Current control method for low-temperature charging of battery pack and dynamic optimal current estimation system

Abstract

The invention discloses a current control method for low-temperature charging of a battery pack and a dynamic optimal current estimation system, and relates to the field of batteries, and the method comprises the steps: S1, obtaining the charging state data of each battery cell; s2, estimating ohmic internal resistance and polarization internal resistance of each battery cell; s3, extracting characteristic values of ohm internal resistance and polarization internal resistance of the battery pack; s4, calculating brand-new battery cell total internal resistance of the battery cells in the battery pack and current real-time total internal resistance of the battery cells; s5, calculating the dynamic optimal current of the battery pack; and S6, controlling the charging current in the next time period according to the dynamic optimal current. The system comprises an acquisition module; an internal resistance estimation module; a feature value extraction module; a total internal resistance calculation module; and an optimal current calculation module. According to the method, the dynamic optimal current of the battery pack in the current time period can be effectively obtained, the optimal current serves as the basis of the charging current, then the purpose of avoiding charging overheating or internal short circuit of the battery pack under the low-temperature charging working condition is achieved, meanwhile, the charging efficiency is considered, and the charging time is saved.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a current control method and a dynamic optimal current estimation system for low-temperature charging of a battery pack. Background Art

[0002] As the production and sales of electric vehicles continue to grow rapidly, the safety of electric vehicles has gradually become a focus of widespread attention and discussion. According to relevant statistics, the majority of safety accidents involving electric vehicles originate from the onboard power battery, particularly during the charging process. Electric vehicle power batteries are typically composed of several cells connected in series and parallel, and are monitored by a battery management system, including various information collection, charge and discharge control, and thermal management. Currently, electric vehicle power batteries on the market primarily use lithium-ion batteries as single cells. However, due to their inherent material properties, lithium-ion batteries have inherently unstable operating performance and are easily affected by physical changes such as overcharging, overheating, and collisions, causing the battery temperature to rise sharply, leading to thermal runaway and serious consequences.

[0003] To reduce the probability of thermal runaway and ensure safe operation of electric vehicle power batteries, it is necessary to study the charging process of power battery packs under low-temperature conditions and, in turn, to control the charging process. On the one hand, the internal resistance of power batteries increases dramatically under low-temperature conditions, making overheating or internal short circuits highly likely to occur during charging, leading to thermal runaway. On the other hand, improving the charging efficiency of power batteries under low-temperature conditions requires controlling the charging current of the battery pack. Therefore, a current control method and a dynamic optimal current estimation system for low-temperature charging of battery packs are provided. Summary of the Invention

[0004] The purpose of the present invention is to address the above-mentioned problems and provide a current control method and a dynamic optimal current estimation system for low-temperature charging of a battery pack, which can effectively obtain the dynamic optimal current of the battery pack in the current time period, and use the optimal current as the basis for controlling the charging current. In this way, the battery pack can avoid overheating or internal short circuit under low-temperature charging conditions, prevent thermal runaway, and at the same time take into account charging efficiency and save charging time.

[0005] The technical solution adopted by the present invention is as follows: A current control method for low-temperature charging of a battery pack comprises the following steps: S1: Obtain the charging status data of each battery cell in the current time period; the charging status data includes the ambient temperature, surface temperature, terminal voltage and charging current at different times; specifically, the ambient temperature around the battery cell is Te, the surface temperature of the battery cell is Ts, the terminal voltage of the battery cell is U, and the data sampling period is t. Based on the characteristics of battery cell charging, the charging status data in each cycle does not change much and can be considered to be consistent. Therefore, the charging status data obtained at time nt can be used as the charging status data in the nth cycle, and the next time period is the time in the n+1th cycle (that is, the time period from time nt to time (n+1)t), and t is the length of one cycle; then the ambient temperature Te sequence data of each battery cell can be expressed as: {Te(1,t), Te(2,t), Te(3,t)...Te(N,t)}; {Te(1,2t), Te(2,2t), Te(3,2t)...Te(N,2t)}; … {Te(1,nt), Te(2,nt), Te(3,nt)...Te(N,nt)}; Where: N is the serial number of the battery cell, n is the number of cycles for data collection, the value of n is a natural number including 0, and t is the length of time for one cycle; the charging status data collected at the ntth moment is used as the charging status data within the nth cycle; for example, Te(1,t) is the ambient temperature value collected when the No. 1 battery cell is charged at the tth moment, which is used as the charging status data within the first cycle (0-t); Te(1,2t) is the ambient temperature value collected when the No. 1 battery cell is charged at the 2tth moment, which is used as the charging status data within the second cycle (t-2t).

