TWI880775B - Battery capacity calculation method and battery capacity calculation system - Google Patents

Abstract

A battery capacity calculation method includes acquiring an ideal open circuit voltage curve of a battery, discharging the battery by using a discharge current, detecting N discharge voltages corresponding to N depth of discharges (DODs) when the battery is discharged, generating N internal resistances corresponding to the N DODs according to the ideal open circuit voltage curve, the discharge current, and the N discharge voltages, adjusting a distribution of at least one internal resistance sampling point on a battery resistance curve according to (N-1) slopes of the N internal resistance sampling points on the battery resistance curve corresponding to the N internal resistances so as to fit the battery resistance curve, and updating N internal resistances after the distribution of the N internal resistance sampling points is adjusted. N is a positive integer greater than 2.

Description

Battery power calculation method and battery power calculation system

The present invention describes a battery power calculation method and a battery power calculation system, and in particular, a low-complexity and high-accuracy battery power calculation method and a battery power calculation system.

With the rapid development of technology, various portable electronic products have been launched. In recent years, users have been demanding high performance, long battery life, and miniaturization for portable electronic products. Therefore, the use and management of batteries in portable electronic products are important design considerations. There are many factors that affect the battery power state, such as battery charge and discharge rate, temperature, battery aging state, etc. In order to correctly obtain the correct battery power state, many systems will adopt a highly complex calculation method to achieve it. For example, when obtaining the battery power state (State of Charge, SOC) today, the open circuit voltage method (Open Circuit Voltage, OCV) or the coulomb counting method (Coulomb Counting) will be adopted.

Since there are too many factors that affect the battery status, and individual factors will affect each other, calculating the battery status becomes a complex problem. For example, the simple open circuit voltage method or coulomb counting method cannot accurately calculate the battery status. Therefore, in current technology, high-dimensional regression calculations, iterative operations, or neural networks (NN) are often combined to increase accuracy. However, the cost of introducing high-dimensional regression calculations, iterative operations, or neural networks is high complexity and high energy consumption. In other words, if a highly complex algorithm is introduced to calculate the battery status, it will accelerate the power consumption of portable electronic products and reduce their battery life.

Therefore, developing a low-complexity and high-accuracy battery charge calculation system is an important design issue.

An embodiment of the present invention proposes a battery capacity calculation method. The battery capacity calculation method includes obtaining an ideal open-circuit voltage curve of the battery, discharging the battery with a discharge current, detecting N discharge voltages corresponding to N discharge depths when the battery is discharged, generating N internal resistances corresponding to N discharge depths according to the ideal open-circuit voltage curve, the discharge current, and the N discharge voltages, adjusting the distribution of at least one internal resistance sampling point on the battery resistance curve according to the (N-1) slopes of the N internal resistance sampling points corresponding to the N internal resistances on the battery resistance curve so that the N internal resistance sampling points fit the battery resistance curve, and updating the N internal resistances after the distribution of the N internal resistance sampling points is adjusted. N is a positive integer greater than 2.

Another embodiment of the present invention provides a battery capacity calculation system. The battery capacity calculation system includes a battery, a current detection circuit, a voltage detection circuit, a memory, and a processor. The current detection circuit is coupled to the battery. The voltage detection circuit is coupled to the battery. The memory is used to store data. The processor is coupled to the current detection circuit, the voltage detection circuit, and the memory. After the processor obtains the ideal open-circuit voltage curve of the battery from the memory, it discharges the battery using a discharge current. When the battery is discharged, the processor controls the voltage detection circuit to detect N discharge voltages corresponding to N discharge depths. The discharge current is detected by the current detection circuit. The processor generates N internal resistors corresponding to N discharge depths according to the ideal open-circuit voltage curve, the discharge current, and N discharge voltages. The processor adjusts the distribution of at least one internal resistor sampling point on the battery resistance curve according to the (N-1) slopes of the N internal resistor sampling points corresponding to the N internal resistors on the battery resistance curve, so that the N internal resistor sampling points fit the battery resistance curve. After the distribution of the N internal resistor sampling points is adjusted, the processor updates the N internal resistors. N is a positive integer greater than 2.

