What Is Functional Residual Capacity?

The remaining capacity refers to the remaining capacity of an energy storage device such as a battery after a certain period of use. General battery modeling methods can be divided into two categories: one is the physical modeling method; the other is the system identification and parameter estimation modeling method.

The amount of electricity or the discharge time of a battery under specified conditions (including discharge intensity, discharge current, and discharge termination voltage) is called the battery capacity, and the unit is A · h or A · min. The car battery is a reversible DC power source, which can convert chemical energy into electrical energy as well as chemical energy. It is connected in parallel with the generator.

The remaining capacity refers to the remaining capacity of an energy storage device such as a battery after a certain period of use.

At home and abroad, the state of charge (SOC) is generally used to indicate the remaining capacity of the battery. It is an important parameter that directly reflects the sustainable power supply capacity and health status of the battery. Because the battery has different types, uses, and external environment, there are many influencing factors, so its prediction methods are various, and the battery models used are different. General battery modeling methods can be divided into two categories: one is the physical modeling method; the other is the system identification and parameter estimation modeling method.

Prediction of remaining capacity physical modeling method

(1) Discharge test method. The discharge test method is recognized as the most reliable estimation method. The battery is continuously discharged to a predetermined zero point at a current of a certain discharge rate, and the product of the discharge current and time is the remaining capacity. The discharge test method is mainly used in the laboratory to calculate the charging efficiency of the battery pack, check the estimated accuracy, or used for the maintenance of the battery, and is applicable to all batteries. However, this method has two obvious disadvantages that require a lot of time and the ongoing work of the human battery has to be interrupted and cannot be predicted online in real time. Static backup batteries can be used, but for important occasions, this method takes a certain risk, because during discharge, the system operates without battery backup. Once the main power supply fails or the mains power is interrupted, the entire system will be paralyzed. Cause immeasurable losses.

(2) Antime method. The ampere-time method essentially treats the battery as a black box, and considers that the amount of electricity flowing into the battery and the amount of electricity flowing out of the battery have a certain proportional relationship, regardless of the internal structure and external electrical characteristics of the battery, so this method is suitable for various battery. At the same time, it can be seen that the problems in the application of the ampere-time method require the calibration of the initial value to accurately calculate the charge and discharge efficiency. The current must be accurately measured. The inaccurate current measurement will cause calculation errors. There will be a cumulative error of current integration in the long-term. In the case of severe current fluctuations, the error is large. Therefore, when the ampere-hour method is used in practical applications, factors such as charge and discharge rate, temperature, battery aging, and self-discharge rate are generally compensated according to the use environment and conditions.

(3) Density method. The density method is mainly used in lead-acid batteries. Because the density of the electrolyte gradually increases during the charging process, it gradually decreases during the discharge process, and the battery capacity and density have a certain linear relationship. Therefore, it can be predicted by measuring the density of the electrolyte. Since the density method needs to measure the electrolyte, it is mainly used in open-type lead-acid batteries. If a more accurate density-capacity sensor can be developed, it can be planted into a sealed battery during production in extremely important situations.

(4) Open circuit voltage method. Open circuit voltage refers to the terminal voltage of the battery in the open circuit state, which is close to the battery electromotive force in value. The open-circuit voltage method is established according to a certain linear proportional relationship between the remaining capacity of the battery and the open-circuit voltage. The size of the remaining capacity can be directly obtained by measuring the open-circuit voltage. Its advantage is that it does not depend on the size, size and discharge speed of the battery, and only uses the open circuit voltage as a test parameter, which is relatively simple. ^[1]

Remaining capacity system identification and parameter estimation model prediction

(1) Neural network method. As the battery is a complex non-linear system, it is difficult to establish an accurate mathematical model for its charging and discharging process. The neural network has the characteristics of distributed parallel processing, non-linear mapping, and adaptive learning, which can better reflect the basic characteristics of non-linear, and can give corresponding output when there is external excitation, so it can simulate the battery dynamics to a certain extent Characteristics, estimate SOC.

Most of the estimated batteries use a typical 3-layer artificial neural network. Generally, the discharge current, terminal voltage, and temperature of the battery are directly collected or a combination of variable current measurement methods is used to determine the electromotive force and internal resistance as the input of the neural network model, and the SOC as the output. The input and output layer neurons are generally linear functions. The number of hidden layer nodes depends on the complexity of the problem and the accuracy of the analysis. It can be determined according to the convergence speed of the network during the training process and the error after training. The artificial neural network method is suitable for various storage batteries, but the error of this method is greatly affected by the training data and training methods, and the noise interference in actual use affects the learning and application of the network.

(2) Kalman filtering method. The core idea of Kalman filter theory is to make an optimal estimation in the sense of minimum variance for the state of the dynamic system, which is suitable for both linear systems and nonlinear systems.

When applying the Kalman filter method to estimate, a battery model suitable for Kalman filter estimation must first be established, and the model must have two characteristics: 1) it can better reflect the dynamic characteristics of the battery, and the order cannot be too high. Reduce the calculation amount of the processor to facilitate the implementation of the project; 2) The model must be able to accurately reflect the relationship between the battery electromotive force and the terminal voltage, so that the closed-loop estimation has higher accuracy. ^[2]