CN110361653A - A kind of SOC estimation method and system based on hybrid accumulator - Google Patents

Abstract

The present invention relates to a method and system for estimating SOC based on a hybrid energy storage device, comprising: obtaining state parameters of a battery, and setting preset parameters; establishing an equivalent circuit model, and constructing a set of vector parameters including preset parameters, etc. The effective circuit model solves the terminal voltage of the battery according to the state parameters and vector parameters; establishes the SOC estimation model, and the SOC estimation model solves the charge state of the battery according to the terminal voltage and vector parameters; establishes the least squares model, and the least squares model is used to calculate the vector The parameters are iteratively calculated, and the vector parameters after iterative calculation are returned to the equivalent circuit model and the SOC estimation model; repeat steps 2-4; this estimation method can realize the closed-loop correction of the SOC estimation process and effectively improve the estimation accuracy of the SOC , to reduce the error, not only better meet the actual needs, but also more suitable for the estimation and monitoring of the state of charge in the hybrid energy storage device, and more conducive to online and remote monitoring of the state of charge of the hybrid energy storage device.

Description

A SOC estimation method and system based on a hybrid energy storage device

technical field

The invention relates to the technical field of energy storage equipment, in particular to an SOC estimation method and system based on a hybrid energy storage device.

Background technique

With the development of new energy technology, more and more hybrid energy storage devices (or systems) are used; hybrid energy storage devices are usually equipped with several supercapacitors and several battery packs, and each battery pack contains several batteries; Compared with batteries, supercapacitors have the characteristics of large capacity and fast charging and discharging. As a new energy storage device and a hybrid energy storage device composed of batteries, it has been widely used, and the parameter monitoring of hybrid energy storage devices has become an industry research. the key of.

In the prior art, it is usually necessary to monitor or estimate the state parameters of the hybrid energy storage device, such as charging and discharging voltage, current, internal resistance of the device or state of charge (SOC for short, full name is State of Charge), etc., in order to grasp The operating state of the hybrid energy storage device is also convenient for control and scheduling; among these state parameters, parameters such as charge and discharge voltage, current, and internal resistance of the device can usually be directly measured, but the state of charge of the hybrid energy storage device is usually It cannot be directly measured, but needs to be estimated. The state of charge is also called the remaining capacity, which represents the ratio of the remaining capacity of the battery after it has been used for a period of time or left unused for a long time to the capacity of the fully charged state, usually expressed as a percentage. Its value ranges from 0 to 1. When SOC=0, it means that the battery is fully discharged; when SOC=1, it means that the battery is fully charged; the state of charge of the hybrid energy storage device is an important parameter index of the hybrid energy storage device, not It is an indispensable decision-making factor, and it is also an important parameter to optimize energy management in hybrid energy storage devices, improve battery capacity and energy utilization, prevent battery overcharge and overdischarge, and ensure battery safety and service life during use.

In the prior art, for the estimation of the state of charge (SOC) in conventional batteries, domestic and foreign scholars have proposed some methods, such as the ampere-hour integration method, Kalman filter method, adaptive Kalman filter method, etc. However, on the one hand, These methods usually have some shortcomings. For example, the ampere-hour integration method is simple and easy to implement, but the cumulative error caused by current sampling and other factors gradually increases, resulting in an increase in the SOC estimation error, which cannot meet the long-term use requirements in actual engineering; The Kalman filter method is widely used because of its small amount of calculation and easy implementation; the adaptive Kalman filter algorithm often does not consider the temperature factor and the charge-discharge rate factor. The reason is that under ideal conditions in the laboratory, These two factors do not change much, but in practical engineering applications, such as the energy feedback process of electric vehicles, temperature and charge-discharge rate will have a great impact on the SOC estimation accuracy of the battery. On the other hand, these methods are usually suitable for conventional batteries (or battery packs), not for hybrid energy storage devices. In addition, the methods used in the prior art for estimating the state of charge (SOC) in hybrid energy storage devices have relatively large errors. In the actual use process, there are usually problems of low precision and not meeting the actual needs.

Contents of the invention

The purpose of the present invention is to improve the problems existing in the prior art. The existing SOC estimation method is not suitable for the SOC estimation of the hybrid energy storage device, and the SOC estimation accuracy is low, the error is large, and the problems that cannot meet the actual needs; the present invention adopts The technical solution is:

A method for estimating SOC based on a hybrid energy storage device, comprising the steps of:

Step 1. Obtain the state parameters of the battery in the hybrid energy storage device and set preset parameters. The state parameters include the open circuit voltage, load current, and internal resistance of the battery obtained through acquisition. The preset parameters include the polarization of the battery resistance, polarized capacitance;

Step 2, establishing an equivalent circuit model of the hybrid energy storage device, and constructing a set of vector parameters including the preset parameters, the equivalent circuit model solves the terminal voltage of the battery according to the state parameters and vector parameters;

Step 3, establishing an SOC estimation model of the hybrid energy storage device, and the SOC estimation model solves the state of charge of the battery according to the terminal voltage and the vector parameter;

Step 4, establishing a least squares model, the least squares model is established according to the adaptive forgetting factor complete least squares method, the least squares model is used to iteratively calculate the vector parameters, and the iteratively calculated The vector parameters are returned to the equivalent circuit model and the SOC estimation model;

Step 5, repeat steps 2 to 4.

