A method for estimating and dynamically calibrating the power of a BMS system

Abstract

The application discloses a BMS system electric quantity estimation and dynamic intelligent calibration method, and belongs to the technical field of battery energy storage. The method corrects the battery electric quantity SOC value calculated based on the ampere-hour method according to the electric characteristics of a battery factory and the dynamic temperature compensation value and the power compensation value of the battery product, and obtains the battery voltage and the electric quantity SOC performance curve of the battery product itself by the corresponding relationship between the battery voltage and the electric quantity SOC value. The corresponding relationship curve is divided into multiple voltage intervals according to the nonlinear characteristics of the curve, and the SOC value of each interval is set according to the battery voltage and the electric quantity SOC performance curve of the battery product when the battery product leaves the factory. The SOC value SOCi calculated in the charging and discharging process is compared with the set SOC value (Si+1), and the deviation coefficient Ki is obtained as an intelligent automatic correction parameter of the battery electric quantity SOC value, so that the electric quantity estimation deviation is effectively controlled, the accuracy of the SOC value is improved, the overcharging and overdischarging of the battery are avoided, and the operation life of the battery and the efficiency of the energy storage system are improved.

Description

Technical Field

[0001] This invention belongs to the field of battery energy storage technology, specifically relating to a method for battery management system power estimation and dynamic intelligent calibration. Background Technology

[0002] The development and utilization of renewable energy can effectively address energy crises and environmental issues, and is a crucial pillar for energy transition and low-carbon economic development in countries worldwide. With technological advancements and declining costs, renewable energy has experienced rapid growth in recent years. The penetration rate of new energy sources will further increase, meaning that the power system will need to accommodate more fluctuating power sources. The application of energy storage technology in conjunction with renewable energy generation can provide peak shaving and valley filling services, as well as smoothing output fluctuations and tracking forecast curves, making it one of the effective ways to solve the grid connection problems of renewable energy.

[0003] Currently, electrochemical energy storage, especially lithium-ion battery energy storage, is one of the main application methods for large-scale energy storage systems. Avoiding overcharging and over-discharging of lithium-ion batteries is fundamental to the safe and healthy operation of lithium-ion battery energy storage systems, requiring effective monitoring of the dynamic charge and discharge parameters of the lithium-ion batteries and obtaining the State of Charge (SOC) value. While existing technologies offer various methods for detecting and calculating SOC values, these methods lack dynamic discrimination and correction capabilities, leading to a gradual increase in accumulated deviations. (Some algorithms utilize complex calculation methods and iterative data sampling to improve accuracy, but the initial state and conditions of each charge and discharge cycle are different, resulting in less than ideal sample data selection and iteration effects.) This necessitates programmed battery maintenance and recalibration after a period of operation, only to repeat this process after calibration, leading to resource waste and unhealthy operation, potentially causing malfunctions and operational risks. Summary of the Invention

[0004] To overcome the above-mentioned shortcomings, this invention discloses a battery power estimation and dynamic intelligent calibration method for a BMS system. The BMS system monitors and reads the battery's voltage, current, and temperature parameters in real time, and obtains the current battery SOC value SOCi. Its characteristic is that during charging and discharging, it dynamically reads the current battery voltage value Vi according to a set cycle time t, selects the corresponding initial SOC value (SOCi-1) based on the corresponding SOC voltage value range Vti, and calculates the current SOC value SOCi. The calculation method is as follows:

[0005] SOCi=(SOCi-1)+Vi*Ii*t+Wi+Pi+Ki;

[0006] Where: SOCi is the current SOC value, SOCi-1 is the previous SOC value, Vi is the current monitored voltage value, Ii is the current current value, t is the cycle time, Wi is the temperature compensation value, Pi is the power compensation value, Ki is the SOC calculation deviation coefficient, and "*" is the multiplication sign;

[0007] During battery charging and discharging, when transitioning from the current voltage range Vti to the next voltage range Vti+1, the BMS compares and determines the deviation coefficient Ki between the obtained battery SOC value SOCi and the SOC value SOCi+1 in the set voltage range Vt+1. By dynamically correcting the deviation coefficient, the BMS intelligently calibrates and calculates the current SOC value, ensuring that the SOC accuracy remains within a controllable range.

