Hierarchical control method, system and device for battery energy management

Abstract

The embodiment of the invention provides a hierarchical control method, system and device for battery energy management, and belongs to the technical field of discharge control of batteries. The control method comprises the following steps: acquiring total required power; separating high-frequency power from the total demand power by adopting a second-order high-pass filter; subtracting the total required power from the high-frequency power to obtain low-frequency power; inputting the high-frequency power into a super capacitor to obtain super capacitor power; subtracting the high-frequency power from the power of the super capacitor; adding the difference value of the high-frequency power and the power of the super capacitor and the low-frequency power to obtain the total power required by the battery and the lithium battery; and determining optimal battery power and lithium battery power by adopting a preset optimization algorithm according to the total required power of the battery and the lithium battery by taking the minimum equivalent hydrogen consumption as an optimization target and taking the start-stop times, the power change rate and the maximum power which influence the performance degradation of the battery as constraint conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of battery discharge control, and in particular to a battery energy management hierarchical control method, system and device. Background Art

[0002] When a battery car accelerates or brakes, the power demand fluctuates greatly, causing battery performance degradation and bus voltage fluctuations. Supercapacitors have the advantages of long life and high power density. When the car accelerates or brakes, they provide or absorb peak power, which can slow down the performance degradation of batteries and batteries. Therefore, the hybrid power system composed of batteries, lithium batteries and supercapacitors is a typical power supply solution for battery cars. When considering the energy management strategy of how to allocate the power of these three power sources, a sliding average filter is usually used to low-pass filter the demand power to suppress the impact of load power fluctuations on battery durability. However, this low-pass filtering method will produce a large time lag, resulting in the output result being not real-time, affecting the effectiveness of the energy management control strategy. In addition, low-pass filtering is required at each layer, which makes the algorithm complex and further causes the adverse effect of filtering delay on the effectiveness of the algorithm. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a battery energy management hierarchical control method, system and device, which can avoid time lag while achieving filtering.

[0004] In order to achieve the above object, an embodiment of the present invention provides a battery energy management hierarchical control method, including:

[0005] Get the total required power;

[0006] Using a second-order high-pass filter to separate high-frequency power from the total required power;

[0007] Subtracting the total required power from the high frequency power to obtain the low frequency power;

[0008] Inputting the high frequency power into a super capacitor to obtain super capacitor power;

[0009] Subtracting the high frequency power from the supercapacitor power;

[0010] Add the difference between the high frequency power and the super capacitor power and the low frequency power to obtain the total power required by the battery and the lithium battery;

[0011] With the minimum equivalent hydrogen consumption as the optimization goal, the number of starts and stops that affect battery performance degradation, the power change rate and the maximum power as constraints, the preset optimization algorithm is used to determine the optimal battery power and lithium battery power based on the total power requirements of the battery and lithium battery.

[0012] Optionally, using a second-order high-pass filter to separate high-frequency power and low-frequency power from the total required power includes:

[0013] According to formula (1) and formula (2), the high frequency power is separated out.

[0014] P f =P t G HPF (s), (1)

[0015]

[0016] P lf =P t -P f , (3)

[0017] Among them, P f is the high frequency power, P t is the total required power, G HPF (s) is the transfer function, s is the Laplace operator, α is the damping coefficient, ω p is the cutoff frequency of the filter, P lf is the low frequency power.

[0018] Optionally, inputting the high frequency power into a super capacitor to obtain super capacitor power comprises:

[0019] Determine the difference power according to formula (3):

[0020]

[0021] Among them, P Δ is the difference power, ΔSOC is the average state of charge of the supercapacitor, C is the capacitance of the supercapacitor, U DC is the terminal voltage of the supercapacitor;

[0022] The supercapacitor power is determined according to formula (4):

[0023] P c =P Δ +P f , (4)

[0024] Among them, P c is the supercapacitor power.

