CN110361653A - A kind of SOC estimation method and system based on hybrid accumulator - Google Patents
A kind of SOC estimation method and system based on hybrid accumulator Download PDFInfo
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Abstract
The present invention relates to a kind of SOC estimation method and system based on hybrid accumulator, including, obtain the state parameter of battery, and parameter preset is set；Equivalent-circuit model is established, and constructs one group of vector parameter including parameter preset, equivalent-circuit model solves the end voltage of battery according to state parameter and vector parameter；SOC appraising model is established, SOC appraising model solves the state of charge of battery according to end voltage and vector parameter；Least square model is established, the vector parameter after iterative calculation is returned to equivalent-circuit model and SOC appraising model for being iterated calculating to vector parameter by least square model；Repeat step 2-4；This evaluation method, the Closed-cycle correction to SOC estimation process may be implemented, effectively improve the estimation precision of SOC, reduce error, not only more meet actual demand, and it is more suitable for estimation, monitoring etc. to state of charge in hybrid accumulator, it is more advantageous to and online, long-range monitoring is carried out to the state of charge of hybrid accumulator.
Description
Technical Field
The invention relates to the technical field of energy storage equipment, in particular to a hybrid energy storage device-based SOC estimation method and system.
Background
With the development of new energy technology, hybrid energy storage devices (or systems) are used more and more; 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 respectively comprises a plurality of storage batteries; compared with a storage battery, the super capacitor has the characteristics of high capacity and quick charge and discharge, and is widely used as a new hybrid energy storage device formed by matching energy storage equipment with the storage battery, and parameter monitoring of the hybrid energy storage device becomes a key point of industrial research.
In the prior art, State parameters of the hybrid energy storage device, such as charging and discharging voltages, currents, internal resistance or State of Charge (SOC for short, State of Charge) of the device, and the like, need to be monitored or estimated, so as to grasp the operating State of the hybrid energy storage device, and facilitate control and scheduling; among the state parameters, parameters such as charging and discharging voltage, current, internal resistance of the device and the like can be directly measured, but the state of charge of the hybrid energy storage device cannot be directly measured, and needs to be estimated, and the state of charge is also called residual capacity, which represents the ratio of the residual capacity of the battery after being used for a period of time or left unused for a long time to the capacity of the battery in a full charging state, and is usually expressed in percentage. The value range is 0-1, when the SOC is 0, the battery is completely discharged, and when the SOC is 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 ensuring safety and service life of the battery in the use process.
In the prior art, for the estimation of the state of charge (SOC) in a conventional storage battery, scholars at home and abroad propose methods such as an ampere-hour integration method, a kalman filtering method, an adaptive kalman filtering method and the like, however, on the one hand, the methods usually have some defects, for example, the ampere-hour integration method is simple and easy to implement, but the SOC estimation error is increased due to the gradual increase of accumulated errors caused by current sampling and other factors, and the requirements of long-term use in practical engineering cannot be met; the Kalman filtering method is widely used because of the characteristics of small calculated amount and easy realization; the reason why the adaptive kalman filter algorithm does not usually consider the temperature factor and the charge-discharge rate factor is that the two factors do not change much under ideal conditions in a laboratory, but in practical engineering applications, such as an energy feedback process of an electric vehicle, the temperature and the charge-discharge rate will have great influence on the SOC estimation accuracy of the battery. On the other hand, the methods are generally applicable to conventional storage batteries (or storage battery packs) and are not applicable to hybrid energy storage devices, and besides, 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, the problems of low precision and no satisfaction of actual requirements generally exist.
Disclosure of Invention
The invention aims to solve the problems that the existing 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 the actual requirement in the prior art; the technical scheme adopted by the invention is as follows:
a SOC estimation method based on a hybrid energy storage device comprises 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 open-circuit voltage, load current and internal resistance of the battery acquired through collection, and the preset parameters comprise polarization resistance and polarization capacitance of the battery;
step 2, establishing an equivalent circuit model of the hybrid energy storage device, establishing a group of vector parameters including the preset parameters, and solving the terminal voltage of the battery by the equivalent circuit model according to the state parameters and the vector parameters;
step 3, establishing an SOC estimation model of the hybrid energy storage device, and solving the charge state of the battery according to the terminal voltage and the vector parameters by the SOC estimation model;
step 4, establishing a least square model, wherein the least square model is established according to a self-adaptive forgetting factor complete least square method, and is used for performing iterative computation on the vector parameters and returning the vector parameters after the iterative computation to the equivalent circuit model and the SOC estimation model;
and 5, repeating the steps 2 to 4.
According to the scheme, the hybrid energy storage single model is subjected to circuit analysis to establish an equivalent circuit model, the terminal voltage data of the battery is solved through the acquired state parameters of the battery and the set preset parameters, 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 an adaptive forgetting factor complete least square method to realize closed-loop correction of the SOC estimation process, so that the estimation precision of the SOC can be effectively improved, the error is reduced, the actual requirement is better met, the hybrid energy storage single model is more suitable for estimating and monitoring the charge state in the hybrid energy storage device, and the online and remote monitoring of the charge state of the hybrid energy storage device is more facilitated.
