CN116381520A - Lithium battery SOC on-line monitoring method and system based on switch high-frequency resonance reactance - Google Patents
Lithium battery SOC on-line monitoring method and system based on switch high-frequency resonance reactance Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 93
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- YJZYDPRMWYWYCG-UHFFFAOYSA-N mppf Chemical compound COC1=CC=CC=C1N1CCN(CCN(C(=O)C=2C=CC(F)=CC=2)C=2N=CC=CC=2)CC1 YJZYDPRMWYWYCG-UHFFFAOYSA-N 0.000 claims 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/02—Arrangements for measuring frequency, e.g. pulse repetition rate; Arrangements for measuring period of current or voltage
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The invention relates to a lithium battery SOC on-line monitoring method and system based on a switch high-frequency resonance reactance, wherein the method comprises the following steps: s1, constructing a PWM switch resonance loop; s2, A/D sampling and model signal conditioning are carried out, and a high-frequency current sensor is used for collecting self-excitation high-frequency PWM (pulse-Width modulation) switch oscillation current i of the lithium battery energy storage system sw_res The method comprises the steps of carrying out a first treatment on the surface of the S3, extracting the characteristic parameter f of the resonant frequency of the oscillating current 0 Calculating resonance reactance X under different charge states SOC res The method comprises the steps of carrying out a first treatment on the surface of the Step S4, fitting SOC-X res Measurement results of (2) to obtain SOC-X res Is a regression function of (2); s5, measuring and calculating resonance reactance X on line res And carrying out regression function monitoring on the state of charge (SOC) of the lithium battery. Compared with the prior art, the invention has the advantages of no disturbance, high sensitivity and temperature dry resistanceThe harassment is strong.
Description
Technical Field
The invention relates to the technical field of on-line monitoring of lithium battery charge states, in particular to a lithium battery SOC on-line monitoring method and system based on a switch high-frequency resonance reactance.
Background
The lithium BATTERY is widely applied to the field of new energy electric automobiles, and a BATTERY management system (Battery MANAGEMENT SYSTEM, BMS) is a main factor for restricting the future development of the electric automobile, wherein the estimation of the state of charge (SOC) of the lithium BATTERY is a core function of the BMS, and the main task of the lithium BATTERY is to pre-warn and process abnormal working conditions of the lithium BATTERY, so that the safe operation of the lithium BATTERY is ensured. Therefore, on-line monitoring of the SOC of the lithium battery has important significance for improving the running reliability of the system and reducing the running and maintenance cost. Lithium batteries are complex electrochemical devices, and because of their obvious nonlinear behavior in the internal and external conditions, lithium battery SOC cannot be measured directly, and must be estimated by measuring current, voltage, and temperature parameters and building a suitable electrochemical or mathematical model, lithium battery SOC on-line monitoring is very challenging.
The lithium battery SOC was monitored by high frequency AC injection in "Method for online battery AC impedance spectrum measurement using dc-dc power converter duty-cycle control" by Xia Z et al. However, the injection of the additional excitation source inevitably interferes with the normal operation of the lithium ion battery system, resulting in problems of poor safety, reduced efficiency, and the like.
Through retrieval, chinese patent application CN114859235A discloses a lithium battery SOC estimation method based on extended Kalman filtering, which establishes a discrete nonlinear system state and an observation equation through a lithium battery equivalent model and estimates the lithium battery SOC according to a linear system state and the observation equation required by a reconstructed extended Kalman filtering algorithm. However, the extended Kalman filtering algorithm has strong dependence on the equivalent model of the lithium battery, and the nonlinear function of the observation equation ignores higher order terms when linearization is adopted, so that linearization errors are easy to generate, and the SOC estimation accuracy is insufficient.
Chinese patent application CN114757340A discloses a lithium battery health state prediction method and system based on neural network integration. According to the method, a plurality of lithium battery health state prediction models are constructed according to data acquired in a lithium battery cyclic charge-discharge process and a plurality of preset neural networks, the results are integrated, and the integrated lithium battery health state prediction models are obtained through iterative adjustment to calculate the lithium battery health state. Although the method does not need to establish an accurate lithium battery model, has the advantages of high nonlinearity, strong self-learning property and the like, the training process is too complex and is excessively dependent on sample data.
