathematical expression of dynamic power virtual inertia control is:

[0011]

[0012] Where J is the virtual inertia coefficient, D v is the damping factor;

[0013] Step 4: Directly control the fuel cell output current to indirectly control the fuel cell output power P fc , define the fuel cell output current reference i fc_ref Equal to the transient power ΔP divided by the fuel cell output voltage v fc , fuel cell output current reference i fc_ref The mathematical expression is

[0014]

[0015] Furthermore, in step 3, the virtual inertia coefficient J is adaptively adjusted. The virtual inertia coefficient J is equal to the initial virtual inertia coefficient J0 plus the adjustment coefficient β multiplied by the absolute value of the load dynamic power change rate. The mathematical expression of the virtual inertia coefficient J is:

[0016]

[0017] Among them, P load (k) is the load power sampling value at time k, P load (k-1) is the load power sampling value at time k-1, T sample is the sampling time.

[0018] Furthermore, in step 3, the damping factor D v Perform adaptive adjustment, damping factor D v The value of is related to the state of charge SOC of the energy storage device, and the damping factor D v The value of is:

[0019] When SOC min ≤SOC≤SOC1, D v =0;

[0020] When SOC1<SOC≤SOC max hour,

[0021] Among them, SOC min , SOC max They are the lower and upper limits of SOC when the energy storage device is working normally, and SOC1 is the damping factor D v The upper limit critical value of SOC is 0, and the coefficient ε is defined according to the actual situation.

[0022] Furthermore, the fuel cell output power reference value P in step 3 fc_ref The value of the load power P load In order to protect the fuel cell, the operating power range is set: the maximum operating power is P fc_max , the minimum operating power is P fc_min ; Thus, the fuel cell output power reference value P is obtained fc_ref for:

[0023]

[0024] The coefficient α (α≥1) is defined as the charging factor so that the fuel cell output power reference value P fc_ref Can be greater than or equal to the load power P load , so as to achieve the purpose of charging the energy storage device by the fuel cell.

[0025] Furthermore, the value of the charging factor α is related to the SOC of the energy storage device. The method for determining the value of the charging factor α is:

[0026] When SOC min When ≤SOC≤SOC2,

[0027] When SOC2<SOC≤SOC max When α=1;

[0028] Among them, SOC min , SOC max are the lower and upper limits of the SOC when the energy storage device is working normally, respectively. SOC2 is the lower critical value of the SOC when the charging factor α is 1. The coefficient δ is defined according to the actual situation.

[0029] The beneficial effects of adopting this technical solution are:

[0030] The present invention discloses a dynamic power adaptive virtual inertia control method for a fuel cell hybrid system. First, a dynamic power virtual inertia control method is proposed based on the output characteristics of the fuel cell in the hybrid system, so that the fuel cell autonomously bears low-frequency power, while the energy storage device passively provides high-frequency power. Second, the virtual inertia coefficient is adaptively adjusted based on the load dynamic power change rate to achieve the purpose of adaptively smoothing the fuel cell output power according to the load power change rate. Then, based on the dynamic power adaptive virtual inertia control, a dynamic power adaptive distribution method is proposed. This method realizes system dynamic power distribution by adaptively adjusting the charging factor and damping factor. The present invention effectively smooths the high-speed fluctuations of the fuel cell output power, improves the fuel cell output characteristics and operating performance, and can adaptively distribute the system dynamic power.

[0031] The present invention realizes real-time suppression of high-speed fluctuation of fuel cell output power in a fuel cell hybrid power system from a bottom-level control perspective, with a short control step and rapid control.

[0032] The present invention enables the fuel cell unit in the fuel cell hybrid power system to independently bear the low-frequency power through dynamic power virtual inertia control, while the energy storage device passively provides high-frequency power.

[0033] The present invention adaptively adjusts the virtual inertia coefficient J by introducing the load dynamic power change rate, so as to achieve the purpose of adaptively smoothing the high-speed fluctuation of the fuel cell output power according to the load power change rate, thereby improving the fuel cell output characteristics and operating performance.

