CN110361653B - SOC estimation method and system based on hybrid energy storage device - Google Patents
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Abstract
The invention relates to a SOC estimation method and system based on a hybrid energy storage device, comprising the steps of obtaining state parameters of a battery and setting preset parameters; establishing an equivalent circuit model, constructing a group of vector parameters including preset parameters, and solving the terminal voltage of the battery according to the state parameters and the vector parameters by the equivalent circuit model; establishing an SOC estimation model, and solving the charge state of the battery according to terminal voltage and vector parameters by the SOC estimation model; establishing a least square model, wherein the least square model is used for carrying out iterative computation on vector parameters, and returning the vector parameters after the iterative computation to the equivalent circuit model and the SOC estimation model; repeating the steps 2-4; the estimation method can realize closed-loop correction of the SOC estimation process, effectively improve the estimation precision of the SOC, reduce errors, more meet actual requirements, is more suitable for estimating, monitoring and the like of the charge state in the hybrid energy storage device, and is more beneficial to online and remote monitoring of the charge state of the hybrid energy storage device.
Description
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
With the development of new energy technology, hybrid energy storage devices (or systems) are increasingly used; a plurality of super capacitors and a plurality of storage battery packs are usually arranged in the hybrid energy storage device, and each storage battery pack comprises a plurality of storage batteries; compared with the storage battery, the super capacitor has the characteristics of large capacity and rapid charge and discharge, and is widely used as a novel energy storage device matched with a hybrid energy storage device formed by the storage battery, and the parameter monitoring of the hybrid energy storage device is also an important point of industrial research.
In the prior art, state parameters of the hybrid energy storage device, such as Charge-discharge voltage, current, internal resistance or State of Charge (SOC for short, referred to as State of Charge) of the device, etc., are generally required to be monitored or estimated, so as to grasp the operation State of the hybrid energy storage device, and also facilitate control and scheduling; among these state parameters, parameters such as charge-discharge voltage, current, internal resistance of the device, etc. can be measured directly, but the state of charge of the hybrid energy storage device cannot be measured directly, and estimation is required, and the state of charge is also called residual capacity, which is represented by the ratio of the residual capacity of the battery after being used for a period of time or being left unused for a long period of time to the capacity of the battery in its fully charged state, and is often expressed as a percentage. The value range is 0-1, when SOC=0, the battery is completely discharged, and when SOC=1, the battery is completely full; the state of charge of the hybrid energy storage device is an important parameter index of the hybrid energy storage device, is not only an indispensable decision factor, but also an important parameter for optimizing energy management in the hybrid energy storage device, improving battery capacity and energy utilization rate, preventing overcharge and overdischarge of the battery, and guaranteeing the safety and service life of the battery in the use process.
In the prior art, for estimating the state of charge (SOC) in a conventional storage battery, domestic and foreign scholars propose methods such as an ampere-hour integration method, a Kalman filtering method, an adaptive Kalman filtering method and the like, however, on one hand, the methods generally have some defects, for example, the ampere-hour integration method is simple and easy to operate, but the accumulated error caused by factors such as current sampling gradually increases, so that the SOC estimation error increases, and the long-term use requirement in practical engineering cannot be met; the Kalman filtering method is widely used because of the characteristics of small calculation amount and easy realization; the adaptive kalman filtering algorithm does not consider the temperature factor and the charge-discharge multiplying power factor, because under the ideal condition of a laboratory, the two factors are not changed greatly, but in practical engineering application, such as an electric automobile energy feedback process, the temperature and the charge-discharge multiplying power can have great influence on the SOC estimation accuracy of the battery. On the other hand, these methods are generally applicable to conventional storage batteries (or storage battery packs), are not applicable to hybrid energy storage devices, and in addition, the methods for estimating the state of charge (SOC) in the hybrid energy storage devices in the prior art have large errors, and in the actual use process, there are generally problems that the accuracy is low and the actual requirements are not met.
Disclosure of Invention
The invention aims to solve the problems that the prior SOC estimation method is not suitable for SOC estimation of a hybrid energy storage device, has low SOC estimation precision and large error and cannot meet actual requirements in the prior art; the technical scheme adopted by the invention is as follows:
an SOC estimation method based on a hybrid energy storage device includes the steps of:
step 1, acquiring state parameters of a battery in a hybrid energy storage device, and setting preset parameters, wherein the state parameters comprise open-circuit voltage, load current and internal resistance of the battery obtained through acquisition, and the preset parameters comprise polarization resistance and polarization capacitance of the battery;
step 2, an equivalent circuit model of the hybrid energy storage device is established, a group of vector parameters including the preset parameters are established, and the equivalent circuit model solves the terminal voltage of the battery according to the state parameters and the vector parameters;
Step 3, an SOC estimation model of the hybrid energy storage device is established, and the SOC estimation model solves the charge state of the battery according to the terminal voltage and the vector parameters;
Step 4, a least square model is established, wherein the least square model is established according to a self-adaptive forgetting factor complete least square method, and is used for carrying out iterative computation on the vector parameters and returning the vector parameters after iterative computation to the equivalent circuit model and the SOC estimation model;
And 5, repeating the steps 2 to 4.