The existing method for estimating the SOC of the lithium battery has the advantages of simple principle and convenient calculation, but the aging of the battery, accumulated error and the like restrict the calculation precision of the ampere-hour method. The open circuit voltage method needs to be kept stand for a long time, and is difficult to meet the requirement of online estimation. The accuracy of the Kalman filtering method is very dependent on an accurate lithium battery equivalent model, and the influence of temperature is considered, so that the SOC estimation accuracy of the Kalman filtering method at different temperatures is greatly different.
Aiming at the defects of the prior art, a lithium battery SOC online monitoring method with high sensitivity and strong temperature interference resistance is needed to be set.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the on-line monitoring method and the system for the lithium battery SOC based on the switch high-frequency resonance reactance, which have the advantages of no disturbance, high sensitivity and strong temperature disturbance resistance.
The aim of the invention can be achieved by the following technical scheme:
according to a first aspect of the invention, there is provided an on-line monitoring method for lithium battery SOC based on switching high frequency resonance reactance, the method comprising the steps of:
s1, constructing a PWM switch resonance loop;
s2, A/D sampling and model signal conditioning are carried out, and a high-frequency current sensor is used for collecting self-excitation high-frequency PWM (pulse-Width modulation) switch oscillation current i of the lithium battery energy storage system sw_res ；
S3, extracting the characteristic parameter f of the resonant frequency of the oscillating current 0 Calculating resonance reactance X under different charge states SOC res ；
Step S4, fitting SOC-X res Measurement results of (2) to obtain SOC-X res Is a regression function of (2);
s5, measuring and calculating resonance reactance X on line res And carrying out regression function monitoring on the state of charge (SOC) of the lithium battery.
Preferably, the step S1 specifically includes:
a bidirectional DC-DC converter is arranged between the lithium battery and the load and is used for voltage regulation and charge-discharge current control; if the DC-DC circuit is provided with an input capacitor for smoothing the input voltage, a resonance loop is formed between the lithium battery and the input capacitor under the excitation of the PWM switch;
if no input capacitor exists in the loop, an additional small metal polypropylene film MPPF input capacitor C is arranged at the part close to the lithium battery in The input capacitor and the lithium battery form an LC series resonance circuit, and C is regulated in The capacitance of (2) may adjust the frequency of the resonant circuit.
Preferably, the step S2 specifically includes: collecting self-excited high-frequency PWM (pulse-Width modulation) switch oscillation current i of lithium battery energy storage system by using high-frequency current sensor sw_res The upper limit bandwidth of the current sensor is larger than the oscillating current frequency of the PWM switch, and the lower limit frequency is larger than the carrier frequency K times of the current transformer.
Preferably, the lower limit frequency is greater than 2 times the carrier frequency of the current transformer.
Preferably, the step S3 specifically includes: intercepting PWM switch oscillation current segment i sw_res Obtaining the resonant frequency f of the oscillating current by using a frequency domain analysis method 0 And calculate the resonant reactance X res The expression is:
X res ≈1/(2πf 0 C res ) (1)
wherein C is res Is resonance capacitance equal to input capacitance C in 。
Preferably, the step S4 specifically includes:
setting the resonant frequency f of the oscillating current 0 Step S3 is repeated under different charge states SOC to obtain resonance reactance X under different states res The method comprises the steps of carrying out a first treatment on the surface of the SOC-X using methods including but not limited to least squares res Is measured by (a)The result is subjected to function fitting to obtain SOC-X res Is a regression function of (a).
Preferably, the regression function is an exponential regression function.
According to a second aspect of the present invention, there is provided a system based on the on-line monitoring method of lithium battery SOC based on the switched high frequency resonant reactance, the system comprising:
the PWM switch resonance loop module comprises a lithium battery, an input capacitor and a PWM ripple current excitation source;
the A/D sampling and signal conditioning module is used for collecting self-excited high-frequency PWM (pulse-Width modulation) switch oscillation current i of the lithium battery energy storage system by using a high-frequency current sensor sw_res ；
The resonance reactance calculation module is used for calculating the resonance frequency characteristic parameter f according to the extracted oscillating current 0 Calculating resonance reactance X under different charge states SOC res ；
Fitting regression module for fitting SOC-X res Measurement results of (2) to obtain SOC-X res Is a regression function of (2);
the on-line monitoring module is used for measuring and calculating the resonance reactance X on line res And carrying out regression function monitoring on the state of charge (SOC) of the lithium battery.
According to a third aspect of the present invention there is provided an electronic device comprising a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method of any one of the above when executing the program.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of any one of the above.
