Energy management strategy, system and equipment of hybrid energy storage system and medium

Abstract

The invention discloses an energy management strategy, system, equipment and medium for a hybrid energy storage system, and relates to the field of hybrid energy storage, and the energy management strategy for the hybrid energy storage system comprises the steps: calculating the load power based on the voltage of a battery module and the load current; inputting the load power into sliding discrete fast Fourier transform to obtain two adjacent load power frequency spectrums; determining a frequency corresponding to the load power in the newest load power spectrum as an adaptive cut-off frequency; and outputting a low-frequency component based on the adaptive cut-off frequency through an adaptive digital filter, and performing power distribution based on the low-frequency component. Therefore, the sliding discrete fast Fourier transform is utilized to obtain the two adjacent load power frequency spectrums, and the frequency corresponding to the load power in the newest load power frequency spectrum is determined as the adaptive cut-off frequency, so that the updating speed of the adaptive cut-off frequency is accelerated, and the power distribution efficiency of the hybrid energy storage system is improved.

Description

Technical Field

[0001] The present invention relates to the field of hybrid energy storage, and particularly to an energy management strategy, system, device and medium for a hybrid energy storage system. Background Art

[0002] The increasingly tense energy crisis and the development demand for low-carbon energy have promoted the utilization of renewable energy. The most widely used renewable energy sources are wind energy and solar energy. The electric energy output by the power generation systems of these energy sources is intermittent, which reduces the safety, stability, reliability and power quality of the power grid. Energy storage devices, which have functions such as electric energy storage, peak shaving and valley filling, suppressing the output fluctuations of new energy, and emergency backup, are necessary links in the new power system. Common energy storage devices can be divided into power-type and energy-type. Power-type energy storage devices have advantages such as high power density and fast response speed, but the disadvantage is that the energy density is relatively small, such as supercapacitors, superconducting energy storage, flywheel energy storage, etc.; energy-type energy storage devices have a large energy density, but the disadvantage is that the response speed is slow and not suitable for frequent charge and discharge, such as lithium batteries and pumped-storage hydroelectricity. Due to the limitations of the disadvantages of power-type and energy-type energy storage devices, it is difficult for a single energy storage device to meet the requirements of the new power system with a large amount of renewable energy access. Therefore, it is necessary to combine two or more energy storage devices to form a hybrid energy storage system to give full play to the technical complementarity of each energy storage device.

[0003] The energy management strategy, also known as the power distribution strategy, plays a crucial role in the hybrid energy storage system and is usually regarded as its "brain" because it can control all components in the hybrid energy storage system and improve the performance of the hybrid energy storage system. Traditional energy management strategies can be divided into three categories: rule-based energy management strategies, optimization-based energy management strategies, and artificial intelligence-based energy management strategies. Rule-based energy management strategies are a control method based on repeated tests and summaries; optimization-based control (i.e., dynamic programming and equivalent consumption minimization strategies) is an advanced energy management strategy used to address problems that occur when the new power system operates under unexpected or complex conditions. Artificial intelligence-based energy management strategies have the highest performance in most aspects. However, it requires pre-learning or a large amount of data for training to obtain the corresponding performance. Rule-based energy management strategies have the advantages of high efficiency, high speed, low cost and low complexity, but the reliability is relatively low. Optimization-based and artificial intelligence-based energy management strategies can effectively improve the reliability of rule-based energy management strategies. However, they all have to sacrifice other performances, such as efficiency and algorithm complexity.

[0004] In summary, how to combine the advantages of traditional energy management strategies while making up for their deficiencies, so as to take into account high speed and high efficiency, and thus make full use of the advantages of the high energy density and high power density technical complementarity of the hybrid energy storage system is an urgent problem to be solved. Summary of the Invention

[0005] The object of the present invention is to provide an energy management strategy, system, device and medium for a hybrid energy storage system, which can use the sliding discrete fast Fourier transform to obtain the load power spectra of two adjacent loads, and determine the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency, thereby accelerating the update speed of the adaptive cut-off frequency and improving the power distribution efficiency of the hybrid energy storage system.

[0006] In a first aspect, the present application discloses an energy management strategy for a hybrid energy storage system, which is applied to the microcontroller unit of the hybrid energy storage system. The hybrid energy storage system includes an adaptive digital filter and a battery module. The energy management strategy for the hybrid energy storage system includes:

[0007] Obtain the battery module voltage and the load current;

[0008] Calculate the load power based on the battery module voltage and the load current;

[0009] Input the load power into the sliding discrete fast Fourier transform to obtain the load power spectra of two adjacent loads;

[0010] Determine the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency;

[0011] Output the low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, and perform power distribution based on the low-frequency component.

