CN110596604A - A lithium battery SOC estimation method based on the ampere-hour integration method - Google Patents

Abstract

The present invention provides a lithium battery SOC estimation method based on the ampere-hour integration method. The ampere-hour integration method is used to estimate the SOC state of the lithium battery, wherein the initial state of charge and discharge, battery capacity, and Coulomb The efficiency value is corrected. The correction method is to obtain multiple sets of initial data of the same parameter through various channels, and calculate the average value of the initial data to obtain the corrected data. The initial data acquisition includes the part obtained by using the neural network. , the neural network is trained using the corresponding data of the lithium battery in the long-term use process. Since each parameter in the ampere-hour integration method has been corrected, the error of the finally obtained lithium battery SOC state is also small, so that the battery can be adjusted Correctly judge the use status of the lithium battery to facilitate subsequent maintenance or replacement of the lithium battery.

Description

A lithium battery SOC estimation method based on the ampere-hour integration method

technical field

The invention relates to the technical field of battery management, in particular to a lithium battery SOC estimation method based on an ampere-hour integration method.

Background technique

In the UAV flight system, the state of charge (SOC) of the power battery is an important parameter of the battery state, which is used to directly reflect the remaining power of the battery. The battery SOC is also an important basis for the UAV control system to formulate an optimal energy management strategy. , accurately estimating the SOC value of the power battery has important research significance for prolonging the battery life, improving the safety and reliability of the battery, and improving its performance.

Battery SOC is affected by many factors and cannot be directly measured by sensors. It must be estimated by measuring physical quantities such as battery voltage, operating current, and temperature and using certain mathematical models and algorithms. Currently, the commonly used methods include open circuit voltage method and ampere-hour integration method. , neural network method and Kalman filter method, and the ampere-hour integration method is widely used because of its low cost and convenient measurement. The expression of the ampere-hour integration method is: It can be seen that the battery SOC is related to the initial state of charge and discharge _SOCO , the total battery capacity C, and the battery Coulombic efficiency η. However, the above parameters used when using the ampere-hour integration method will have certain errors, resulting in the measurement of the battery SOC. Not accurate enough to correctly judge the usage status of the battery.

Contents of the invention

In view of this, the present invention proposes a lithium battery SOC estimation method based on the ampere-hour integration method, and uses the ampere-hour integration method to estimate the SOC state of the lithium battery, wherein each parameter in the ampere-hour integration method is corrected to finally obtain The error of the battery SOC state is small, and the battery use state can be correctly judged.

Technical scheme of the present invention is realized like this:

A lithium battery SOC estimation method based on the ampere-hour integration method, comprising the following steps:

Step S1. Read the battery SOC stored in the database after the previous use as the first initial state of charge and discharge; obtain ambient temperature information and pressure information as the input of the trained first neural network, and obtain the second initial state of charge and discharge. State and the third initial state of charge and discharge, calculate the average value of the first initial state of charge and discharge, the second initial state of charge and discharge, and the third initial state of charge and discharge to obtain the correction value SOC _O of the initial state of charge and discharge;

Step S2, obtain the SOH value of the battery state of health, and obtain the first battery capacity; obtain the battery usage time as the input of the trained second neural network to obtain the second battery capacity; obtain the full charge time and discharge time, and thus obtain the third battery capacity and the fourth battery capacity; obtain the average value of the first battery capacity, the second battery capacity, the third battery capacity, and the fourth battery capacity to obtain the battery capacity correction value C;

Step S3, obtain the battery discharge power and the battery charge power, and obtain the first Coulombic efficiency; obtain the ambient temperature information as the input of the third neural network, obtain the second Coulombic efficiency, and obtain the first Coulombic efficiency and the second Coulombic efficiency The average value of obtains Coulombic efficiency correction value η;

Step S4, obtaining the charging and discharging current I of the battery;

Step S5, using the ampere-hour integration method to obtain the battery SOC state according to SOC _O , C, η and I.

Preferably, the following steps are also included:

Step S6 , storing the SOC value when the battery stopped discharging into the database as the first charging and discharging initial state when calculating the battery SOC state next time.

Preferably, the specific steps for obtaining the SOH value of the battery state of health in the step S2 and obtaining the first battery capacity are as follows:

Step S21, using the internal resistance of the battery to obtain the SOH value of the battery state of health, the formula for obtaining the SOH value is:

Wherein, R _O is the internal resistance of the lithium battery at the end of its service life, R _n is the internal resistance of the lithium battery when it leaves the factory, and R is the internal resistance measured during use of the battery;

Step S22, the first battery capacity C ₁ =SOH*C _N , where C _N is the rated capacity of the battery.