[0008] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized in that the SOC value corresponds to a voltage value range, which is the voltage parameter range range corresponding to the SOC value in the performance parameters given by the battery product. The range of the voltage value range Vti corresponding to the SOC value is also different depending on the accuracy of the SOC parameter value.

[0009] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized in that the temperature compensation is the temperature compensation value given by the battery product manufacturer.

[0010] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized in that the power compensation is the power compensation value given by the battery product manufacturer.

[0011] The BMS system power estimation and dynamic intelligent calibration method is characterized by calculating the deviation coefficient Ki as the deviation Sk between the currently calculated SOC value SOCi when entering the next voltage interval Vti after the current voltage interval Vti ends and the SOC value SOCi+1 set for the next voltage interval Vti+1, divided by the number of cycles Ts of the current voltage interval Vti, i.e., the number of times the SOC value is calculated within the current voltage interval Vti. Ki takes a positive value when SOCi+1 is large and a negative value when SOCi+1 is small.

[0012] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized in that the cycle time is the interval t between every two sampling calculations set by the BMS system.

[0013] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized by the following BMS operation control flow:

[0014] 1) Based on the battery's product performance specifications chart from the manufacturer, the BMS sets multiple voltage ranges and the SOC value of each voltage range, and reads the corresponding temperature compensation value Wi and the corresponding power compensation value Pi.

[0015] 2) When the BMS is running for the first time, the SOC value corresponding to the voltage range set for the current voltage value is used as the previous SOC value SOCi-1;

[0016] 3) BMS settings: Ts = 1;

[0017] 4) The BMS monitors the relevant parameters of the battery and collects voltage parameter Vi, current parameter Ii, and temperature parameter W according to the set time interval;

[0018] 5) Determine if the parameters are abnormal;

[0019] 6) If yes, then issue an alarm; otherwise, continue with 7).

[0020] 7) BMS calculation: SOCi=(SOCi-1)+Vi*Ii*t+Wi+Pi+Ki

[0021] 8) Judgment: Monitor whether the voltage range has entered the next range;

[0022] 9) If yes, continue to execute 10); if no, calculate Ts = Ts + 1 and execute 4).

[0023] 10) BMS compares and calculates: Sk = SOCi - (SOCi + 1), Ki = -Sk / Ts;

[0024] 11) Enter the next voltage range; execute 3).

[0025] This invention discloses a battery management system (BMS) power estimation and dynamic intelligent calibration method, belonging to the field of battery energy storage technology. It corrects the battery's state of charge (SOC) value calculated using the ampere-hour method based on the battery's factory electrical characteristics and the battery product's dynamic temperature and power compensation values. Furthermore, by obtaining the battery product's voltage-to-SOC performance curve from the correlation between battery voltage and SOC value, and considering the non-linearity of the curve, the correlation curve is divided into multiple voltage intervals. Each interval has a set SOC value based on the battery product's factory voltage-to-SOC performance curve. During charging and discharging, the calculated SOC value SOCi is compared with the set SOC value SOCi+1, and a deviation coefficient Ki is obtained as an intelligent automatic correction parameter for the battery's SOC value. This effectively controls power estimation deviation, improves the accuracy of the SOC value, avoids overcharging and over-discharging of the battery, thereby improving battery life and the efficiency of the energy storage system. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the operation control of a power estimation and dynamic intelligent calibration method for a BMS system. Detailed Implementation

[0027] As an example, a method for estimating power consumption and dynamic intelligent calibration of a BMS system is described in conjunction with the accompanying drawings. However, the described embodiments are only a part of, and not all, of the embodiments of the present invention applied to the method for estimating power consumption and dynamic intelligent calibration of a BMS system. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. The technology and solutions of the present invention are not limited to the content given in this example.