[0012] Optionally, before outputting the low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, it further includes:

[0013] Determine the cut-off frequency range of the adaptive digital filter;

[0014] Determine the cut-off frequency with the smallest difference from the adaptive cut-off frequency within the cut-off frequency range as the new adaptive cut-off frequency;

[0015] Outputting the low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter includes:

[0016] Output the low-frequency component through the adaptive digital filter based on the new adaptive cut-off frequency.

[0017] Optionally, determining the cut-off frequency range of the adaptive digital filter includes:

[0018] Determine the preset frequency threshold greater than the fundamental frequency of the power grid as the lower limit of the cut-off frequency;

[0019] Determine the frequency with the largest proportion in the latest load power spectrum;

[0020] Determine the time constant of the adaptive digital filter based on the frequency with the largest proportion;

[0021] Take the reciprocal of the product of the time constant and 2 as the upper limit of the cut-off frequency;

[0022] Determine the range between the lower limit of the cut-off frequency and the upper limit of the cut-off frequency as the cut-off frequency range of the adaptive digital filter.

[0023] Optionally, the adaptive digital filter is an adaptive discrete digital low-pass filter.

[0024] Optionally, power distribution based on the low-frequency component includes:

[0025] Determine the high-frequency component based on the low-frequency component;

[0026] Allocate the low-frequency component to the battery module;

[0027] Allocate the high-frequency component to the power-type energy storage device.

[0028] Optionally, determining the high-frequency component based on the low-frequency component includes:

[0029] Determine whether the low-frequency component is greater than zero;

[0030] If the low-frequency component is greater than zero, the high-frequency component is the load power minus the low-frequency component;

[0031] If the low-frequency component is less than zero, the high-frequency component is the load power.

[0032] Optionally, the battery module is a lithium battery module, and the power-type energy storage device is a supercapacitor.

[0033] In a second aspect, the present application discloses an energy management system for a hybrid energy storage system, which is applied to a microcontroller unit of the hybrid energy storage system. The hybrid energy storage system includes an adaptive digital filter and a battery module. The energy management system for the hybrid energy storage system includes:

[0034] A first acquisition module for acquiring the battery module voltage and the load current;

[0035] A calculation module for calculating the load power based on the battery module voltage and the load current;

[0036] A second acquisition module for inputting the load power into a sliding discrete fast Fourier transform to obtain two adjacent load power spectra;

[0037] A determination module, configured to determine the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency;

[0038] An allocation module, configured to output a low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, and perform power allocation based on the low-frequency component.

[0039] In a third aspect, the present application discloses an electronic device, including:

[0040] A memory, configured to store a computer program;

[0041] A processor, configured to execute the computer program to implement the steps of the hybrid energy storage system energy management strategy as described above.

[0042] In a fourth aspect, the present application discloses a computer-readable storage medium, configured to store a computer program, wherein the computer program, when executed by a processor, implements the steps of the hybrid energy storage system energy management strategy as described above.

[0043] The present application provides a hybrid energy storage system energy management strategy, system, device and medium. The hybrid energy storage system energy management strategy includes: obtaining the battery module voltage and the load current; calculating the load power based on the battery module voltage and the load current; inputting the load power into a sliding discrete fast Fourier transform to obtain two adjacent load power spectra; determining the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency; outputting a low-frequency component based on the adaptive cut-off frequency through an adaptive digital filter, and performing power allocation based on the low-frequency component. It can be seen that the present application uses a sliding discrete fast Fourier transform to obtain two adjacent load power spectra, and determines the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency, which speeds up the update speed of the adaptive cut-off frequency and improves the power allocation efficiency of the hybrid energy storage system. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the prior art and the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a flowchart of a hybrid energy storage system energy management strategy disclosed by the present invention;

[0046] Figure 2 It is a flowchart of a specific hybrid energy storage system energy management strategy disclosed by the present invention;

[0047] Figure 3 A structural schematic diagram of an energy management system for a hybrid energy storage system disclosed by the present invention;

[0048] Figure 4 A structural diagram of an electronic device disclosed by the present invention. Specific implementation manners

[0049] The core of the present invention is to provide an energy management strategy, system, device and medium for a hybrid energy storage system, which can use the sliding discrete fast Fourier transform to obtain the load power spectra of two adjacent loads, and determine the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency, accelerating the update speed of the adaptive cut-off frequency and improving the power distribution efficiency of the hybrid energy storage system.