Compared with the prior art, the invention has the following advantages:
1) Undisturbed: the self-oscillation current existing in the system is obtained, so that the SOC is monitored in the running process of the lithium battery; the method has the advantages that no additional high-frequency excitation source is needed, the interference to the normal running state of the lithium battery system is avoided, and the lithium battery SOC can be continuously and sensitively extracted in real time for monitoring the lithium battery high-frequency reactance.
2) The sensitivity is high: the sensitivity of the equivalent impedance of the lithium ion battery in the high frequency band to SOC and temperature is far higher than that in the middle and low frequency bands.
3) The temperature interference resistance is strong: the high-frequency reactance of the lithium battery is sensitive to the SOC and is hardly influenced by temperature, and the on-line monitoring of the SOC of the lithium battery can be realized under the condition of not being influenced by temperature.
Drawings
FIG. 1 is a flow chart of a method for on-line monitoring of lithium battery SOC using PWM switching oscillation;
FIG. 2 is a diagram of an experimental system framework;
FIG. 3 is a PWM switching oscillation of a battery and an input capacitive current in a lithium battery energy storage system;
FIG. 4 is an FFT of switching oscillating current;
fig. 5 is a graph showing the high-frequency resonance reactance characteristics of the on-line measurement test battery module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Examples
The embodiment provides a lithium battery SOC on-line monitoring method based on a switch high-frequency resonance reactance, which comprises the following steps:
s1, constructing a PWM switch resonance loop;
s2, A/D sampling and model signal conditioning are carried out, and a high-frequency current sensor is used for collecting self-excitation high-frequency PWM (pulse-Width modulation) switch oscillation current i of the lithium battery energy storage system sw_res ；
S3, extracting the characteristic parameter f of the resonant frequency of the oscillating current 0 Calculating resonance reactance X under different charge states SOC res ；
Step S4, fitting SOC-X res Measurement results of (2) to obtain SOC-X res Is a regression function of (2);
s5, measuring and calculating resonance reactance X on line res And carrying out regression function monitoring on the state of charge (SOC) of the lithium battery.
Next, the method of the present invention will be described in detail.
(1) Constructing a PWM switch resonance loop: a bi-directional DC-DC converter is typically installed between the lithium battery and the load for voltage regulation and charge-discharge current control. If the DC-DC circuit itself has an input capacitor installed for smoothing the input voltage, a resonant tank will be formed between the lithium battery and the input capacitor under PWM switching actuation. If there is no input capacitor in the system, an additional small metal polypropylene film (MPPF) input capacitor C can be arranged near the lithium battery in The input capacitance and the lithium battery form an LC series resonant circuit. By adjusting C in The capacitance of (2) may adjust the frequency of the resonant circuit.
(2) Signal conditioning and a/D sampling: collecting self-excited high-frequency PWM (pulse-Width modulation) switch oscillation current i of lithium battery energy storage system by using high-frequency current sensor sw_res The upper bandwidth of the current sensor should be greater than the PWM switching oscillation current frequency (e.g., hundreds of kHz). The lower limit frequency is more than 2 times of the carrier frequency of the converter, so that interference of system fundamental waves and switching harmonics is filtered.
(3) Extracting resonance characteristic parameter f 0 Calculating resonant reactance X res : intercepting PWM switch oscillation current segment i sw_res Obtaining the oscillating current resonant frequency f using frequency domain analysis methods including, but not limited to, fast fourier decomposition 0 And calculate the resonant reactance X according to equation (1) res Wherein C is res Is resonance capacitance equal to input capacitance C in 。
X res ≈1/(2πf 0 C res ) (1)
(4) Fitting SOC-X res Regression function: respectively under different SOC states (setting oscillation current resonant frequency f 0 Repeating the step (3) to obtain the resonance reactance X under different states res The method comprises the steps of carrying out a first treatment on the surface of the Using methods including but not limited to least squares for SOC-X res Performing function fitting on the measurement results of (2) to obtain SOC-X res Is a function of the exponential regression of (c).
(5) Online measurement X res Monitoring the lithium battery SOC: resonant reactance X to be measured and calculated on line res Substituted into SOC-X res In the exponential regression function, the lithium battery SOC is monitored on line.