[0034] The present invention adjusts the charging factor α and the damping factor D in real time based on the underlying dynamic power adaptive virtual inertia control through the change of SOC. vThe purpose of dynamic power adaptive distribution of fuel cell hybrid power system is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of a method for dynamic power adaptive virtual inertia control of a fuel cell hybrid system according to the present invention;

[0036] Figure 2 Schematic diagram of virtual inertia of the fuel cell hybrid system of the present invention;

[0037] Figure 3 This is a block diagram of the dynamic power adaptive virtual inertia control in the present invention;

[0038] Figure 4 The topology of the fuel cell hybrid system in the embodiment of the present invention;

[0039] Figure 5 This is a circuit topology and control loop block diagram of a fuel cell unit in an embodiment of the present invention;

[0040] Figure 6 This is a block diagram of the energy storage unit circuit topology and control loop in an embodiment of the present invention;

[0041] Figure 7 The dynamic response of the system under the adaptive adjustment of the virtual inertia coefficient in the embodiment of the present invention;

[0042] Figure 8 This is the dynamic response of the system under actual regenerative load in the embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to the accompanying drawings.

[0044] In this embodiment, see Figure 1 As shown, the present invention proposes a dynamic power adaptive virtual inertia control method for a fuel cell hybrid system, comprising the steps of:

[0045] Step 1: The fuel cell hybrid system consists of a fuel cell unit, an energy storage unit, and a traction load; the fuel cell output power P of the entire system is obtained. fc , energy storage device output power P es , traction load power P load The balance between the three:

[0046] P fc +P es =P load ;

[0047] Step 2: Since the energy storage device has a certain inertial support capability, such as Figure 2 As shown, there is a transient power fluctuation ΔP in the traction load load When the energy storage device can provide virtual inertia power ΔP vir , then the following equilibrium relationship is obtained:

[0048] P fc +P es +ΔP vir =P load +ΔP load ;

[0049] Step 3: In order to realize that the energy storage device can passively provide virtual inertia power ΔP to the system during transient state vir , dynamic power virtual inertia control is proposed for the fuel cell unit: fuel cell output power reference value P fc_ref Subtract the fuel cell output power P fc The deviation of the transient power ΔP is obtained through the first-order inertia control link, and the mathematical expression of dynamic power virtual inertia control is:

[0050]

[0051] Where J is the virtual inertia coefficient, D v is the damping factor;

[0052] Step 4: Directly control the fuel cell output current to indirectly control the fuel cell output power P fc , define the fuel cell output current reference i fc_ref Equal to the transient power ΔP divided by the fuel cell output voltage v fc , fuel cell output current reference i fc_ref The mathematical expression is

[0053]

[0054] As an optimization solution 1 of the above embodiment, the virtual inertia coefficient J in step 3 can be adaptively adjusted. The virtual inertia coefficient J is equal to the initial virtual inertia coefficient J0 plus the adjustment coefficient β multiplied by the absolute value of the load dynamic power change rate. The mathematical expression of the virtual inertia coefficient J is:

[0055]

[0056] Among them, P load (k) is the load power sampling value at time k, P load (k-1) is the load power sampling value at time k-1, T sample is the sampling time.

[0057] As the optimization solution 2 of the above embodiment, the damping factor D in step 3 v(D v ≥0) can be adaptively adjusted, damping factor D v The value of is related to the state of charge (SOC) of the energy storage device, and the damping factor D v The value of is:

[0058] When SOC min ≤SOC≤SOC1, D v =0.

[0059] When SOC1<SOC≤SOC max hour,

[0060] Among them, SOC min , SOC max They are the lower and upper limits of SOC when the energy storage device is working normally, and SOC1 is the damping factor D v When the SOC is 0, the upper limit critical value of the SOC, the coefficient ε can be defined according to the actual situation. min =20%, SOC max =80%, SOC1=60%, ε=2.

