SOC estimation method for BMS algorithm design (Part 2)

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Hello everyone! I am glad to meet you again. This article is the second in the series of articles on [BMS Algorithm Design]. This issue mainly introduces the first method of battery SOC estimation - direct estimation method. Let's learn together!


In fact, various test methods, models and algorithms for estimating battery SOC have been proposed and developed, and each method has its own advantages and disadvantages. The figure below is a summary of the SOC estimation methods, which are also the algorithms to be discussed in this series of articles (the blue words are the main methods to be explained in this issue).

Several typical SOC estimation methods:

In direct measurement methods, physical measurements such as the voltage and impedance of the battery are used to estimate SOC. The most commonly used direct measurement methods are: open circuit voltage method, terminal voltage method, impedance method and spectroscopy method.


Open Circuit Voltage method (OCV)


OCV is the thermodynamic potential of a battery under no-load conditions, and it has a nonlinear relationship with the battery's SOC. OCV is usually obtained through offline OCV testing at a specific ambient temperature and aging stage. Although the OCV method is relatively accurate, it requires a period of rest time to estimate the SOC, so it is difficult to be used directly in practical applications (usually combined with other algorithms). OCV appears in the equivalent circuit model as an ideal variable voltage source, and its overvoltage is increased by the remaining resistance and capacitance components in the equivalent circuit. In addition, the OCV-SOC curve relationship between batteries is also different, so the SOC estimation algorithm directly using this changing OCV-SOC curve data may produce an unacceptable error result. The traditional OCV-SOC curve is obtained by measuring the OCV at each SOC stage. This relationship varies with the change of battery capacity, and even for batteries with the same structure and materials, the results are different. However, estimating the OCV of each battery at each SOC to determine the effectiveness of the estimation process is a very time-consuming process.

The hysteresis of OCV has a great influence on the estimation of SOC. Hysteresis can be defined as the difference between OCV during charging and discharging. Therefore, we can say that the OCV information alone is not enough to determine the SOC, and we also need to take the historical charge and discharge data into consideration.


Moreover, different types of lithium-ion battery electrodes have different hysteresis (electrodes with lithium iron phosphate as active material have hysteresis). In order to accurately analyze the impact of hysteresis on battery SOC estimation or equivalent circuit parameters, the impact of hysteresis should be measured based on battery SOC value or capacity. The OCV-SOC function can be implemented by analytical expression or table lookup, among which the analytical method has many advantages, including data processing efficiency.


Terminal Voltage method


It can be said that there are only a few studies that show that the terminal voltage method of lithium-ion batteries can be used to determine their SOC. This method is based on the fact that when the battery is discharged due to internal resistance, the terminal voltage will drop and the electromotive force is equal to the terminal voltage.


Impedence method


In order to calculate the SOC using the impedance method, we have to record both the voltage and the current at different excitation frequencies, since the impedance of the battery depends on the frequency. The principle consists in injecting a current over a certain frequency range to find the impedance. The change in impedance is negligible at high SOC values, but it rises rapidly when the SOC reaches a certain low SOC level. Among the many methods, EIS (Electrochemical Impedance Spectroscopy) is considered an important source of information about the complex electrochemical processes inside the battery. Although many methods for estimating the SOC are based on EIS, the direct use of EIS is very complex. As a method, an impedance model is built based on the EIS data, which is presented in the form of a Nyquist plot, where the measured impedance is plotted as real and imaginary parts. The Nyquist plot impedance spectrum is divided into three parts: low frequency region, medium frequency region and high frequency region. Since this division simplifies the identification of parameters, the SOC can be estimated using the method based on the ECM model. Note: The Nyquist diagram is a method of graphically representing the frequency characteristics of a system. A graph that represents the frequency response in polar coordinates through its amplitude-frequency characteristics and phase-frequency characteristics is called an amplitude-phase diagram, or Nyquist diagram. The above is this issue’s introduction to the direct measurement method in the SOC estimation algorithm. The next article will continue to introduce other estimation algorithms (counting method). See you next time! In order to prevent some friends from not seeing the first article, the original link of the first article is attached: Introduction to Battery SOC in BMS Algorithm Design (I) If you have different opinions, please scan the QR code below to follow this official account. We look forward to communicating with you. References: CNKI, Energy Journal, related books, etc.



Reference address:SOC estimation method for BMS algorithm design (Part 2)

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