Preferably, in step 2, the equivalent circuit model includes an SOC model and a first-order davining model, where the first-order davining model is:
Vt＝Voc-Vp-IRs
in the SOC model, the relation among the state of charge, the open-circuit voltage and the load current is respectively as follows:
SOC(t)＝ηI(t)/Q
wherein the variable s is 2(q-1)/tsV (q +1), q is a discrete operator, tsIs the sampling interval, VqIs an intermediate variable, VtIs terminal voltage of battery, VocOpen circuit voltage of battery, RsIs the internal resistance of the battery, RpIs the polarization resistance of the battery, CpThe subscript k denotes the data collected or calculated the k-th time, which is the polarization capacitance of the cell.
Preferably, the vector parameters include a first set of vector parameters and a second set of vector parameters, and the first set of vector parameters is: thetak＝[a1,kb0,kb1,k]TThe second set of vector parameters isWherein,
wherein, the variable Vp＝Vt-Voc，VtIs terminal voltage, VocIs an open circuit voltage, a1,k、b0,k、b1,kThe index k indicates the data acquired or calculated at the k-th time and the index k-1 indicates the data acquired or calculated at the k-1 th time, respectively, for the three intermediate variables.
In the scheme, the vector parameters for iterative updating comprise a first group of vector parameters and a second group of vector parameters, wherein the first group of vector parameters mainly describe actual physical parameters of a battery in the hybrid energy storage device, the second group of vector parameters mainly describe state parameters of the battery in the hybrid energy storage device at different moments, and the vector parameters are updated when the SOC is estimated every time, so that the estimated value is corrected, and the acquisition of the high-precision SOC is facilitated.
Preferably, in the step 2, the calculation process of obtaining the terminal voltage includes: firstly, the formula (1) is subjected to Laplace transform to obtain
Vq(s)/I(s)＝(Rs+Rp+RsCps)/(1+RpCps) (2)
Bilinear transformation is carried out on the formula (2) to obtain
Vq(q-1)/I(q-1)＝(b0+b1q-1)/(1+a1q-1) (3)
Converting equation (3) to a discrete time domain representation:
then, the estimated value of the terminal voltage is
Wherein the variable s is 2(q-1)/tsV (q +1), q is a discrete operator, tsIs the sampling interval, VqIs an intermediate variable, VtIs terminal voltage of battery, VocOpen circuit voltage of battery, RsIs the internal resistance of the battery, RpIs the polarization resistance of the battery, CpThe subscript k denotes the data collected or calculated the k-th time, which is the polarization capacitance of the cell.
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 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, V, to be estimatedpIs the polarization voltage to be estimated; the SOC estimation formula established according to the model state matrix is as follows:
wherein, variableL is the feedback gain, VtTerminal voltage at time t;f is the battery model function (thevenin theorem) for the estimated value of the terminal voltage at time t;
and,
wherein C is a fitting parameter, p1、p2And (3) a correction parameter provided for the least square model, wherein eta is the coulombic efficiency.
Preferably, in the step 4, the iterative update equation in the least square model is,
wherein, mukIs the correction factor and, further,
wherein,
wherein, variable
Variables ofWhereinWherein E is desirability
Variables of
Wherein
Variables ofWhereinIn order to collect the variance of the voltage,for collecting current variance
g1,k、g2,k、g3,kAnd respectively represent three constructors in the k-th iteration calculation. In the scheme, the least square model is established according to a self-adaptive forgetting factor complete least square method, specific data such as data with long interval time can be artificially forgotten in the calculation process, not only can the calculation precision be improved, but also the data amount required to be processed can be reduced, the calculation efficiency is improved, and the method is particularly suitable for online monitoring of the charge state of the hybrid energy storage device.
Preferably, the open-circuit voltage is acquired by adopting a multi-channel analog switch and a floating measurement method.
A SOC estimation system based on a 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 voltage, current and internal resistance of the hybrid energy storage device and transmitting the voltage, current and internal resistance to the controller, the controller estimates the charge state according to the voltage, current and internal resistance, the voltage, current, internal resistance and charge state are transmitted to the data receiving unit through the data transmitting unit, and the data processing unit acquires the voltage, current, internal resistance and charge state from the data receiving unit, transmits the voltage, current, internal resistance and charge state to the data storage unit for storage and transmits the voltage, current, internal resistance and charge state to 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 perform real-time online monitoring on various parameters in the hybrid energy storage device, such as voltage, current, internal resistance, charge state and the like, and is beneficial to remotely knowing and mastering the actual operation condition of each hybrid energy storage device.
Preferably, the controller adopts an STM32 chip or an ARM chip.