The embodiment also provides a system based on the on-line monitoring method of the lithium battery SOC based on the switch high-frequency resonance reactance, which comprises the following steps:
the PWM switch resonance loop module comprises a lithium battery, an input capacitor and a PWM ripple current excitation source;
the A/D sampling and signal conditioning module is used for collecting self-excited high-frequency PWM (pulse-Width modulation) switch oscillation current i of the lithium battery energy storage system by using a high-frequency current sensor sw_res ；
The resonance reactance calculation module is used for calculating the resonance frequency characteristic parameter f according to the extracted oscillating current 0 Calculating resonance reactance X under different charge states SOC res ；
Fitting regression module for fitting SOC-X res Measurement results of (2) to obtain SOC-X res Is a regression function of (2);
the on-line monitoring module is used for measuring and calculating the resonance reactance X on line res And carrying out regression function monitoring on the state of charge (SOC) of the lithium battery.
Taking a group of lithium battery modules as an example, the lithium battery module is formed by connecting 12 SANYO NCR18650-GA batteries (the nominal voltage is 3.6V, the minimum rated capacity is 3200 mAh) in series through spot welding nickel strips, and the two groups of 6 batteries are connected in parallel; the bidirectional DC-DC converter is provided with a filter inductor and a direct current capacitor for suppressing PWM current and voltage ripple, and is provided with an MPPF input capacitor of 1 mu F near the lithium battery module, and the experimental system structure is shown in figure 2. The PWM oscillation current is measured by a high-frequency current probe CYBERTEK CP8030B with the bandwidth of 50MHz, and the signal acquisition and processing are completed by a high-speed digital oscilloscope (Pico 5444D) and a host.
First, the DC-DC converter is controlled to discharge (occupy) the lithium battery module at a current of about 10A (1.5C)Space ratio=0.5, switching frequency=10 kHz); then collecting PWM switch oscillation current waveforms in the lithium battery energy storage system as shown in figure 3, and analyzing the switch oscillation current i in a time window of 12.5 mu s at a sampling frequency of 125MHz by utilizing FFT function in MATLAB sw_res Fragments. By switching oscillating current I per segment sw_res (f) The FFT amplitude in units is shown in FIG. 4, and the resonant frequency f is obtained from the FFT function analysis 0 And calculate the resonant reactance X res . The oscillation characteristic parameters measured on line in the 100% SOC state of the lithium battery pack are respectively as follows: f (f) 0 ＝241.742kHz、X res ＝686.773mΩ。
The steps are repeated, the equivalent resonance reactance characteristics of the lithium battery module at different temperatures and different SOC states are measured on line through a PWM switch oscillation method (as shown in figure 5), and the fact that the method for measuring the SOC of the lithium battery is hardly affected by temperature can be seen.
Based on the online extraction result of FIG. 5, SOC-X is obtained by least square method res Is subjected to function fitting to obtain SOC-X res Is a function of the exponential regression of (a):
and (3) randomly selecting five test points, measuring the equivalent resonance reactance of the lithium ion battery module on line by using a PWM (pulse-width modulation) switch oscillation method, and carrying the measured value into a preset regression function to obtain the SOC of the lithium battery. The battery system is disconnected at the same time when the online test is finished, the battery module is still tested for 2h and the open circuit voltage is measured, and the SOC of the battery at the moment is obtained through an open circuit voltage method (OCV) ocv . The experimental results are shown in Table 1, and the on-line monitoring of the SOC of the lithium battery and the SOC obtained by the open-circuit voltage method by the PWM switch oscillation method provided by the method ocv In contrast, the average error rate is 2.24% and the maximum single error is less than 4%.
On-line off-line SOC estimation result
TABLE 1
| X res (mΩ) | SOC PWM | OCV(V) | SOC OCV | ΔSOC |
| 687.16 | 90.68％ | 24.81 | 92.24％ | 1.56％ |
| 692.86 | 74.57％ | 23.88 | 78.16％ | 3.59％ |
| 705.99 | 54.08％ | 22.38 | 51.32％ | -2.76％ |
| 725.01 | 34.48％ | 21.63 | 31.97％ | -2.51％ |
| 765.38 | 5.77％ | 20.18 | 6.56％ | 0.79％ |
The electronic device of the present invention includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) or computer program instructions loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
A plurality of components in a device are connected to an I/O interface, comprising: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, speakers, and the like; a storage unit such as a magnetic disk, an optical disk, or the like; and communication units such as network cards, modems, wireless communication transceivers, and the like. The communication unit allows the device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processing unit performs the respective methods and processes described above, for example, the methods S1 to S4. For example, in some embodiments, methods S1-S4 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device via the ROM and/or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods S1 to S4 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform methods S1-S4 by any other suitable means (e.g., by means of firmware).