In the scheme, an equivalent circuit model is established by circuit analysis on the hybrid energy storage monomer model, terminal voltage data of the battery are solved through collected state parameters and set preset parameters of the battery, then an SOC estimation model is established, the SOC estimation model estimates the charge state of the battery according to the terminal voltage data, and finally the vector parameters related to the equivalent circuit model and the SOC estimation model are updated by adopting a self-adaptive forgetting factor complete least square method, so that closed loop correction of an SOC estimation process is realized, the estimation precision of the SOC can be effectively improved, errors are reduced, the actual requirements are more met, and the method is more suitable for estimating, monitoring and the like of the charge state in the hybrid energy storage device, and is more beneficial to online and remote monitoring of the charge state of the hybrid energy storage device.
Preferably, in the step 2, the equivalent circuit model includes an SOC model and a first-order Dai Weining model, where the first-order Dai Weining model is:
Vt＝Voc-Vp-IRs
in the SOC model, the relations between the state of charge and the open circuit voltage and the load current are respectively:
SOC(t)＝ηI(t)/Q
Wherein, the variable s=2 (q-1)/t s/(q+1), q is a discrete operator, t s is a sampling interval, V q is an intermediate variable, V t is a terminal voltage of the battery, V oc is an open circuit voltage of the battery, R s is an internal resistance of the battery, R p is a polarization resistance of the battery, C p is a polarization capacitance of the battery, and subscript k represents data acquired or calculated for the kth time.
Preferably, the vector parameters include a first set of vector parameters and a second set of vector parameters, the first set of vector parameters being: θ k＝[a1,k b0,k b1,k]T, the second set of vector parameters isWherein,
Wherein, the variable V p＝Vt-Voc,Vt is terminal voltage, V oc is open-circuit voltage, a1, k, b0, k, b1, k are three intermediate variables respectively, the subscript k represents the data collected or calculated for the kth time, and the subscript k-1 represents the data collected or calculated for the kth-1 time.
In this scheme, the vector parameters for iterative update include a first set of vector parameters mainly describing actual physical parameters of the battery in the hybrid energy storage device and a second set of vector parameters mainly describing state parameters of the battery in the hybrid energy storage device at different moments, and the estimated values are corrected by updating the vector parameters each time the SOC is estimated, so that it is beneficial to obtain a high-precision SOC.
Preferably, in the step 2, the calculation process for obtaining the terminal voltage is as follows: firstly, carrying out Laplace transformation on the formula (1) to obtain
Vq(s)/I(s)＝(Rs+Rp+RsCps)/(1+RpCps) (2)
Performing bilinear transformation on the formula (2) to obtain
Vq(q-1)/I(q-1)＝(b0+b1q-1)/(1+a1q-1) (3)
Converting the equation (3) into a discrete time domain representation:
Then, the estimated value of the terminal voltage is
Wherein, the variable s=2 (q-1)/t s/(q+1), q is a discrete operator, t s is a sampling interval, V q is an intermediate variable, V t is a terminal voltage of the battery, V oc is an open circuit voltage of the battery, R s is an internal resistance of the battery, R p is a polarization resistance of the battery, C p is a polarization capacitance of the battery, and subscript k represents data acquired or calculated for the kth time.
Preferably, the relation between the state of charge and the open circuit voltage is obtained by fitting a relation 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, where the model state matrix is
x＝[Vp,SOC]T
Wherein, SOC is the state of charge to be estimated, and V p is the polarization voltage to be estimated; the SOC estimation formula established according to the model state matrix is:
Wherein the variables are L is feedback gain, and V t is terminal voltage at time t; an estimated value of terminal voltage at time t, F being a battery model function (Dai Weining theorem);
And, in addition, the method comprises the steps of,
Wherein, C is a fitting parameter, p 1、p2 is a correction parameter provided by the least squares model, and eta is coulombic efficiency.
Preferably, in the step 4, the iterative update equation in the least squares model is,
Wherein mu k is the correction factor, and,
Wherein,
Wherein the variables are
Variable(s)Wherein/>Wherein E is the desire
Variable(s)
Wherein the method comprises the steps of
Variable(s)Wherein/>To collect the variance of the voltage,/>To collect current variance
G 1,k、g2,k、g3,k, representing three constructors at the kth iteration calculation, respectively. In the scheme, the least square model is established according to the self-adaptive forgetting factor complete least square method, and specific data such as data with longer interval time can be forgotten artificially in the calculation process, so that the calculation accuracy can be improved, the data quantity required to be processed can be reduced, the calculation efficiency can be improved, and the method is particularly suitable for on-line monitoring of the charge state of the hybrid energy storage device.
Preferably, the open circuit voltage is collected by adopting a multichannel analog switch and a floating ground measurement method.