Further, the controller adopts STM32F 103. STM32F103 is moderate in price, and multichannel input has satisfied data acquisition input, and high computing power has satisfied 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, wherein n is a natural number, the acquisition groups respectively comprise a differential circuit, an operational amplifier, an optical coupling isolating switch and an A/D converter which are connected in parallel at two ends of a super capacitor or two ends of a 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 coupling isolating switch, the output end of the optical coupling isolating switch is connected with the A/D converter, the output end of the A/D converter is connected with the controller, the controller respectively calculates corresponding voltage values according to data acquired by each acquisition group, and adds n voltage values to obtain the voltage.
Preferably, the optical coupling isolation switch adopts a PC817A optical coupling switch. The PC817A optical coupling switch has good linear performance, and is cheap and suitable for mass use.
In a preferred embodiment, the current collecting module includes a hall sensor, a current signal converter, and an a/D converter, the hall sensor has an output connected to an input of the current signal converter, the current signal converter has an output connected to an input of the a/D converter, the a/D converter has an output connected to the controller, wherein the hall sensor is configured to convert a current signal in a circuit to be tested 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, the a/D converter is configured to convert the analog voltage signal into a data signal and transmit the data signal to the controller, 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 to the storage battery pack and is used for acquiring voltage response signals at two ends of the storage battery pack, and an alternating current constant current source for generating a sinusoidal signal; the output end of the alternating current differential circuit and the output end of the alternating current constant current source are respectively connected with the input end of the analog multiplier, 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 by the sinusoidal signal, the low-pass filter is used for converting an 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 hybrid energy storage 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 sending unit and the controller.
Compared with the prior art, the SOC estimation method and the SOC estimation system based on the hybrid energy storage device provided by the invention have the advantages that firstly, the hybrid energy storage monomer model is subjected to circuit analysis to establish an equivalent circuit 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 SOC in the hybrid energy storage device is effectively estimated; and finally, performing data analysis on the open-circuit voltage output by the SOC estimation model by adopting a self-adaptive forgetting factor complete least square method, iteratively updating the vector parameters, and then returning the updated vector parameters to the equivalent circuit model and the SOC estimation model, wherein the equivalent circuit model and the SOC estimation model perform next charge state SOC calculation according to the updated vector parameters, so that closed-loop correction of the SOC estimation process is realized, the SOC estimation precision can be effectively improved, the error is reduced, the actual requirement is better met, the method is more suitable for estimating and monitoring the charge state in the hybrid energy storage device, and the like, and is more favorable for performing online and remote monitoring on the charge state 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 needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a hybrid energy storage device-based SOC estimation method according to embodiment 1 of the present invention.
Fig. 2 is a schematic model framework diagram 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 the SOC estimation system based on the hybrid energy storage device according to embodiment 2 of the present invention.
Fig. 6 is a block diagram of an internal resistance acquisition 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 technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of 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 present invention, 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 derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1 and fig. 2, the present embodiment provides a method for estimating SOC based on a hybrid energy storage device, including 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 open-circuit voltage V of the battery acquired through acquisitionocLoad current I, internal resistance RsThe preset parameter comprises polarization resistance R of the batterypAnd a polarization capacitor CpCoulombic efficiency eta, charge capacity Q and the like, so as to facilitate the establishment and calculation of a subsequent model;
for example, in the present embodiment, a multi-channel analog switch may be preferentially adopted to collect the open-circuit voltage V in combination with a floating measurement methodoc。
Step 2, establishing an equivalent circuit model of the hybrid energy storage device, establishing a group of vector parameters including the preset parameters, and solving the terminal voltage V of the battery by the equivalent circuit model according to the state parameters and the vector parameterst；
By way of example, the equivalent circuit model may be built according to an equivalent circuit diagram as shown in the figure, and the equivalent circuit model includes an SOC model and a first-order davining model, where the first-order davining model is:
Vt＝Voc-Vp-IRs (2)
in the SOC model, the relation among the state of charge, the open-circuit voltage and the load current is respectively as follows:
SOC(t)＝ηI(t)/Q (4)
wherein, VocIs an open circuit voltage, VpIs a polarization voltage, VtTo terminal voltage, RsFor internal resistance of collection, RpIs a polarization resistance, CpThe current is a polarization capacitor, I is the collected load current, c is a fitting coefficient, n is equal to 4, Q is the rated charge quantity of the hybrid energy storage device, eta is the coulombic efficiency, and SOC is the charge state;
in a preferred embodiment provided by 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: thetak＝[a1,kb0,kb1,k]TThe second set of vector parameters isWherein,
wherein, the variable Vp＝Vt-Voc，VtIs terminal voltage, VocIs an open circuit voltage, a1,k、b0,k、b1,kThe subscript k represents the data acquired or calculated at the kth time, and the subscript k-1 represents the data acquired or calculated at the kth-1 time, which will not be described in detail later.