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), etc.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The lithium battery SOC on-line monitoring method based on the switch high-frequency resonance reactance is characterized by comprising the following steps:
s1, constructing a PWM switch resonance loop;
s2, A/D sampling and model signal conditioning are carried out, and a high-frequency current sensor is used for collecting self-excitation high-frequency PWM (pulse-Width modulation) switch oscillation current i of the lithium battery energy storage system sw_res ；
S3, extracting the characteristic parameter f of the resonant frequency of the oscillating current 0 Calculating resonance reactance X under different charge states SOC res ；
Step S4, fitting SOC-X res Measurement results of (2) to obtain SOC-X res Is a regression function of (2);
s5, measuring and calculating resonance reactance X on line res And carrying out regression function monitoring on the state of charge (SOC) of the lithium battery.
2. The on-line monitoring method of the lithium battery SOC based on the switch high-frequency resonance reactance of claim 1, wherein the step S1 specifically includes:
a bidirectional DC-DC converter is arranged between the lithium battery and the load and is used for voltage regulation and charge-discharge current control; if the DC-DC circuit is provided with an input capacitor for smoothing the input voltage, a resonance loop is formed between the lithium battery and the input capacitor under the excitation of the PWM switch;
if no input capacitor exists in the loop, an additional small metal polypropylene film MPPF input capacitor C is arranged at the part close to the lithium battery in The input capacitor and the lithium battery form an LC series resonance circuit, and C is regulated in The capacitance of (2) may adjust the frequency of the resonant circuit.
3. The on-line lithium battery SOC monitoring method based on switch high-frequency resonance reactance of claim 2, wherein the method comprises the following steps ofThe step S2 specifically includes: collecting self-excited high-frequency PWM (pulse-Width modulation) switch oscillation current i of lithium battery energy storage system by using high-frequency current sensor sw_res The upper limit bandwidth of the current sensor is larger than the oscillating current frequency of the PWM switch, and the lower limit frequency is larger than the carrier frequency K times of the current transformer.
4. The on-line lithium battery SOC monitoring method based on the switch high-frequency resonance reactance of claim 3, wherein the lower limit frequency is 2 times greater than the carrier frequency of the converter.
5. The on-line monitoring method of the lithium battery SOC based on the switch high-frequency resonance reactance of claim 2, wherein the step S3 specifically comprises: intercepting PWM switch oscillation current segment i sw_res Obtaining the resonant frequency f of the oscillating current by using a frequency domain analysis method 0 And calculate the resonant reactance X res The expression is:
X res ≈1/(2πf 0 C res ) In the formula (1), C res Is resonance capacitance equal to input capacitance C in 。
6. The on-line monitoring method of the lithium battery SOC based on the switch high-frequency resonance reactance of claim 1, wherein the step S4 specifically comprises:
setting the resonant frequency f of the oscillating current 0 Step S3 is repeated under different charge states SOC to obtain resonance reactance X under different states res The method comprises the steps of carrying out a first treatment on the surface of the SOC-X using methods including but not limited to least squares res Performing function fitting on the measurement results of (2) to obtain SOC-X res Is a regression function of (a).
7. The on-line monitoring method of lithium battery SOC based on a switched high frequency resonant reactance of claim 6, wherein the regression function is an exponential regression function.
8. A system based on the on-line monitoring method of lithium battery SOC based on switching high frequency resonance reactance of claim 1, characterized in that the system comprises:
the PWM switch resonance loop module comprises a lithium battery, an input capacitor and a PWM ripple current excitation source;
the A/D sampling and signal conditioning module is used for collecting self-excited high-frequency PWM (pulse-Width modulation) switch oscillation current i of the lithium battery energy storage system by using a high-frequency current sensor sw_res ；
The resonance reactance calculation module is used for calculating the resonance frequency characteristic parameter f according to the extracted oscillating current 0 Calculating resonance reactance X under different charge states SOC res ；
Fitting regression module for fitting SOC-X res Measurement results of (2) to obtain SOC-X res Is a regression function of (2);
the on-line monitoring module is used for measuring and calculating the resonance reactance X on line res And carrying out regression function monitoring on the state of charge (SOC) of the lithium battery.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the program, implements the method according to any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1-7.
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