100:Battery power calculation system

10:Battery

11: Current detection circuit

12: Voltage detection circuit

13: Memory

14: Processor

C _OCV : Ideal open circuit voltage curve

C _BAT : Open circuit voltage curve

DOD(n): nth discharge depth

V _OCV (n): nth ideal discharge voltage

V _BAT (n): nth discharge voltage

△V: voltage difference

R(n): nth internal resistance

RS(n): nth internal resistance sampling point

C _R : Battery resistance curve

RS(n-1): the n-1th internal resistance sampling point

RS(n+1): the n+1th internal resistance sampling point

S1, S1’: first slope

S2, S2’: Second slope

RS’(m-1): m-1th internal resistance sampling point

RS’(m+1): m+1th internal resistance sampling point

RS’(m): mth internal resistance sampling point

DOD(m): mth discharge depth

S701 to S706: Steps

Figure 1 is a block diagram of an embodiment of the battery capacity calculation system of the present invention.

Figure 2 is a schematic diagram of obtaining the nth ideal discharge voltage and the nth discharge voltage in the battery capacity calculation system of Figure 1.

Figure 3 is a schematic diagram of obtaining the nth internal resistance in the battery capacity calculation system of Figure 1.

FIG. 4 is a schematic diagram showing the distribution of at least one internal resistance sampling point on the battery resistance curve adjusted according to two adjacent slopes in the first mode in the battery capacity calculation system of FIG. 1.

FIG5 is a schematic diagram showing the distribution of at least one internal resistance sampling point on the battery resistance curve adjusted according to two adjacent slopes in the battery capacity calculation system of FIG1 in the second mode.

Figure 6 is a schematic diagram showing the adjustment of the distribution of N internal resistance sampling points on the battery resistance curve in the battery capacity calculation system of Figure 1.

Figure 7 is a flow chart of the battery power calculation system in Figure 1 executing the battery power calculation method.

FIG. 1 is a block diagram of an embodiment of a battery capacity calculation system 100 of the present invention. The battery capacity calculation system 100 includes a battery 10, a current detection circuit 11, a voltage detection circuit 12, a memory 13, and a processor 14. The battery 10 of the capacity calculation system 100 can be any type of battery, such as a rechargeable battery, a lithium battery, or a nickel-hydrogen battery. The battery 10 can also be a battery pack, which includes a group of battery cells connected in series. The current detection circuit 11 is coupled to the battery 10 to detect the current of the battery 10 when it is discharged or charged. The voltage detection circuit 12 is coupled to the battery 10 to detect the voltage at both ends of the battery 10 when it is discharged or charged. It should be understood that the voltage detection circuit 12 can be coupled to the positive and negative ends of the battery 10. When the battery 10 is a battery pack, the voltage detection circuit 12 can also be coupled to the positive and negative ends of each battery cell in the battery pack through a multiplexer. Any reasonable hardware changes belong to the scope disclosed by the present invention. The memory 13 is used to store data. The processor 14 is coupled to the current detection circuit 11, the voltage detection circuit 12 and the memory 13. The processor 14 can be any form of computing unit, such as a central processing unit or a microprocessor. The memory 13 can be a flash memory or a random access memory, but is not limited thereto. In the battery capacity calculation system 100, after the processor 14 obtains the ideal open circuit voltage curve of the battery 10 from the memory 13, the battery 10 can be discharged using the discharge current. When the battery 10 is discharged, the processor 14 can control the voltage detection circuit 12 to detect N discharge voltages corresponding to N discharge depths. The discharge current can be detected by the current detection circuit 11. The processor 14 can generate N internal resistors corresponding to N discharge depths according to the ideal open-circuit voltage curve, the discharge current, and the N discharge voltages. Furthermore, the processor 14 can adjust the distribution of at least one internal resistor sampling point on the battery resistance curve according to the (N-1) slopes of the N internal resistor sampling points corresponding to the N internal resistors on the battery resistance curve, so that the N internal resistor sampling points fit the battery resistance curve. Finally, the processor 14 can update the N internal resistors after the distribution of the N internal resistor sampling points is adjusted. N is a positive integer greater than 2. The details of the battery capacity calculation method executed by the battery capacity calculation system 100 will be described in detail later.