The calculation process for obtaining the terminal voltage through the step is as follows: firstly, the formula (1) is subjected to Laplace transform to obtain
Vq(s)/I(s)＝-(Rs+Rp+RsRpCps)/(1+RpCps) (6)
Bilinear transformation is carried out on the formula (6) to obtain
Vq(q-1)/I(q-1)＝(b0+b1q-1)/(1+a1q-1) (7)
Converting equation (7) to a discrete time domain representation:
then, an estimate of the terminal voltageThereby, an estimated value of the terminal voltage can be calculated;
wherein the variable s is 2(q-1)/tsV (q +1), q is a discrete operator, tsIs the sampling interval, VqIs an intermediate variable, VtIs terminal voltage of battery, VocOpen circuit voltage of battery, RsIs the internal resistance of the battery, RpIs the polarization resistance of the battery, CpThe subscript k denotes the data collected or calculated the k-th time, which is the polarization capacitance of the cell.
In a preferred embodiment, in the step (2), the state of charge SOC and the open-circuit voltage V areocBy fitting the open circuit voltage VocObtaining a relation model between the state of charge SOC and the fitted open-circuit voltage VocThe relation model between the state of charge (SOC) and the SOC is a mature prior art, and can be realized through one-time charging and discharging process of the hybrid energy storage device, and is not described again here.
Step 3, establishing an SOC estimation model of the hybrid energy storage device, and solving the charge state of the battery according to the terminal voltage and the vector parameters by the SOC estimation model;
by way of example, the SOC estimation model includes a model state matrix and an SOC estimation formula, where the model state matrix is
x＝[Vp,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 as follows:
wherein, variableL is the feedback gain, VtTerminal voltage at time t;f is a battery model function (davining theorem) for an estimate of the terminal voltage at time t, and,
wherein C is a fitting parameter, p1、p2And (3) a correction parameter provided for the least square model, wherein eta is the coulombic efficiency.
By means of the SOC estimation model, the state of charge SOC can be estimated, and the polarization voltage V can be obtainedpBy the above formula, the open-circuit voltage V can be calculatedocIs estimated, and an intermediate variable VqTerminal voltage V of batterytEtc. to update the relevant parameters in the equivalent circuit model at the next calculation.
Step 4, establishing a least square model, wherein the least square model is established according to a self-adaptive forgetting factor complete least square method, and is used for performing iterative computation on the vector parameters and returning the vector parameters after the iterative computation to the equivalent circuit model and the SOC estimation model;
in this embodiment, for example, the iterative update equation is,
wherein, mukIs the correction factor and, further,
wherein,
wherein, variable
Variables ofWhereinWherein E is desirability
Variables of
Wherein
Variables ofWhereinIn order to collect the variance of the voltage,for collecting current variance
g1,k、g2,k、g3,kAnd respectively represent three constructors in the k-th iteration calculation.
Using least squares model to a first set of vector parameters thetak＝[a1,kb0,kb1,k]TAnd performing iterative updating so as to improve the calculation precision of the SOC during the next calculation.
Step 5, repeating the step 2, the step 3 and the step 4; and obtaining the SOC with high precision through repeated iterative calculation.
According to the SOC estimation method provided by the embodiment, firstly, a hybrid energy storage monomer model is subjected to circuit analysis to establish an equivalent circuit 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 a hybrid energy storage device can be effectively estimated; finally, data analysis is carried out on the open-circuit voltage Voc output by the SOC estimation model by adopting a self-adaptive forgetting factor complete least square method, the vector parameters are updated in an iterative manner and then returned to the equivalent circuit model and the SOC estimation model, the equivalent circuit model and the SOC estimation model carry out next charge state SOC calculation according to the updated vector parameters, and therefore closed-loop correction of the SOC estimation process is achieved, the SOC estimation precision can be effectively improved, errors are reduced, the actual requirements are better met, the method is more suitable for estimating and monitoring the charge state in the hybrid energy storage device, and the charge state of the hybrid energy storage device can be monitored online and remotely; for example, to verify the accuracy of the estimation method, the estimation method provided in this embodiment and the ampere-hour integration method commonly used in the prior art are respectively adopted to estimate the SOC of the same hybrid energy storage device during the discharging process, and the SOC of the hybrid energy storage device is actually measured at different times, the experimental data are shown in table 1,
table 1 comparative experimental data
As can be seen from table 1, compared with the conventional ampere-hour integral estimation method, the SOC estimated by the estimation method provided by the present embodiment is closer to the true value, and the relative error is usually 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, including a data acquisition unit, a controller, a data transmission unit for transmitting data, and a cloud platform, where the cloud platform includes a data reception unit, a data storage unit, a data processing unit, and a display unit adapted to the data transmission unit, the data acquisition unit and the data transmission unit are respectively connected to the controller, and the data reception unit, the data storage unit, and the display unit are respectively connected to the data processing unit, as shown in fig. 3, where,
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, transmits the voltage, the current, the internal resistance and the charge state to the data storage unit for storage and transmits the voltage, the current, the internal resistance and the charge state to 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 perform real-time online monitoring on various parameters in the hybrid energy storage device, such as voltage, current, internal resistance, charge state and the like, and is beneficial to remotely knowing and mastering the actual operation condition of each hybrid energy storage device.