[0050] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Traditional energy management strategies can be divided into three categories: rule-based energy management strategies, optimization-based energy management strategies, and artificial intelligence-based energy management strategies. The rule-based energy management strategy is a control method summarized based on repeated tests; the optimization-based control (i.e., dynamic programming and equivalent consumption minimization strategy) is an advanced energy management strategy used to address problems that occur when a new power system operates under unexpected or complex conditions. The artificial intelligence-based energy management strategy has the highest performance in most aspects. However, it requires pre-learning or a large amount of data for training to obtain the corresponding performance. The rule-based energy management strategy has the advantages of high efficiency, high speed, low cost and low complexity, but has low reliability. The optimization-based and artificial intelligence-based energy management strategies can effectively improve the reliability of the rule-based energy management strategy. However, they must all sacrifice other performances, such as efficiency and algorithm complexity. Therefore, the present application provides an energy management strategy for a hybrid energy storage system, which can balance high speed and high efficiency.

[0052] Please refer to Figure 1 as shown in Figure 1 A flowchart of an energy management strategy for a hybrid energy storage system disclosed by the present invention.

[0053] This energy management strategy for a hybrid energy storage system is applied to the microcontroller unit of the hybrid energy storage system. The hybrid energy storage system includes an adaptive digital filter and a battery module. The energy management strategy for the hybrid energy storage system includes:

[0054] S11. Obtain the battery module voltage and the load current;

[0055] S12. Calculate the load power based on the battery module voltage and the load current.

[0056] In this embodiment, in order to obtain the battery module voltage and the load current to calculate the load power, the battery module voltage and the load current can be directly monitored through the detection circuit built in the hybrid energy storage system; then the load power can be calculated based on the monitored battery module voltage and the load current.

[0057] S13. Input the load power into the sliding discrete fast Fourier transform to obtain two adjacent load power spectra.

[0058] The traditional discrete Fourier transform has problems of complex calculation and delay. Each calculation requires re-filling the data window to obtain the subsequent spectrum, which causes the traditional discrete Fourier transform to be unable to update the cut-off frequency of the adaptive filter in a timely manner according to the current load condition. The equation of the discrete Fourier transform is as follows:

[0059] ; (1)

[0060] In the formula, \(x[n]\) represents the time-domain signal, represents the frequency-domain signal, \(k\) represents the \(k\)th frequency point, \(k = 0, 1, \ldots, N - 1\), \(n\) represents the sequence number of the \(n\)th data in the window, \(n = 0, 1, \ldots, N - 1\), \(j\) is the imaginary unit, and \(j\) satisfies , represents the accumulation, is the complex exponential function.

[0061] The sliding discrete fast Fourier transform can obtain two adjacent spectra. When the microcontroller unit continuously acquires data, the frequency with the largest proportion can be continuously updated according to the latest spectrum, and the sliding discrete fast Fourier transform speeds up the update speed of the adaptive cut-off frequency. Moreover, the sliding discrete fast Fourier transform can update the spectrum sample by sample, rather than waiting until the entire data window is filled for calculation as in the traditional Fourier transform. This characteristic of updating sample by sample enables the sliding discrete fast Fourier transform to respond faster to signal changes, thereby improving the time resolution. At the same time, the sliding discrete fast Fourier transform supports data windows with arbitrary step sizes and can also adjust the size and step size of the data window according to actual needs to further optimize the time resolution. Among them, the frequency resolution is inversely proportional to the number of sampling points. The sliding discrete fast Fourier transform can increase the number of sampling points to improve the frequency resolution while maintaining the calculation efficiency. For example, increasing the number of sampling points from 1024 to 2048 can double the frequency resolution. The equation of the sliding discrete fast Fourier transform is as follows:

[0062] ; (2)

[0063] In the formula, n + 1 represents the serial number of the (n + 1)-th data in the window.

[0064] S14. Determine the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency.

[0065] To further optimize the filtering effect of the adaptive digital filter, the cut-off frequency of the adaptive digital filter needs to be dynamically adjusted according to the change of the load power spectrum. The sliding discrete fast Fourier transform can obtain two adjacent spectra. When the microcontroller unit continuously acquires data, the frequency with the largest proportion will be continuously updated according to the latest spectrum. At this time, determine the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency, and use the adaptive cut-off frequency as the cut-off frequency of the adaptive digital filter, which can realize the real-time update of the cut-off frequency of the adaptive digital filter to optimize the smoothing effect of the output signal of the adaptive digital filter.