Preferably, in the step S2, the specific steps of obtaining the full charge time and the full discharge time, and obtaining the third battery capacity and the fourth battery capacity are as follows: using the following formula to obtain the third battery capacity and the fourth battery capacity:

Among them, C ₃ is the third battery capacity, T ₃ is the collected full charge time, C _N is the battery rated capacity, T _N3 is the rated full charge time, C ₄ is the fourth battery capacity, T ₄ is the collected discharge time , T _N4 is the rated discharge time.

Preferably, the expression of the first Coulombic efficiency in the step S3 is:

Among them, Q _dis is the discharge capacity of the battery, and Q _cha is the charge capacity of the battery.

Preferably, in the step S4, a current sensor is used to collect the charging and discharging current I of the battery.

Preferably, the expression of the ampere-hour integral method in the step S5 is:

Preferably, the first neural network, the second neural network and the third neural network are trained from data stored by various types of lithium batteries during long-term use.

Compared with prior art, the beneficial effect of the present invention is:

The present invention provides a lithium battery SOC estimation method based on the ampere-hour integration method. The traditional ampere-hour integration method is used to estimate the SOC state of the lithium battery, and the parameters used in the ampere-hour integration method are corrected, including charging Discharge initial state, battery capacity and coulombic efficiency, each parameter has taken at least two sets of initial data to obtain the correction value, and the initial data also includes the part obtained by using the neural network combined with the data during the long-term use of the lithium battery, Therefore, the various parameters of the ampere-hour integration method can be corrected, and the difference in the SOC state of the lithium battery caused by the error of each parameter can be reduced, so that the error of the finally obtained battery SOC state is small, so that the use state of the battery can be corrected. judge.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings that need to be used in the description of the embodiments. Obviously, the drawings in the following description are only preferred embodiments of the present invention. For those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

FIG. 1 is a flow chart of an embodiment of a lithium battery SOC estimation method based on the ampere-hour integration method of the present invention.

Detailed ways

In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

Referring to Fig. 1, a lithium battery SOC estimation method based on the ampere-hour integration method provided by the present invention comprises the following steps:

Step S1. Read the battery SOC stored in the database after the previous use as the first initial state of charge and discharge; obtain ambient temperature information and pressure information as the input of the trained first neural network, and obtain the second initial state of charge and discharge. State and the third initial state of charge and discharge, calculate the average value of the first initial state of charge and discharge, the second initial state of charge and discharge, and the third initial state of charge and discharge to obtain the correction value SOC _O of the initial state of charge and discharge;

Step S2, obtain the SOH value of the battery state of health, and obtain the first battery capacity; obtain the battery usage time as the input of the trained second neural network to obtain the second battery capacity; obtain the full charge time and discharge time, and thus obtain the third battery capacity and the fourth battery capacity; obtain the average value of the first battery capacity, the second battery capacity, the third battery capacity, and the fourth battery capacity to obtain the battery capacity correction value C;

Step S3, obtain the battery discharge power and the battery charge power, and obtain the first Coulombic efficiency; obtain the ambient temperature information as the input of the third neural network, obtain the second Coulombic efficiency, and obtain the first Coulombic efficiency and the second Coulombic efficiency The average value of obtains Coulombic efficiency correction value η;

Step S4, obtaining the charging and discharging current I of the battery;

Use the current sensor to detect and save the measured battery charge and discharge current I in real time;

Step S5, using the ampere-hour integration method to obtain the battery SOC state according to SOC ₀ , C, η and I;

Step S6 , storing the SOC value when the battery stopped discharging into the database as the first charging and discharging initial state when calculating the battery SOC state next time.

A lithium battery SOC estimation method based on the ampere-hour integration method of the present invention uses the ampere-hour integration method to estimate the SOC state of the lithium battery, wherein the multiple parameters used in the ampere-hour integration method are corrected, Therefore, the error of the finally obtained SOC state of the lithium battery is small, so that the use state of the battery can be judged, and subsequent maintenance or replacement is facilitated.