In this scheme, the circuit analysis of the hybrid energy storage unit model was first established to establish an equivalent circuit model, and the terminal voltage data of the battery was solved through the collected state parameters of the battery and the set preset parameters, and then established SOC estimation model, the SOC estimation model estimates the state of charge of the battery according to the terminal voltage data, and finally uses the adaptive forgetting factor complete least squares method to update the vector parameters involved in the equivalent circuit model and the SOC estimation model to realize the SOC estimation The closed-loop correction of the process can effectively improve the estimation accuracy of SOC and reduce the error, which not only meets the actual needs, but also is more suitable for the estimation and monitoring of the state of charge in the hybrid energy storage device, which is more conducive to the hybrid energy storage device. The state of charge is monitored online and remotely.

Preferably, in the step 2, the equivalent circuit model includes a SOC model and a first-order Thevenin model, wherein the first-order Thevenin model is:

V _t =V _oc -V _p -IR _s

In the SOC model, the relational expressions between the state of charge, the open circuit voltage and the load current are respectively:

SOC(t)=ηI(t)/Q

Among them, the variable s=2(q-1)/t _s /(q+1), q is a discrete operator, t _s is the sampling interval, V _q is the intermediate variable, V _t is the terminal voltage of the battery, V _oc battery The open circuit voltage of the battery, R _s is the internal resistance of the battery, R _p is the polarization resistance of the battery, C _p is the polarization capacitance of the battery, and the subscript k indicates the data collected or calculated for the kth time.

Preferably, the vector parameters include a first group of vector parameters and a second group of vector parameters, the first group of vector parameters is: θ _k =[a _1,k b _0,k b _1,k ] ^T , the second The group vector arguments are in,

Among them, the variable V _p =V _t -V _oc , V _t is the terminal voltage, V _oc is the open circuit voltage, a _1,k , b _0,k , b _1,k are three intermediate variables respectively, and the subscript k represents the first The data collected or calculated for k times, the subscript k-1 indicates the data collected or calculated for the k-1th time.

In this solution, the vector parameters used for iterative update include a first set of vector parameters and a second set of vector parameters, wherein the first set of vector parameters mainly describe the actual physical parameters of the battery in the hybrid energy storage device, and the second set The two sets of vector parameters mainly describe the state parameters of the battery at different moments in the hybrid energy storage device. By updating the vector parameters each time the SOC is estimated, the estimated value can be corrected, which is conducive to obtaining high precision. the SOC.

Preferably, in the step 2, the calculation process for obtaining the terminal voltage is: first carry out the Laplace transform of the formula (1), to obtain

V _q (s)/I(s)＝(R _s +R _p +R _s C _p s)/(1+R _p C _p s) (2)

Carrying out bilinear transformation on (2), we get

V _q (q ^-1 )/I(q ^-1 )＝(b ₀ +b ₁ q ^-1 )/(1+a ₁ q ^-1 ) (3)

Convert formula (3) into discrete time domain representation:

Then, the estimated terminal voltage is

Among them, the variable s=2(q-1)/t _s /(q+1), q is a discrete operator, t _s is the sampling interval, V _q is the intermediate variable, V _t is the terminal voltage of the battery, V _oc battery The open circuit voltage of the battery, R _s is the internal resistance of the battery, R _p is the polarization resistance of the battery, C _p is the polarization capacitance of the battery, and the subscript k indicates the data collected or calculated for the kth time.

Preferably, the relational expression between the state of charge and the open circuit voltage is obtained by fitting the relational model between the open circuit voltage and the state of charge. The operation is simple and effective.

Preferably, in the step 3, the SOC estimation model includes a model state matrix and an SOC estimation formula, wherein the model state matrix is

x＝[V _p ,SOC] ^T

Wherein, SOC is the state of charge to be estimated, and _Vp is the polarization voltage to be estimated; the SOC estimation formula established according to the model state matrix is:

where the variable L is the feedback gain, V _t is the terminal voltage at time t; is the estimated value of the terminal voltage at time t, and F is a battery model function (Thevenin's theorem);

and,

Among them, C is the fitting parameter, p ₁ and p ₂ are the correction parameters provided by the least squares model, and η is the Coulombic efficiency.

Preferably, in the step 4, the iterative update equation in the least squares model is,

Wherein, μ _k is the correction factor, and,

in,

where the variable

variable in where E is the expectation

variable

in

variable in is the variance of the collected voltage, To collect current variance

g _1,k , g _2,k , g _3,k , respectively represent the three constructors in the k-th iterative calculation. In this solution, the least squares model is established based on the adaptive forgetting factor complete least squares method, which can artificially forget specific data during the calculation process, such as data with long intervals, not only The calculation accuracy is improved, and it is beneficial to reduce the amount of data to be processed and improve the calculation efficiency, and is especially suitable for online monitoring of the charge state of the hybrid energy storage device.

Preferably, the open circuit voltage is collected by using a multi-channel analog switch and a floating measurement method.