[0061] As the optimization solution 3 of the above embodiment, the fuel cell output power reference value P in step 3 fc_ref The value of the load power P load In order to protect the fuel cell, the operating power range is set: the maximum operating power is P fc_max , the minimum operating power is P fc_min Fuel cell output power reference value P fc_ref The specific mathematical expression is:

[0062]

[0063] The coefficient α (α≥1) is defined as the charging factor so that the fuel cell output power reference value P fc_ref Can be greater than or equal to the load power P load , which can achieve the purpose of charging the energy storage device with the fuel cell.

[0064] The value of the charging factor α is related to the SOC of the energy storage device. The value of the charging factor α is determined as follows:

[0065] When SOC min When ≤SOC≤SOC2,

[0066] When SOC2<SOC≤SOC max When α=1;

[0067] Among them, SOC min , SOC max are the lower and upper limits of the SOC when the energy storage device is working normally, SOC2 is the lower limit critical value of the SOC when the charging factor α is 1, and the coefficient δ can be defined according to the actual situation. min =20%, SOC max =80%, SOC2=40%, δ=2.

[0068] Therefore, based on the proposed dynamic power adaptive virtual inertia control, the charging factor α and the damping factor D are adaptively adjusted in real time according to the change of SOC. v The control block diagram of the dynamic power adaptive virtual inertia control method for a fuel cell hybrid system proposed by the present invention can be shown as follows: Figure 3 shown.

[0069] like Figure 4 As shown, the fuel cell hybrid system in this embodiment consists of a fuel cell unit, an energy storage unit using a battery as an energy storage device, and a traction load. The fuel cell is connected to the DC bus via a unidirectional DC / DC converter. The traction load consists of a bidirectional DC / AC converter and a traction motor. The energy storage device is connected to the DC bus via a bidirectional DC / DC converter. The energy storage unit can recover the braking regenerative energy of the traction load and assist the fuel cell in meeting the instantaneous load power demand.

[0070] The dynamic power adaptive virtual inertia control method for a fuel cell hybrid power system proposed in the present invention has universality.

[0071] Therefore, in order to verify the feasibility and effectiveness of the proposed control method, the Boost converter is selected as the interface converter between the fuel cell and the DC bus. Figure 5 As shown in FIG, the circuit topology and control loop block diagram of the fuel cell unit in this embodiment are shown. The control loop is composed of current inner loop control and dynamic power adaptive virtual inertia control.

[0072] Similarly, the Buck / Boost converter is selected as the interface converter between the battery and the DC bus. Figure 6 As shown in FIG, the circuit topology and control loop block diagram of the energy storage unit in this embodiment are shown. The energy storage unit uses traditional voltage-current dual closed-loop control to achieve the purpose of maintaining the DC bus voltage stability.

[0073] like Figure 7 As shown in FIG, the dynamic response of the system under the adaptive adjustment of the virtual inertia coefficient in this embodiment. Figure 7It can be seen that the virtual inertia coefficient J is adaptively adjusted with the fluctuation of load power. The more severe the load power fluctuation is, the larger the inertia coefficient is.

[0074] like Figure 8 As shown in FIG, the dynamic response of the system under the actual regenerative load in this embodiment is shown, where Figure 8 (a) is the system dynamic response without any control, Figure 8 (b), 8(c) and 8(d) are the dynamic responses of the system under the proposed control when the initial SOC is about 50%, 30% and 70%, respectively. Figure 8 (a) and Figure 8 (b) It can be seen that the fuel cell output power fluctuation based on the proposed control is smoother. On the contrary, under the proposed control, the instantaneous power fluctuation of the battery is more severe. In addition, Figure 8 (b) Figure 8 (c) and Figure 8 As shown in (d), adaptive power distribution can be achieved for a fuel cell hybrid system. When the initial SOC is 50%, the load power demand is largely met by the fuel cell, while the battery provides high-speed unbalanced power. When the initial SOC is 30%, the load power demand is met by the fuel cell, and the excess power of the fuel cell is used to charge the battery. When the initial SOC is 70%, the fuel cell and battery share the load power demand. The results show that the proposed method is effective and helps improve the operating performance of the fuel cell.