[0066] It should be noted that the cut-off frequency of the adaptive digital filter is the frequency point at which the adaptive digital filter starts to down-convert or filter the input signal and corresponds to a point on the signal frequency response curve. When some frequency components of the input signal are lower than or equal to the adaptive cut-off frequency, these frequency components can pass through the adaptive digital filter, while the frequency components higher than the adaptive cut-off frequency will be filtered or attenuated. The adaptive filter used in this hybrid energy storage system energy management strategy does not need to converge to a stable state, which can reduce the algorithm complexity and improve the robustness, thus showing better performance.

[0067] S15. Output the low-frequency component based on the adaptive cut-off frequency through an adaptive digital filter, and perform power distribution based on the low-frequency component.

[0068] When the input signal of the adaptive digital filter is the load power, the frequency components in the load power that are lower than or equal to the adaptive cut-off frequency can pass through the adaptive digital filter. Among them, the frequency components in the load power that are lower than or equal to the adaptive cut-off frequency are the low-frequency components. After the adaptive digital filter outputs the low-frequency component, the microcontroller unit can allocate the low-frequency component to the battery module and the high-frequency component to the power-type energy storage device to meet the requirements of the new power system with a large amount of renewable energy access. Specifically, when the low-frequency component is positive and the high-frequency component is negative, the power-type energy storage device will absorb this part of the energy to smooth the output power of the battery module; when the low-frequency component is negative, the high-frequency component is the load power, that is, the power-type energy storage device needs to bear all the load power; if the load power is negative, the power-type energy storage device will absorb this part of the feedback energy.

[0069] It should be noted that since the adaptive cut-off frequency determined by the frequency corresponding to the load power in the latest load power spectrum may not be within the cut-off frequency range of the adaptive digital filter, the frequency with the smallest difference from the adaptive cut-off frequency within the cut-off frequency range of the adaptive digital filter should be used as the new adaptive cut-off frequency, and the new adaptive cut-off frequency is the cut-off frequency of the adaptive digital filter. Specifically, if the adaptive cut-off frequency is greater than the upper limit of the cut-off frequency of the adaptive digital filter, the upper limit of the cut-off frequency of the adaptive digital filter is used as the cut-off frequency of the adaptive digital filter; if the adaptive cut-off frequency is within the cut-off frequency range of the adaptive digital filter, the adaptive cut-off frequency is used as the cut-off frequency of the adaptive digital filter.

[0070] It can be seen that this application uses the sliding discrete fast Fourier transform to obtain two adjacent load power spectra, and determines the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency, which speeds up the update speed of the adaptive cut-off frequency and improves the power distribution efficiency of the hybrid energy storage system.

[0071] Based on the above embodiments:

[0072] Specifically, please refer to Figure 2 as shown in Figure 2 which is a flowchart of a specific energy management strategy for a hybrid energy storage system disclosed in the present invention.

[0073] As an optional embodiment, before outputting the low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, it further includes:

[0074] Determine the cut-off frequency range of the adaptive digital filter;

[0075] Determine the cut-off frequency with the smallest difference from the adaptive cut-off frequency within the cut-off frequency range as the new adaptive cut-off frequency;

[0076] Output the low-frequency component based on the adaptive cut-off frequency through an adaptive digital filter, including:

[0077] Output the low-frequency component based on the new adaptive cut-off frequency through an adaptive digital filter.

[0078] Since the adaptive cut-off frequency determined from the frequency corresponding to the load power in the latest load power spectrum may not be within the cut-off frequency range of the adaptive digital filter, in order to optimize the filtering effect and improve the reliability of the energy management strategy of the hybrid energy storage system, it is necessary to first determine the cut-off frequency range of the adaptive digital filter, then use the frequency with the smallest difference from the adaptive cut-off frequency within the cut-off frequency range of the adaptive digital filter as the new adaptive cut-off frequency, then use the new adaptive cut-off frequency as the cut-off frequency of the adaptive digital filter, and finally output the low-frequency component based on the new adaptive cut-off frequency through the adaptive digital filter.