A SOC estimation system based on a hybrid energy storage device, including a data acquisition unit, a controller, a data sending unit for transmitting data, and a cloud platform, and the cloud platform includes a data receiving unit adapted to the data sending unit , a data storage unit, a data processing unit, and a display unit, the data acquisition unit and the data sending unit are respectively connected to the controller, the data receiving unit, the data storage unit, and the display unit are respectively connected to the data processing unit, in,

The data acquisition unit is used to collect the voltage, current and internal resistance of the hybrid energy storage device, and transmit them to the controller, and the controller estimates the state of charge according to the voltage, current and internal resistance, and sends The unit sends the voltage, current, internal resistance and state of charge to the data receiving unit, and the data processing unit obtains the voltage, current, internal resistance and state of charge from the data receiving unit and sends it to the The data storage unit stores and sends to the display unit for display. This estimation system has a simple and compact structure, not only suitable for more accurate estimation of the state of charge of the hybrid energy storage device, but also for various parameters in the hybrid energy storage device, such as voltage, current, internal resistance, state of charge, etc., Real-time online monitoring is conducive to remote understanding and mastering the actual operation status of each hybrid energy storage device.

Preferably, the controller is an STM32 chip or an ARM chip.

Further, the controller is STM32F103. The price of STM32F103 is moderate, the multi-channel input meets the data acquisition input, and the high computing power meets the real-time requirements of SOC estimation.

Preferably, the data collection unit includes a voltage collection module for collecting voltage, a current collection module for collecting current, and an internal resistance collection module for collecting internal resistance.

In a preferred solution, the voltage acquisition module includes n acquisition groups, where n is a natural number, and the acquisition groups respectively include a differential circuit connected in parallel at both ends of the supercapacitor or at both ends of the battery pack, an operational amplifier, an optocoupler isolating switch, and A/D converter, wherein the output end of the differential circuit is connected to the input end of the operational amplifier, the output end of the operational amplifier is connected to the input end of the optocoupler isolation switch, and the output of the optocoupler isolation switch is terminal is connected with the A/D converter, and the output terminal of the A/D converter is connected with the controller, and the controller calculates the corresponding voltage value according to the data collected by each collection group, and The n voltage values are added to obtain the voltage.

Preferably, the optocoupler isolating switch adopts PC817A optocoupler switch. PC817A optocoupler switch has good linear performance, and it is cheap and suitable for mass use.

In a preferred solution, the current acquisition module includes a Hall element sensor, a current signal converter, and an A/D converter arranged on the battery pack, and the output end of the Hall element sensor is connected to the input end of the current signal converter , the output end of the current signal converter is connected with the input end of the A/D converter, and the output end of the A/D converter is connected with the controller, wherein the Hall element sensor is used to The current signal is converted into an analog current signal, and transmitted to a current signal converter, and the current signal converter is used to convert the analog current signal into a corresponding analog voltage signal, and transmitted to the A/D converter, A/D conversion The device is used to convert the analog voltage signal into a data signal and transmit it to the controller, and the controller calculates the current according to the digital signal.

In a preferred solution, the internal resistance acquisition module includes an analog multiplier, a low-pass filter, a DC amplifier, an A/D converter, and an AC circuit connected in parallel to the battery pack and used to collect voltage response signals at both ends of the battery pack. A differential circuit and an AC constant current source for generating sinusoidal signals; the output terminals of the AC differential circuit and the AC constant current source are respectively connected to the input terminals of the analog multiplier, and the output terminals of the analog multiplier are connected to the low The input of the pass filter is connected, the output of the low-pass filter is connected with the input of the DC amplifier, the output of the DC amplifier is connected with the input of the A/D converter, and the output of the A/D converter The terminal is connected to the controller; wherein, the analog multiplier is used to multiply the voltage response signal with the sinusoidal signal, the low-pass filter is used to convert the AC signal into a DC signal, and the DC amplifier is used to convert the DC signal For amplification, the A/D converter is used to convert the amplified DC signal into a digital signal and transmit it to the controller, and the controller calculates the internal resistance according to the digital signal.

Preferably, the data processing unit is a PC or a server.

Preferably, the data storage unit is a hard disk.

Preferably, the display unit is a display.

Preferably, the data sending unit is a WiFi wireless sending chip, and the data receiving unit is an Ethernet card compatible with the WiFi wireless sending chip.

Optionally, the WiFi wireless sending chip is ESP8266, and the Ethernet card is a TP-LINK network card.

In a further solution, it also includes a DC-DC module for converting 12V voltage into 3.3V and/or 5V voltage, the input end of the DC-DC module is connected to the output end of the hybrid energy storage device, and the output The terminals are respectively connected with the data sending unit and the controller.

Compared with the prior art, using the SOC estimation method and system based on the hybrid energy storage device provided by the present invention, firstly, the circuit analysis of the hybrid energy storage unit model is carried out to establish an equivalent circuit model, and the self-defined vector parameters, and based on the terminal voltage data provided by the equivalent circuit model, an SOC estimation model was established to effectively estimate the state of charge SOC in the hybrid energy storage device; Data analysis is performed on the open circuit voltage, and the vector parameters are iteratively updated, and then returned to the equivalent circuit model and the SOC estimation model. The equivalent circuit model and the SOC estimation model perform the calculation of the next state of charge SOC according to the updated vector parameters , so as to realize the closed-loop correction of the SOC estimation process, which can effectively improve the estimation accuracy of SOC and reduce the error, which not only meets the actual needs, but also is more suitable for the estimation and monitoring of the charge state in the hybrid energy storage device, which is more conducive Online and remote monitoring of the state of charge of the hybrid energy storage device.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus It should be regarded as a limitation on the scope, and those skilled in the art can also obtain other related drawings based on these drawings without creative work.