The SOC estimation system based on the hybrid energy storage device comprises a data acquisition unit, a controller, a data transmission unit for transmitting data and a cloud platform, wherein the cloud platform comprises a data receiving unit, a data storage unit, a data processing unit and a display unit which are matched with the data transmission unit, the data acquisition unit and the data transmission unit are respectively connected with the controller, the data receiving unit, the data storage unit and the display unit are respectively connected with the data processing unit,
The data acquisition unit is used for acquiring the voltage, the current and the internal resistance of the hybrid energy storage device and transmitting the voltage, the current and the internal resistance to the controller, the controller estimates the charge state according to the voltage, the current and the internal resistance and transmits the voltage, the current, the internal resistance and the charge state to the data receiving unit through the data transmitting unit, and the data processing unit obtains the voltage, the current, the internal resistance and the charge state from the data receiving unit and transmits the voltage, the current, the internal resistance and the charge state to the data storage unit for storage and the display unit for display. The estimation system is simple and compact in structure, is suitable for accurately estimating the charge state of the hybrid energy storage device, can monitor various parameters in the hybrid energy storage device, such as voltage, current, internal resistance, charge state and the like, in real time on line, and is beneficial to remotely knowing and mastering the actual running condition of each hybrid energy storage device.
Preferably, the controller adopts an STM32 chip or an ARM chip.
Further, the controller adopts STM32F103.STM32F103 is moderate in price, multiple paths of input meet data acquisition input, and high computing power meets the real-time requirement of SOC estimation.
Preferably, the data acquisition unit comprises a voltage acquisition module for acquiring voltage, a current acquisition module for acquiring current and an internal resistance acquisition module for acquiring internal resistance.
In a preferred scheme, the voltage acquisition module comprises n acquisition groups, n is a natural number, each acquisition group comprises a differential circuit, an operational amplifier, an optical coupler isolating switch and an A/D converter, wherein the differential circuit, the operational amplifier, the optical coupler isolating switch and the A/D converter are connected in parallel with two ends of the super capacitor or two ends of the storage battery, the output end of the differential circuit is connected with the input end of the operational amplifier, the output end of the operational amplifier is connected with the input end of the optical coupler isolating switch, the output end of the optical coupler isolating switch is connected with the A/D converter, the output end of the A/D converter is connected with the controller, and the controller calculates corresponding voltage values according to data acquired by each acquisition group respectively and adds the n voltage values to obtain the voltage.
Preferably, the optocoupler isolation switch is a PC817A optocoupler switch. The PC817A optocoupler switch has good linear performance, and is low in cost and suitable for mass use.
In a preferred scheme, the current acquisition module comprises a hall element sensor, a current signal converter and an A/D converter, wherein the hall element sensor is arranged on a storage battery, 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, the output end of the A/D converter is connected with the controller, the hall element sensor is used for converting a current signal in a tested circuit into an analog current signal and transmitting the analog current signal to the current signal converter, the current signal converter is used for converting the analog current signal into a corresponding analog voltage signal and transmitting the analog voltage signal to the A/D converter, the A/D converter is used for converting the analog voltage signal into a digital signal and transmitting the digital signal to the controller, and the controller calculates the current according to the digital signal.
In a preferred scheme, the internal resistance acquisition module comprises an analog multiplier, a low-pass filter, a direct current amplifier, an A/D converter, an alternating current differential circuit which is connected in parallel with the storage battery and is used for acquiring voltage response signals at two ends of the storage battery, and an alternating current constant current source which is used for generating sine signals; the output end of the alternating current differential circuit is connected with the input end of the analog multiplier respectively, the output end of the analog multiplier is connected with the input end of the low-pass filter, the output end of the low-pass filter is connected with the input end of the direct current amplifier, the output end of the direct current amplifier 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; the analog multiplier is used for multiplying the voltage response signal with the sine signal, the low-pass filter is used for converting the alternating current signal into a direct current signal, the direct current amplifier is used for amplifying the direct current signal, the A/D converter is used for converting the amplified direct current signal into a digital signal and transmitting the digital signal 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 transmitting unit is a WiFi wireless transmitting chip, and the data receiving unit is an ethernet card adapted to the WiFi wireless transmitting chip.
Optionally, the WiFi wireless transmitting chip is an ESP8266, and the ethernet card is a TP-LINK network card.
In a further scheme, the device further comprises a DC-DC module for converting 12V voltage into 3.3V and/or 5V voltage, wherein the input end of the DC-DC module is connected with the output end of the hybrid energy storage device, and the output end of the DC-DC module is respectively connected with the data transmission unit and the controller.
Compared with the prior art, the SOC estimation method and system based on the hybrid energy storage device provided by the invention have the advantages that firstly, the equivalent circuit model is built by circuit analysis on the hybrid energy storage monomer model, vector parameters are customized, and then, the SOC estimation model is built according to terminal voltage data provided by the equivalent circuit model, so that the state of charge (SOC) in the hybrid energy storage device is effectively estimated; and finally, carrying out data analysis on open-circuit voltage output by the SOC estimation model by adopting a self-adaptive forgetting factor complete least square method, carrying out iterative update on the vector parameters, and returning to the equivalent circuit model and the SOC estimation model, wherein the equivalent circuit model and the SOC estimation model carry out calculation on the next state of charge (SOC) according to the updated vector parameters, so that closed-loop correction on the SOC estimation process is realized, the estimation accuracy of the SOC can be effectively improved, errors are reduced, the actual requirements are more met, and the method is more suitable for estimating, monitoring and the like on the state of charge in the hybrid energy storage device, and is more beneficial to carrying out on-line and remote monitoring on the state of charge of the hybrid energy storage device.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a SOC estimation method based on a hybrid energy storage device according to embodiment 1 of the present invention.