[0025] Optionally, the objective function of the optimization algorithm is formula (5),

[0026]

[0027] Where J is the objective function, k1 and k2 are equivalent factors, and P fc is the battery power, LHV H2 is the lower calorific value of hydrogen after complete combustion of the battery, η fc is the working efficiency of the battery, δ is the equivalent correction value, P bat is the lithium battery power.

[0028] On the other hand, the present invention further provides a battery energy management hierarchical control system, the control system comprising a processor, and the processor is configured to execute any of the control methods described above.

[0029] In another aspect, the present invention further provides a battery energy management hierarchical control device, the control device comprising:

[0030] A power acquisition module, used to acquire the total required power;

[0031] A second-order high-pass filter, used for separating high-frequency power and low-frequency power from the total required power;

[0032] A first operation module, used for subtracting the total required power from the high frequency power to obtain the low frequency power;

[0033] A supercapacitor module, used to obtain supercapacitor power according to the high-frequency power;

[0034] A second operation module, used for subtracting the high frequency power from the super capacitor power;

[0035] The third operation module is used to add the difference between the high-frequency power and the supercapacitor power and the low-frequency power to obtain the total power required by the battery and the lithium battery.

[0036] The algorithm model module is used to minimize the equivalent hydrogen consumption as the optimization goal, and the number of starts and stops, power change rate and maximum power that affect battery performance degradation as constraints. A preset optimization algorithm is used to determine the optimal battery power and lithium battery power based on the total power required by the battery and lithium battery.

[0037] Optionally, the second-order high-pass filter is used to:

[0038] According to formula (1) and formula (2), the high frequency power and the low frequency power are separated.

[0039] P f =P t G HPF (s), (1)

[0040]

[0041] Among them, P fis the high frequency power, P t is the total required power, G HPF (s) is the transfer function, s is the Laplace operator, α is the damping coefficient, ω p is the cut-off frequency of the filter.

[0042] Optionally, the supercapacitor module is used for:

[0043] Determine the difference power according to formula (3):

[0044]

[0045] Among them, P Δ is the difference power, ΔSOC is the average state of charge of the supercapacitor module, C is the capacitance value of the supercapacitor module, U DC is the terminal voltage of the supercapacitor module;

[0046] The supercapacitor power is determined according to formula (4):

[0047] P c =P Δ +P f , (4)

[0048] Among them, P c is the supercapacitor power.

[0049] Optionally, the objective function of the optimization algorithm is formula (5),

[0050]

[0051] Where J is the objective function, k1 and k2 are equivalent factors, and P fc is the battery power, LHV H2 is the lower calorific value of hydrogen after complete combustion of the battery, η fc is the working efficiency of the battery, δ is the equivalent correction value, P bat is the lithium battery power.

[0052] On the other hand, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, wherein the instructions are used to be read by a machine so that the machine executes any of the control methods described above.

[0053] Through the above technical scheme, the battery energy management hierarchical control method, system and device provided by the present invention use a second-order high-pass filter to separate power, and then determine the total power required by the battery and lithium battery in combination with the characteristics of the supercapacitor. Compared with the prior art, the method provided by the present invention uses a high-pass filter to subtract the original total power requirement from the high-pass filter, thereby replacing the existing low-pass filter and eliminating the lag effect caused by the low-pass filter.

[0054] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0056] Figure 1 is a flow chart of a battery energy management hierarchical control method according to an embodiment of the present invention;

[0057] Figure 2 is a flow chart of a battery energy management hierarchical control method according to an embodiment of the present invention;

[0058] Figure 3 4 is a structural block diagram of a battery energy management hierarchical control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0060] like Figure 1 FIG. 1 is a flow chart of a battery energy management hierarchical control method according to an embodiment of the present invention. Figure 1 In the control method, the control method may include the following steps:

[0061] In step S10, the total required power is obtained;

[0062] In step S11, a second-order high-pass filter is used to separate high-frequency power from the total required power;

[0063] In step S12, the total required power and the high frequency power are subtracted to obtain the low frequency power;

