Estimation method, system and equipment for energy state of energy storage battery and medium

Abstract

The invention discloses a method, a system and equipment for estimating the energy state of an energy storage battery and a medium. The method comprises the following steps: acquiring impedance of a lithium ion battery; the method comprises the following steps: drawing an energy state change diagram of an impedance real part and an impedance imaginary part along with the battery under different frequencies by taking the energy state of the battery as a horizontal coordinate and taking the impedance real part and the impedance imaginary part as vertical coordinates, connecting two points of the energy state of 0% and the energy state of 100% of the battery under all the frequencies by using straight lines, calculating the slopes of all the straight lines, and comparing all the slopes, selecting the frequency corresponding to the straight line with the maximum slope absolute value as the characteristic frequency; and calculating the energy state of the battery in the current state according to the impedance real part or imaginary part under the characteristic frequency. According to the method, the energy state of the energy storage battery can be estimated by using the alternating-current impedance data under the single frequency point, the accuracy is high, and the method is easy for engineering application.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage batteries, and particularly relates to a method, a system, a device and a medium for estimating the energy state of an energy storage battery. Background Art

[0002] As the core component of an energy storage system, the performance of an energy storage battery directly affects the safe, stable and efficient operation of the entire system. And the State of Energy (SOE) of the battery, as a key parameter reflecting the remaining power of the battery, is the basis for functions such as charge and discharge control and safety warning of the Battery Management System (BMS). As the brain and "housekeeper" of the battery system, BMS undertakes the important mission of ensuring the safe, stable and efficient operation of the battery. And SOE plays an indispensable role in it and is the basic support for functions such as charge and discharge control and safety warning of BMS. In the charge and discharge control link, SOE plays an even more precise regulation role. BMS will accurately control the charge and discharge process of the battery according to the value of SOE. When SOE is at a low level, in order to avoid damage to the battery caused by over-discharge, BMS will limit the discharge power of the battery and even issue a charging reminder; when SOE is at a high level, BMS will reasonably arrange the charging strategy of the battery according to the actual situation to ensure that the charging process is both fast and safe and avoid overcharging. In terms of safety warning, SOE is one of the important bases for BMS to judge the safety state of the battery. When there is an abnormal fluctuation or SOE reaches a dangerous threshold, BMS will quickly issue a safety warning to prompt the user to take measures in time to prevent safety accidents such as overheating and combustion of the battery. For example, when SOE suddenly drops rapidly, BMS may detect a short circuit or other faults inside the battery, and at this time it will immediately issue an alarm, reminding the user to stop using the battery and perform maintenance in time.

[0003] Currently, the main data used in the actual operation of energy storage power stations for energy management is the State of Charge (SOC). However, the output of energy storage power stations is generally evaluated by power and energy, but SOC can only reflect the capacity state of the battery system and cannot reflect the power and energy state of the battery. Therefore, there is an urgent need for a method for estimating the SOE of energy storage batteries. Summary of the Invention

[0004] In order to fill the gap in the evaluation of SOE in the energy storage system, the present invention provides a method, a system, a device and a medium for estimating the energy state of an energy storage battery. This method accurately evaluates the energy state of the energy storage battery by using the real part and the imaginary part at the characteristic frequency of the alternating current impedance spectrum.

[0005] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0006] In the first aspect of the present invention, a method for estimating the energy state of a storage battery is provided, including the following steps:

[0007] Obtain the impedance of the lithium-ion battery;

[0008] Taking the energy state of the battery as the abscissa, and the real part and imaginary part of the impedance as the ordinates respectively, plot the graphs of the real part and imaginary part of the impedance changing with the energy state of the battery at different frequencies. Connect the two points of the energy state of 0% battery and 100% battery at all frequencies with a straight line, calculate the slopes of all the straight lines, compare the magnitudes of all the slopes, and select the frequency corresponding to the straight line with the largest absolute value of the slope as the characteristic frequency;

[0009] Calculate the energy state of the battery in the current state according to the real part or imaginary part of the impedance at the characteristic frequency.