Fig. 2 is a schematic diagram of a model framework of an SOC estimation method based on a hybrid energy storage device according to 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 according to embodiment 2 of the present invention.
Fig. 4 is a schematic circuit diagram of a voltage acquisition module in the SOC estimation system based on the hybrid energy storage device according to embodiment 2 of the present invention.
Fig. 5 is a block diagram of a current collection module in an SOC estimation system based on a hybrid energy storage device according to embodiment 2 of the present invention.
Fig. 6 is a block diagram of an internal resistance collection module in the SOC estimation system based on the hybrid energy storage device according to embodiment 2 of the present invention.
Fig. 7 is a schematic circuit diagram of a data transmitting unit in the SOC estimation system based on the hybrid energy storage device according to embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Example 1
Referring to fig. 1 and 2, the present embodiment provides a SOC estimation method based on a hybrid energy storage device, which includes the following steps:
step 1, acquiring state parameters of a battery in a hybrid energy storage device, and setting preset parameters, wherein the state parameters comprise an open-circuit voltage V oc, a load current I and an internal resistance R s of the battery, which are acquired through acquisition, and the preset parameters comprise a polarization resistance R p, a polarization capacitance C p, coulomb efficiency eta, charge capacity Q and the like of the battery so as to facilitate the establishment and calculation of a subsequent model;
as an example, in this embodiment, the open circuit voltage V oc may be collected by preferentially using a multi-channel analog switch in combination with the floating measurement method.
Step 2, an equivalent circuit model of the hybrid energy storage device is established, a group of vector parameters including the preset parameters are established, and the equivalent circuit model solves the terminal voltage V t of the battery according to the state parameters and the vector parameters;
By way of example, the equivalent circuit model may be built from an equivalent circuit diagram as shown, the equivalent circuit model including an SOC model and a first-order Dai Weining model, wherein the first-order Dai Weining model is:
Vt＝Voc-Vp-IRs (2)
in the SOC model, the relations between the state of charge and the open circuit voltage and the load current are respectively:
SOC(t)＝ηI(t)/Q (4)
wherein, V oc is open circuit voltage, V p is polarization voltage, V t is terminal voltage, R s is collected internal resistance, R p is polarization resistance, C p is polarization capacitance, I is collected load current, C is fitting coefficient, n is equal to 4, Q is rated charge of the hybrid energy storage device, eta is coulomb efficiency, and SOC is charge state;
In a preferred solution provided in this embodiment, the vector parameters include a first set of vector parameters and a second set of vector parameters, where the first set of vector parameters is: θ k＝[a1,k b0,k b1,k]T, the second set of vector parameters is Wherein,
Wherein, the variable V p＝Vt-Voc,Vt is a terminal voltage, V oc is an open-circuit voltage, a 1,k、b0,k、b1,k is three intermediate variables, the subscript k represents the data collected or calculated for the kth time, and the subscript k-1 represents the data collected or calculated for the kth-1 time, which will not be described in detail.
The calculation process for obtaining the terminal voltage through the step comprises the following steps: firstly, carrying out Laplace transformation on the formula (1) to obtain
Vq(s)/I(s)＝-(Rs+Rp+RsRpCps)/(1+RpCps) (6)
Performing bilinear transformation on the formula (6) to obtain
Vq(q-1)/I(q-1)＝(b0+b1q-1)/(1+a1q-1) (7)
Converting the equation (7) into a discrete time domain representation:
Then, the estimated value of the terminal voltage Thereby, the estimated value of the terminal voltage can be calculated;
Wherein, the variable s=2 (q-1)/t s/(q+1), q is a discrete operator, t s is a sampling interval, V q is an intermediate variable, V t is a terminal voltage of the battery, V oc is an open circuit voltage of the battery, R s is an internal resistance of the battery, R p is a polarization resistance of the battery, C p is a polarization capacitance of the battery, and subscript k represents data acquired or calculated for the kth time.
In a preferred embodiment, in the step (2), the relation between the state of charge SOC and the open circuit voltage V oc is obtained by fitting a relation model between the open circuit voltage V oc and the state of charge SOC, and the fitting of the relation model between the open circuit voltage V oc and the state of charge SOC is a very mature prior art, which is achieved by a single charge-discharge process of the hybrid energy storage device, which is not described herein again.
Step 3, an SOC estimation model of the hybrid energy storage device is established, and the SOC estimation model solves the charge state of the battery according to the terminal voltage and the vector parameters;
by way of example, the SOC estimation model includes a model state matrix and an SOC estimation formula, wherein the model state matrix is
x＝[Vp,SOC]T (9)
Wherein, SOC is the charge state to be estimated, and Vp is the polarization voltage to be estimated; the SOC estimation formula established according to the model state matrix is:
Wherein the variables are L is feedback gain, and V t is terminal voltage at time t; For the estimated value of the terminal voltage at time t, F is a battery model function (Dai Weining theorem), and,
Wherein, C is a fitting parameter, p 1、p2 is a correction parameter provided by the least squares model, and eta is coulombic efficiency.
The SOC estimation model can be used to estimate the SOC of the state of charge and obtain an estimated value of the polarization voltage V p, and the estimated value of the open-circuit voltage V oc, the intermediate variable V q, the estimated value of the terminal voltage V t of the battery, and the like can be calculated by the foregoing formula, so as to update the relevant parameters in the equivalent circuit model at the next calculation.