Fig. 1 is a flow chart of an SOC estimation method based on a hybrid energy storage device provided in Embodiment 1 of the present invention.

Fig. 2 is a schematic diagram of a model framework of a SOC estimation method based on a hybrid energy storage device provided in Embodiment 1 of the present invention.

Fig. 3 is a schematic structural diagram of an SOC estimation system based on a hybrid energy storage device provided in Embodiment 2 of the present invention.

FIG. 4 is a schematic circuit diagram of a voltage acquisition module in an SOC estimation system based on a hybrid energy storage device provided in Embodiment 2 of the present invention.

FIG. 5 is a block diagram of a current acquisition module in an SOC estimation system based on a hybrid energy storage device provided in Embodiment 2 of the present invention.

FIG. 6 is a block diagram of an internal resistance acquisition module in an SOC estimation system based on a hybrid energy storage device provided in Embodiment 2 of the present invention.

FIG. 7 is a schematic circuit diagram of a data sending unit in an SOC estimation system based on a hybrid energy storage device provided in Embodiment 2 of the present invention.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention.

Example 1

Please refer to FIG. 1 and FIG. 2. In this embodiment, a SOC estimation method based on a hybrid energy storage device is provided, including the following steps:

Step 1, obtain the state parameters of the battery in the hybrid energy storage device, and set the preset parameters, the state parameters include the open circuit voltage V _oc of the battery obtained through acquisition, the load current I, the internal resistance R _s , the preset parameters Including battery polarization resistance R _p , polarization capacitance C _p , coulombic efficiency η, charge capacity Q, etc., for the establishment and calculation of subsequent models;

As an example, in this embodiment, the open-circuit voltage V _oc may be collected preferentially by combining a multi-channel analog switch with a floating measurement method.

Step 2, establishing an equivalent circuit model of the hybrid energy storage device, and constructing a set of vector parameters including the preset parameters, the equivalent circuit model solves the terminal voltage V _t of the battery according to the state parameters and vector parameters;

As an example, the equivalent circuit model can be established according to the equivalent circuit diagram as shown in the figure, the equivalent circuit model includes a SOC model and a first-order Thevenin model, wherein the first-order Thevenin model is:

V _t =V _oc -V _p -IR _s (2)

In the SOC model, the relational expressions between the state of charge, the open circuit voltage and the load current are respectively:

SOC(t)=ηI(t)/Q (4)

Among them, _Voc is the open circuit voltage, _Vp is the polarization voltage, _Vt is the terminal voltage, _Rs is the collected internal resistance, _Rp is the polarization resistance, _Cp is the polarization capacitance, I is the collected load current, c is the fitting coefficient, n is equal to 4, Q is the rated charge of the hybrid energy storage device, η is the Coulombic efficiency, and SOC is the state of charge;

In the preferred solution provided by this embodiment, the vector parameters include a first set of vector parameters and a second set of vector parameters, wherein the first set of vector parameters is: θ _k =[a _{1, k} b _{0, k} b _1,k ] ^T , the second set of vector parameters is in,

Among them, the variable V _p =V _t -V _oc , V _t is the terminal voltage, V _oc is the open circuit voltage, a _1,k , b _0,k , b _1,k are three intermediate variables respectively, and the subscript k represents the first The data collected or calculated for k times, the subscript k-1 indicates the data collected or calculated for the k-1th time, and will not be described in detail below.

The calculation process for obtaining the terminal voltage through this step is as follows: first carry out Laplace transform on the formula (1), and obtain

V _q (s)/I(s)＝-(R _s +R _p +R _s R _p C _p s)/(1+R _p C _p s) (6)

Carrying out bilinear transformation on (6), we get

V _q (q ^-1 )/I(q ^-1 )＝(b ₀ +b ₁ q ^-1 )/(1+a ₁ q ^-1 ) (7)

Convert equation (7) into discrete time domain representation:

Then, the estimated value of the terminal voltage From this an estimate of the terminal voltage can be calculated;

Among them, the variable s=2(q-1)/t _s /(q+1), q is a discrete operator, t _s is the sampling interval, V _q is the intermediate variable, V _t is the terminal voltage of the battery, V _oc battery The open circuit voltage of the battery, R _s is the internal resistance of the battery, R _p is the polarization resistance of the battery, C _p is the polarization capacitance of the battery, and the subscript k indicates the data collected or calculated for the kth time.

In a preferred solution, in the step (2), the relational expression between the state of charge SOC and the open circuit voltage V _oc is obtained by fitting the relational model between the open circuit voltage V _oc and the state of charge SOC, Fitting the relationship model between the open circuit voltage V _oc and the state of charge SOC is a very mature prior art, which can be realized through one charge and discharge process of the hybrid energy storage device, and will not be repeated here.

Step 3, establishing an SOC estimation model of the hybrid energy storage device, and the SOC estimation model solves the state of charge of the battery according to the terminal voltage and the vector parameter;

As an example, the SOC estimation model includes a model state matrix and an SOC estimation formula, wherein the model state matrix is

x=[V _p ,SOC] ^T (9)

Wherein, SOC is the state of charge to be estimated, and Vp is the polarization voltage to be estimated; the SOC estimation formula established according to the model state matrix is:

where the variable L is the feedback gain, V _t is the terminal voltage at time t; is the estimated value of the terminal voltage at time t, F is the battery model function (Thevenin's theorem), and,

Among them, C is the fitting parameter, p ₁ and p ₂ are the correction parameters provided by the least squares model, and η is the Coulombic efficiency.