FIG. 2 is a schematic diagram of obtaining the nth ideal discharge voltage V _OCV (n) and the nth discharge voltage V _BAT (n) in the battery capacity calculation system 100. As mentioned above, the processor 14 can obtain the ideal open circuit voltage curve _COCV of the battery 10 from the memory 13. The ideal open circuit voltage curve _COCV can be preset and stored in the memory 13. In other embodiments, the equations or lookup tables of all ideal open circuit voltages V _OCV on the ideal open circuit voltage curve _COCV can be pre-stored in the memory 13. As shown in FIG. 2 , the ideal open circuit voltage curve _COCV can be viewed as a curve showing the relationship between the discharge voltage V and the discharge depth DOD of the battery 10 when the battery 10 has no internal resistance (e.g., a battery just out of the factory). Here, the Y-axis is the discharge voltage V, which represents the voltage measured when the battery 10 is discharged. The X-axis is the discharge depth DOD. The discharge depth DOD is defined as the ratio of the fully charged state to the amount of electricity currently discharged. The range of the discharge depth DOD is between 0% and 100%. In the battery charge calculation system 100, the processor 14 can obtain N ideal discharge voltages corresponding to N discharge depths on the ideal open circuit voltage curve _COCV in the memory 13. Furthermore, the processor 14 can generate N internal resistors corresponding to N discharge depths according to the ideal open-circuit voltage curve _COCV , N discharge voltages, and discharge currents. For example, in FIG. 2, the nth discharge depth DOD(n) can be x%. At the nth discharge depth DOD(n), the processor 14 can obtain the nth ideal discharge voltage V _OCV (n) according to the ideal open-circuit voltage curve _COCV . Furthermore, the processor 14 can control the voltage detection circuit 12 to detect the actual value of the nth discharge voltage V _BAT (n). It should be understood that since the battery 10 will generate internal resistance due to the influence of many factors such as the charge and discharge rate, temperature, battery aging state, etc., its actual n-th discharge voltage V _BAT (n) will be affected by the internal resistance and will be smaller than the ideal discharge voltage V _OCV (n). Therefore, after obtaining the n-th ideal discharge voltage V _OCV (n) and the n-th discharge voltage V _BAT (n), the processor 14 can obtain the voltage difference △V at the n-th discharge depth DOD (n). Therefore, the voltage difference △V can be interpreted as: the difference between the ideal discharge voltage V _OCV (n) and the actual discharge voltage V _BAT (n) due to the degradation of the battery discharge performance. Furthermore, the open circuit voltage curve C _BAT in FIG. 2 is an actual measured value, and its sampled voltage value will be smaller than the sampled voltage value of the ideal open circuit voltage curve _COCV .

FIG. 3 is a schematic diagram of obtaining the nth internal resistance R(n) in the battery capacity calculation system of FIG. 1. After obtaining the nth ideal discharge voltage V _OCV (n) and the nth discharge voltage V _BAT (n), the processor 14 can calculate the nth internal resistance R(n), which is expressed as:

Where I is the discharge current, which can be preset and detected by the current detection circuit 11. It should be understood that the nth internal resistance R(n) can be approximated by a moving average mechanism to increase the accuracy of the nth internal resistance R(n). However, the present invention is not limited to this. After the nth internal resistance R(n) is obtained, the processor 14 can mark the nth internal resistance sampling point RS(n) on the battery resistance curve _CR at the nth discharge depth DOD(n). It should be understood that, as mentioned above, N internal resistance sampling points RS(1) to RS(N) are considered. The purpose of the battery capacity calculation system 100 is to optimize the distribution of the N internal resistance sampling points RS(1) to RS(N) on the battery resistance curve _CR so that the N internal resistance sampling points RS(1) to RS(N) fit the battery resistance curve _CR . Therefore, when the internal resistance sampling points RS(1) to RS(N) are updated, a very accurate battery resistance curve _CR can be generated to facilitate the calculation of the battery capacity. The details of how to adjust the distribution of the N internal resistance sampling points RS(1) to RS(N) on the battery resistance curve _CR will be described in detail later.