Step 4, a least square model is established, wherein the least square model is established according to a self-adaptive forgetting factor complete least square method, and is used for carrying out iterative computation on the vector parameters and returning the vector parameters after iterative computation to the equivalent circuit model and the SOC estimation model;
By way of example, in this embodiment, the iterative update equation in question is,
Wherein mu k is the correction factor, and,
Wherein,
Wherein the variables are
Variable(s)Wherein/>Wherein E is the desire
Variable(s)
Wherein the method comprises the steps of
Variable(s)Wherein/>To collect the variance of the voltage,/>To collect current variance
G 1,k、g2,k、g3,k, representing three constructors at the kth iteration calculation, respectively.
And iteratively updating the first group of vector parameters theta k＝[a1,k b0,k b1,k]T by utilizing the least square model so as to improve the calculation accuracy of the state of charge SOC in the next calculation.
Step 5, repeating the step2, the step3 and the step 4; and obtaining the high-precision SOC through repeated iterative computation.
According to the SOC estimation method provided by the embodiment, firstly, an equivalent circuit model is established by circuit analysis on the hybrid energy storage monomer model, vector parameters are customized, and then an SOC estimation model is established according to terminal voltage data provided by the equivalent circuit model, so that the state of charge (SOC) in the hybrid energy storage device can be effectively estimated; finally, carrying out data analysis on open-circuit voltage Voc output by the SOC estimation model by adopting a self-adaptive forgetting factor complete least square method, carrying out iterative update on the vector parameters, and returning to the equivalent circuit model and the SOC estimation model, wherein the equivalent circuit model and the SOC estimation model carry out calculation on the next state of charge SOC according to the updated vector parameters, so that closed-loop correction on the SOC estimation process is realized, the estimation precision of the SOC can be effectively improved, errors are reduced, the actual requirements are more met, and the method is more suitable for estimating, monitoring and the like on the state of charge in the hybrid energy storage device, and is more beneficial to carrying out on-line and remote monitoring on the state of charge of the hybrid energy storage device; for example, in order to verify the accuracy of the estimation method, the estimation method provided by the embodiment and the ampere-hour integration method commonly used in the prior art are adopted to estimate the SOC of the same hybrid energy storage device in the discharging process, and the SOC of the hybrid energy storage device is actually measured at different moments, the experimental data are shown in table 1,
Table 1 comparative experimental data
As can be seen from Table 1, the SOC estimated by the estimation method provided by the embodiment is closer to the true value than the conventional ampere-hour integral estimation method, and the relative error is generally within 5%, so that the estimation accuracy is higher.
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, which includes a data acquisition unit, a controller, a data transmission unit for transmitting data, and a cloud platform, where the cloud platform includes a data receiving unit, a data storage unit, a data processing unit, and a display unit, which are adapted to the data transmission unit, where the data acquisition unit and the data transmission unit are respectively connected to the controller, and the data receiving unit, the data storage unit, and the display unit are respectively connected to the data processing unit, as shown in fig. 3,
The data acquisition unit is used for acquiring voltage (namely open-circuit voltage), current (namely load current) and internal resistance of the hybrid energy storage device and transmitting the voltage, the current and the internal resistance to the controller, the controller estimates a charge state according to the voltage, the current and the internal resistance and transmits the voltage, the current, the internal resistance and the charge state to the data receiving unit through the data transmitting unit, and the data processing unit obtains the voltage, the current, the internal resistance and the charge state from the data receiving unit and transmits the voltage, the current, the internal resistance and the charge state to the data storage unit for storage and the display unit for display. The estimation system is simple and compact in structure, is suitable for accurately estimating the charge state of the hybrid energy storage device, can monitor various parameters in the hybrid energy storage device, such as voltage, current, internal resistance, charge state and the like, in real time on line, and is beneficial to remotely knowing and mastering the actual running condition of each hybrid energy storage device.
It may be appreciated that the data transmitting unit and the data receiving unit may be connected by a wired connection, such as a network cable connection, or may be connected by a wireless connection, such as a wifi connection, a wireless network connection, or the like.
In a preferred embodiment, the controller may employ an STM32 chip or an ARM chip, and in this embodiment, the controller employs an STM32F103, for example. STM32F103 is moderate in price, multiple paths of input meet data acquisition input, and high computing power meets the real-time requirement of SOC estimation. It will be appreciated that other types of controllers, such as a single-chip microcomputer, etc., may also be employed by those skilled in the art.
It will be appreciated that the algorithm provided in example 1 is preset in the controller to process the collected voltage, current, internal resistance, etc. data to estimate the state of charge of the hybrid energy storage device.
Preferably, the data acquisition unit comprises a voltage acquisition module for acquiring voltage, a current acquisition module for acquiring current and an internal resistance acquisition module for acquiring internal resistance.