[0079] Specifically, if the adaptive cut-off frequency is greater than the upper limit of the cut-off frequency of the adaptive digital filter, then use the upper limit of the cut-off frequency of the adaptive digital filter as the cut-off frequency of the adaptive digital filter; if the adaptive cut-off frequency is within the cut-off frequency range of the adaptive digital filter, then use the adaptive cut-off frequency as the cut-off frequency of the adaptive digital filter.

[0080] It can be seen that in this embodiment, before outputting the low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, first determine the cut-off frequency range of the adaptive digital filter, and determine the cut-off frequency with the smallest difference from the adaptive cut-off frequency within the cut-off frequency range as the new adaptive cut-off frequency, so that the adaptive digital filter outputs the low-frequency component based on the new adaptive cut-off frequency, optimizing the filtering effect, improving the accuracy of the low-frequency component output by the adaptive cut-off frequency, and increasing the reliability of the energy management strategy of the hybrid energy storage system.

[0081] As an optional embodiment, determining the cut-off frequency range of the adaptive digital filter includes:

[0082] Determine the lower limit of the cut-off frequency as a preset frequency threshold greater than the fundamental frequency of the power grid;

[0083] Determine the frequency with the largest proportion in the latest load power spectrum;

[0084] Determine the time constant of the adaptive digital filter based on the frequency with the largest proportion;

[0085] Take the reciprocal of the product of the time constant and as the upper limit of the cut-off frequency;

[0086] Determine the range between the lower cut-off frequency and the upper cut-off frequency as the cut-off frequency range of the adaptive digital filter.

[0087] Specifically, due to the time constant of the adaptive digital filter can be continuously adjusted according to the frequency with the largest proportion in the latest load power spectrum, and the smoothing effect is related to the time constant of the discrete digital low-pass filter In this case, the lower cut-off frequency and the upper cut-off frequency should be preset. Among them, the lower cut-off frequency of the adaptive digital filter should be higher than the fundamental frequency of the power grid (such as 50Hz or 60Hz) to avoid excessive filtering of the fundamental signal by the adaptive digital filter; the upper cut-off frequency of the adaptive digital filter is related to the time constant of the adaptive digital filter The relationship is as follows:

[0088] ; (3)

[0089] In this embodiment, a preset frequency threshold greater than the fundamental frequency of the power grid can be determined as the lower cut-off frequency. Before calculating the upper cut-off frequency, the frequency with the largest proportion in the latest load power spectrum can be determined first, then the time constant of the adaptive digital filter can be determined based on the frequency with the largest proportion, and then the upper cut-off frequency can be calculated by formula (3). Finally, the range between the lower cut-off frequency and the upper cut-off frequency is determined as the cut-off frequency range of the adaptive digital filter.

[0090] It should be noted that in signal processing, we usually focus on the frequency components with the largest energy or amplitude in the signal, because these components often reflect the main characteristics of the signal. For example, in the scenario of load power change, the frequency with the largest proportion may correspond to the main harmonic or fundamental components.

[0091] In addition, the adaptive filter generally considers the entire load curve, rather than just n steps. If there is no sharp change in the power curve within n steps, the sliding discrete Fourier transform will obtain the corresponding adaptive cut-off frequency fadaptive(a). On the contrary, if the power curve within n steps has the characteristic of sharp change, the sliding discrete Fourier transform will obtain the corresponding adaptive cut-off frequency fadaptive(b). Since fadaptive(a) > fadaptive(b), when using the adaptive digital filter with fadaptive(b) for power distribution, a higher-frequency load component will be obtained than when using the adaptive digital filter with fadaptive(a). Given this characteristic, the proposed adaptive discrete digital filter energy management strategy can optimize the utilization of the energy of the power-type energy storage device while obtaining a certain smoothing effect. It should be noted that if the sudden change of the load causes a step response, the power curve within n steps will change sharply, and its energy is concentrated in the low-frequency component, so fadaptive(b) is even lower at this time; while the stationary load may have more periodic fluctuations, resulting in a higher fadaptive(a).

[0092] It can be seen that in this embodiment, the preset frequency threshold greater than the fundamental frequency of the power grid is determined as the lower limit of the cut-off frequency, and the reciprocal of the product of the time constant and 2 is determined as the upper limit of the cut-off frequency, and the range between the lower limit of the cut-off frequency and the upper limit of the cut-off frequency is determined as the cut-off frequency range of the adaptive digital filter, which can effectively output the fundamental wave signal, suppress high-frequency noise at the same time, and avoid signal distortion or over-filtering caused by too high or too low cut-off frequencies.