Among them, the initial state of charge and discharge, battery capacity and Coulomb efficiency are mainly corrected. For the initial state of charge and discharge, the SOC state of the battery after the previous use is read from the database first, and this is used as the first charge and discharge initial state SOC _O1 , then obtain ambient temperature information and pressure information, and input the ambient temperature information and pressure information into the first neural network, and the first neural network processes the ambient temperature information to obtain the second charge and discharge initial state SOC _O2 , the first The neural network processes the pressure information to obtain the third charge and discharge initial state SOC _O3 , and takes the average value of SOC _O1 , SOC _O2 , and SOC _O3 to obtain the correction value SOC _O of the charge and discharge initial state, namely:

Among them, SOC _O1 can be directly read from the database, while SOC _O2 and SOC _O3 need to be processed by the first neural network. For SOC _O2 and SOC _O3 , the obtained environmental temperature information and pressure information are multi-group , after the first neural network processes the collected multiple sets of environmental temperature information, the obtained data is averaged to obtain the second charging and discharging initial state SOC _O2 , similarly the first neural network will also process the collected multiple sets of pressure information And the results are averaged to obtain the third charge and discharge initial state SOC _O3 . The first neural network in this embodiment has been trained before use, and the data used for training are different types of lithium batteries during long-term use. The initial state of charge and discharge at different temperatures and pressures, so after obtaining the ambient temperature information and pressure information, the second initial state of charge and discharge and the third initial state of charge and discharge can be obtained through the first neural network, the correction value of the initial state of charge and discharge SOC _O is the average value of SOC _O1 , SOC _O2 , and SOC _O3 , so the error of the correction value SOC _O of the initial state of charge and discharge obtained by combining the previous battery SOC value and the initial state of charge and discharge obtained according to the neural network is very large. Small.

For the battery capacity, after obtaining multiple sets of initial data, the average value is calculated, where the initial data includes the first battery capacity C ₁ , the second battery capacity C ₂ , the third battery capacity C ₃ , and the fourth battery capacity C ₄ , wherein the specific steps for obtaining the first battery capacity _C1 are:

Step S21, using the internal resistance of the battery to obtain the SOH value of the battery state of health, the formula for obtaining the SOH value is:

Wherein, R _O is the internal resistance of the lithium battery at the end of its service life, R _n is the internal resistance of the lithium battery when it leaves the factory, and R is the internal resistance measured during use of the battery;

Step S22, the first battery capacity C ₁ =SOH*C _N , where C _N is the rated capacity of the battery.

As for the second battery capacity C ₂ , it is processed by the second neural network. The input data of the second neural network is the service time of the lithium battery, and the output is the second battery capacity C ₂ , which is the same as the first neural network. , the second neural network has been trained before use, and the data used for training is the battery capacity corresponding to different use durations of the lithium battery during long-term use.

For the _third battery capacity C3 and the fourth battery capacity _C4 , the third battery capacity and the fourth battery capacity are obtained using the following formula:

Among them, C ₃ is the third battery capacity, T ₃ is the collected full charge time, C _N is the battery rated capacity, T _N3 is the rated full charge time, C ₄ is the fourth battery capacity, T ₄ is the collected discharge time , T _N4 is the rated discharge time.

The _third battery capacity C3 and the fourth battery capacity _C4 are calculated based on the fact that batteries with different capacities have different discharge time and full charge time in daily use, but the rated capacity, The rated full charge time and the rated full discharge time are fixed. As the battery capacity continues to decrease, the full charge time and the full discharge time are also decreasing, and the changes are linear. The ratio of the charging duration (or the rated discharge duration) to the battery rated capacity is equal to the ratio of the collected full charge duration (or the collected discharge duration), so the _third battery capacity C3 can be calculated according to the equation and a fourth battery capacity C ₄ .

After obtaining the first battery capacity C ₁ , the second battery capacity C ₂ , the third battery capacity C ₃ , and the fourth battery capacity C ₄ , the battery capacity correction value Therefore, the error of the battery capacity is reduced.

For the acquisition of Coulombic efficiency, firstly, the first Coulombic efficiency η ₁ is obtained according to the battery discharge power and the battery charge power, where In the formula, _Qdis is the discharge capacity of the battery, _Qcha is the charge capacity of the battery, and then the second Coulombic efficiency η ₂ is obtained, and the second Coulombic efficiency η ₂ is obtained by processing the third neural network, and the third neural network is using The training has been completed before, and the training data is the coulombic efficiency of lithium batteries at different ambient temperatures during long-term use. Therefore, after inputting the measured ambient temperature into the third neural network, the third neural network The output after processing is the second Coulombic efficiency η ₂ , and the expression of the Coulombic efficiency correction value η is Therefore, the correction of the Coulombic efficiency can be realized, and the error of the Coulombic efficiency can be minimized.

Preferably, the expression of the ampere-hour integral method in the step S5 is:

After obtaining the charging and discharging initial state correction value SOC _O , battery capacity correction value C, coulombic efficiency correction value η, and battery charge and discharge current I, the above parameters are brought into the expression of the ampere-hour integral method to obtain the battery SOC state, when the battery ends the use state, the battery SOC state at the end state is stored in the database, as the first charge and discharge initial state of the next estimated battery SOC state, in the above formula, the integral represents the charge and discharge current I of the battery Integrating over time [0, t], the discharge current I is positive and the charge current I is negative.