As shown in fig. 4, in a preferred solution, the voltage acquisition module includes n acquisition groups, where n is a natural number, the acquisition groups include a differential circuit connected in parallel to two ends of the super capacitor or two ends of the storage battery, an operational amplifier, an optical coupling isolation switch, and an a/D converter, where an output end of the differential circuit is connected to an input end of the operational amplifier, an output end of the operational amplifier is connected to an input end of the optical coupling isolation switch, an output end of the optical coupling isolation switch is connected to the a/D converter, and an output end of the a/D converter is connected to the controller, and the controller calculates corresponding voltage values according to data acquired by each acquisition group, and adds n voltage values to obtain the voltage. As an example, when a super capacitor and two storage battery packs are arranged in the hybrid energy storage device, the voltage acquisition module comprises three acquisition groups for respectively acquiring the voltage values of the super capacitor and the two storage battery packs; as shown in the figure, in the voltage acquisition module, by combining the multichannel analog switch technology with the floating ground measurement technology and using the multichannel analog switch technology to measure the voltages (charge-discharge voltage and open-circuit voltage) of the hybrid energy storage device, firstly, the multichannel analog switch technology can group the hybrid energy storage device monomers, and only one voltage signal is processed at a time, so that the problem that the overall common-mode voltage of the hybrid energy storage device is too high can be effectively solved; secondly, in the floating ground measurement technology, an optical coupler isolating switch is utilized to enable a measurement circuit and an internal circuit of a chip to be not supplied with ground, so that the measurement circuit is not powered by energy storage equipment, the discharge of the measurement circuit to the energy storage device is reduced, and the precision is improved; finally, the collected analog signals are converted into digital signals through A/D, and because the super capacitor usually needs to be subjected to overvoltage protection by the voltage equalizing module, the voltage signals are simultaneously transmitted to the voltage equalizing module, and the controller supplies power to the measuring circuit, so that the measuring precision can be improved.
In this embodiment, the optocoupler isolation switch is a PC817A optocoupler switch. The PC817A optocoupler switch has good linear performance, and is low in cost and suitable for mass use.
Since the charging current and the discharging current of the storage battery must be maintained within specific ranges to ensure that the storage battery can normally work, each storage battery pack is formed by connecting a plurality of storage batteries in series, so that each storage battery pack must be provided with a current acquisition module, in a preferred scheme, the current acquisition module comprises a hall element sensor arranged on the storage battery pack, a current signal converter and an a/D converter, as shown in fig. 5, an output end of the hall element sensor is connected with an input end of the current signal converter, an output end of the current signal converter is connected with an input end of the a/D converter, an output end of the a/D converter is connected with the controller, wherein the hall element sensor is used for converting a current signal in a measured circuit into an analog current signal and transmitting the analog current signal to the current signal converter, the current signal converter is used for converting the analog current signal into a corresponding analog voltage signal and transmitting the analog voltage signal to the a/D converter, and the a/D converter is used for converting the analog voltage signal to a digital signal and transmitting the analog voltage signal to the controller, and the digital signal to the controller is used for calculating the current load (the current load) according to the current data in the energy storage device).
The hybrid energy storage device generates internal resistance polarization after multiple charging and discharging, thereby influencing the service life; the internal resistance caused by the series connection is also involved in the series connection process, so that the internal resistance monitoring of the single energy storage device is also necessary; because the super capacitor bank is basically consistent with the storage battery bank in model, an alternating current injection method is the most widely used at present, in a preferable scheme, a frequency multiplication signal can be amplified after an alternating current excitation power supply is externally added and then a phase-locked amplifying circuit is adopted, at the moment, high-precision acquisition is realized through a low-pass filter, and at the moment, the excitation voltage and the internal resistance to be acquired have the following characteristics:
U0＝C|Z|cosθ＝CR
Wherein U 0 is an excitation voltage source, Z is impedance, R is internal resistance, cos theta is a power factor angle, and C is excitation current; as an example, as shown in fig. 6, in this embodiment, the internal resistance acquisition module 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 for acquiring voltage response signals at two ends of the battery pack, and an ac constant current source used for generating sinusoidal signals; the output end of the alternating current differential circuit is connected with the input end of the analog multiplier respectively, the output end of the analog multiplier is connected with the input end of the low-pass filter, the output end of the low-pass filter is connected with the input end of the direct current amplifier, the output end of the direct current amplifier 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; the analog multiplier is used for multiplying the voltage response signal with the sine signal, the low-pass filter is used for converting the alternating current signal into a direct current signal, the direct current amplifier is used for amplifying the direct current signal, the A/D converter is used for converting the amplified direct current signal into a digital signal and transmitting the digital signal to the controller, and the controller calculates the internal resistance according to the digital signal.
In a preferred embodiment, the data processing unit may be a PC or a server. By way of example, in this embodiment, the data processing unit is a server.
In a preferred scheme, the data storage unit is a device with a storage function, such as a hard disk, a magnetic disk and the like.
In a preferred embodiment, the display unit may preferably be a display, and those skilled in the art will understand that the display unit includes a display, but is not limited to a display, for example, the display unit may also be a mobile phone, a tablet, etc., which are not listed here.