[0093] As an alternative embodiment, the adaptive digital filter is an adaptive discrete digital low-pass filter.

[0094] Specifically, when the load power is input into the adaptive discrete digital low-pass filter, the adaptive discrete digital low-pass filter will output a smoothed power curve. The smoothed power curve mainly reflects the low-frequency component of the load power. Since the energy density of the battery module is large and it is suitable for bearing the fluctuations of the low-frequency component, the low-frequency component is usually borne by the battery module. However, the disadvantage of the battery module is that the number of charge and discharge cycles is limited. The smoothed power curve can reduce the frequent charge and discharge times of the battery module, thereby prolonging the life of the battery module.

[0095] This embodiment uses a first-order low-pass filter as the design reference for the digital low-pass filter. The equation of the first-order low-pass filter is as follows:[[]]

[0096] ; (4)

[0097] where H(s) is the transfer function, is the RC time constant and s is the Laplace variable.

[0098] Through the backward Euler method and Z-transform, a continuous-time low-pass filter can be converted into a discrete-time digital low-pass filter. The discrete-time digital low-pass filter is the adaptive discrete digital low-pass filter, and the equation of the adaptive discrete digital low-pass filter can be derived:

[0099] ; (5)

[0100] where y[n] represents the nth output of the adaptive discrete digital low-pass filter; y[n - 1] represents the (n - 1)th output of the adaptive discrete digital low-pass filter; u[n] represents the input of the adaptive discrete digital low-pass filter, is the sampling period.

[0101] It can be seen that in this embodiment, through the backward Euler method and Z-transform, the continuous-time low-pass filter is converted into an adaptive discrete digital low-pass filter.

[0102] As an alternative embodiment, the hybrid energy storage system further includes a power-type energy storage device for power distribution based on low-frequency components, including:

[0103] Determine the high-frequency component based on the low-frequency component;

[0104] Allocate the low-frequency component to the battery module;

[0105] Allocate the high-frequency component to the power-type energy storage device.

[0106] Specifically, the load power is input into the adaptive digital filter. After filtering, the adaptive digital filter outputs the low-frequency component, and then the high-frequency component is determined based on the low-frequency component. Since the energy density of the battery module is large, the low-frequency component usually has a large amplitude and a slow fluctuation frequency, and long-term charge and discharge are required to smooth the fluctuation. Therefore, the low-frequency component is allocated to the battery module; the power density of the power-type energy storage device is large and the response speed is fast. The high-frequency component has a small amplitude and a fast fluctuation frequency, and fast charge and discharge are required to suppress the fluctuation. Therefore, the high-frequency component is allocated to the power-type energy storage device. When the power of the load suddenly changes, the sudden change in power will be absorbed by the power-type energy storage device, which can reduce the damage to the battery module caused by power mutation.

[0107] It can be seen that in this embodiment, the low-frequency component is allocated to the battery module and the high-frequency component is allocated to the power-type energy storage device, which can utilize the long-term charge and discharge characteristics of the battery module to smooth the fluctuation of the low-frequency component, and the fast charge and discharge characteristics of the power-type energy storage device to suppress the fluctuation. When the power of the load suddenly changes, the sudden change in power will be absorbed by the power-type energy storage device, avoiding damage to the battery module caused by power mutation.

[0108] As an alternative embodiment, determining the high-frequency component based on the low-frequency component includes:

[0109] Determine whether the low-frequency component is greater than zero;

[0110] If the low-frequency component is greater than zero, the high-frequency component is the load power minus the low-frequency component;

[0111] If the low-frequency component is less than zero, the high-frequency component is the load power.

[0112] Specifically, when the load power is input into the adaptive digital filter, the adaptive digital filter outputs the low-frequency component after filtering. If the low-frequency component is greater than zero, the high-frequency component is the load power minus the low-frequency component. At this time, the power-type energy storage device provides or absorbs additional energy to prevent the power of the battery module from rising sharply; if the low-frequency component is less than zero, the high-frequency component is the load power, and the power-type energy storage device provides energy for the load or absorbs the high-frequency component in the load, and the battery module is charged.

[0113] It can be seen that in this embodiment, when the low-frequency component is greater than zero, the power-type energy storage device provides or absorbs additional energy to prevent the power of the battery module from rising sharply; when the low-frequency component is less than zero, the power-type energy storage device provides energy for the load or absorbs the high-frequency component in the load, and the battery module is charged, improving the efficiency of the hybrid energy storage system.