It can be understood that the data sending unit and the data receiving unit can be connected in a wired mode, such as a network cable connection, or in a wireless mode, such as a wifi connection or a wireless network connection.
In a preferred scheme, the controller may adopt an STM32 chip or an ARM chip, for example, in the present embodiment, the controller adopts an STM32F 103. STM32F103 is moderate in price, and multichannel input has satisfied data acquisition input, and high computing power has satisfied the real-time requirement of SOC estimation. It is understood that other types of controllers, such as a single chip, may be used by those skilled in the art.
It can be understood that the algorithm provided in embodiment 1 is preset in the controller, so as to process the acquired data of voltage, current, internal resistance, and the like, and 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 embodiment, the voltage acquisition module includes n acquisition groups, where n is a natural number, the acquisition groups respectively include a differential circuit, an operational amplifier, an optical coupling isolation switch, and an a/D converter, which are connected in parallel to two ends of a super capacitor or two ends of a storage battery, 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, an output end of the a/D converter is connected to the controller, and the controller respectively calculates corresponding voltage values according to data acquired by each acquisition group, and adds up n voltage values to obtain the voltage. For example, when a super capacitor and two storage battery packs are arranged in the hybrid energy storage device, the voltage acquisition module includes three acquisition groups, which are respectively used for acquiring voltage values of the super capacitor and the two storage battery packs; as shown in the figure, in the voltage acquisition module, the multichannel analog switch technology is combined with the floating ground measurement technology and used for measuring the voltage (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 path of voltage signal is processed at a certain time, so that the problem of overhigh overall common-mode voltage of the hybrid energy storage device can be effectively solved; secondly, in the floating ground measurement technology, the optical coupling isolating switch is utilized to enable the measurement circuit and the internal circuit of the chip to be free of ground supply, so that the measurement circuit is not powered by energy storage equipment, discharging of the measurement circuit to an energy storage device is reduced, and precision is improved; finally, the acquired analog signals are converted into digital signals through A/D, and because the super capacitor usually needs the voltage equalizing module to perform overvoltage protection, voltage signals can be transmitted to the voltage equalizing module at the same time, and the controller supplies power to the measuring circuit, so that the measuring precision can be improved.
In the embodiment, the optical coupling isolation switch adopts a PC817A optical coupling switch. The PC817A optical coupling switch has good linear performance, and is cheap and suitable for mass use.
In order to ensure the normal operation of the storage battery, the charging current and the discharging current of the storage battery must be maintained within a specific range, and usually the storage battery pack is formed by connecting a plurality of storage batteries in series, so each storage battery pack must be provided with a current collecting module, in a preferred scheme, the current collecting module comprises a hall element sensor, a current signal converter and an a/D converter which are arranged on the storage battery pack, as shown in fig. 5, 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, wherein the hall element sensor is used for converting the current signal in the 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 data signal and transmitting the data signal to the controller, and the controller calculates current according to the digital signal so as to obtain real-time current data (load current) in the hybrid energy storage device.
The hybrid energy storage device can generate internal resistance polarization after being charged and discharged for many times, so that the service life is influenced; the internal resistance caused by 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 module, the alternating current injection method is most widely used at present, in a preferred scheme, a frequency multiplication signal is amplified after an alternating current excitation power supply is additionally added and a phase-locked amplifying circuit is passed, high-precision acquisition is realized through a low-pass filter at the moment, and the excitation voltage and the internal resistance to be acquired have the following steps:
U0＝C|Z|cosθ＝CR
wherein, U0Is an excitation voltage source, Z is impedance, R is internal resistance, cos theta is a power factor angle, and C is excitation current; by way of example, as shown in fig. 6, in the present 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 for generating a sinusoidal signal; the output end of the alternating current differential circuit and the output end of the alternating current constant current source are respectively connected with the input end of the analog multiplier, 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 by the sinusoidal signal, the low-pass filter is used for converting an 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. For example, in this embodiment, the data processing unit is a server.
In a preferred embodiment, the data storage unit is a device with a storage function, such as a hard disk or a magnetic disk.
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, 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 adapted to the WiFi wireless transmitting chip; by way of example, in the present embodiment, the WiFi wireless transmitting chip is ESP2866, which has powerful on-chip processing and storage capabilities, includes an antenna switch and a power management converter, and has self-help troubleshooting and a low-power sleep mode. The method is realized only by SPI interface connection, and is very practical for the environment with limited environment and relatively complex environment, in the invention, SOCKET is edited based on TCP/IP protocol, and Hash algorithm encryption is carried out on the data to be sent in and sent out, so that the reliability of the data is ensured; the Ethernet card is a TP-LINK network card, and stable transmission and storage of a large amount of data imported to the cloud are guaranteed.