In a preferred scheme, the data transmitting unit is a WiFi wireless transmitting chip, and the data receiving unit is an Ethernet card which is matched with the WiFi wireless transmitting chip; by way of example, in this embodiment, the WiFi wireless transmission chip is ESP2866, which has a powerful on-chip processing and storage capability, including an antenna switch and a power management converter, while having a self-service troubleshooting, low power sleep mode. The invention is practical to the environment with limited environment and relatively complex environment only by being connected through an SPI interface, edits SOCKET based on TCP/IP protocol, encrypts the incoming and transmitted data by a Hash algorithm, and ensures the reliability of the data; the Ethernet card is a TP-LINK network card, so that stable transmission and storage of a large amount of data entering the cloud are ensured.
In a further scheme, the device further comprises a DC-DC module for converting 12V voltage into 3.3V and/or 5V voltage, wherein the input end of the DC-DC module is connected with the output end of the hybrid energy storage device, and the output end of the DC-DC module is respectively connected with the data transmission unit and the controller. In this embodiment, the DC-DC module may use the hybrid energy storage device itself to supply power to the power consumption components in the estimation system, which is more beneficial to integrating the data acquisition unit, the controller, the data transmission unit, and the like in the estimation system into the existing hybrid energy storage device; the DC-DC module may be a DC-DC module commonly used in the prior art, for example, in this embodiment, the DC-DC module includes a topology design based on a BUCK conversion circuit, including a synchronous rectification circuit formed by two NMOS transistors, a schottky diode, and an inductor, and is configured to convert the 12V voltage output by the hybrid energy storage device into 3.3V, so as to provide a 2.5-3.3V voltage signal required by the ESP2866 chip in the data sending unit, where the NMOS driving signal is provided by the controller, so that when other external devices are replaced or used, the required voltage signal can be changed to obtain the target voltage signal, thereby implementing high multiplexing.
As an example, as shown in fig. 7, the DC-DC circuit is connected to the VSS port and the GND port of the ESP2866 chip respectively, the data transmitted by the controller is connected to the TXD port and the RXD port of the ESP2866 chip respectively, and then the GPIO (18) switch is closed to write in a hardware program once, so as to write and encrypt the protocol of the data; and finally, opening a switch to finish writing, wherein the data encryption uses a Hash algorithm MD5 to sign, so that the integrity and safety of the data from the monitoring end to the cloud platform are ensured. In order to encrypt the data packet by MD5, firstly judging the length of the data packet, if not, filling the data packet, and carrying out 32-round iterative encryption according to the given initial 4-set 32-bit round encryption generation module and the defined round encryption function, wherein the data is 128 data blocks with 1-set bits; and finally, splicing 4 groups of 32-bit result data to output 128-bit Hash values, and finally adding the 128-bit Hash values to the tail end of a protocol data packet to form a data packet format.
In this embodiment, the cloud platform uses Windows Server 2012 as a service system, mySQL as a database, JSP as a front-end design, and a back-end data receiving processing language. The collected hybrid energy storage data is monitored by the distributed controller to be analyzed and the SOC is estimated online. And finally, uniformly presenting the information to other users and maintenance personnel.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention.
Claims (5)
1. The SOC estimation method based on the hybrid energy storage device is characterized by comprising the following steps of:
step 1, acquiring state parameters of a battery in a hybrid energy storage device, and setting preset parameters, wherein the state parameters comprise open-circuit voltage, load current and internal resistance of the battery obtained through acquisition, and the preset parameters comprise polarization resistance and polarization capacitance of the battery;
step 2, an equivalent circuit model of the hybrid energy storage device is established, a group of vector parameters including the preset parameters are established, and the equivalent circuit model solves the terminal voltage of the battery according to the state parameters and the vector parameters; the equivalent circuit model comprises an SOC model and a first-order Dai Weining model, wherein the first-order Dai Weining model is as follows:
Vt＝Voc-Vp-IRs
in the SOC model, the relations between the state of charge and the open circuit voltage and the load current are respectively:
SOC(t)＝ηI(t)/Q
wherein, V oc is open circuit voltage, V p is polarization voltage, V t is terminal voltage, R s is collected internal resistance, R p is polarization resistance, C p is polarization capacitance, I is collected load current, C is fitting coefficient, n is equal to 4, Q is rated charge of the hybrid energy storage device, eta is coulomb efficiency, and SOC is charge state;
The vector parameters include a first set of vector parameters and a second set of vector parameters, the first set of vector parameters being: θ k＝[a1,k b0,k b1,k]T, the second set of vector parameters is Wherein,
Wherein, the variable V p＝Vt-Voc,Vt is terminal voltage, V oc is open-circuit voltage, a 1,k、b0,k、b1,k is three intermediate variables respectively, the subscript k represents data acquired or calculated for the kth time, and the subscript k-1 represents data acquired or calculated for the kth-1 time;
the calculation process for obtaining the terminal voltage comprises the following steps: firstly, carrying out Laplace transformation on the formula (1) to obtain
Vq(s)/I(s)＝(Rs+Rp+RsCps)/(1+RpCps) (2)
Performing bilinear transformation on the formula (2) to obtain
Vq(q-1)/I(q-1)＝(b0+b1q-1)/(1+a1q-1) (3)
Converting the equation (3) into a discrete time domain representation:
Then, the estimated value of the terminal voltage is
Wherein, the variable s=2 (q-1)/t s/(q+1), q is a discrete operator, t s is a sampling interval, V q is an intermediate variable, V t is a terminal voltage of a battery, open-circuit voltage of a V oc battery, R s is internal resistance of the battery, R p is polarization resistance of the battery, C p is polarization capacitance of the battery, and subscript k represents data acquired or calculated for the kth time;