[0006] Similarly, the surface temperature Ts series data of each battery cell can be expressed as: {Ts(1,t), Ts(2,t), Ts(3,t)...Ts(N,t)}; {Ts(1,2t), Ts(2,2t), Ts(3,2t)...Ts(N,2t)}; … {Ts(1,nt), Ts(2,nt), Ts(3,nt)...Ts(N,nt)}.

[0007] The terminal voltage U sequence data of each battery cell can be expressed as: {U(1,t), U(2,t), U(3,t)...U(N,t)}; {U(1,2t), U(2,2t), U(3,2t)...U(N,2t)}; … {U(1,nt), U(2,nt), U(3,nt)...U(N,nt)}.

[0008] The charging current I series data of each battery cell can be expressed as: {I(1,t), I(2,t), I(3,t)...I(N,t)}; {I(1,2t), I(2,2t), I(3,2t)...I(N,2t)}; … {I(1,nt), I(2,nt), I(3,nt)...I(N,nt)}.

[0009] It should be noted that at 0s, when charging is about to begin, there is no terminal voltage and charging current for the battery cell, so there is no need to record the terminal voltage and charging current data at 0s; and since there is no terminal voltage and charging current for the battery cell, there is no need to use the subsequent long short-term memory neural network model to estimate the ohmic internal resistance and polarization internal resistance at 0s, and therefore, there is no need to obtain the ambient temperature and surface temperature at 0s.

[0010] S2: Estimate the ohmic internal resistance and polarization internal resistance of each battery cell based on the charging state data; the specific steps are as follows: pre-build a long short-term memory neural network model, take the charging state data of each battery cell as input, and use the long short-term memory neural network model to estimate the ohmic internal resistance and polarization internal resistance of each battery cell. The estimation effect is as follows: Figure 3 、 Figure 4 As shown in Figure 2, the estimated value is basically equal to the actual value; the steps for constructing the long short-term memory neural network model are as follows: S201. Based on experimental test data of battery cells under various low-temperature charging conditions, including six types of data: ambient temperature, surface temperature, terminal voltage, charging current, ohmic internal resistance, and polarization internal resistance of the battery cells, where ambient temperature, surface temperature, terminal voltage, and charging current are input data and ohmic internal resistance or polarization internal resistance is output data, the data are used to train a long short-term memory neural network estimation model; S202: Optimizing the parameters of the long short-term memory neural network model by using a particle swarm optimization algorithm.

[0011] The estimated ohmic internal resistance Ro can be expressed as: {Ro(1,t), Ro(2,t), Ro(3,t)...Ro(N,t)}; {Ro(1,2t), Ro(2,2t), Ro(3,2t)...Ro(N,2t)}; … {Ro(1,nt), Ro(2,nt), Ro(3,nt)...Ro(N,nt)}.

[0012] The estimated polarization internal resistance Rp can be expressed as: {Rp(1,t), Rp(2,t), Rp(3,t)...Rp(N,t)}; {Rp(1,2t), Rp(2,2t), Rp(3,2t)...Rp(N,2t)}; … {Rp(1,nt), Rp(2,nt), Rp(3,nt)...Rp(N,nt)}.

[0013] It should be noted that since there is no terminal voltage and charging current at 0s, there is no need to predict the ohmic internal resistance Ro and polarization internal resistance Rp through the long short-term memory neural network model, that is, there is no need to record the ohmic internal resistance Ro and polarization internal resistance Rp at 0s.