In a further scheme, the hybrid energy storage 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 sending unit and the controller. In this embodiment, the DC-DC module can use the hybrid energy storage device itself to supply power to the power consuming components in the estimation system, which is more beneficial to integrating the data acquisition unit, the controller, the data sending unit, and the like in the estimation system into the existing hybrid energy storage device; the DC-DC module may adopt 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, and includes a synchronous rectification circuit composed of two NMOS transistors, a schottky diode, and an inductor, and is configured to convert a 12V voltage output by the hybrid energy storage device into 3.3V to provide a 2.5-3.3V voltage signal required by an ESP2866 chip in the data transmission unit, and the NMOS driving signal is provided by the controller, so that when other external devices are replaced or used, the required voltage signal can obtain a target voltage signal by changing the driving signal, thereby implementing high multiplexing.
For example, as shown in fig. 7, the DC-DC circuit is respectively connected to a VSS port and a GND port of the ESP2866 chip, data transmitted by the controller is respectively connected to a TXD port and an RXD port of the ESP2866 chip, and then the GPIO (18) switch is closed to perform hardware program writing once, so as to implement protocol writing and encryption of the data; and finally, opening a switch to finish writing, wherein the data is encrypted and signed by using a Hash algorithm MD5, so that the completeness and safety of the data from the monitoring end to the cloud platform are ensured. For MD5 encryption of a data packet, firstly, the length of the data packet needs to be judged, if the length of the data packet is not enough, the data packet needs to be filled, the data is a data block with 128 bits as 1 group, and 32-round iterative encryption is carried out by matching with a defined round encryption function according to a given initial 4-group 32-bit round encryption generation module; finally, 4 groups of 32-bit result data are spliced to output a 128-bit Hash value, and finally the Hash value is added to the end of the 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, and JSP as a front-end design and a backend data receiving and processing language. And analyzing the hybrid energy storage data monitored and collected by the distributed controller and estimating the SOC on line. And finally, uniformly presenting the data to other users and maintenance personnel.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A SOC estimation method based on a hybrid energy storage device is characterized by comprising 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 open-circuit voltage, load current and internal resistance of the battery acquired through collection, and the preset parameters comprise polarization resistance and polarization capacitance of the battery;
step 2, establishing an equivalent circuit model of the hybrid energy storage device, establishing a group of vector parameters including the preset parameters, and solving the terminal voltage of the battery by the equivalent circuit model according to the state parameters and the vector parameters;
step 3, establishing an SOC estimation model of the hybrid energy storage device, and solving the charge state of the battery according to the terminal voltage and the vector parameters by the SOC estimation model;
step 4, establishing a least square model, wherein the least square model is established according to a self-adaptive forgetting factor complete least square method, and is used for performing iterative computation on the vector parameters and returning the vector parameters after the iterative computation to the equivalent circuit model and the SOC estimation model;
and 5, repeating the steps 2 to 4.
2. The hybrid energy storage device-based SOC estimation method according to claim 1, wherein in the step 2, the equivalent circuit model includes an SOC model and a first order davining model, wherein the first order davining model is:
Vt＝Voc-Vp-IRs
in the SOC model, the relation among the state of charge, the open-circuit voltage and the load current is respectively as follows:
SOC(t)＝ηI(t)/Q
wherein, VocIs an open circuit voltage, VpIs a polarization voltage, VtTo terminal voltage, RsFor internal resistance of collection, RpIs a polarization resistance, CpThe polarization capacitance is adopted, I is the collected load current, c is the fitting coefficient, n is equal to 4, Q is the rated charge quantity of the hybrid energy storage device, eta is the coulombic efficiency, and SOC is the charge state.
3. The hybrid energy storage device-based SOC estimation method of claim 2, wherein the vector parameters comprise a first set of vector parameters and a second set of vector parameters, the first set of vector parameters being: thetak＝[a1,k b0,k b1,k]TThe second set of vector parameters isWherein,
wherein, the variable Vp＝Vt-Voc，VtIs terminal voltage, VocIs an open circuit voltage, a1,k、b0,k、b1,kThe index k indicates the data acquired or calculated at the k-th time and the index k-1 indicates the data acquired or calculated at the k-1 th time, respectively, for the three intermediate variables.
4. The hybrid energy storage device-based SOC estimation method according to claim 3, wherein in the step 2, the calculation process for obtaining the terminal voltage is: firstly, the formula (1) is subjected to Laplace transform to obtain
Vq(s)/I(s)＝(Rs+Rp+RsCps)/(1+RpCps) (2)
Bilinear transformation is carried out on the formula (2) to obtain
Vq(q-1)/I(q-1)＝(b0+b1q-1)/(1+a1q-1) (3)
Converting equation (3) to a discrete time domain representation:
then, the estimated value of the terminal voltage is
Wherein the variable s is 2(q-1)/tsV (q +1), q is a discrete operator, tsIs the sampling interval, VqIs an intermediate variable, VtIs terminal voltage of battery, VocOpen circuit voltage of battery, RsIs the internal resistance of the battery, RpIs the polarization resistance of the battery, CpThe subscript k denotes the data collected or calculated the k-th time, which is the polarization capacitance of the cell.