[0028] like Figure 1 As shown, a method for battery power estimation and dynamic intelligent calibration in a BMS system is disclosed. The BMS system monitors and reads the battery's voltage, current, and temperature parameters in real time, and obtains the current state of charge (SOC) value, SOCi. Its characteristic is that during charging and discharging, the current battery voltage value Vi is dynamically read according to a set cycle time t, and the corresponding initial SOC value SOCi-1 is selected based on the corresponding SOC voltage range Vti. The current SOC value SOCi is then calculated using the following method:

[0029] SOCi=(SOCi-1)+Vi*Ii*t+Wi+Pi+Ki;

[0030] Where: SOCi is the current SOC value, SOCi-1 is the previous SOC value, Vi is the current monitored voltage value, Ii is the current current value, t is the cycle time (in hours), Wi is the temperature compensation value, Pi is the power compensation value, Ki is the SOC calculation deviation coefficient, and "*" is the multiplication sign;

[0031] During operation, when moving from the current voltage range Vti to the next voltage range Vti+1, the BMS compares and determines the deviation coefficient Ki between the obtained battery SOC value SOCi and the SOC value SOCi+1 in the set voltage range Vt+1. By dynamically correcting the deviation coefficient, the BMS intelligently calibrates and calculates the current SOC value, ensuring that the SOC accuracy remains within a controllable range.

[0032] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized in that the SOC voltage value range corresponds to the SOC value corresponding to the voltage parameter of the battery product standard. Considering the accuracy of parameter value selection, the SOC value corresponds to a voltage value range Vti (e.g., an SOC value with an accuracy of 1% corresponds to a voltage range of hundreds of millivolts, and an SOC value with an accuracy of 2% corresponds to a larger voltage range).

[0033] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized in that the temperature compensation is the temperature compensation value given by the battery product manufacturer.

[0034] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized in that the power compensation is the power compensation value given by the battery product manufacturer.

[0035] The BMS system power estimation and dynamic intelligent calibration method is characterized by calculating the deviation coefficient Ki as the deviation Sk between the currently calculated SOC value SOCi when entering the next voltage interval Vti after the current voltage interval Vti ends and the SOC value SOCi+1 set for the next voltage interval Vti+1, divided by the number of cycles Ts of the current voltage interval Vti, i.e., the number of times the SOC value is calculated within the current voltage interval Vti. Ki takes a positive value when SOCi+1 is large and a negative value when SOCi+1 is small.

[0036] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized in that the cycle time is the interval t between every two sampling calculations set by the BMS system.

[0037] The aforementioned BMS system power estimation and dynamic intelligent calibration method is characterized by the following BMS operation control flow:

[0038] 1) Based on the battery's product performance specifications chart from the manufacturer, the BMS sets multiple voltage ranges and the SOC value of each voltage range, and reads the corresponding temperature compensation value Wi and the corresponding power compensation value Pi.

[0039] 2) When the BMS is running for the first time, the SOC value corresponding to the voltage range set for the current voltage value is used as the previous SOC value SOCi-1;

[0040] 3) BMS settings: Ts = 1;

[0041] 4) The BMS monitors the relevant parameters of the battery and collects voltage parameter Vi, current parameter Ii, and temperature parameter W according to the set time interval;

[0042] 5) Determine if the parameters are abnormal;

[0043] 6) If yes, then issue an alarm; otherwise, continue with 7).

[0044] 7) BMS calculation: SOCi=(SOCi-1)+Vi*Ii*t+Wi+Pi+Ki

[0045] 8) Judgment: Monitor whether the voltage range has entered the next range;

[0046] 9) If yes, continue to execute 10); if no, calculate Ts = Ts + 1 and execute 4).

[0047] 10) BMS compares and calculates: Sk = SOCi - SOCi + 1, Ki = -Sk / Ts;

[0048] 11) Enter the next voltage range; execute 3).

[0049] This invention discloses a battery management system (BMS) power estimation and dynamic intelligent calibration method, belonging to the field of lithium battery energy storage technology. It corrects the battery's state of charge (SOC) value calculated using the ampere-hour method based on the battery's factory electrical characteristics and standard temperature and power compensation values, achieving higher accuracy. Furthermore, it obtains a battery voltage-to-SOC curve at the time of manufacture by establishing a correlation between the battery's voltage and SOC value. Taking into full account the nonlinearity of the curve, the curve is divided into multiple voltage intervals, each more closely approximating a linear relationship. The SOC value for each voltage interval is set based on the factory voltage-to-SOC curve. During charging and discharging, the calculated SOC value (SOCi) is compared with the set SOC value (SOCi+1), and a deviation coefficient (Ki) is obtained as an intelligent automatic compensation parameter for the battery's SOC value. This effectively controls power estimation deviation, improves the accuracy of the SOC value, and effectively avoids overcharging and over-discharging, thereby improving battery life and the efficiency of the energy storage system.