[0010] Further, obtaining the impedance of the lithium-ion battery includes the following steps: After the lithium-ion battery is fully charged, discharge the battery at the rated power. Measure the AC impedance spectrum once every 2% - 10% of the energy state of the battery and let it stand for more than 2 hours until the energy state of the battery reaches 0%, so as to obtain the impedance.

[0011] Further, charge the battery with the energy calibrated at the rated power until the cut-off voltage is 3.65V, and let it stand for more than 2 hours. At this time, the state is recorded as the energy state of 100% battery; discharge the battery at the rated power until the cut-off voltage is 2.0V, and let it stand for more than 2 hours. At this time, the state is recorded as the energy state of 0% battery.

[0012] Further, the AC impedance spectrum adopts the current excitation method, the excitation current is not greater than 0.15 times the 1C current of the battery, the frequency range is 10kHz - 0.01Hz, and the number of frequency points is 6 - 10 at each order of magnitude.

[0013] Further, calculating the energy state of the battery in the current state according to the real part or imaginary part of the impedance at the characteristic frequency includes the following steps:

[0014] Using the curve fitting method, obtain the relationship between the energy state of the battery and the real part or imaginary part at the characteristic frequency. By testing the real part or imaginary part of the impedance at the characteristic frequency and inputting it into the relationship, obtain the energy state of the battery in the current state.

[0015] Further, calculating the energy state of the battery in the current state according to the real part or imaginary part of the impedance at the characteristic frequency includes the following steps:

[0016] Taking the real part and the imaginary part of the impedance at the characteristic frequency as input parameters and the state of energy of the battery as the output parameter, an evaluation model for the state of energy is built. By testing the real part and the imaginary part at the characteristic frequency and inputting them into the evaluation model for the state of energy, the state of energy of the battery in the current state is obtained.

[0017] Furthermore, the evaluation model for the state of energy is built by a neural network algorithm or a support vector regression algorithm.

[0018] In the second aspect of the present invention, an estimation system for the state of energy of a storage battery is provided, including:

[0019] An impedance acquisition module, configured to acquire the impedance of a lithium-ion battery;

[0020] A characteristic frequency acquisition module, configured to plot a graph of the real part and the imaginary part of the impedance varying with the state of energy at different frequencies with the state of energy as the abscissa and the real part and the imaginary part of the impedance as the ordinates respectively, connect the two points of the state of energy of 0% battery and the state of energy of 100% battery at all frequencies with a straight line, calculate the slopes of all the straight lines, compare the magnitudes of all the slopes, and select the frequency corresponding to the straight line with the largest absolute value of the slope as the characteristic frequency;

[0021] A state of energy calculation module, configured to calculate the state of energy of the battery in the current state according to the real part or the imaginary part of the impedance at the characteristic frequency.

[0022] In the third aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the estimation method for the state of energy of the storage battery is implemented.

[0023] In the fourth aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the estimation method for the state of energy of the storage battery is implemented.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] In the present invention, by taking the state of energy of the battery as the abscissa and the real part and the imaginary part of the impedance as the ordinates respectively, a graph of the change of the real part and the imaginary part of the impedance with the state of energy of the battery at different frequencies is plotted. The two points of the state of energy of 0% battery and 100% battery at all frequencies are connected by a straight line, the slopes of all the straight lines are calculated, the magnitudes of all the slopes are compared, and the frequency corresponding to the straight line with the largest absolute value of the slope is selected as the characteristic frequency; then, according to the real part or the imaginary part of the impedance at the characteristic frequency, the state of energy of the battery in the current state is calculated. The method for estimating the state of energy of the energy storage battery of the present invention can estimate the state of energy of the energy storage battery by using the AC impedance data at a single frequency point, has high accuracy, and is easy to be applied in engineering. Description of the Drawings

[0026] 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 the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0027] Figure 1 It is a flowchart of the method for estimating the state of energy of the energy storage battery of the present invention;

[0028] Figure 2 It is a schematic diagram of the system for estimating the state of energy of the energy storage battery;

[0029] Figure 3 It is a curve of the change of the real part of the impedance with the frequency under different SOEs;

[0030] Figure 4 It is a curve of the change of the imaginary part of the impedance with the frequency under different SOEs;

[0031] Figure 5 It is a curve of the change of the real part of the impedance with the SOE;

[0032] Figure 6 It is a curve of the change of the imaginary part of the impedance with the SOE;

[0033] Figure 7 It is a curve of relationship fitting; Detailed Embodiments

[0034] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the 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 scope of protection of the present invention.