[0064] In step S13, high frequency power is input into the super capacitor to obtain super capacitor power;

[0065] In step S14, the high frequency power and the super capacitor power are subtracted;

[0066] In step S15, the difference between the high frequency power and the super capacitor power and the low frequency power are added to obtain the total power required by the battery and the lithium battery;

[0067] In step S16, the optimization goal is to minimize the equivalent hydrogen consumption, and the number of starts and stops, power change rate and maximum power that affect battery performance degradation are used as constraints. The preset optimization algorithm is used to determine the optimal battery power and lithium battery power based on the total power requirements of the battery and lithium battery.

[0068] In this Figure 1 In the method shown, step S10 is used to obtain the total required power. The method for obtaining the required total power may be, for example, by accessing the current output power demand of the vehicle.

[0069] Steps S11 to S12 are used to obtain low-frequency power. Since the commonly used method for obtaining low-frequency power in the prior art is to filter by a sliding average filter, this method will cause a lag in power transfer. Therefore, in this embodiment of the present invention, step S11 is filtered by a low-hysteresis high-frequency filter, and then combined with step S12, the total required power and the high-frequency power are subtracted to obtain the low-frequency power, thereby avoiding the lag effect caused by the low-frequency filter itself. Specifically, in one example of the present invention, step S11 can also be expressed as separating the high-frequency power according to formula (1) and formula (2),

[0070] P f =P t G HPF (s), (1)

[0071]

[0072] Among them, P f is the high frequency power, P t is the total required power, G HPF (s) is the transfer function, s is the Laplace operator, α is the damping coefficient, ω p is the cut-off frequency of the filter.

[0073] Steps S13 to S15 are used to correct the low-frequency power so that the output battery and total battery power can accurately express the total power demand of the current vehicle. In particular, step S13 inputs the high-frequency power into the supercapacitor to obtain the supercapacitor power. Specifically, the method for determining the supercapacitor power can be to first determine the difference power according to formula (3),

[0074]

[0075] Among them, P Δ is the difference power, ΔSOC is the average state of charge of the supercapacitor, C is the capacitance of the supercapacitor, and U DC is the terminal voltage of the supercapacitor.

[0076] Then determine the supercapacitor power according to formula (4):

[0077] P c =P Δ +P f , (4)

[0078] Among them, P c is the supercapacitor power.

[0079] Finally, the optimal power allocation scheme for the lithium battery power is completed by the optimization algorithm of step S16. The optimization algorithm can be in various forms known to those skilled in the art, including but not limited to particle swarm optimization, neural network model algorithm, etc. In one example of the present invention, considering that the optimization process needs to consider both the combustion of hydrogen and the output power of the lithium battery, the objective function of the optimization algorithm can be formula (6):

[0080]

[0081] Among them, J is the objective function, k1 and k2 are equivalent factors, and P fc is the battery power, LHV H2 is the lower calorific value of hydrogen after complete combustion of the battery, η fc is the working efficiency of the battery, δ is the equivalent correction value, P bat For lithium battery power.

[0082] On the other hand, the present invention further provides a battery energy management hierarchical control system, which may include a processor configured to execute any of the control methods described above.

[0083] In another aspect, the present invention also provides a battery energy management hierarchical control device, such as Figure 3As shown, the control device may include a power acquisition module 1, a second-order high-pass filter 2, a first operation module 3, a supercapacitor module 4, a second operation module 5, a third operation module 6 and an algorithm model module 7. Among them, the power acquisition module 1 can be used to obtain the total required power. The second-order high-pass filter 2 can be used to separate the high-frequency power from the total required power. The first operation module 3 can be used to subtract the total required power from the high-frequency power to obtain the low-frequency power. The supercapacitor module 4 can be used to obtain the supercapacitor power according to the high-frequency power. The second operation module 5 is used to subtract the high-frequency power from the supercapacitor power. The third operation module 6 can be used to add the difference between the high-frequency power and the supercapacitor power and the low-frequency power to obtain the total power required by the battery and the lithium battery. The algorithm model module 7 can be used to minimize the equivalent hydrogen consumption as the optimization target, the number of starts and stops that affect the battery performance degradation, the power change rate and the maximum power as the constraint conditions, and the preset optimization algorithm is used to determine the optimal battery power and lithium battery power according to the total power required by the battery and the lithium battery.

