What is the remaining capacity prediction method of battery?


  Author :Iflowpower – Portable Power Station Supplier

1 Preface Valve Controlled Sealed Lead Acid (VRLA) Battery Since there is a small size, explosion-proof, voltage stable, no pollution, light weight, high discharge performance, small maintenance, low price, etc., is favored by various industries, Widely used in postal, electricity, transportation, aerospace, emergency lighting, communication, etc. VRLA batteries have become one of the key components of the system, and its safe and reliable operation is directly related to the reliable operation of the entire device.

However, during use, because the remaining capacity cannot be accurately predicted, the accident causes the accident, and the heavy market is a tragedy. Therefore, a valid battery management system must be established to accurately predict the remaining capacity of the battery, which is the most basic and most important task in the battery management system [1] [2]. At present, there is generally used in China and abroad to indicate the remaining capacity of the battery.

SOC is an important parameter that directly reflects sustainable power supply capacity and health of batteries. Since VRLA batteries have different types, uses, and external environments, SOC has many influencing factors, so they are predicted by various methods, and the battery model used is not the same. The modeling method of the general battery can be divided into two major categories: one is a physical modeling method; the other is the system's identification and parameter estimation modeling method [3].

2 Physical modeling method predicts the SOC2.1 discharge test method discharge test method is a recognized most reliable SOC estimation method. The battery is continuously discharged to a predetermined SOC zero point, and the product of discharge current and time is the remaining capacity.

The discharge test method is mainly used in the laboratory computing battery pack charging efficiency, inspection of SOC estimation accuracy or maintenance of the battery, suitable for all batteries. However, there are two obvious disadvantages: (1) require a lot of time and humanity; (2) The work on the battery has to be interrupted, unable to online prediction in real time. For static backup batteries, it is necessary to take this method for important occasions.

During the discharge period, the system is running without battery backups, once the main power is problematic or the mains interruption, the entire system will be paralyzed. Incidentless loss. Document [4] describes the discharging test method and precautions, but requires a lot of manual operation; literature [5] uses the power environment monitoring system to realize the discharge test management of the battery pack, save time and efficiency, but the accuracy is very low.

Can only determine the performance of the battery pack without accurately estimating the remaining capacity. 2.2 Ambar Measurement Actual Method is the most common method of SOC estimation, the calculation formula is: (1) where the SOC0 is the charge and discharge start time, the CN is the rated capacity, η is charge and discharge efficiency and is not a constant ( It is assumed that the charging current direction is positive, the discharge current direction is negative), and the SOC is the charge state of the current time.

The safety of the safety of the safety is a black box that is considered to have a certain proportional relationship with the electricity amount of the battery flowing out of the battery, regardless of the structure and external electrical characteristics of the battery, so this method is suitable for various Battery. As can be seen from the same equation (1), the problems existing in the application: (1) require calibration of the SOC initial value; (2) requires precise calculation of charge and discharge efficiency; (3) to accurately measure current, current measurement According to the SOC calculation error, there is a cumulative error of current integration; (4) is large in the case of high temperature state and current fluctuations. Therefore, when an Astronautics is employed in practical applications, it is generally compensated for factors such as charge and discharge rate, temperature, battery aging, and self-discharge rate according to the use of environment and conditions.

Document [6] uses the safety of the AC, the Peukert equation, temperature correction, and SOH combined with the SOH, and the SOC of the static rear preparation valve-controlled lead-acid battery is estimated to be between the two states of the battery capacity of zero to the capacity of one cycle. In this cycle, the measurement battery calculates SOH to calculate the total capacity of standard current discharge or charging at standard temperatures. Its SOC calculation accuracy can reach 0.

1%, and the calculation formula is: Document [7] considers compensation for battery charge and discharge rate, temperature, battery aging, and self-discharge ratio, and corrects the accumulated error through self-tuning, and utilized a large number of experiments. The resulting single battery voltage value and the capacity relationship coefficient, corrected the inconsistency of the battery, and correct the formula (4). Where: ks is the relationship coefficient, and ΔU is the difference between the voltage at the low voltage in the battery pack and the average voltage of all monomer batteries: Document [8], using an open circuit voltage method to obtain an initial SOC, after the safety time method Various compensation, its SOC estimation accuracy is within 6%.

In addition, safety law is often used in conjunction with Kalmann (Detailed discussion in Kalman filtering). 2.3 Density Method Density Method is mainly used in lead-acid batteries.

Since the electrolyte density gradually becomes higher during charging, gradually decrease during discharge, and the battery capacity and density have a certain linear relationship, so the size of the SOC can be predicted by measuring the density of the electrolyte [9]. Since the density method needs to be measured, it is mainly used in an open-type lead-acid battery. If a higher precision density-capacity sensor can be developed, it can be implanted in a sealed battery when it is produced.