FIG. 4 is a schematic diagram of adjusting the distribution of at least one internal resistance sampling point on the battery resistance curve _CR according to two adjacent slopes in the first mode in the battery capacity calculation system 100. In FIG. 4, the processor 14 can obtain the (n-1)th internal resistance sampling point RS(n-1), the nth internal resistance sampling point RS(n), and the (n+1)th internal resistance sampling point _RS (n+1) corresponding to the (n-1)th internal resistance, the nth internal resistance, and the (n+1)th internal resistance among the N internal resistances on the battery resistance curve CR. The (n-1)th internal resistance sampling point RS(n-1), the nth internal resistance sampling point RS(n), and the (n+1)th internal resistance sampling point RS(n+1) are three consecutive sampling points on the battery resistance curve _CR . Then, the processor 14 can obtain the first slope S1 of the (n-1)th internal resistance sampling point RS(n-1) and the nth internal resistance sampling point RS(n). The first slope S1 can be expressed as:

The (n-1)th internal resistance sampling point RS(n-1) corresponds to the (n-1)th discharge depth DOD(n-1), and the (n)th internal resistance sampling point RS(n) corresponds to the (n)th discharge depth DOD(n). Similarly, the processor 14 can obtain the second slope S2 of the (n)th internal resistance sampling point RS(n) and the (n+1)th internal resistance sampling point RS(n+1). The second slope S2 can be expressed as:

<S _TH The (n+1)th internal resistance sampling point RS(n+1) corresponds to the (n+1)th discharge depth DOD(n+1). Then, the processor 14 can compare the first slope S1 and the second slope S2 to adjust the distribution of at least one internal resistance sampling point on the battery resistance curve _CR . For example, the processor 14 can set a threshold value S _TH , and the threshold value S _TH is a positive number between 0 and 1. In addition, the processor 14 can compare the first slope S1 and the second slope S2 to generate a difference value between the first slope S1 and the second slope S2. In FIG. 4, if the difference between the first slope S1 and the second slope S2 is less than the threshold value S _TH , it means that the three consecutive sampling points RS(n-1), RS(n), and RS(n+1) do not change much. Therefore, the battery resistance curve _CR can be linearly approximated. The processor 14 can reduce the number of samples between the (n-1)th internal resistance sampling point RS(n-1) and the (n+1)th internal resistance sampling point RS(n+1), as shown below:

< S _TH

FIG. 5 is a schematic diagram of the distribution of at least one internal resistance sampling point on the battery resistance curve _CR adjusted according to two adjacent slopes in the second mode in the battery capacity calculation system 100. In FIG. 5, the processor 14 can obtain the (m-1)th internal resistance sampling point RS'(m-1), the mth internal resistance sampling point RS'(m), and the (m+1)th internal resistance sampling point _RS '(m+1) corresponding to the (m-1)th internal resistance, the mth internal resistance, and the (m+1)th internal resistance among the N internal resistances on the battery resistance curve CR. The (m-1)th internal resistance sampling point RS'(m-1), the mth internal resistance sampling point RS'(m), and the (m+1)th internal resistance sampling point RS'(m+1) are three consecutive sampling points on the battery resistance curve _CR . Then, the processor 14 can obtain the first slope S1' of the (m-1)th internal resistance sampling point RS'(m-1) and the mth internal resistance sampling point RS'(m). The first slope S1' can be expressed as:

The (m-1)th internal resistance sampling point RS'(m-1) corresponds to the (m-1)th discharge depth DOD'(m-1), and the (m)th internal resistance sampling point RS'(m) corresponds to the (m)th discharge depth DOD'(m). Similarly, the processor 14 can obtain the second slope S2' of the (m)th internal resistance sampling point RS'(m) and the (m+1)th internal resistance sampling point RS'(m+1). The second slope S2' can be expressed as:

S _TH The (m+1)th internal resistance sampling point RS'(m+1) corresponds to the (m+1)th discharge depth DOD'(m+1). Then, the processor 14 can compare the first slope S1' and the second slope S2' to adjust the distribution of at least one internal resistance sampling point on the battery resistance curve _CR . For example, the processor 14 can compare the first slope S1' and the second slope S2' to generate a difference value between the first slope S1' and the second slope S2'. In FIG. 5, if the difference value between the first slope S1' and the second slope S2' is greater than or equal to the threshold value S _TH , it means that the three consecutive sampling points RS'(m-1), RS'(m), and RS'(m+1) change dramatically. Therefore, the battery resistance curve _CR may be inflected at the sampling point RS'(m). The processor 14 may increase the number of samples between the (m-1)th internal resistance sampling point RS'(m-1) and the (m+1)th internal resistance sampling point RS'(m+1), as shown below:

S _T

FIG. 6 is a schematic diagram showing that the distribution of N internal resistance sampling points on the battery resistance curve _CR in the battery capacity calculation system 100 has been adjusted. As mentioned above, the processor 14 can compare the (N-1) slopes corresponding to the N internal resistance sampling points on the battery resistance curve _CR in pairs to adjust the distribution of the N internal resistance sampling points. As shown in FIG. 6, after the distribution of the N internal resistance sampling points is adjusted, the difference between two adjacent slopes of the (N-1) slopes on the battery resistance curve _CR will be less than the threshold value S _TH . In other words, the battery capacity calculation system 100 can adjust the distribution of the N internal resistance sampling points in a dynamic distance manner. Therefore, the battery resistance curve _CR can be accurately fitted with a limited number of N internal resistance sampling points. Also because the battery resistance curve _CR can be accurately fitted with a limited number of N internal resistance sampling points, when the battery resistance curve _CR is combined with open circuit voltage (OCV) or coulomb counting (Coulomb Counting) and other technologies, there will be a more accurate battery capacity estimation result. For example, when the battery resistance curve _CR is generated, at a discharge depth of y%, the discharge voltage measured by the voltage detection circuit 12 can be superimposed on the voltage difference derived from the discharge current and the internal resistance. Therefore, the discharge efficiency of the battery 10 or other applications can be determined.

FIG. 7 is a flow chart of the battery power calculation method executed by the battery power calculation system 100. The flow chart of the battery power calculation method includes steps S701 to S706. Steps S701 to S706 are described as follows: Step S701: Obtain an ideal open circuit voltage curve _COCV of the battery 10; Step S702: Discharge the battery 10 using a discharge current I; Step S703: When the battery 10 is discharged, detect N discharge voltages corresponding to N discharge depths; Step S704: Generate N internal resistances corresponding to N discharge depths based on the ideal open circuit voltage curve _COCV , the discharge current I, and the N discharge voltages; Step S705: Adjust the battery resistance curve C _R based on the (N-1) slopes of the N internal resistance sampling points corresponding to the N internal resistances on the battery resistance curve C R. _R so that the N internal resistance sampling points fit the battery resistance curve _CR ; Step S706: after the distribution of the N internal resistance sampling points is adjusted, the N internal resistances are updated.

The details of step S701 to step S706 have been described in detail above, so they will not be repeated here. The battery capacity calculation system 100 can detect the internal resistance and optimize the distribution of the limited internal resistance to fit the battery resistance curve with the least distortion. Since the number of internal resistances (N) can be customized, the complexity of the subsequent update of the internal resistance can also be adjusted. Therefore, the battery capacity calculation system of the present invention can realize a battery capacity calculation method with adjustable complexity and high calculation accuracy.