[0035] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0036] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present invention may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

[0037] In the present invention, terms such as "module", "device", "system", etc. refer to related entities applied to a computer, such as hardware, a combination of hardware and software, software, or software in execution. Specifically, for example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable component, an execution thread, a program, and / or a computer. Also, an application program or a script program running on a server, and the server may both be components. One or more components may be in a process and / or thread of execution, and the components may be localized on one computer and / or distributed between two or more computers, and may be run by various computer-readable media. Components may also communicate through local and / or remote processes according to a signal having one or more data packets, for example, a signal from data that interacts with another component in a local system, a distributed system, and / or interacts with other systems through a network on the Internet.

[0038] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising" and "including", not only include those elements, but also include other elements not expressly listed, or also include elements inherent to such a process, method, article, or device. Without further limitation, the elements defined by the statement "comprising..." do not exclude the existence of additional identical elements in the process, method, article, or device including the said elements.

[0039] Explanation of the meanings of terms in the present invention:

[0040] Battery Management System (BMS);

[0041] State of Charge (SOC);

[0042] State of Energy (SOE) of the battery

[0043] Currently, the chargeable or dischargeable power of the energy storage power station is represented by the state of charge (SOC). However, the energy storage power station now charges and discharges in terms of power, so it is no longer applicable to use SOC. Therefore, it is more appropriate to use the state of energy (SOE). Based on the above considerations, this patent provides a method for estimating the state of energy of an energy storage battery.

[0044] See Figure 1 , a method for estimating the state of energy of an energy storage battery according to the present invention includes the following steps:

[0045] (1) Acquisition of basic data

[0046] 1) Battery energy calibration

[0047] Take a lithium-ion battery and perform three charge and discharge cycles on it according to its rated power. The charge and discharge voltage range is 2.5 - 3.65V, and take the discharge energy of the third cycle as the calibrated energy.

[0048] 2) Definition of 100% SOE and 0% SOE

[0049] Charge the battery with the calibrated energy at the rated power until the cut-off voltage is 3.65V, and let it stand for more than 2 hours. At this time, the state is recorded as 100% SOE; discharge the battery at the rated power until the cut-off voltage is 2.0V, and let it stand for more than 2 hours. At this time, the state is recorded as 0% SOE.

[0050] 3) Acquisition of impedance data

[0051] After the lithium-ion battery is fully charged, discharge the battery at the rated power. Measure the AC impedance spectrum once every 2% - 10% SOE discharge and let it stand for more than 2 hours until 0% SOE to obtain the impedance.

[0052] Among them, the AC impedance spectrum adopts the current excitation method. The excitation current is not greater than 0.15 times the 1C current of the battery (for example, if the battery capacity is 20Ah, its 1C current is 20A, and the maximum excitation current is 0.15 * 20A = 3A). The frequency range is 10kHz - 0.01Hz, and the number of frequency points is 6 - 10 at each order of magnitude.

[0053] (2) Selection of characteristic frequency points

[0054] Take SOE as the abscissa, and the real and imaginary parts of the impedance as the ordinates respectively, and draw the graphs of the real and imaginary parts of the impedance varying with SOE at different frequencies. Connect the two points of 0% SOE and 100% SOE at all frequencies with a straight line, and calculate the slope of all the straight lines, denoted as a fi, compare the magnitudes of all slopes, and select the frequency corresponding to the line with the largest absolute value of the slope as the characteristic frequency for SOE estimation.