[0114] As an alternative embodiment, the battery module is a lithium battery module, and the power-type energy storage device is a super capacitor.

[0115] Specifically, when the load power is input into the adaptive digital filter, the adaptive digital filter outputs the low-frequency component after filtering. If the low-frequency component is greater than zero, the high-frequency component is the load power minus the low-frequency component. At this time, the super capacitor provides or absorbs additional energy to prevent the power of the lithium battery module from rising sharply; if the low-frequency component is less than zero, the high-frequency component is the load power, and the super capacitor provides energy for the load or absorbs the high-frequency component in the load, and the lithium battery module is charged.

[0116] It can be seen that in this embodiment, the lithium battery module is used as the battery module, and the super capacitor is used as the power-type energy storage device. The lithium battery module undertakes the low-frequency component and the super capacitor undertakes the high-frequency component, giving full play to the advantages of the two energy storage devices and improving the overall performance of the hybrid energy storage system.

[0117] Correspondingly, referring to Figure 3 as shown Figure 3 is a schematic structural diagram of an energy management system for a hybrid energy storage system disclosed by the present invention.

[0118] The present invention also provides an energy management system for a hybrid energy storage system, which is applied to a microcontroller unit of the hybrid energy storage system. The hybrid energy storage system includes an adaptive digital filter and a battery module. The energy management system for the hybrid energy storage system includes:

[0119] A first acquisition module 11, configured to acquire the battery module voltage and the load current;

[0120] A calculation module 12, configured to calculate the load power based on the battery module voltage and the load current;

[0121] A second acquisition module 13, configured to input the load power into a sliding discrete fast Fourier transform to obtain two adjacent load power spectra;

[0122] A determination module 14, configured to determine the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency;

[0123] An allocation module 15, configured to output a low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, and perform power allocation based on the low-frequency component.

[0124] It can be seen that the present application uses a sliding discrete fast Fourier transform to obtain two adjacent load power spectra, determines the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency, speeds up the update speed of the adaptive cut-off frequency, and improves the power allocation efficiency of the hybrid energy storage system.

[0125] In some specific embodiments, the energy management system for the hybrid energy storage system further includes:

[0126] A first determination unit, configured to determine the cut-off frequency range of the adaptive digital filter;

[0127] A second determination unit, configured to determine the cut-off frequency with the smallest difference from the adaptive cut-off frequency within the cut-off frequency range as the new adaptive cut-off frequency;

[0128] The allocation module 15 includes:

[0129] An output unit, configured to output the low-frequency component based on the new adaptive cut-off frequency through the adaptive digital filter.

[0130] In some specific embodiments, the first determination unit includes:

[0131] A third determination unit, configured to determine a preset frequency threshold greater than the fundamental frequency of the power grid as the lower limit of the cut-off frequency;

[0132] A fourth determination unit, configured to determine the frequency with the largest proportion in the latest load power spectrum;

[0133] A fifth determination unit, configured to determine a time constant of the adaptive digital filter based on the frequency with the largest proportion;

[0134] A sixth determination unit, configured to determine a reciprocal of a product of the time constant and 2 as an upper cut-off frequency;

[0135] A seventh determination unit, configured to determine a range between the lower cut-off frequency and the upper cut-off frequency as a cut-off frequency range of the adaptive digital filter.

[0136] In some specific embodiments, the adaptive digital filter is an adaptive discrete digital low-pass filter.

[0137] In some specific embodiments, the hybrid energy storage system further includes a power-type energy storage device, and the distribution module 15 includes:

[0138] An eighth determination unit, configured to determine a high-frequency component based on the low-frequency component;

[0139] A first distribution unit, configured to distribute the low-frequency component to the battery module;

[0140] A second distribution unit, configured to distribute the high-frequency component to the power-type energy storage device.

[0141] In some specific embodiments, the distribution module 15 includes:

[0142] A judgment unit, configured to judge whether the low-frequency component is greater than zero;

[0143] A ninth determination unit, configured to, if the low-frequency component is greater than zero, set the high-frequency component as the load power minus the low-frequency component;

[0144] A tenth determination unit, configured to, if the low-frequency component is less than zero, set the high-frequency component as the load power.

[0145] In some specific embodiments, the battery module is a lithium battery module, and the power-type energy storage device is a super capacitor.

[0146] Furthermore, an embodiment of the present application also discloses an electronic device, Figure 4A structural diagram of an electronic device disclosed by the present invention. The content in the figure should not be regarded as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the hybrid energy storage system energy management strategy disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0147] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of this application, and no specific limitation is imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application needs, and no specific limitation is made here.