Step 3, an SOC estimation model of the hybrid energy storage device is established, the SOC estimation model solves the charge state of the battery according to the terminal voltage and the vector parameters, and the SOC estimation model comprises a model state matrix and an SOC estimation formula, wherein the model state matrix is as follows:
x＝[Vp,SOC]T
wherein, SOC is the charge state to be estimated, and Vp is the polarization voltage to be estimated; the SOC estimation formula established according to the model state matrix is:
Wherein the variables are L is feedback gain, and V t is terminal voltage at time t; /(I)The estimated value of the terminal voltage at the time t is F, which is a battery model function;
And, in addition, the method comprises the steps of,
Wherein, C is a fitting parameter, p 1、p2 is a correction parameter provided by a least squares model, and eta is coulomb efficiency;
Step 4, a least square model is established, wherein the least square model is established according to a self-adaptive forgetting factor complete least square method, and is used for carrying out iterative computation on the vector parameters and returning the vector parameters after iterative computation to the equivalent circuit model and the SOC estimation model; the iteratively updated equations in the least squares model are,
Wherein mu k is a correction factor, and,
Wherein,
Wherein the variables are
Variable(s)Wherein/>Wherein E is the desire;
Variable(s)
Wherein the T 1＝[T]1:3,1:3 is defined as the total number of the components,
Variable(s)Wherein/>To collect the variance of the voltage,/>To collect current variance;
g 1,k、g2,k、g3,k, representing three constructors during the kth iterative computation respectively;
And 5, repeating the steps 2 to 4.
2. The SOC estimation system based on the hybrid energy storage device, adopting the SOC estimation method as set forth in claim 1, is characterized in that the system comprises a data acquisition unit, a controller, a data transmission unit for transmitting data, and a cloud platform, the cloud platform comprises a data receiving unit, a data storage unit, a data processing unit and a display unit which are matched with the data transmission unit, the data acquisition unit and the data transmission unit are respectively connected with the controller, the data receiving unit, the data storage unit and the display unit are respectively connected with the data processing unit,
The data acquisition unit is used for acquiring the voltage, the current and the internal resistance of the hybrid energy storage device and transmitting the voltage, the current and the internal resistance to the controller, the controller estimates the charge state according to the voltage, the current and the internal resistance and transmits the voltage, the current, the internal resistance and the charge state to the data receiving unit through the data transmitting unit, and the data processing unit obtains the voltage, the current, the internal resistance and the charge state from the data receiving unit and transmits the voltage, the current, the internal resistance and the charge state to the data storage unit for storage and the display unit for display.
3. The hybrid energy storage device based SOC estimation system of claim 2 wherein the data acquisition unit includes a voltage acquisition module for acquiring voltage, a current acquisition module for acquiring current, and an internal resistance acquisition module for acquiring internal resistance; the voltage acquisition module comprises n acquisition groups, n is a natural number, each acquisition group comprises a differential circuit, an operational amplifier, an optical coupler isolating switch and an A/D converter, wherein the differential circuits are connected in parallel with two ends of the super capacitor or two ends of the storage battery, the output ends of the differential circuits are connected with the input ends of the operational amplifier, the output ends of the operational amplifier are connected with the input ends of the optical coupler isolating switch, the output ends of the optical coupler isolating switch are connected with the A/D converter, the output ends of the A/D converter are connected with the controller, the controller calculates corresponding voltage values according to data acquired by each acquisition group, and n voltage values are added to obtain the voltage.
4. The SOC estimation system based on the hybrid energy storage device of claim 3, wherein the current collection module includes a hall element sensor disposed in the battery pack, a current signal converter, and an a/D converter, an output end of the hall element sensor is connected to an input end of the current signal converter, an output end of the current signal converter is connected to an input end of the a/D converter, an output end of the a/D converter is connected to the controller, wherein the hall element sensor is configured to convert a current signal in the tested circuit into an analog current signal and transmit the analog current signal to the current signal converter, the current signal converter is configured to convert the analog current signal into a corresponding analog voltage signal and transmit the analog voltage signal to the a/D converter, and the a/D converter is configured to convert the analog voltage signal into a digital signal and transmit the digital signal to the controller, and the controller calculates the current according to the digital signal.
5. The hybrid energy storage device based SOC estimation system of claim 3 wherein the internal resistance acquisition module comprises 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 for acquiring a voltage response signal across the battery pack, and an ac constant current source for generating a sinusoidal signal; the output end of the alternating current differential circuit is connected with the input end of the analog multiplier respectively, the output end of the analog multiplier is connected with the input end of the low-pass filter, the output end of the low-pass filter is connected with the input end of the direct current amplifier, the output end of the direct current amplifier 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; the analog multiplier is used for multiplying the voltage response signal with the sine signal, the low-pass filter is used for converting the alternating current signal into a direct current signal, the direct current amplifier is used for amplifying the direct current signal, the A/D converter is used for converting the amplified direct current signal into a digital signal and transmitting the digital signal to the controller, and the controller calculates the internal resistance according to the digital signal.
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