[0014] S3: Extract the characteristic values ​​of the ohmic internal resistance and polarization internal resistance of the battery pack; the characteristic values ​​are the maximum value Roe of the ohmic internal resistance between each battery cell in the battery pack and the maximum value Rpe of the polarization internal resistance between each battery cell.

[0015] The characteristic value Roe of the ohmic internal resistance in each time period can be expressed as follows: Roe(0)=max{Ro(1,0), Ro(2,0), Ro(3,0),..., Ro(N,0)}; Roe(t)=max{Ro(1,t), Ro(2,t), Ro(3,t),..., Ro(N,t)}; Roe(2t)=max{Ro(1,2t), Ro(2,2t), Ro(3,2t),..., Ro(N,2t)}; ︙ Roe(nt)=max{Ro(1,nt), Ro(2,nt), Ro(3,nt),..., Ro(N,nt)}.

[0016] It should be noted that the ohmic internal resistance Ro at 0s can be obtained by applying a small current square charging current pulse to the battery pack, measuring the ohmic internal resistance Ro of each battery cell at 0s, and directly performing step S3 with the ohmic internal resistance Ro to obtain the characteristic value Roe(0s) of the ohmic internal resistance.

[0017] The characteristic value Rpe of the polarization internal resistance in each time period can be expressed as follows: Rpe=max{Rp(1,t), Rp(2,t), Rp(3,t),..., Rp(N,t)}; Rpe(2t)=max{Rp(1,2t), Rp(2,2t), Rp(3,2t),..., Rp(N,2t)}; ︙ Rpe(nt)=max{Rp(1,nt), Rp(2,nt), Rp(3,nt),..., Rp(N,nt)}.

[0018] S4: Calculate the total internal resistance of the new cells in the battery pack and the real-time total internal resistance of the cells at different times; the details are as follows: The real-time total internal resistance of the battery cell Ric=Roe+Rpe, that is, the real-time total internal resistance of the battery cell Ric can be expressed as follows: Ric(0)=Roe(0); Ric=Roe(t)+Rpe(t); Ric(2t)=Roe(2t)+Rpe(2t); ︙ Ric(nt)=Roe(nt)+Rpe(nt).

[0019] The total internal resistance of a new battery cell is Rin = Ron + Rpn; where Ron is the minimum ohmic internal resistance of a new battery cell when the ambient temperature is 25°C and the SOC is in the range of 20% to 100%. Rpn is the minimum polarization internal resistance of a new battery cell when charged at Im at an ambient temperature of 25°C. Im is the maximum current value allowed for continuous charging of the battery cell at 25°C.

[0020] S5: Calculate the dynamic optimal current of the battery pack. The dynamic optimal current Ip=(Rin / Ric)*Im, that is, the dynamic optimal current is as follows: Ip(0)=(Rin / Ric(0))*Im; Ip(t)=(Rin / Ric)*Im; Ip(2t)=(Rin / Ric(2t))*Im; ︙ Ip(nt)=(Rin / Ric(ntnt))*Im.

[0021] S6: Control the charging current in the next time period according to the dynamic optimal current; specifically, the currently estimated dynamic optimal current is used as the charging control current for the next time period; that is, after obtaining Ip(0s), the charging current in the first cycle (0-t time period) is controlled to be equal to Ip(0s); after obtaining Ip(t), the charging current in the first cycle (t-2t time period) is controlled to be equal to Ip(t), and so on.