Through the SOC estimation model, the SOC of the state of charge can be estimated, and the estimated value of the polarization voltage V _p can be obtained. Through the above formula, the estimated value of the open circuit voltage V _oc , the intermediate variable V _q , and the terminal voltage of the battery can be calculated. The estimated value of V _t , etc., so that the relevant parameters in the equivalent circuit model can be updated in the next calculation.

Step 4, establishing a least squares model, the least squares model is established according to the adaptive forgetting factor complete least squares method, the least squares model is used to iteratively calculate the vector parameters, and the iteratively calculated The vector parameters are returned to the equivalent circuit model and the SOC estimation model;

As an example, in this embodiment, the iterative update equation in the above is,

Wherein, μ _k is the correction factor, and,

in,

where the variable

variable in where E is the expectation

variable

in

variable in is the variance of the collected voltage, To collect current variance

g _1,k , g _2,k , g _3,k , respectively represent the three constructors in the k-th iterative calculation.

The least squares model is used to iteratively update the first group of vector parameters θ _k =[a _1,k b _0,k b _1,k ] ^T , so as to improve the calculation accuracy of the state of charge SOC in the next calculation.

Step 5, repeat step 2, step 3 and step 4; obtain high-precision SOC through multiple iterative calculations.

In the SOC estimation method provided in this embodiment, first, the circuit analysis of the hybrid energy storage unit model is carried out to establish an equivalent circuit model, and the vector parameters are customized, and then based on the terminal voltage data provided by the equivalent circuit model, the The SOC estimation model is established, which can effectively estimate the state of charge SOC in the hybrid energy storage device; finally, the open circuit voltage Voc output by the SOC estimation model is analyzed using the adaptive forgetting factor full least squares method, and the vector parameters are iteratively updated , and then returned to the equivalent circuit model and the SOC estimation model, the equivalent circuit model and the SOC estimation model calculate the next state of charge SOC according to the updated vector parameters, so as to realize the closed-loop correction of the SOC estimation process, which can effectively improve The estimation accuracy of SOC and the reduction of errors not only meet the actual needs, but also are more suitable for the estimation and monitoring of the state of charge in the hybrid energy storage device, and are more conducive to online and remote monitoring of the state of charge of the hybrid energy storage device; as For example, in order to verify the accuracy of this estimation method, the estimation method provided in this embodiment and the ampere-hour integration method commonly used in the prior art are used to estimate the SOC during the discharge process of the same hybrid energy storage device, and the The SOC of the hybrid energy storage device was actually measured at all times, and the experimental data are shown in Table 1.

Table 1 comparative experimental data

It can be seen from Table 1 that compared with the traditional ampere-hour integral estimation method, the SOC estimated by the estimation method provided by this embodiment is closer to the real value, and the relative error is usually within 5%, which has a higher estimation accuracy .

Example 2

According to the estimation method provided in Embodiment 1, Embodiment 2 provides an SOC estimation system based on a hybrid energy storage device, including a data acquisition unit, a controller, a data transmission unit for transmitting data, and a cloud platform. The cloud platform includes a data receiving unit, a data storage unit, a data processing unit and a display unit adapted to the data sending unit, the data collecting unit and the data sending unit are connected to the controller respectively, and the data receiving unit Unit, data storage unit, and display unit are respectively connected to the data processing unit, as shown in Figure 3, wherein,

The data acquisition unit is used to collect the voltage (i.e. open circuit voltage), current (i.e. load current) and internal resistance of the hybrid energy storage device, and transmit them to the controller. Estimate the charge state, and send the voltage, current, internal resistance and charge state to the data receiving unit through the data sending unit, and the data processing unit obtains the voltage, current, internal resistance and charge state from the data receiving unit The internal resistance and charge state are sent to the data storage unit for storage, and sent to the display unit for display. This estimation system has a simple and compact structure, not only suitable for more accurate estimation of the state of charge of the hybrid energy storage device, but also for various parameters in the hybrid energy storage device, such as voltage, current, internal resistance, state of charge, etc., Real-time online monitoring is conducive to remote understanding and mastering the actual operation status of each hybrid energy storage device.

It can be understood that the data sending unit and the data receiving unit may be connected by a wired connection, such as a network cable connection, or a wireless connection, such as a wifi connection, a wireless network connection, or the like.

In a preferred solution, the controller may use an STM32 chip or an ARM chip. As an example, in this embodiment, the controller uses an STM32F103. The price of STM32F103 is moderate, the multi-channel input meets the data acquisition input, and the high computing power meets the real-time requirements of SOC estimation. It can be understood that those skilled in the art may also use other types of controllers, such as single-chip microcomputers and the like.

It can be understood that the algorithm provided in Embodiment 1 is preset in the controller, so as to process the collected data such as voltage, current, internal resistance, etc., so as to estimate the state of charge of the hybrid energy storage device.

Preferably, the data collection unit includes a voltage collection module for collecting voltage, a current collection module for collecting current, and an internal resistance collection module for collecting internal resistance.