[0148] In addition, the memory 22, as a carrier for resource storage, may be a read-only memory, a random access memory, a disk, or an optical disc, etc. The resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.

[0149] Among them, the operating system 221 is used to manage and control each hardware device and the computer program 222 on the electronic device 20, and it may be Windows Server, Netware, Unix, Linux, etc. The computer program 222 may further include a computer program capable of completing other specific tasks in addition to the computer program capable of implementing the hybrid energy storage system energy management strategy executed by the electronic device 20 disclosed in any of the foregoing embodiments.

[0150] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the message display method of the instant interactive media platform disclosed above. For the specific steps of this method, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated here.

[0151] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts among the various embodiments may be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts may be referred to the description of the method part.

[0152] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0153] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy management strategy for a hybrid energy storage system, characterized in that, A microcontroller unit applied to a hybrid energy storage system, the hybrid energy storage system including an adaptive digital filter and a battery module, and the energy management strategy of the hybrid energy storage system includes: Obtain the battery module voltage and the load current; Calculate the load power based on the battery module voltage and the load current; Input the load power into a sliding discrete fast Fourier transform to obtain two adjacent load power spectra; Determine the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency; Output a low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, and perform power distribution based on the low-frequency component.

2. The energy management strategy of the hybrid energy storage system according to claim 1, wherein Before outputting the low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, it further includes: Determine the cut-off frequency range of the adaptive digital filter; Determine the cut-off frequency with the smallest difference from the adaptive cut-off frequency within the cut-off frequency range as the new adaptive cut-off frequency; Outputting the low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter includes: Output the low-frequency component based on the new adaptive cut-off frequency through the adaptive digital filter.

3. The energy management strategy of the hybrid energy storage system according to claim 2, characterized in that Determining the cut-off frequency range of the adaptive digital filter includes: Determine a preset frequency threshold greater than the fundamental frequency of the power grid as the lower cut-off frequency; Determine the frequency with the largest proportion in the latest load power spectrum; Determine the time constant of the adaptive digital filter based on the frequency with the largest proportion; Determine the reciprocal of the product of the time constant and 2 as the upper limit of the cut-off frequency; Determine the range between the lower cut-off frequency and the upper cut-off frequency as the cut-off frequency range of the adaptive digital filter.

4. The energy management strategy of the hybrid energy storage system according to claim 1, wherein The adaptive digital filter is an adaptive discrete digital low-pass filter.

5. The energy management strategy of the hybrid energy storage system according to any one of claims 1 to 4, characterized in that, The hybrid energy storage system further includes a power-type energy storage device, and performing power distribution based on the low-frequency component includes: Determine a high-frequency component based on the low-frequency component; Allocate the low-frequency component to the battery module; Allocate the high-frequency component to the power-type energy storage device.

6. The energy management strategy of the hybrid energy storage system according to claim 5, wherein Determining the high-frequency component based on the low-frequency component includes: Judge whether the low-frequency component is greater than zero; If the low-frequency component is greater than zero, the high-frequency component is the load power minus the low-frequency component; If the low-frequency component is less than zero, the high-frequency component is the load power.

7. The energy management strategy of the hybrid energy storage system according to claim 5, characterized in that The battery module is a lithium battery module, and the power-type energy storage device is a super capacitor.

8. A hybrid energy storage system energy management system, characterized in that, A microcontroller unit applied to a hybrid energy storage system, the hybrid energy storage system including an adaptive digital filter and a battery module, and the energy management system of the hybrid energy storage system includes: A first acquisition module for acquiring the battery module voltage and the load current; A calculation module for calculating the load power based on the battery module voltage and the load current; A second acquisition module for inputting the load power into a sliding discrete fast Fourier transform to obtain two adjacent load power spectra; A determination module for determining the frequency corresponding to the load power in the latest load power spectrum as the adaptive cut-off frequency; A distribution module, configured to output a low-frequency component based on the adaptive cut-off frequency through the adaptive digital filter, and perform power distribution based on the low-frequency component.

9. An electronic device, characterized in that, Comprising: A memory, configured to store a computer program; A processor, configured to execute the computer program to implement the steps of the hybrid energy storage system energy management strategy according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, For storing a computer program, wherein when the computer program is executed by a processor, the steps of the hybrid energy storage system energy management strategy according to any one of claims 1 to 7 are implemented.

Images