5. The hybrid energy storage device-based SOC estimation method of claim 4, wherein in the step 3, the SOC estimation model comprises a model state matrix and an SOC estimation formula, wherein the model state matrix is
x＝[Vp,SOC]T
Wherein SOC is the state of charge, V, to be estimatedpIs the polarization voltage to be estimated; the SOC estimation formula established according to the model state matrix is as follows:
wherein, variableL is the feedback gain, VtTerminal voltage at time t;f is a battery model function, which is an estimate of the terminal voltage at time t;
and,
wherein C is a fitting parameter, p1、p2And (3) a correction parameter provided for the least square model, wherein eta is the coulombic efficiency.
6. The hybrid energy storage device-based SOC estimation method of claim 5, wherein in the step 4, the iterative update equation in the least squares model is,
wherein, mukIs the correction factor and, further,
wherein,
wherein, variable
Variables ofWhereinWherein E isTo make expectations
Variables of
Wherein
Variables ofWhereinIn order to collect the variance of the voltage,for collecting current variance
g1,k、g2,k、g3,kAnd respectively represent three constructors in the k-th iteration calculation.
7. An SOC estimation system based on a hybrid energy storage device is characterized by comprising 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 SOC estimation system comprises a data acquisition unit, a controller, a data transmission unit for transmitting data, and a cloud platform for receiving data,
the data acquisition unit is used for acquiring voltage, current and internal resistance of the hybrid energy storage device and transmitting the voltage, current and internal resistance to the controller, the controller estimates the charge state according to the voltage, current and internal resistance, the voltage, current, internal resistance and charge state are transmitted to the data receiving unit through the data transmitting unit, and the data processing unit acquires the voltage, current, internal resistance and charge state from the data receiving unit, transmits the voltage, current, internal resistance and charge state to the data storage unit for storage and transmits the voltage, current, internal resistance and charge state to the display unit for display.
8. The hybrid energy storage device-based SOC estimation system of claim 7, wherein 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; the voltage acquisition module comprises n acquisition groups, wherein n is a natural number, the acquisition groups respectively comprise a differential circuit, an operational amplifier, an optical coupling isolating switch and an A/D converter which are connected in parallel at two ends of a super capacitor or two ends of a 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 coupling isolating switch, the output end of the optical coupling isolating switch is connected with the A/D converter, the output end of the A/D converter is connected with the controller, the controller respectively calculates corresponding voltage values according to data acquired by each acquisition group, and adds n voltage values to obtain the voltage.
9. The SOC estimation system of claim 8, wherein the current collection module comprises a Hall element sensor disposed on the battery pack, a current signal converter, and an A/D converter, an output of the Hall element sensor is connected to an input of the current signal converter, an output of the current signal converter is connected to an input of the A/D converter, and an output of the A/D converter is connected to the controller, wherein the Hall element sensor is configured to convert a current signal in the circuit under test 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 data signal, and transmitting the digital signal to the controller, and calculating the current by the controller according to the digital signal.
10. The hybrid energy storage device-based SOC estimation system of claim 8, wherein the internal resistance collection 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 and used for collecting voltage response signals across the battery pack, and an ac constant current source for generating sinusoidal signals; the output end of the alternating current differential circuit and the output end of the alternating current constant current source are respectively connected with the input end of the analog multiplier, 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 by the sinusoidal signal, the low-pass filter is used for converting an 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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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111914394A (en) * | 2020-06-29 | 2020-11-10 | 哈尔滨工程大学 | Super capacitor parameter online estimation method based on double least square method |