[0055] (3) SOE Estimation Method

[0056] Method 1: Use curve fitting methods such as polynomial fitting to obtain the relationship between SOE and the real part / imaginary part at the characteristic frequency, denoted as SOE = f(Z′ fi ), or SOE = f(Z″ fi ), where fi is the characteristic frequency, Z′ fi is the real part of the impedance, and Z″ fi is the imaginary part of the impedance. By measuring the real part or imaginary part of the impedance at the characteristic frequency and inputting it into the relationship, the SOE in the current state can be obtained.

[0057] Method 2: Use the real part and imaginary part of the impedance at the characteristic frequency as input parameters, use algorithms such as neural network algorithms and support vector regression as the core algorithms, and use SOE as the output parameter to build an SOE state evaluation model. By measuring the real part and imaginary part at the characteristic frequency and inputting them into the SOE state evaluation model, the SOE in the current state can be obtained.

[0058] Example 1

[0059] Take a 20Ah lithium iron phosphate battery as an example.

[0060] (1) Acquisition of Basic Data

[0061] 1) Battery Energy Calibration

[0062] Take a lithium iron phosphate battery with a nominal capacity of 20Ah and a rated energy of 64Wh. Charge and discharge it three times at a power of 32W, with the charge and discharge voltage range of 2.5 - 3.65V. Take the discharge energy of the third time as the calibrated energy, and the actually measured energy is 62.6Wh.

[0063] 2) Definition of 100% SOE and 0% SOE

[0064] Charge the battery after energy calibration at the rated power until the cut-off voltage is 3.65V, and let it stand for more than 2h. At this time, the state is recorded as 100% SOE; discharge the battery at the rated power until the cut-off voltage is 2.0V, and let it stand for more than 2h. At this time, the state is recorded as 0% SOE.

[0065] 3) After the lithium-ion battery is fully charged, discharge the battery at the rated power. Every time it discharges 5% SOE, measure the AC impedance spectrum once after standing for more than 2h until 0% SOE to obtain the impedance. As Figure 3 shown, it is the change trend of the real part Z’ of the impedance at different frequencies under different SOEs. From Figure 3It can be seen that in the high-frequency region, the real part overlap degree under different SOEs is relatively high and it is difficult to distinguish. As the frequency decreases, the real parts of different SOEs gradually separate and show a monotonically decreasing trend. As Figure 4 shown, it is the change trend of the imaginary part Z” of the impedance at different frequencies under different SOEs. It can be seen from Figure 4 that, similarly, in the high-frequency region, the imaginary part overlap degree under different SOEs is relatively high.

[0066] Among them, the alternating current impedance spectrum adopts the current excitation method, the excitation current is 3A, the frequency range is 1000Hz to 0.1Hz, and the number of frequency points is 6 at each order of magnitude.

[0067] (2) Taking SOE as the abscissa and the real part and imaginary part of the impedance as the ordinates respectively, draw the graphs of the real part and imaginary part of the impedance changing with SOE at different frequencies. As Figure 5 shown, it is the graph of the real part of the impedance changing with SOE, Figure 6 shown, it is the graph of the imaginary part of the impedance changing with SOE. Connect the two points of 0% SOE and 100% SOE at all frequencies with a straight line. As Figure 5 shown by the straight line marked ① in, connect the two points of 0% SOE and 100% SOE at 0.1Hz, denoted as a 0.1 , and so on to calculate the slopes of all the straight lines, denoted as a fi . Compare the magnitudes of all the slopes, and select the frequency corresponding to the straight line with the largest absolute value of the slope as the characteristic frequency for SOE estimation. As Figure 6 shown by the straight line marked ② in, connect the two points of 0% SOE and 100% SOE at 0.1Hz, and so on to calculate the slopes of all the straight lines, and select the frequency corresponding to the straight line with the largest absolute value of the slope. In this embodiment, the absolute values of the slopes of the straight lines of the real part and imaginary part of the impedance at 0.1Hz are the largest, which are 0.00073 and 0.0011 respectively. Therefore, 0.1Hz is denoted as the characteristic frequency for SOE estimation.