[0084] In this Figure 3 In the control device shown, the power acquisition module 1 is used to obtain the total required power. The method for obtaining the required total power may be, for example, by connecting to the output power demand of the current vehicle. The second-order high-pass filter 2 and the first operation module 3 are used to obtain low-frequency power. Since the commonly used method for obtaining low-frequency power in the prior art is to filter by a sliding average filter, this method will cause a lag in power transfer. Therefore, in this embodiment of the present invention, the second-order high-pass filter 2 is filtered by a low-hysteresis high-frequency filtering method, and then combined with the first operation module 3, the total required power and the high-frequency power are subtracted to obtain the low-frequency power, thereby avoiding the lag effect caused by the low-frequency filter itself. Specifically, in one example of the present invention, the second-order high-pass filter 2 can also be used to separate the high-frequency power according to formula (1) and formula (2),

[0085] P f =P t G HPF (s), (1)

[0086]

[0087] Among them, P f is the high frequency power, P t is the total required power, G HPF (s) is the transfer function, s is the Laplace operator, α is the damping coefficient, ω p is the cut-off frequency of the filter.

[0088] The supercapacitor module 4, the second operation module 5, and the third operation module 6 are used to correct the low-frequency power so that the output battery and total battery power can accurately express the total power demand of the current vehicle. The supercapacitor module 4 is used to obtain the supercapacitor power according to the high-frequency power. Specifically, the method for determining the supercapacitor power can be to first determine the difference power according to formula (3),

[0089]

[0090] Among them, P Δ is the difference power, ΔSOC is the average state of charge of the supercapacitor, C is the capacitance of the supercapacitor, and U DC is the terminal voltage of the supercapacitor.

[0091] Then determine the supercapacitor power according to formula (4):

[0092] P c =P Δ +P f , (4)

[0093] Among them, P c is the supercapacitor power.

[0094] Finally, the optimal power allocation scheme for the lithium battery power is completed by the optimization algorithm built into the algorithm model module 7. The optimization algorithm can be in various forms known to those skilled in the art, including but not limited to particle swarm optimization, neural network model algorithm, etc. In one example of the present invention, considering that the optimization process needs to consider both the combustion of hydrogen and the output power of the lithium battery, the objective function of the optimization algorithm can be formula (6):

[0095]

[0096] Among them, J is the objective function, k1 and k2 are equivalent factors, and P fc is the battery power, LHV H2 is the lower calorific value of hydrogen after complete combustion of the battery, η fc is the working efficiency of the battery, δ is the equivalent correction value, P bat For lithium battery power.

[0097] On the other hand, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, wherein the instructions are used to be read by a machine so that the machine executes any of the control methods described above.

[0098] Through the above technical scheme, the battery energy management hierarchical control method, system and device provided by the present invention use a second-order high-pass filter to separate power, and then determine the total power required by the battery and lithium battery in combination with the characteristics of the supercapacitor. Compared with the prior art, the method provided by the present invention uses a high-pass filter to subtract the original total power requirement from the high-pass filter, thereby replacing the existing low-pass filter and eliminating the lag effect caused by the low-pass filter.