[0022] A dynamic optimal current estimation system for low-temperature charging of a battery pack is applied to a current control method for low-temperature charging of a battery pack, comprising an acquisition module, an internal resistance estimation module, a characteristic value extraction module, and a calculation module; wherein: The acquisition module is used to collect the current charging status data of each battery cell in the battery pack; The input terminal of the internal resistance estimation module is connected to the output terminal signal of the acquisition module. Based on the collected charging state data, the internal resistance of each battery cell is estimated to obtain the ohmic internal resistance and polarization internal resistance data; The input end of the feature value extraction module is connected to the output end signal of the internal resistance estimation module, and is used to extract the maximum value Roe of the ohmic internal resistance between each battery cell in the battery pack, and the maximum value Rpe of the polarization internal resistance between each battery cell; The input end of the calculation module is connected to the output end signal of the eigenvalue extraction module, and is used to calculate the total internal resistance Rin of the new battery cell and the real-time total internal resistance Ric of the battery cell, as well as calculate the dynamic optimal current Ip based on the total internal resistance Rin of the new battery cell, the real-time total internal resistance Ric of the battery cell and the maximum current value Im allowed for continuous charging of the battery cell at 25°C, and output the dynamic optimal current Ip.

[0023] Furthermore, the calculation module includes a total internal resistance calculation module and an optimal current calculation module; wherein: The input end of the total internal resistance calculation module is connected to the output end signal of the characteristic value extraction module to calculate the total internal resistance Rin of the new battery cell and the real-time total internal resistance Ric of the battery cell; The input end of the optimal current calculation module is connected to the output end signal of the total internal resistance calculation module, and is used to calculate the dynamic optimal current Ip and output the dynamic optimal current Ip.

[0024] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention can obtain the dynamic optimal current of the current time period as the basis for controlling the charging current of the battery pack in the next time period. That is, the charging current of the battery pack in the next time period is equal to the currently estimated dynamic optimal current, so that the battery pack will not overheat or short-circuit under low-temperature charging conditions, avoiding thermal runaway, while taking into account charging efficiency and saving charging time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will now be described by way of example with reference to the accompanying drawings, in which: Figure 1 A flow chart of the method disclosed in the present invention; Figure 2 A schematic diagram of the system disclosed in the present invention; Figure 3 This is a comparison chart of the battery cell ohmic internal resistance estimation effect based on the long short-term memory neural network model; Figure 4 A comparison chart of the cell polarization internal resistance estimation effect based on the long short-term memory neural network model; Figure 5 This is the charging current curve of the battery pack under low temperature charging conditions.

[0026] Markings in the figure: 1-acquisition module; 2-internal resistance estimation module; 3-eigenvalue extraction module; 4-total internal resistance calculation module; 5-optimal current calculation module. DETAILED DESCRIPTION

[0027] In the description of this specification, it should also be noted that, unless otherwise clearly stipulated and limited, the terms "setting", "installation", "connection" and "connection" should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be a connection between the internal parts of two components.

[0028] The "low temperature" in this manual is determined in comparison with the suitable temperature for charging lithium batteries, that is, low temperature refers to a temperature lower than the suitable temperature for charging lithium batteries. For example, the suitable temperature for charging lithium batteries is usually 10℃-35℃, and low-temperature charging refers to charging in an environment below 10℃.

[0029] Example 1 like Figure 1 、 Figure 5 As shown, a battery pack consisting of 10 18650 lithium battery cells is charged at low temperature under the conditions of initial charge SOC = 10%, health state SOH = 86%, ambient temperature of -15°C, and charging time of 10 minutes. Current control is performed using a current control method for low temperature charging of a battery pack disclosed in this specification; the method comprises the following steps: S1: Obtain charging status data of each battery cell; the details are as follows.

[0030] The BMS is used to indirectly or directly obtain the real-time charging status data of the battery pack during the charging process, including the surface temperature Ts, terminal voltage U, ambient temperature Te, and charging current I of each battery cell. The data sampling period is t=30s (based on the battery charging characteristics, the battery charging status data will not change too much in a short period of time. This embodiment only takes t=30s as an example. According to actual conditions, the cycle time can be shortened to 10s or 5s, so that the charging status data collected at a certain moment can be used as the charging status data within the cycle).

[0031] S2: Estimate the ohmic internal resistance and polarization internal resistance of each battery cell based on the charge state data; the details are as follows.

[0032] First, the long short-term memory neural network model is trained in advance using test data under various low-temperature charging conditions; the training steps include step S201 and step S202.