[0068] (3) SOE estimation method

[0069] As Figure 7 shown, taking SOE as the y-axis and the real part of the impedance as the x-axis, draw a curve, and use polynomial fitting to obtain the relationship formula between SOE and the real part at the characteristic frequency as follows:

[0070] SOE = -8151.39 + 2.20727e 7 *Z’ - 2.1606e 10 Z’ 2 + 9.17277e 12 Z’ 3 - 1.43528e 15 Z’ 4

[0071] R2 = 0.99752

[0072] Wherein, R 2 is an index used in statistics to measure the goodness of fit of a regression model. The closer it is to 1, the higher the degree of fit.

[0073] (4) Method verification

[0074] Another lithium iron phosphate battery with the same model of 20 Ah is taken. In order to verify the accuracy of the method, its SOE is adjusted to 45% SOE as a comparison value. Under this state, its impedance at 0.1 Hz is tested, and the real part of its impedance is obtained as 0.00146 Ω. Substituting it into the relational expression in step (3), the SOE is obtained as 44.8%, and the absolute error is only 0.2%.

[0075] The present invention extracts a frequency in the low frequency of the AC impedance as a characteristic frequency point, and evaluates the SOE of the energy storage battery with the real part or the imaginary part or the combination of both at the characteristic frequency point.

[0076] The following is an apparatus embodiment of the present invention, which can be used to execute the method embodiment of the present invention. For the details not disclosed in the apparatus embodiment, please refer to the method embodiment of the present invention.

[0077] See Figure 2 , in an embodiment of the present invention, there is provided an estimation system for the energy state of an energy storage battery, which can implement the above-mentioned estimation method for the energy state of an energy storage battery. Specifically, the estimation system for the energy state of an energy storage battery includes an impedance acquisition module, a characteristic frequency acquisition module, and an energy state calculation module;

[0078] The impedance acquisition module is used to acquire the impedance of the lithium-ion battery;

[0079] The characteristic frequency acquisition module is used to plot the graphs of the real part and the imaginary part of the impedance varying with the SOE at different frequencies with the SOE as the abscissa and the real part and the imaginary part of the impedance as the ordinates respectively, connect the two points of the energy state of the 0% battery and the energy state of the 100% battery at all frequencies with a straight line, calculate the slopes of all the straight lines, compare the magnitudes of all the slopes, and select the frequency corresponding to the straight line with the largest absolute value of the slope as the characteristic frequency;

[0080] The energy state calculation module is used to calculate the energy state of the battery in the current state according to the real part or the imaginary part of the impedance at the characteristic frequency.

[0081] In the impedance acquisition module, acquiring the impedance of the lithium-ion battery includes the following steps: after the lithium-ion battery is fully charged, the battery is discharged at the rated power, and the AC impedance spectrum is measured once after standing for more than 2 h for every 2% - 10% of the energy state of the battery until the energy state of the 0% battery, and the impedance is obtained.

[0082] The alternating current impedance spectrum adopts a current excitation method, where the excitation current is not greater than 0.15 times the 1C current of the battery, the frequency range is 10 kHz to 0.01 Hz, and the number of frequency points is 6 - 10 per order of magnitude.

[0083] In the energy state calculation module, according to the real part or imaginary part of the impedance at the characteristic frequency, the energy state of the battery in the current state is calculated, including the following steps:

[0084] Using the curve fitting method, obtain the relationship between the energy state of the battery and the real part or imaginary part at the characteristic frequency. By testing the real part or imaginary part of the impedance at the characteristic frequency and inputting it into the relationship, the energy state of the battery in the current state is obtained.

[0085] The SOE state evaluation model is built through a neural network algorithm or a support vector regression algorithm.

[0086] All relevant contents of each step involved in the embodiments of the foregoing method for estimating the energy state of a storage battery can be cited in the function description of the corresponding functional modules of the method and system for estimating the energy state of a storage battery in the embodiments of the present invention, and will not be repeated here.

[0087] The division of modules in the embodiments of the present invention is illustrative, merely a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present invention, the functional modules can be integrated in one processor, or exist separately physically, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0088] In another embodiment of the present invention, a computer device is provided. The computer device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of the method for estimating the energy state of the energy storage battery.