[0099] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0100] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing 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 comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0104] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0105] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0106] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0107] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A battery energy management hierarchical control method, characterized in that: The control method comprises: Get the total required power; Using a second-order high-pass filter to separate high-frequency power from the total required power; Subtracting the total required power from the high frequency power to obtain the low frequency power; Inputting the high frequency power into a super capacitor to obtain super capacitor power; Subtracting the high frequency power from the supercapacitor power; Add the difference between the high frequency power and the super capacitor power and the low frequency power to obtain the total power required by the battery and the lithium battery; With the minimum equivalent hydrogen consumption as the optimization goal, the number of starts and stops that affect battery performance degradation, the power change rate and the maximum power as constraints, the preset optimization algorithm is used to determine the optimal battery power and lithium battery power based on the total power requirements of the battery and lithium battery.

2. The control method according to claim 1, characterized in that: Using a second-order high-pass filter to separate high-frequency power from the total required power includes: According to formula (1) and formula (2), the high frequency power is separated out. P f =P t G HPF (s), (1) Among them, P f is the high frequency power, P t is the total required power, G HPF () is the transfer function, s is the Laplace operator, α is the damping coefficient, ω p is the cut-off frequency of the filter.

3. The control method according to claim 1, characterized in that: Inputting the high frequency power into the super capacitor to obtain the super capacitor power includes: Determine the difference power according to formula (3): Among them, P Δ is the difference power, ΔSOC is the average state of charge of the supercapacitor, C is the capacitance of the supercapacitor, U DC is the terminal voltage of the supercapacitor; The supercapacitor power is determined according to formula (4): P c =P Δ +P f , (4) Among them, P c is the supercapacitor power.

4. The control method according to claim 1, characterized in that: The objective function of the optimization algorithm is formula (5), Where J is the objective function, k1 and k2 are equivalent factors, and P fc is the battery power, LHV H2 is the lower calorific value of hydrogen after complete combustion of the battery, η fc is the working efficiency of the battery, δ is the equivalent correction value, P bat is the lithium battery power.

5. A battery energy management hierarchical control system, characterized in that: The control system comprises a processor, and the processor is configured to execute the control method according to any one of claims 1 to 4.

6. A battery energy management hierarchical control device, characterized in that: The control device comprises: A power acquisition module, used to acquire the total required power; A second-order high-pass filter, used to separate high-frequency power from the total required power; A first operation module, used for subtracting the total required power from the high frequency power to obtain the low frequency power; A supercapacitor module, used to obtain supercapacitor power according to the high-frequency power; A second operation module, used for subtracting the high frequency power from the super capacitor power; The third operation module is used to add the difference between the high-frequency power and the supercapacitor power and the low-frequency power to obtain the total power required by the battery and the lithium battery. The algorithm model module is used to minimize the equivalent hydrogen consumption as the optimization goal, and the number of starts and stops, power change rate and maximum power that affect battery performance degradation as constraints. A preset optimization algorithm is used to determine the optimal battery power and lithium battery power based on the total power required by the battery and lithium battery.

7. The control method according to claim 6, characterized in that: The second-order high-pass filter is used to: According to formula (1) and formula (2), the high frequency power and the low frequency power are separated. P f =P t G HPF (s), (1) Among them, P f is the high frequency power, P t is the total required power, G HPF () is the transfer function, s is the Laplace operator, α is the damping coefficient, ω p is the cut-off frequency of the filter.

8. The control device according to claim 6, characterized in that: The supercapacitor module is used for: Determine the difference power according to formula (3): Among them, P Δ is the difference power, ΔSOC is the average state of charge of the supercapacitor module, C is the capacitance value of the supercapacitor module, and U DC is the terminal voltage of the supercapacitor module; The supercapacitor power is determined according to formula (4): P c =P Δ +P f , (4) Among them, P c is the supercapacitor power.

9. The control device according to claim 6, characterized in that: The objective function of the optimization algorithm is formula (5), Where J is the objective function, k1 and k2 are equivalent factors, and P fc is the battery power, LHV H2 is the lower calorific value of hydrogen after complete combustion of the battery, η fc is the working efficiency of the battery, δ is the equivalent correction value, P bat is the lithium battery power.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and the instructions are used to be read by a machine so that the machine executes the control method according to any one of claims 1 to 4.

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