In summary, the present invention describes a battery capacity calculation method and a battery capacity calculation system. The battery capacity calculation system can use the ideal open-circuit voltage curve, discharge current and discharge voltage to obtain a finite number of internal resistors. In addition, the battery capacity calculation system can also use the difference between two adjacent slopes of the finite number of internal resistors on the battery resistance curve to determine whether to adjust the distribution of the finite number of internal resistors. Finally, when the difference between all pairs of adjacent slopes on the battery resistance curve is less than a threshold value, the distribution of the finite number of internal resistors can be optimized. Therefore, the battery capacity calculation system can use the finite number of internal resistors to fit a battery resistance curve with minimal distortion. In the battery capacity calculation system, since the number of internal resistors can be customized, the complexity of updating the internal resistors can also be adjusted. Therefore, the battery capacity calculation system of the present invention can realize a battery capacity calculation method with adjustable complexity and high calculation accuracy.

The above is only the preferred embodiment of the present invention. All equivalent changes and modifications made within the scope of the patent application of the present invention shall fall within the scope of the present invention.

100:Battery power calculation system

10:Battery

11: Current detection circuit

12: Voltage detection circuit

13: Memory

14: Processor

Claims (20)

A battery capacity calculation method comprises: Obtaining an ideal open circuit voltage curve of a battery; Discharging the battery with a discharge current; Detecting N discharge voltages corresponding to N discharge depths when the battery is discharged; Generating N internal resistances corresponding to the N discharge depths based on the ideal open circuit voltage curve, the discharge current and the N discharge voltages; Adjusting a distribution of at least one internal resistance sampling point on a battery resistance curve based on (N-1) slopes of N internal resistance sampling points corresponding to the N internal resistances on a battery resistance curve, so that the N internal resistance sampling points fit the battery resistance curve; and After the distribution of the N internal resistance sampling points is adjusted, the N internal resistances are updated; wherein N is a positive integer greater than 2. The method as described in claim 1, wherein the ideal open-circuit voltage curve includes a curve of a relationship between a discharge voltage and a discharge depth of the battery when the battery has no internal resistance, and the ideal open-circuit voltage curve is preset and stored in a memory. The method as described in claim 1, wherein generating the N internal resistances corresponding to the N discharge depths according to the ideal open-circuit voltage curve, the discharge current and the N discharge voltages comprises: obtaining the N ideal discharge voltages corresponding to the N discharge depths on the ideal open-circuit voltage curve; and generating the N internal resistances corresponding to the N discharge depths according to the N ideal discharge voltages, the N discharge voltages and the discharge current. The method as claimed in claim 3, wherein an nth internal resistor among the N internal resistors is expressed as: Where I is the discharge current, is one of the N ideal discharge voltages, the nth ideal discharge voltage, is an nth discharge voltage among N discharge voltages, and a range of the N discharge depths is between 0% and 100%. The method as described in claim 1, wherein each of the N internal resistors is approximated using a moving average mechanism. The method as described in claim 1, wherein adjusting the distribution of the at least one internal resistance sampling point on the battery resistance curve according to the (N-1) slopes of the N internal resistance sampling points corresponding to the N internal resistances on the battery resistance curve so that the N internal resistance sampling points fit the battery resistance curve comprises: Obtaining an (n-1)th internal resistance sampling point, an nth internal resistance sampling point and an (n+1)th internal resistance sampling point corresponding to an (n-1)th internal resistance, an nth internal resistance and an (n+1)th internal resistance of the N internal resistances on the battery resistance curve, respectively; Obtaining a first slope of the (n-1)th internal resistance sampling point and the nth internal resistance sampling point; Obtain a second slope of the nth internal resistance sampling point and the (n+1)th internal resistance sampling point; and Compare the first slope with the second slope to adjust the distribution of at least one internal resistance sampling point on the battery resistance curve; Wherein a range of n is 1 to N. The method as described in claim 6 further comprises: Setting a threshold value; Wherein the threshold value is a positive number between 0 and 1. The method as described in claim 7, wherein comparing the first slope with the second slope to adjust the distribution of the at least one internal resistance sampling point on the battery resistance curve comprises: Comparing the first slope with the second slope to generate a difference value between the first slope and the second slope; and If the difference value is greater than or