Claims

1. A method for battery power estimation and dynamic intelligent calibration in a battery management system (BMS), wherein the BMS system monitors and reads the battery's voltage, current, and temperature parameters in real time, and obtains the current battery's state of charge (SOC) value (SOCi); characterized in that... During charging and discharging, the current battery voltage value Vi is dynamically read according to the set cycle time t, and the corresponding initial SOC value SOCi-1 is selected according to the corresponding SOC voltage value range Vti. The current SOC value SOCi is then calculated using the following method: SOCi=(SOCi-1)+Vi*Ii*t+Wi+Pi+Ki; Where: SOCi is the current SOC value, SOCi-1 is the previous SOC value, Vi is the current monitored voltage value, Ii is the current current value, t is the cycle time, Wi is the temperature compensation value, Pi is the power compensation value, Ki is the SOC calculation deviation coefficient, and "*" is the multiplication sign; During battery charging and discharging, when transitioning from the current voltage range Vti to the next voltage range Vti+1, the BMS compares and determines the deviation coefficient Ki between the obtained battery SOC value SOCi and the SOC value SOCi+1 in the set voltage range Vt+1. By dynamically correcting the deviation coefficient, the BMS intelligently calibrates and calculates the current SOC value, ensuring that the SOC accuracy remains within a controllable range.

2. The method for power estimation and dynamic intelligent calibration of a BMS system according to claim 1, characterized in that: The SOC value corresponds to a voltage range, which is the range of voltage parameters corresponding to the SOC value in the performance parameters given by the battery product. The range of voltage values ​​Vti corresponding to the SOC value will also be different depending on the accuracy of the SOC parameter.

3. The method for power estimation and dynamic intelligent calibration of a BMS system according to claim 1, characterized in that: Temperature compensation refers to the temperature compensation value given by the manufacturer when the battery product leaves the factory.

4. The method for power estimation and dynamic intelligent calibration of a BMS system according to claim 1, characterized in that: The power compensation is the power compensation value given by the battery product manufacturer.

5. The method for power estimation and dynamic intelligent calibration of a BMS system according to claim 1, characterized in that: The deviation coefficient Ki is calculated as the deviation Sk between the currently calculated SOC value SOCi when entering the next voltage interval Vti+1 after the current voltage interval Vti ends, and the SOCi+1 set for the next voltage interval Vti+1, divided by the number of cycles Ts of the current voltage interval Vti, i.e., the number of times the SOC value is calculated within the current voltage interval Vti. Ki takes a positive value when SOCi+1 is large and a negative value when SOCi+1 is small.

6. The method for power estimation and dynamic intelligent calibration of a BMS system according to claim 1, characterized in that: The cycle time is the interval t between every two sampling calculations set by the BMS system.

7. The method for power estimation and dynamic intelligent calibration of a BMS system according to claim 1, characterized in that: The BMS operation control process is as follows: 1) Based on the battery's product performance specifications chart from the manufacturer, the BMS sets multiple voltage ranges and the SOC value of each voltage range, and reads the corresponding temperature compensation value Wi and the corresponding power compensation value Pi. 2) When the BMS is running for the first time, the SOC value corresponding to the voltage range set for the current voltage value is used as the previous SOC value SOCi-1; 3) BMS settings: Ts = 1; 4) The BMS monitors the relevant parameters of the battery and collects voltage parameter Vi, current parameter Ii, and temperature parameter W according to the set time interval; 5) Determine if the parameters are abnormal; 6) If yes, then issue an alarm; otherwise, continue with 7). 7) BMS calculation: SOCi=(SOCi-1)+Vi*Ii*t+Wi+Pi+Ki 8) Judgment: Monitor whether the voltage range has been entered; 9) If yes, continue to execute 10); if no, calculate Ts = Ts + 1 and execute 4). 10) BMS compares and calculates: Sk = SOCi - (SOCi + 1), Ki = -Sk / Ts; 11) Enter the next voltage range; execute 3).

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