| CN111965544A (en) * | 2020-07-17 | 2020-11-20 | 江苏大学 | Method for estimating minimum envelope line SOC of vehicle parallel power battery based on voltage and current dual constraints |
| CN112186278A (en) * | 2020-09-23 | 2021-01-05 | 珠海冠宇动力电池有限公司 | Lithium ion battery matching method |
| CN112505566A (en) * | 2020-12-21 | 2021-03-16 | 河南克能新能源科技有限公司 | Dynamic impedance judgment system and method in battery charging and discharging process |
| CN112965001A (en) * | 2021-02-09 | 2021-06-15 | 重庆大学 | Power battery pack fault diagnosis method based on real vehicle data |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120028000A (en) * | 2010-09-14 | 2012-03-22 | 충북대학교 산학협력단 | A method for the soc estimation of li-ion battery and a system for its implementation |
| WO2015188610A1 (en) * | 2014-06-11 | 2015-12-17 | 北京交通大学 | Method and device for estimating state of charge of battery |
| CN106443459A (en) * | 2016-09-06 | 2017-02-22 | 中国第汽车股份有限公司 | Evaluation method of state of charge of vehicle lithium ion power battery |
| CN107064811A (en) * | 2017-03-01 | 2017-08-18 | 华南理工大学 | A kind of lithium battery SOC On-line Estimation methods |
| CN107390127A (en) * | 2017-07-11 | 2017-11-24 | 欣旺达电动汽车电池有限公司 | A kind of SOC estimation method |
| CN107576919A (en) * | 2017-10-20 | 2018-01-12 | 广东石油化工学院 | Power battery charged state estimating system and method based on ARMAX models |
| CN108445402A (en) * | 2018-02-28 | 2018-08-24 | 广州小鹏汽车科技有限公司 | A kind of lithium-ion-power cell state-of-charge method of estimation and system |
| WO2018161486A1 (en) * | 2017-03-08 | 2018-09-13 | 深圳市海云图新能源有限公司 | Method and system for estimating soc of power battery on the basis of dynamic parameters |
| CN109164391A (en) * | 2018-07-12 | 2019-01-08 | 杭州神驹科技有限公司 | A kind of power battery charged state estimation on line method and system |
| WO2019113999A1 (en) * | 2017-12-14 | 2019-06-20 | 江苏大学 | Gas-liquid dynamic model-based accurate lithium-ion battery soc estimation method |
| CN211086534U (en) * | 2019-07-25 | 2020-07-24 | 北方民族大学 | SOC estimation system based on hybrid energy storage device |
-
2019
- 2019-07-25 CN CN201910676870.3A patent/CN110361653B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120028000A (en) * | 2010-09-14 | 2012-03-22 | 충북대학교 산학협력단 | A method for the soc estimation of li-ion battery and a system for its implementation |
| WO2015188610A1 (en) * | 2014-06-11 | 2015-12-17 | 北京交通大学 | Method and device for estimating state of charge of battery |
| CN106716158A (en) * | 2014-06-11 | 2017-05-24 | 北京交通大学 | Method and device for estimating state of charge of battery |
| CN106443459A (en) * | 2016-09-06 | 2017-02-22 | 中国第汽车股份有限公司 | Evaluation method of state of charge of vehicle lithium ion power battery |
| CN107064811A (en) * | 2017-03-01 | 2017-08-18 | 华南理工大学 | A kind of lithium battery SOC On-line Estimation methods |
| WO2018161486A1 (en) * | 2017-03-08 | 2018-09-13 | 深圳市海云图新能源有限公司 | Method and system for estimating soc of power battery on the basis of dynamic parameters |
| CN107390127A (en) * | 2017-07-11 | 2017-11-24 | 欣旺达电动汽车电池有限公司 | A kind of SOC estimation method |
| CN107576919A (en) * | 2017-10-20 | 2018-01-12 | 广东石油化工学院 | Power battery charged state estimating system and method based on ARMAX models |
| WO2019113999A1 (en) * | 2017-12-14 | 2019-06-20 | 江苏大学 | Gas-liquid dynamic model-based accurate lithium-ion battery soc estimation method |
| CN108445402A (en) * | 2018-02-28 | 2018-08-24 | 广州小鹏汽车科技有限公司 | A kind of lithium-ion-power cell state-of-charge method of estimation and system |
| CN109164391A (en) * | 2018-07-12 | 2019-01-08 | 杭州神驹科技有限公司 | A kind of power battery charged state estimation on line method and system |
| CN211086534U (en) * | 2019-07-25 | 2020-07-24 | 北方民族大学 | SOC estimation system based on hybrid energy storage device |
Non-Patent Citations (2)
| Title |
|---|
| 刘小菡;王顺利;苏杰;顾鹏程;马程;: "结合电路等效和UKF-EKF的锂电池SOC估算方法研究", 电源世界, no. 08 * |
| 张东华;马燕;陈思琪;朱国荣;陈伟;: "锂电池模型参数估计与荷电状态估算研究", 武汉理工大学学报(信息与管理工程版), no. 02 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111914394A (en) * | 2020-06-29 | 2020-11-10 | 哈尔滨工程大学 | Super capacitor parameter online estimation method based on double least square method |
| CN111965544A (en) * | 2020-07-17 | 2020-11-20 | 江苏大学 | Method for estimating minimum envelope line SOC of vehicle parallel power battery based on voltage and current dual constraints |
| CN112186278A (en) * | 2020-09-23 | 2021-01-05 | 珠海冠宇动力电池有限公司 | Lithium ion battery matching method |
| CN112186278B (en) * | 2020-09-23 | 2021-10-19 | 珠海冠宇动力电池有限公司 | Lithium ion battery matching method |
| CN112505566A (en) * | 2020-12-21 | 2021-03-16 | 河南克能新能源科技有限公司 | Dynamic impedance judgment system and method in battery charging and discharging process |
| CN112965001A (en) * | 2021-02-09 | 2021-06-15 | 重庆大学 | Power battery pack fault diagnosis method based on real vehicle data |
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