[0033] S201. Based on experimental test data of battery cells under various low-temperature charging conditions, including six types of data: ambient temperature, surface temperature, terminal voltage, charging current, ohmic internal resistance, and polarization internal resistance of the battery cells, where ambient temperature, surface temperature, terminal voltage, and charging current are input data and ohmic internal resistance or polarization internal resistance is output data, the data are used to train a long short-term memory neural network estimation model; S202, optimize the parameters of the long short-term memory neural network model by particle swarm optimization algorithm. The estimation effect of the long short-term memory neural network model has been described in the invention content of this specification. Figure 3 、 Figure 4 shown.

[0034] Based on the charging status data collected in step S1, the ambient temperature, surface temperature, terminal voltage, and charging current are used as input data of the long short-term memory neural network estimation model to estimate the ohmic internal resistance or polarization internal resistance of each battery cell.

[0035] It should be noted that, since there is no polarization internal resistance in all battery cells at 0s (about to be charged), a small current square charging current pulse can be applied to the battery pack to measure the ohmic internal resistance of each battery cell at 0s. The ohmic internal resistance can be used to directly perform step S3 to obtain the ohmic internal resistance characteristic value, without the need to perform step S2 to estimate the ohmic internal resistance and polarization internal resistance.

[0036] S3: Extract the characteristic values ​​of the ohmic internal resistance and polarization internal resistance of the battery pack; the characteristic values ​​are the maximum value Roe of the ohmic internal resistance between each battery cell in the battery pack and the maximum value Rpe of the polarization internal resistance between each battery cell.

[0037] In this embodiment, the ohmic internal resistance and the characteristic value of the ohmic internal resistance of the battery cell are shown in Table 1.

[0038] Table 1: Ohmic internal resistance Ro and characteristic value Roe of battery cells (in mΩ)

[0039] In this embodiment, the polarization internal resistance and polarization internal resistance characteristic values ​​of the battery cell are shown in Table 2.

[0040] Table 2: Polarization internal resistance Rp and polarization internal resistance characteristic value Rpe of battery cells (unit: mΩ)

[0041] S4: Calculate the total internal resistance of the new cells and the current real-time total internal resistance of the cells in the battery pack; where the current real-time total internal resistance of the cells is Ric=Roe+Rpe; the total internal resistance of the new cells is Rin=Ron+Rpn; where Ron is the minimum ohmic internal resistance of the new cells when the ambient temperature is 25°C and the SOC is in the range of 20% to 100%, Rpn is the minimum polarization internal resistance of the new cells when charged at Im at an ambient temperature of 25°C, and Im is the maximum current value allowed for continuous charging of the cells at 25°C. By measuring another brand new battery cell of the same product and specifications, it is known that when the battery cell is brand new, the ambient temperature is 25°C, the SOC is in the range of 20% to 100%, the minimum ohmic internal resistance Ron of the battery cell is 56mΩ, the minimum polarization internal resistance Rpe of the battery cell is 3.8mΩ, and the maximum current value Im allowed for continuous charging at an ambient temperature of 25°C is 1.2A. The total internal resistance Rin of the brand new battery cell is 59.8mΩ.

[0042] S5: Calculate the dynamic optimal current of the battery pack based on the total internal resistance of the new battery cells and the real-time total internal resistance; the dynamic optimal current Ip=(Rin / Ric)*Im.

[0043] In this embodiment, the real-time total internal resistance and dynamic optimal current of the battery cell are shown in Table 3.

[0044] Table 3: Real-time total internal resistance of battery cells

[0045] S6: Control the charging current in the next time period according to the dynamic optimal current; Figure 5 As shown, the currently estimated dynamic optimal current is used as the charging control current for the next time period; that is, after obtaining Ip(0s)=0.316A, the charging current in the first cycle (0-30s time period) is controlled to be equal to 0.316A; after obtaining Ip(30s)=0.299A, the charging current in the second cycle (30s-60s time period) is controlled to be equal to 0.299A, and so on.

