The current mainstream SOC estimation methods include open circuit voltage method, current integration method, Kalman filter method and neural network method. The first two algorithms are more commonly used, and the latter two algorithms will not be discussed here. The principle of the open circuit voltage method is to use the relatively fixed functional relationship between the open circuit voltage and the SOC when the battery is left standing for a long time, so as to estimate the SOC based on the open circuit voltage. Previously commonly used lead-acid battery electric bicycles used this method to estimate SOC. The open circuit voltage method is simple and convenient, but it also has many shortcomings:

　　1. The battery must be left standing for a long time, but electric vehicles start frequently and the open circuit voltage is difficult to stabilize in a short period of time;

　　2. There is a voltage platform in batteries, especially lithium iron phosphate batteries. During the period of SOC 30%-80%, the terminal voltage and SOC curve are approximately straight lines;

　　3. The batteries are at different temperatures or different life periods. Although the open circuit voltage is the same, the actual SOC may be significantly different;

　　As shown in the picture below, we are using this kind of electric bicycle. If the current SOC shows 100%, the voltage drops when accelerating and the battery capacity may show 80%. When the acceleration stops, the voltage rises and the battery capacity jumps back to 100%.

　　The current integration method to estimate SOC is currently widely used in electric bicycles, garden tools, energy storage and other fields. Although many different BMS manufacturers use the current integration method to estimate SOC, the accuracy varies due to the different algorithm designs, hardware circuits, and current sampling chip selections. The current integration method is also called the ampere-hour integration method (also called the current integration method or the Coulomb counting method). Its essence is to estimate the SOC of the battery by accumulating the amount of electricity charged or discharged when the battery is charging or discharging. Compared with other SOC estimation methods, the current integration method is relatively reliable and can dynamically estimate the SOC value of the battery, so it is widely used.

　　The simplified formula of the current integration method is as follows:

　　It is not difficult to see from the above formula that there are errors in this estimation method, which mainly come from three aspects:

　　1. Error caused by current sampling

　　Sampling accuracy sampling interval

　　2. Error caused by changes in battery capacity

　　Temperature changes, battery aging, different charge and discharge rates, battery self-discharge

　　3. SOC

　　Initial SOC estimation is difficult

　　Final SOC process round-off error

　　This method only uses the external characteristics of the battery as the basis for SOC estimation. To a certain extent, it ignores the impact of battery self-discharge rate, aging degree and charge and discharge rate on battery SOC. Long-term use will also lead to the accumulation and expansion of measurement errors, so it is necessary to introduce The relevant correction coefficient corrects the accumulated error.

　　The following is a brief introduction to the SOC estimation method used by our BMS protection board. The main purpose of our algorithm is to improve the limitations of the current integration method for calculating SOC:

　　●The first time the battery pack is powered on, the open circuit voltage method is used to estimate SOC. When powering on for the first time, roughly estimate the current remaining capacity of the battery pack, that is, SOC, based on the two-dimensional relationship between voltage and remaining capacity given by the battery pack manufacturer.

　　●After the first cycle, the current integration method (ampere-hour method) is used to calculate SOC. At this time, the accuracy of current sampling determines the accuracy of the ampere-hour method for estimating SOC. Therefore, the choice of front-end sampling chip is very important. Our BMS selection is from abroad. The front-end chip ensures that the current sampling is as accurate as possible.

　　●In response to the problem of battery aging, we correct the full charge capacity of the battery pack in real time after each complete charge and discharge cycle of the battery pack, so that we can more accurately obtain the actual full charge capacity of the battery pack. This is like if you have an oil drum with a capacity of 200L (the design capacity of the battery pack). After using it for a period of time, the shape of the oil drum has changed. We don’t know its actual capacity, but we can know each time the oil drum is used. The volume of oil required from empty to full (a complete charging capacity is 180L as shown below), we can use this volume (a complete charging and discharging time) to calculate the actual capacity of the oil barrel (the actual capacity of the battery pack) Make a slight correction, that is, from emptying the battery (due to different actual battery pack application scenarios, the lower limit of the correction may not necessarily be emptying, and is set according to different actual situations) to full charging. See the picture below. If the full charge capacity is not corrected accurately, even if the remaining capacity is accurate, the SOC will be inaccurate, which will cause the user to make wrong judgments. Therefore, in order to make the SOC more accurate, the remaining capacity and full charge capacity must be as accurate as possible.

　　●To address the problem of self-discharge, when the battery pack is not charging or discharging, we use the average operating current or sleep power consumption multiplied by time to correct the SOC error caused by self-discharge. Our SOC correction algorithm also adds temperature correction. According to the battery characteristics, the battery can release different electric energy at different temperatures. We have added temperature coefficient correction into the algorithm, which will not be explained here. At present, our SOC error is within 4% through a large number of actual tests and applications.

　　Our BMS protection board lithium battery is safer, allows customers to use it with more confidence, and makes the SOC more accurate, so that customers will not misjudge the actual situation due to the inaccuracy of the SOC display, causing unnecessary trouble.

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