[0075] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic power adaptive virtual inertia control of a fuel cell hybrid system, characterized in that: Including steps: Step 1: The fuel cell hybrid system consists of a fuel cell unit, an energy storage device unit, and a traction load. The fuel cell output power P of the entire system is obtained. fc , energy storage device output power P es , traction load power P load The balance between the three: fc +P es =P load ; Step 2: Based on the inertial support capability of the energy storage device, there is a transient power fluctuation ΔP in the traction load load When the virtual inertia power ΔP of the energy storage device is obtained vir , establish a balanced relationship: P fc +P es +ΔP vir =P load +ΔP load ; Step 3: Propose dynamic power virtual inertia control for the fuel cell unit, so that the energy storage device can passively provide virtual inertia power ΔP for the system in transient state. vir The dynamic power virtual inertia control: fuel cell output power reference value P fc_ref Subtract the fuel cell output power P fc The deviation of the transient power ΔP is obtained through the first-order inertia control link, and the mathematical expression of dynamic power virtual inertia control is: Where J is the virtual inertia coefficient, D v is the damping factor; Step 4: Directly control the fuel cell output current to indirectly control the fuel cell output power P fc , define the fuel cell output current reference i fc_ref Equal to the transient power ΔP divided by the fuel cell output voltage v fc , fuel cell output current reference i fc_ref The mathematical expression is 2. A method for dynamic power adaptive virtual inertia control of a fuel cell hybrid system according to claim 1, characterized in that: In step 3, the virtual inertia coefficient J is adaptively adjusted. The virtual inertia coefficient J is equal to the initial virtual inertia coefficient J0 plus the adjustment coefficient β multiplied by the absolute value of the load dynamic power change rate. The mathematical expression of the virtual inertia coefficient J is: Among them, P load (k) is the load power sampling value at time k, P load (k-1) is the load power sampling value at time k-1, T sample is the sampling time.

3. The method for dynamic power adaptive virtual inertia control of a fuel cell hybrid system according to claim 1, characterized in that: In step 3, the damping factor D v Perform adaptive adjustment, damping factor D v The value of is related to the state of charge SOC of the energy storage device, and the damping factor D v The value of is: When SOC min ≤SOC≤SOC1, D v =0; When SOC1<SOC≤SOC max hour, Among them, SOC min , SOC max They are the lower and upper limits of SOC when the energy storage device is working normally, and SOC1 is the damping factor D v The upper limit critical value of SOC is 0, and the coefficient ε is defined according to the actual situation.

4. The method for dynamic power adaptive virtual inertia control of a fuel cell hybrid system according to claim 1, characterized in that: The fuel cell output power reference value P in step 3 fc_ref The value of the load power P load In order to protect the fuel cell, the operating power range is set: the maximum operating power is P fc_max , the minimum operating power is P fc_min ; Thus, the fuel cell output power reference value P is obtained fc_ref for: The coefficient α (α≥1) is defined as the charging factor so that the fuel cell output power reference value P fc_ref Can be greater than or equal to the load power P load , so as to achieve the purpose of charging the energy storage device by the fuel cell.

5. The method for dynamic power adaptive virtual inertia control of a fuel cell hybrid system according to claim 4, characterized in that: The value of the charging factor α is related to the SOC of the energy storage device. The value of the charging factor α is determined as follows: When SOC min When ≤SOC≤SOC2, When SOC2<SOC≤SOC max When α=1; Among them, SOC min , SOC max are the lower and upper limits of the SOC when the energy storage device is working normally, respectively. SOC2 is the lower critical value of the SOC when the charging factor α is 1. The coefficient δ is defined according to the actual situation.

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