As shown in Figure 4, in a preferred solution, the voltage acquisition module includes n acquisition groups, where n is a natural number, and the acquisition groups respectively include differential circuits, computing Amplifier, optocoupler isolating switch and A/D converter, wherein, the output end of described differential circuit is connected with the input end of described operational amplifier, and the output end of operational amplifier is connected with the input end of described optocoupler isolating switch, so The output end of the optocoupler isolating switch is connected with the A/D converter, and the output end of the A/D converter is connected with the controller, and the controller calculates respectively according to the data collected by each collection group corresponding voltage value, and adding n said voltage values to obtain said voltage. As an example, when a supercapacitor and two battery packs are provided in the hybrid energy storage device, the voltage collection module includes three collection groups, which are respectively used to collect the voltage values of the supercapacitor and the two battery packs; as shown in the figure As shown, in this embodiment, in the voltage acquisition module, the multi-channel analog switch technology is combined with the floating measurement technology, and used to measure the voltage (charge and discharge voltage, open circuit voltage) of the hybrid energy storage device. First, the multi-channel The analog switch technology can group the hybrid energy storage devices into groups, and only process one voltage signal at a certain moment, which can effectively solve the problem that the overall common-mode voltage of the hybrid energy storage device is too high; secondly, in the floating measurement technology, The optocoupler isolation switch is used to make the measurement circuit and the internal circuit of the chip not supply ground, so that the measurement circuit is not powered by the energy storage device, which reduces the discharge of the measurement circuit to the energy storage device and improves the accuracy; finally, the collected analog signal is A/D converted into Digital signal, because the supercapacitor usually needs a voltage equalization module for overvoltage protection, so the voltage signal will also be transmitted to the voltage equalization module at the same time, and the controller will supply power to the measurement circuit, which can improve the measurement accuracy.

In this embodiment, the optocoupler isolating switch adopts PC817A optocoupler switch. PC817A optocoupler switch has good linear performance, and it is cheap and suitable for mass use.

In order to ensure the normal operation of the battery, the charging current and discharging current of the battery must be maintained within a specific range. Usually, the battery pack is composed of several batteries connected in series, so each battery pack must be equipped with a The current acquisition module, in a preferred solution, the current acquisition module includes a Hall element sensor, a current signal converter, and an A/D converter arranged on the battery pack, as shown in Figure 5, the Hall element sensor The output end is connected to the input end of the current signal converter, the output end of the current signal converter is connected to the input end of the A/D converter, and the output end of the A/D converter is connected to the controller, wherein the Huo The Er element sensor is used to convert the current signal in the circuit under test into an analog current signal and transmit it to the current signal converter, and the current signal converter is used to convert the analog current signal into a corresponding analog voltage signal and transmit it to the The A/D converter, the A/D converter is used to convert the analog voltage signal into a data signal and transmit it to the controller, and the controller calculates the current according to the digital signal, so as to obtain the current in the hybrid energy storage device real-time current data (load current).

The hybrid energy storage device will produce internal resistance polarization after multiple charges and discharges, which will affect the service life; and the internal resistance caused by the series connection is also involved in the process of series connection, so it is also very important to monitor the internal resistance of the single energy storage device. It is necessary; because the model of the supercapacitor bank is basically the same as that of the battery pack, the most widely used method is the AC injection method. The high-frequency signal is amplified, and then through a low-pass filter to achieve high-precision acquisition. At this time, the relationship between the excitation voltage and the internal resistance to be collected is as follows:

U ₀ ＝C|Z|cosθ＝CR

Wherein, _U0 is an excitation voltage source, Z is an impedance, R is an internal resistance, cosθ is a power factor angle, and C is an excitation current; as an example, as shown in Figure 6, in this embodiment, the internal resistance acquisition module It includes an analog multiplier, a low-pass filter, a DC amplifier, an A/D converter, an AC differential circuit connected in parallel to the battery pack and used to collect voltage response signals at both ends of the battery pack, and an AC constant current source for generating sinusoidal signals The output terminal of the AC differential circuit and the AC constant current source are that the output terminal is connected with the input terminal of the analog multiplier respectively, and the output terminal of the analog multiplier is connected with the input terminal of the low-pass filter, and the output terminal of the low-pass filter The output terminal is connected to the input terminal of the DC amplifier, the output terminal of the DC amplifier is connected to the input terminal of the A/D converter, and the output terminal of the A/D converter is connected to the controller; wherein, the analog The multiplier is used to multiply the voltage response signal with the sinusoidal signal, the low-pass filter is used to convert the AC signal into a DC signal, the DC amplifier is used to amplify the DC signal, and the A/D converter is used to convert the amplified The DC signal is converted into a digital signal and transmitted to the controller, and the controller calculates the internal resistance based on the digital signal.

In a preferred solution, the data processing unit may be a PC or a server. As an example, in this embodiment, the data processing unit is a server.

In a preferred solution, the data storage unit is a device with a storage function such as a hard disk or a magnetic disk.

In a preferred solution, the display unit can preferably use a display. Those skilled in the art can understand that the display unit includes a display, but is not limited to a display. For example, the display unit can also be a mobile phone, a tablet, etc. List them all.

In a preferred solution, the data sending unit is a WiFi wireless sending chip, and the data receiving unit is an Ethernet card compatible with the WiFi wireless sending chip; as an example, in this embodiment, the WiFi wireless The sending chip is ESP2866. ESP has powerful on-chip processing and storage capabilities, including antenna switches and power management converters. It also has self-service troubleshooting and low-power sleep mode. It only needs to be connected through the SPI interface, which is very practical for the limited and relatively complicated environment. In the present invention, the SOCKET is edited based on the TCP/IP protocol, and the sent data is encrypted by the Hash algorithm to ensure data security. Reliability; the Ethernet card is a TP-LINK network card, which ensures the stable transmission and storage of a large amount of data into the cloud.