[0046] In this embodiment, after the battery pack is charged, the maximum temperature of the battery pack is measured to be -10.3°C, indicating no overheating or internal short circuit. Therefore, in the present invention, the current charging state data of each battery cell of the battery pack under low-temperature charging conditions is collected in real time, and then the ohmic internal resistance and polarization internal resistance of each battery cell are estimated based on the collected charging state data. The long short-term memory neural network is optimized using intelligent optimization algorithms such as particle swarm optimization to improve the accuracy of the estimation model, thereby extracting the characteristic values ​​of the current ohmic internal resistance and polarization internal resistance of each battery cell, and calculating the characteristic value of the total internal resistance of each battery cell during the current charging. The total internal resistance of the new battery cell under specific charging conditions is then calculated, and finally the dynamic optimal current of the target battery pack in the current time period is calculated, and the dynamic optimal current is used as the basis for controlling the charging current of the battery pack. Therefore, the present invention can prevent the battery pack from overheating or internal short circuiting under low-temperature charging conditions, avoid thermal runaway, and at the same time take into account charging efficiency and save charging time.

[0047] Example 2 like Figure 2 As shown, a dynamic optimal current estimation system for low-temperature charging of a battery pack is applied to the current control method for low-temperature charging of a battery pack described in Example 1, comprising an acquisition module 1, an internal resistance estimation module 2, a characteristic value extraction module 3, and a calculation module; wherein: Acquisition module 1 is used to collect the current charging status data of each battery cell in the battery pack. It can use a temperature sensor to collect ambient temperature and the surface temperature of the battery cell; it can use a voltage detection circuit similar to the BMS system to obtain the terminal voltage of each battery cell; and it can use a current sensor such as a shunt to collect the charging current. Of course, acquisition module 1 can also be directly connected to the battery pack's BMS system to indirectly obtain the surface temperature, terminal voltage, charging current, and ambient temperature of each battery cell through the BMS system.

[0048] The input end of the internal resistance estimation module 2 is connected to the output end signal of the acquisition module 1. Based on the collected charging state data, the internal resistance of each battery cell is estimated to obtain the ohmic internal resistance and polarization internal resistance data; The input end of the feature value extraction module 3 is connected to the output end signal of the internal resistance estimation module 2, and is used to extract the maximum value Roe of the ohmic internal resistance between each battery cell in the battery pack, and the maximum value Rpe of the polarization internal resistance between each battery cell; The input end of the calculation module is connected to the output end signal of the characteristic value extraction module 3, and is used to calculate the total internal resistance Rin of the new battery cell and the real-time total internal resistance Ric of the battery cell, and calculate the dynamic optimal current Ip based on the total internal resistance Rin of the new battery cell, the real-time total internal resistance Ric of the battery cell and the maximum current value Im allowed for continuous charging of the battery cell at 25°C, and output the dynamic optimal current Ip.

[0049] Furthermore, the calculation module includes a total internal resistance calculation module 4 and an optimal current calculation module 5; wherein: The input end of the total internal resistance calculation module 4 is connected to the output end signal of the characteristic value extraction module 3, and is used to calculate the total internal resistance Rin of the new battery cell and the real-time total internal resistance Ric of the battery cell; The input end of the optimal current calculation module 5 is signal-connected to the output end of the total internal resistance calculation module 4 , and is used to calculate the dynamic optimal current Ip and output the dynamic optimal current Ip.

[0050] The present invention is not limited to the aforementioned specific embodiments, but extends to any new features or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A current control method for low-temperature charging of a battery pack, characterized by: The following steps are involved: S1: Obtain the charging status data of each battery cell in the current time period; S2: Estimate the ohmic internal resistance and polarization internal resistance of each battery cell based on the charge state data; S3: Extract the characteristic values ​​of the ohmic internal resistance and polarization internal resistance of the battery pack; S4: Calculate the new total internal resistance of the battery cells in the battery pack and the current real-time total internal resistance of the battery cells; S5: Calculate the dynamic optimal current of the battery pack based on the total internal resistance of the new battery cells and the real-time total internal resistance; S6: Control the charging current in the next time period according to the dynamic optimal current.