Document [10] [11] [12] uses ultrasonic sensors, low energy γ rays, lead-acid battery capacity sensors to measure the density of lead-acid battery electrolyte density, while literature [11] predicts the density by the fuzzy neural network. Good, but no determination between electrolyte and SOC. 2.

4 Opening Voltage Law Opening Voltage (OpenCIRCUITVOLTAGE) refers to the end voltage in the opening state, close to the battery electromotive force on the value. The open circuit voltage method is established according to the remaining capacity of the battery and the opening voltage there is a certain linear (proportional) relationship, and the size of the remaining capacity can be directly obtained by measuring the open circuit voltage. The advantage is that it does not rely on the battery size, size, and discharge speed, only the open circuit is test parameters, relatively simple [13] [14] [15].

Document [16] describes the relationship between lead-acid batteries open circuit voltage, residual capacity, and electrolyte density, and gives a calculation formula between SOC and open circuit: wherein VBO is the open circuit voltage of the battery, and Vα is filled with electricity. Open circuit voltage, Vb is open-circuit voltage at sufficient discharge, and its size correspondence with different battery manufacturers. When using this method, by measuring the open circuit voltage of the battery, the general check table can obtain an estimated SOC value.

However, there is also a significant disadvantage of the open circuit voltage method: (1) The battery needs to be allowed to reach a steady state, and how the stationary time is determined; (2) As the battery is aging, the remaining electricity decreases, open circuit voltage changes Not obvious, there is no accurate prediction of the remaining electricity; (3) For the traditional battery pack used, the battery is in a state, and the open circuit voltage cannot be measured, and the online measurement cannot be realized. From the current literature, it is generally not used alone using an open circuit voltage method. Since the open circuit voltage method is good in the initial stage of charging, the SOC estimation is good, often combined with the safety of the safety, Karmana.

For a long time to stand for a long time for the battery, the literature [14] uses the battery of the recovery curve of the open circuit in various states, and the prediction formula of the open circuit voltage is obtained by calculating the SOC, predicted value and measurement. The relative error is within 6%. Document [17] [18] [19] normalizes the discharge curve of VRLA battery at different discharge ratios, found that the discharge curve has good consistency, discharge mode, discharge ratio, ambient temperature, and discharge voltage, etc.

The change of factors is very small to this consistency. It is proposed that only the discharge voltage predicts the SOC, the calculation formula is as follows: wherein the TT is the entire discharge time length, and VEND is the discharge termination voltage, VP is the discharge initial voltage. At any time, when the discharge voltage V (T) of the battery is known, the Vu (TU) can be calculated, and the normalized Tu is obtained by the normalized curve, which in turn has a state of charge (the estimation accuracy is within 10%, " Suitable for situations that require low requirements).

Document [20] [21] uses different initial discharge voltages to correspond to different discharge time, by periodically externally externally external flowing load in operation, measure a series of operating voltages, establish a voltage, The temperature is input, the remaining time is the output SOC blur estimation system, thereby obtaining the SOC of the monomer power battery, which is within 1%, which is also referred to as a load voltage method. This method can estimate the SOC of the battery on line, having a good effect in constant current discharge, but does not apply to discharge conditions of substantial or severe fluctuations. 2.

5 Internal resistance (conductance) method of the battery resistance in the battery, the intended internal resistance, the RESISTANCE, and they have close relationships with SOC to implement online measurement. In the battery is in a different battery, its internal resistance value is different, the internal resistance (electrical guide) method is to predict the change of SOC by measuring the change in internal resistance (conductance) during the discharge process. [twenty two].

There is also controversy on the application of internal resistance prediction SOC. Document [23] Test and statistics on the conductance of the valve-controlled sealing lead-acid battery using the conductivity tester, discovered that the discharge time is linearly related to the conductance value, and the correlation coefficient reaches 0.825; in the IEEE 1188-1996 standard, measurement is also proposed.

The necessity of internal resistance, clearly defining the battery internal resistance test at least once a quarter [24]. But the literature [25] [26] [27] [28] The relationship between the internal resistance (conductance) and the remaining capacity of the battery is studied by experimental testing and theoretical analysis, respectively, and the results show that: (1) Valve Control Lead When the battery SOC is 50% or 40%, its internal resistance (or electron conductive) is basically no change, only the SOC is less than 40%, the internal resistance of the battery is rapidly increased; (2) For more than 80% of the capacity The VRLA battery is used online, and the SOC of the battery cannot be detected in line according to the internal resistance (conductance) value; (3) according to the battery electrodide value or the internal resistance value, the battery performance can be determined to a certain extent. The emergence of disputes is related to the statistical methods, mainly related to the accuracy of the tested battery itself and the internal resistance (conductance) tester.