A lithium battery SOC estimation method based on the ampere-hour integration method of the present invention corrects the initial charge and discharge state correction value SOC _O , the battery capacity correction value C, and the Coulomb efficiency correction value η, and collects the SOC value through various ways respectively. The initial data of the parameter, and then calculate the average value of multiple sets of initial data to reduce the error generated by the parameter, and finally use the ampere-hour integration method to estimate the battery SOC state, because the error of each parameter has been reduced. Therefore, the error of the finally obtained battery SOC state is also small, so that the use state of the battery can be correctly judged, and subsequent maintenance or replacement of the lithium battery is facilitated.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of the present invention. within the scope of protection.

Claims (8)

1. A lithium battery SOC estimation method based on the ampere-hour integral method, is characterized in that, comprises the following steps: Step S1. Read the battery SOC stored in the database after the previous use as the first initial state of charge and discharge; obtain ambient temperature information and pressure information as the input of the trained first neural network, and obtain the second initial state of charge and discharge. State and the third initial state of charge and discharge, calculate the average value of the first initial state of charge and discharge, the second initial state of charge and discharge, and the third initial state of charge and discharge to obtain the correction value SOC _O of the initial state of charge and discharge; Step S2, obtain the SOH value of the battery state of health, and obtain the first battery capacity; obtain the battery usage time as the input of the trained second neural network to obtain the second battery capacity; obtain the full charge time and discharge time, and thus obtain the third battery capacity and the fourth battery capacity; obtain the average value of the first battery capacity, the second battery capacity, the third battery capacity, and the fourth battery capacity to obtain the battery capacity correction value C; Step S3, obtain the battery discharge power and the battery charge power, and obtain the first Coulombic efficiency; obtain the ambient temperature information as the input of the third neural network, obtain the second Coulombic efficiency, and obtain the first Coulombic efficiency and the second Coulombic efficiency The average value of obtains Coulombic efficiency correction value η; Step S4, obtaining the charging and discharging current I of the battery; Step S5, using the ampere-hour integration method to obtain the battery SOC state according to SOC _O , C, η and I. 2. a kind of lithium battery SOC estimation method based on ampere-hour integral method according to claim 1, is characterized in that, also comprises the following steps: Step S6 , storing the SOC value when the battery stopped discharging into the database as the first charging and discharging initial state when calculating the battery SOC state next time. 3. A lithium battery SOC estimation method based on the ampere-hour integration method according to claim 1, characterized in that, in the step S2, the SOH value of the battery state of health is obtained, and the specific steps of obtaining the first battery capacity for: Step S21, using the internal resistance of the battery to obtain the SOH value of the battery state of health, the formula for obtaining the SOH value is: Wherein, R _O is the internal resistance of the lithium battery at the end of its service life, R _n is the internal resistance of the lithium battery when it leaves the factory, and R is the internal resistance measured during use of the battery; Step S22, the first battery capacity C ₁ =SOH*C _N , where C _N is the rated capacity of the battery. 4. A lithium battery SOC estimation method based on the ampere-hour integration method according to claim 1, characterized in that, in the step S2, the full charge time and the full discharge time are obtained, and the third battery capacity is thus obtained and the fourth battery capacity are as follows: use the following formula to obtain the third battery capacity and the fourth battery capacity: Among them, C ₃ is the third battery capacity, T ₃ is the collected full charge time, C _N is the battery rated capacity, T _N3 is the rated full charge time, C ₄ is the fourth battery capacity, T ₄ is the collected discharge time , T _N4 is the rated discharge time. 5. A method for estimating lithium battery SOC based on the ampere-hour integration method according to claim 1, wherein the expression of the first Coulombic efficiency in the step S3 is: Among them, Q _dis is the discharge capacity of the battery, and Q _cha is the charge capacity of the battery. 6. A lithium battery SOC estimation method based on the ampere-hour integration method according to claim 1, characterized in that, in the step S4, a current sensor is used to collect the charging and discharging current I of the battery. 7. A lithium battery SOC estimation method based on the ampere-hour integration method according to claim 1, wherein the expression of the ampere-hour integration method in the step S5 is: 8. A lithium battery SOC estimation method based on the ampere-hour integration method according to claim 1, wherein the first neural network, the second neural network and the third neural network are composed of various types of lithium batteries It is trained on data saved during long-term use.