equal to the threshold value, increasing a sampling number between the (n-1)th internal resistance sampling point and the (n+1)th internal resistance sampling point. The method as described in claim 7, wherein comparing the first slope with the second slope to adjust the distribution of the at least one internal resistance sampling point on the battery resistance curve comprises: Comparing the first slope with the second slope to generate a difference value between the first slope and the second slope; and If the difference value is less than the threshold value, reducing a sampling number between the (n-1)th internal resistance sampling point and the (n+1)th internal resistance sampling point. The method as described in claim 7, wherein after the distribution of the N internal resistance sampling points is adjusted, a difference value between two adjacent slopes among the (N-1) slopes on the battery resistance curve is less than the threshold value. A battery capacity calculation system includes: a battery; a current detection circuit coupled to the battery; a voltage detection circuit coupled to the battery; a memory for storing data; and a processor coupled to the current detection circuit, the voltage detection circuit and the memory; wherein the processor obtains an ideal open circuit voltage curve of the battery from the memory, and discharges the battery with a discharge current. When the battery is discharged, the processor controls the voltage detection circuit to detect N discharge depths. discharge) corresponding to N discharge voltages, the discharge current is detected by the current detection circuit, the processor generates N internal resistances corresponding to the N discharge depths according to the ideal open circuit voltage curve, the discharge current and the N discharge voltages, the processor adjusts a distribution of at least one internal resistance sampling point on a battery resistance curve according to the (N-1) slopes of the N internal resistance sampling points corresponding to the N internal resistances on a battery resistance curve, so that the N internal resistance sampling points fit the battery resistance curve, the processor updates the N internal resistances after the distribution of the N internal resistance sampling points is adjusted, and N is a positive integer greater than 2. A system as described in claim 11, wherein the ideal open-circuit voltage curve includes a relationship curve between a discharge voltage and a discharge depth of the battery when the battery has no internal resistance, and the ideal open-circuit voltage curve is preset and stored in the memory. A system as described in claim 11, wherein the processor obtains N ideal discharge voltages corresponding to the N discharge depths on the ideal open-circuit voltage curve in the memory, and the processor generates the N internal resistances corresponding to the N discharge depths based on the N ideal discharge voltages, the N discharge voltages and the discharge current. The system of claim 13, wherein an nth internal resistor among the N internal resistors is represented by: Where I is the discharge current, is one of the N ideal discharge voltages, the nth ideal discharge voltage, is an nth discharge voltage among N discharge voltages, and a range of the N discharge depths is between 0% and 100%. A system as described in claim 11, wherein each of the N internal resistors is approximated using a moving average mechanism. A system as described in claim 11, wherein the processor obtains an (n-1)th internal resistance sampling point, an nth internal resistance sampling point and an (n+1)th internal resistance sampling point corresponding to an (n-1)th internal resistance sampling point, an nth internal resistance sampling point and an (n+1)th internal resistance sampling point of the N internal resistances, respectively, on the battery resistance curve, the processor obtains a first slope between the (n-1)th internal resistance sampling point and the nth internal resistance sampling point, the processor obtains a second slope between the nth internal resistance sampling point and the (n+1)th internal resistance sampling point, the processor compares the first slope with the second slope to adjust the distribution of the at least one internal resistance sampling point on the battery resistance curve, and n ranges from 1 to N. A system as described in claim 16, wherein the processor sets a threshold value, and the threshold value is a positive number between 0 and 1. A system as described in claim 17, wherein the processor compares the first slope and the second slope to generate a difference value between the first slope and the second slope, and if the difference value is greater than or equal to the threshold value, the processor increases a sampling number between the (n-1)th internal resistance sampling point and the (n+1)th internal resistance sampling point. A system as described in claim 17, wherein the processor compares the first slope and the second slope to generate a difference value between the first slope and the second slope, and if the difference value is less than the threshold value, reduces a sampling number between the (n-1)th internal resistance sampling point and the (n+1)th internal resistance sampling point. A system as described in claim 17, wherein after the distribution of the N internal resistance sampling points is adjusted, a difference between two adjacent slopes among the (N-1) slopes on the battery resistance curve is less than the threshold value.

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