Because even with the same manufacturer, the same batch, the same size of the battery, its internal resistance (conductance) also does not consistency, this is determined by the technical level of the battery manufacturer. And the internal resistance of the battery is small, and the SOC has changed, the internal resistance changes are not large, and if the accuracy of the measuring instrument does not meet the requirements, it will be difficult to correspond to the corresponding relationship between the internal resistance and the remaining capacity. Document [29] By impedance spectral measurement, it is pointed out that the changes in ohm acoustic resistance can reflect the changes in SOC, but when SOC is increasing from 16% to 91%, its ohmic internal resistance is small, about 0.

6mΩ. And proposed that when the internal impedance of the battery changes to the sensibility, there is a monotonic function relationship between the corresponding excitation signal and its SOC, and the frequency change range is large, and the resonant frequency of the VRLA battery is used as the transmission of the battery SOC. Initial parameters, this theory is still in the research stage.

At the same time, literature [30] proposes to standardize the manufacturer by selecting internal resistance (conductance) stabilized battery by selecting internal resistance (conductance) stabilized battery in the case of large-scale use of the battery. Production, rather than the precise indicator directly as a battery charge state. From the current literature, data, and internal resistance (conductance) testing products [31] [32] [33] [34] Mainly applied to the internal resistance (conductance) method to the battery failure warning, directly applied to SOC prediction very Less (generally used as one of the SOC influencing factors) combined with voltage method, neural network, etc.

) [36]. And the literature [30] has been conclusively, when the electrical conductivity of the monomer battery is more than 80% of the reference value, the battery is normal, and the capacity is 80% or more; when the conductance value is 60% -80% of the reference value. The capacity is very likely that less than 80%, the battery is in a "normal danger" state, and the full discharge test is required; when the conductance value is 60% of the reference value, the battery is in a "serious risk" state, requiring timely replacement.

3 System Identification and Parameter Estimation Model Method Prediction SOC 2000, system identification and parameter estimation model method began to be applied to battery SOC estimation, and is currently more popular in domestic and foreign research. It is mainly to apply some new methods (mainly manual intelligence algorithms) to model system modeling, which will affect the various factors of SOC into the battery model, and the model is systematically identified and parameter estimated by a large number of tests, and obtains a battery The relationship between some parameters and SOC, and then estimate SOC. Comparable artificial neural network law, vector machine, fuzzy reasoning method, and Kalman filtering method, etc.

3.1 Neural network method Since the battery is a complex nonlinear system, it is difficult to establish an accurate mathematical model for its charging and discharge process. The neural network has a distributed parallel treatment, nonlinear mapping, and adaptive learning, etc.

, which can better reflect the basic characteristics of nonlinearity, and can give corresponding outputs when there is external excitation, so that the battery dynamics can be simulated to a certain extent Features, estimates SOC [36] [37]. Estimating most of the battery SOC uses a typical 3-storey artificial neural network [38] [39]. Generally collect the discharge current, end voltage, and temperature of the battery directly, or use the change of the variable current combined measurement method, determine the input of the electric motion and internal resistance as a neural network model, SOC as an output.

Where input, output layer neurons are generally linear functions; the number of implicit layer nodes depends on the complexity and analysis accuracy of the problem, and can be determined according to the convergence speed and training completion of the network. Artificial neural network method is suitable for various batteries, but the error is affected by training data and training methods, and there is noise interference affecting network learning and application in actual use. From the current literature, the neural network is mainly theoretical.

Document [40] [41] Another Neural Network-Support Vector Machine (SVM) method is used for battery SOC estimation to avoid defects in training time, local optimality, and convergence speed. And the literature [42] further proposes to predict the battery SOC using the associated vector machine (RVM), which is higher than the support vector machine, and the prediction model is also more sparse, but the algorithm is more complicated, and it is necessary to occupy larger computer resources. 3.

2 Fuzzy Logic Flag Logic Law is a fuzzy modeling of the battery, which is based on the input, output test data, and is not limited by the prior knowledge, experience and behavior. This method generally processes the parameters (such as voltage, current, temperature, internal resistance, etc.) as an input variable of the model (e.

g., voltage, in accordance with a large number of battery characteristics test data, the relationship between SOC and current, voltage, temperature and other factors, Design fuzzy rules and make fuzzy reasoning, via anti-ambiguous treatment estimates battery SOC [43] [44] [45]. The main disadvantage of the fuzzy logic method is that a large amount of experimental data is required to obtain fuzzy reasoning rules and experience formulas according to experimental data.