[0089] In one embodiment of the present invention, a computer-readable storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is the memory device in the computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the method for estimating the energy state of the energy storage battery in the above embodiment.

[0090] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0091] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0092] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for estimating the energy state of an energy storage battery, characterized in that, It includes the following steps: Obtain the impedance of the lithium-ion battery; Taking the state of energy of the battery as the abscissa, and the real part and imaginary part of the impedance as the ordinates respectively, plot the variation diagrams of the real part and imaginary part of the impedance with the state of energy of the battery at different frequencies. Connect the two points of the state of energy of 0% battery and 100% battery at all frequencies with a straight line, calculate the slopes of all the straight lines, compare the magnitudes of all the slopes, and select the frequency corresponding to the straight line with the largest absolute value of the slope as the characteristic frequency; Calculate the state of energy of the battery in the current state according to the real part or imaginary part of the impedance at the characteristic frequency.

2. The method for estimating the energy state of the energy storage battery according to claim 1, wherein Obtain the impedance of the lithium-ion battery, including the following steps: After the lithium-ion battery is fully charged, discharge the battery at the rated power. Measure the AC impedance spectrum once every 2% - 10% of the state of energy of the battery, and let it stand for more than 2 h until the state of energy of the battery reaches 0%, so as to obtain the impedance.

3. The method for estimating the energy state of the energy storage battery according to claim 2, wherein The AC impedance spectrum adopts the current excitation method, the excitation current is not greater than 0.15 times the 1C current of the battery, the frequency range is 10 kHz - 0.01 Hz, and the number of frequency points is 6 - 10 at each order of magnitude.

4. The method for estimating the energy state of the energy storage battery according to claim 1, wherein Charge the battery with the calibrated energy at the rated power until the cut-off voltage is 3.65 V, and let it stand for more than 2 h. At this time, the state is recorded as the state of energy of 100% battery; Discharge the battery at the rated power until the cut-off voltage is 2.0 V, and let it stand for more than 2 h. At this time, the state is recorded as the state of energy of 0% battery.

5. The method for estimating the energy state of an energy storage battery according to claim 1, wherein Calculate the state of energy of the battery in the current state according to the real part or imaginary part of the impedance at the characteristic frequency, including the following steps: Use the curve fitting method to obtain the relationship between the state of energy of the battery and the real part or imaginary part at the characteristic frequency. By testing the real part or imaginary part of the impedance at the characteristic frequency and inputting it into the relationship, obtain the state of energy of the battery in the current state.

6. The method for estimating the energy state of the energy storage battery according to claim 1, characterized in that, Calculate the state of energy of the battery in the current state according to the real part or imaginary part of the impedance at the characteristic frequency, including the following steps: Taking the real part and imaginary part of the impedance at the characteristic frequency as the input parameters together, and the state of energy of the battery as the output parameter, build an energy state evaluation model. By testing the real part and imaginary part at the characteristic frequency and inputting them into the energy state evaluation model, obtain the state of energy of the battery in the current state.

7. The method for estimating the energy state of the energy storage battery according to claim 6, wherein The energy state evaluation model is built by the neural network algorithm or the support vector regression algorithm.

8. An estimation system for the energy state of a energy storage battery, characterized in that, It includes: An impedance acquisition module for obtaining the impedance of the lithium-ion battery; A characteristic frequency acquisition module for taking the state of energy as the abscissa, and the real part and imaginary part of the impedance as the ordinates respectively, plotting the variation diagrams of the real part and imaginary part of the impedance with the state of energy at different frequencies, connecting the two points of the state of energy of 0% battery and 100% battery at all frequencies with a straight line, calculating the slopes of all the straight lines, comparing the magnitudes of all the slopes, and selecting the frequency corresponding to the straight line with the largest absolute value of the slope as the characteristic frequency; An energy state calculation module for calculating the state of energy of the battery in the current state according to the real part or imaginary part of the impedance at the characteristic frequency.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the estimation method of the energy state of the energy storage battery as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the described computer program is executed by a processor, it implements the method for estimating the energy state of an energy storage battery according to any one of claims 1 to 7.

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