In a further solution, it also includes a DC-DC module for converting 12V voltage into 3.3V and/or 5V voltage, the input end of the DC-DC module is connected to the output end of the hybrid energy storage device, and the output The terminals are respectively connected with the data sending unit and the controller. In this embodiment, the setting of the DC-DC module can use the hybrid energy storage device itself to supply power to the electrical components in this estimation system, which is more conducive to the data acquisition unit, controller, and data sending unit in this estimation system etc. are integrated into the existing hybrid energy storage device; the DC-DC module can adopt the DC-DC module commonly used in the prior art. As an example, in this embodiment, the DC-DC module includes a topology based on the BUCK conversion circuit The design consists of a synchronous rectification circuit composed of two NMOS transistors, a Schottky diode, and an inductor, which are used to convert the 12V voltage output by the hybrid energy storage device into 3.3V to provide the 2.5V required by the ESP2866 chip in the data sending unit. -3.3V voltage signal, the NMOS drive signal is provided by the controller, so that when replacing or using other external devices, the required voltage signal can be obtained by changing the drive signal to obtain the target voltage signal, thereby achieving a high degree of multiplexing.

As an example, as shown in Figure 7, the DC-DC circuit is respectively connected to the VSS port and the GND port of the ESP2866 chip, and the data transmitted by the controller is respectively connected to the TXD port and the RXD port of the ESP2866 chip, and then the GPIO (18) switch is closed Write a hardware program to realize the protocol writing and encryption of the data; finally turn on the switch to end the writing, in which the Hash algorithm MD5 is used to encrypt the data for signature, thus ensuring the integrity and security of the data from the monitoring terminal to the cloud platform. In order to perform MD5 encryption on the data packet, it is first necessary to judge the length of the data packet, if it is not enough, it needs to be filled, and the data is divided into 128-bit data blocks, and the encryption occurs according to the given initial 4 groups of 32-bit rounds The module cooperates with the defined round encryption function to perform 32 rounds of iterative encryption; finally, 4 sets of 32-bit result data are spliced to output a 128-bit Hash value, and finally added to the end of the protocol data packet to form a data packet format.

In this implementation example, the cloud platform uses Windows Server 2012 as the service system, MySQL as the database, and JSP as the front-end design and back-end data receiving and processing language. The hybrid energy storage data collected by the distributed controller is analyzed and the SOC is estimated online. Finally, it is uniformly presented to other users and maintenance personnel.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Anyone skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present invention. Should be covered within the protection scope of the present invention.

Claims (10)