This method is currently used in simulation and theoretical analysis, and has not been applied to the actual. 3.3 Kalman filtering the core idea of ​​Karman filtering theory is the optimal estimate of the size of the power system, which applies to both linear systems and nonlinear systems [46].

When using Kalman filtering method to estimate SOC, the battery model suitable for Kalman filter estimation is first to be established, and the model must have two features: (1) It can better reflect the dynamic characteristics of the battery, while the order cannot be too high. To reduce the amount of processor's operation, it is easy to implement; (2) The model must accurately reflect the relationship between the battery electromotive force and the terminal voltage, thereby enabling the closed loop estimate of high precision. Commonly used equivalent circuit models have a Randle model (see Figure 1), massimoceraolo model, thevenin model, Shepherd model, etc.

, all parameters are pending parameters, need to be calculated according to experimental data [47] [48]. Figure 1Randles Battery Model In practical applications, Kalman filtering method is usually used in combination with open circuit voltage law and safety. The basic procedure is: When the voltage on the model is used as the system, after the voltage is estimated by Kalman, it is used to obtain a battery electromotive force (or open voltage) using the mathematical relationship in the model, and finally the relationship between the electromotive and SOC.

SOC. Calman mathematics form of the battery model is: State Equation: (9) Observation Equation: (10) An Equation Equation: (11) Input Vector UK, usually include variables such as battery current, temperature, residual capacity and internal resistance The output YK of the system is usually the operating voltage of the battery, and the battery SOC is included in the status quantity xk of the system, and the AK, BK is determined by the parameters obtained by the test, ωk, vk is system noise. The core of the estimated SOC algorithm is to establish a set of recursive equations including SOC estimates and reflecting estimation errors, and covariance matrices are used to give estimation error range.

Equation (11) is the battery model state equation, which describes the basis of the SOC as a status vector. Kalman filter can maintain a good accuracy during the estimation process, and has a strong correction effect on the initialization error, which has a strong inhibitory effect on noise. At present, the SOC prediction of a hybrid vehicle battery that is mainly used in current change.

On the basis of Kalman filter, the literature [49] [50] [51] will extend the Karman and the Colorborne Karman filtering method for estimating SOC. The greatest disadvantage of Kalman filtering method is that its estimated accuracy relies on accuracy of the battery equivalent circuit model, and the establishment of an accurate battery model is the key to the algorithm. Another disadvantage is that the operation is relatively large, you must choose a simple and reasonable battery model and a processor faster.

3.4 Other Method Document [52] The linear model method mentioned, using the linear model on the initial conditions of the measurement error and the error, based on a large amount of battery charging discharge experiment, establishing SOC and its change battery Linear equation of end voltage, current, in formula (12), (13). This method is suitable for small current, SOC changes slowly, but this feature limits its scope of use, and has not been seen in the actual application.

Where SOC (k) is the SOC value of the current time; △ SOC (k) is the change value of the SOC; V (k) and i (k) are the voltage and current of the current time. Β0, β1, β2, and β3 are linear model coefficients obtained by using reference data through the least squares method. The literature [53] proposes that the nonlinear self-return sliding average (Narmax) model is high, the structure is simple, the convergence speed is characterized, and other influencing factors of the battery work voltage and current are model input, and the SOC is used as system noise, and the battery SOC performs real-time estimation, relative error is only 1%, and the applicability of this method needs to be further studied.

It recognizes the model (14) in which Y (t) is the SOC sequence, U1 (T) is a current sequence, U2 (T) is a voltage sequence. Document [54] For the nonlinear relationship between the internal resistance of the battery and the remaining capacity, the SOC of the mixed power vehicle battery unit is predicted by the combined gray GM (1, 1) model group method. The literature [55] establishes the SOC state equation based on the safety time, and proposes the application robust filtering algorithm to predict the SOC of the battery.

It can be seen from the various methods described above. Whether it is a physical modeling method or a system identification and parameter estimation model method, it is based on the measured parameters of the battery (mainly voltage, current, internal resistance, temperature, etc.) and remaining.

The relationship between capacity is based on a large number of experiments to establish a stable battery system model to predict SOC. 4 Summary, SOC prediction method is affected by many factors (discharge current, voltage, temperature, discharge depth, internal resistance, electrolyte density, self-discharge, aging, etc.), the prediction technology of the remaining capacity of VRLA batteries and its construction The model is complicated, and there is no exact and universal prediction method.

The above various SOC prediction methods are advantageous, but in different use environments, in different prediction accuracy, the use of a single predictive method can no longer meet the actual needs, and thus design high-precision data detecting circuits, using multiple methods for combined prediction SOC, especially the combination of a variety of intelligent algorithms and new theories, the SOC is real-time, online, accurate prediction, has become the development direction of the remaining capacity prediction of the battery.

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