2. The current control method according to claim 1, wherein: In step S1 , the charging state data includes the ambient temperature, the surface temperature, the terminal voltage and the charging current.

3. The current control method according to claim 1, wherein: In step S2, the charge state data of each battery cell is used as input, and the ohmic internal resistance and polarization internal resistance of each battery cell are estimated using a long short-term memory neural network model. The steps for constructing the long short-term memory neural network model are as follows: S201. Based on experimental test data of battery cells under various low-temperature charging conditions, including six types of data: ambient temperature, surface temperature, terminal voltage, charging current, ohmic internal resistance, and polarization internal resistance of the battery cells, where ambient temperature, surface temperature, terminal voltage, and charging current are input data and ohmic internal resistance or polarization internal resistance is output data, the data are used to train a long short-term memory neural network estimation model; S202: Optimizing the parameters of the long short-term memory neural network model by using a particle swarm optimization algorithm.

4. The current control method according to claim 1, wherein: In step S3 , the characteristic values ​​are the maximum value Roe of the ohmic internal resistance between the battery cells in the battery pack and the maximum value Rpe of the polarization internal resistance between the battery cells.

5. The current control method according to claim 4, wherein: In step S4, the current real-time total internal resistance of the battery cell is Ric=Roe+Rpe.

6. The current control method according to claim 5, wherein: In step S4, the total internal resistance of the new battery cell Rin=Ron+Rpn; wherein Ron is the minimum ohmic internal resistance of the new battery cell when the ambient temperature is 25°C and the SOC is in the range of 20% to 100%, Rpn is the minimum polarization internal resistance of the new battery cell when charged at Im at an ambient temperature of 25°C, and Im is the maximum current value allowed for continuous charging of the battery cell at 25°C.

7. The current control method according to claim 6, wherein: In step S5 , the dynamic optimal current Ip=(Rin / Ric)*Im.

8. The current control method according to claim 1, wherein: In step S6 , the currently estimated dynamic optimal current is used as the charging control current for the next time period.

9. A dynamic optimal current estimation system for low-temperature charging of a battery pack, applied to a current control method for low-temperature charging of a battery pack according to any one of claims 1 to 8, characterized in that: It includes an acquisition module (1), an internal resistance estimation module (2), a characteristic value extraction module (3) and a calculation module; wherein: The acquisition module (1) is used to collect the current charging state data of each battery cell of the battery pack; The input end of the internal resistance estimation module (2) is connected to the output end signal of the acquisition module (1), and the internal resistance of each battery cell is estimated based on the collected charging state data to obtain ohmic internal resistance and polarization internal resistance data; The input end of the characteristic value extraction module (3) is connected to the output end signal of the internal resistance estimation module (2) to extract the maximum value Roe of the ohmic internal resistance between each battery cell in the battery pack and the maximum value Rpe of the polarization internal resistance between each battery cell; The input end of the calculation module is connected to the output end signal of the characteristic value extraction module (3), and is used to calculate the total internal resistance Rin of the new battery cell and the real-time total internal resistance Ric of the battery cell, and calculate the dynamic optimal current Ip based on the total internal resistance Rin of the new battery cell, the real-time total internal resistance Ric of the battery cell and the maximum current value Im allowed for continuous charging of the battery cell at 25° C., and output the dynamic optimal current Ip.

10. The dynamic optimal current estimation system according to claim 9, characterized in that: The calculation module includes a total internal resistance calculation module (4) and an optimal current calculation module (5); wherein: The input end of the total internal resistance calculation module (4) is connected to the output end signal of the characteristic value extraction module (3) for calculating the total internal resistance Rin of a new battery cell and the real-time total internal resistance Ric of the battery cell; The input end of the optimal current calculation module (5) is signal-connected to the output end of the total internal resistance calculation module (4) for calculating the dynamic optimal current Ip and outputting the dynamic optimal current Ip.

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