1. A method for estimating SOC based on a hybrid energy storage device, comprising the steps of: Step 1. Obtain the state parameters of the battery in the hybrid energy storage device and set preset parameters. The state parameters include the open circuit voltage, load current, and internal resistance of the battery obtained through acquisition. The preset parameters include the polarization of the battery resistance, polarized capacitance; Step 2, establishing an equivalent circuit model of the hybrid energy storage device, and constructing a set of vector parameters including the preset parameters, the equivalent circuit model solves the terminal voltage of the battery according to the state parameters and vector parameters; Step 3, establishing an SOC estimation model of the hybrid energy storage device, and the SOC estimation model solves the state of charge of the battery according to the terminal voltage and the vector parameter; Step 4, establishing a least squares model, the least squares model is established according to the adaptive forgetting factor complete least squares method, the least squares model is used to iteratively calculate the vector parameters, and the iteratively calculated The vector parameters are returned to the equivalent circuit model and the SOC estimation model; Step 5, repeat steps 2 to 4. 2. The SOC estimation method based on a hybrid energy storage device according to claim 1, wherein in said step 2, said equivalent circuit model includes a SOC model and a first-order Thevenin model, wherein said first-order The Thevenin model is: V _t =V _oc -V _p -IR _s In the SOC model, the relational expressions between the state of charge, the open circuit voltage and the load current are respectively: SOC(t)=ηI(t)/Q Among them, _Voc is the open circuit voltage, _Vp is the polarization voltage, _Vt is the terminal voltage, _Rs is the collected internal resistance, _Rp is the polarization resistance, _Cp is the polarization capacitance, I is the collected load current, c is the fitting coefficient, n is equal to 4, Q is the rated charge of the hybrid energy storage device, η is the Coulombic efficiency, and SOC is the state of charge. 3. The SOC estimation method based on a hybrid energy storage device according to claim 2, wherein the vector parameters include a first group of vector parameters and a second group of vector parameters, and the first group of vector parameters is: θ _k ＝[a _1,k b _0,k b _1,k ] ^T , the second set of vector parameters is in, Among them, the variable V _p =V _t -V _oc , V _t is the terminal voltage, V _oc is the open circuit voltage, a _1,k , b _0,k , b _1,k are three intermediate variables respectively, and the subscript k represents the first The data collected or calculated for k times, the subscript k-1 indicates the data collected or calculated for the k-1th time. 4. The method for estimating SOC based on a hybrid energy storage device according to claim 3, characterized in that, in the step 2 shown, the calculation process for obtaining the terminal voltage is as follows: first perform Lap on the formula (1) Lass transform, get V _q (s)/I(s)＝(R _s +R _p +R _s C _p s)/(1+R _p C _p s) (2) Perform bilinear transformation on (2), and get V _q (q ^-1 )/I(q ^-1 )＝(b ₀ +b ₁ q ^-1 )/(1+a ₁ q ^-1 ) (3) Convert formula (3) into discrete time domain representation: Then, the estimated terminal voltage is Among them, the variable s=2(q-1)/t _s /(q+1), q is a discrete operator, t _s is the sampling interval, V _q is the intermediate variable, V _t is the terminal voltage of the battery, V _oc battery The open circuit voltage of the battery, R _s is the internal resistance of the battery, R _p is the polarization resistance of the battery, C _p is the polarization capacitance of the battery, and the subscript k indicates the data collected or calculated for the kth time. 5. The method for estimating SOC based on a hybrid energy storage device according to claim 4, wherein in step 3, the SOC estimating model includes a model state matrix and an SOC estimating formula, wherein the model state matrix for x＝[V _p ,SOC] ^T Wherein, SOC is the state of charge to be estimated, and _Vp is the polarization voltage to be estimated; the SOC estimation formula established according to the model state matrix is: where the variable L is the feedback gain, V _t is the terminal voltage at time t; is the estimated value of the terminal voltage at time t, and F is the battery model function; and, Among them, C is the fitting parameter, p ₁ and p ₂ are the correction parameters provided by the least squares model, and η is the Coulombic efficiency. 6. The SOC estimation method based on a hybrid energy storage device according to claim 5, characterized in that, in the step 4, the iterative update equation in the least squares model is, Wherein, μ _k is the correction factor, and, in, where the variable variable in where E is the expectation variable in variable in is the variance of the collected voltage, To collect current variance g _1,k , g _2,k , g _3,k , respectively represent the three constructors in the k-th iterative calculation. 7. A SOC estimating system based on a hybrid energy storage device, characterized in that it includes a data acquisition unit, a controller, a data sending unit for transmitting data, and a cloud platform, and the cloud platform includes Adapted data receiving unit, data storage unit, data processing unit, and display unit, the data acquisition unit and data sending unit are respectively connected to the controller, and the data receiving unit, data storage unit, and display unit are respectively connected to the The above data processing unit is connected, wherein, The data acquisition unit is used to collect the voltage, current and internal resistance of the hybrid energy storage device, and transmit them to the controller, and the controller estimates the state of charge according to the voltage, current and internal resistance, and sends The unit sends the voltage, current, internal resistance and state of charge to the data receiving unit, and the data processing unit obtains the voltage, current, internal resistance and state of charge from the data receiving unit and sends it to the The data storage unit stores and sends to the display unit for display. 8. The SOC estimating system based on a hybrid energy storage device according to claim 7, wherein the data acquisition unit includes a voltage acquisition module for collecting voltage, a current acquisition module for collecting current, and a The internal resistance acquisition module of internal resistance; the voltage acquisition module includes n acquisition groups, where n is a natural number, and the acquisition groups respectively include a differential circuit, an operational amplifier, and an optocoupler isolation switch connected in parallel at both ends of the supercapacitor or at both ends of the battery pack and the A/D converter, wherein the output end of the differential circuit is connected to the input end of the operational amplifier, the output end of the operational amplifier is connected to the input end of the optocoupler isolation switch, and the optocoupler isolation switch The output end is connected to the A/D converter, the output end of the A/D converter is connected to the controller, and the controller calculates the corresponding voltage value according to the data collected by each collection group, and The n voltage values are added together to obtain the voltage. 9. The SOC estimating system based on a hybrid energy storage device according to claim 8, wherein the current acquisition module includes a Hall element sensor, a current signal converter, and an A/D converter arranged in the battery pack, The output end of the Hall element sensor is connected with the input end of the current signal converter, the output end of the current signal converter is connected with the input end of the A/D converter, and the output end of the A/D converter is connected with the controller, Wherein, the Hall element sensor is used to convert the current signal in the circuit under test into an analog current signal and transmit it to a current signal converter, and the current signal converter is used to convert the analog current signal into a corresponding analog voltage signal , and transmit it to the A/D converter, the A/D converter is used to convert the analog voltage signal into a data signal, and transmit it to the controller, and the controller calculates the current according to the digital signal. 10. The SOC estimation system based on a hybrid energy storage device according to claim 8, wherein the internal resistance acquisition module includes an analog multiplier, a low-pass filter, a DC amplifier, an A/D converter, connected in parallel to The AC differential circuit on the battery pack and used to collect the voltage response signals at both ends of the battery pack and the AC constant current source used to generate sinusoidal signals; The input of the device is connected, the output of the analog multiplier is connected with the input of the low-pass filter, the output of the low-pass filter is connected with the input of the DC amplifier, and the output of the DC amplifier is connected with the A The input of the /D converter is connected, and the output of the A/D converter is connected with the controller; wherein the analog multiplier is used to multiply the voltage response signal with the sinusoidal signal, and the low-pass filter is used to The AC signal is converted into a DC signal, the DC amplifier is used to amplify the DC signal, the A/D converter is used to convert the amplified DC signal into a digital signal, and transmit it to the controller, and the controller calculates according to the digital signal Out of internal resistance.