A balancing control strategy for series battery packs considering aging

Abstract

The present invention discloses a balancing control strategy for a series battery module considering aging, including two parts: distinguishing the battery pack power state and balancing optimization control; the method is to improve the power inconsistency of the series battery pack and increase the available capacity of the battery pack. The capacity, SOC and internal resistance of the battery pack monomer are obtained through battery characteristic experiments, the average power of the battery pack and the minimum capacity of the monomer are compared, and the power state of the battery pack is distinguished as the first type of power state and the second type of power state; different balancing targets and different optimization targets are proposed for battery modules with two different power states, and the targets are optimized using genetic algorithms, so as to achieve the purpose of increasing the available capacity of the battery module.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a balancing control strategy for a series battery pack taking aging into consideration. Background Art

[0002] In order to meet the high power and high voltage requirements of most application scenarios, battery cells are often connected in series to form battery packs. Due to differences in processing technology, even batteries produced by the same manufacturer will have inconsistent battery parameters such as capacity, internal resistance, coulomb efficiency, and self-discharge rate. In addition, the location and environment of each battery after being grouped are different. As the number of times the battery pack is used increases, the inconsistency between batteries will increase, resulting in battery pack capacity loss and battery pack performance degradation, reducing the service life of the battery pack. In order to solve the problem of reduced available capacity and shortened service life of battery packs due to inconsistent capacity and SOC between batteries in series, a balancing control strategy is formulated. The available capacity of the battery pack can be increased and the service life can be extended by balancing the circuit board.

[0003] At present, the common equalization control strategy is mainly based on voltage consistency. When the voltage inconsistency of the batteries in the battery pack reaches the set threshold, the equalization is started, and when the voltage of the batteries in the battery pack becomes consistent, the equalization is turned off. However, since the terminal voltage of the battery has a certain hysteresis characteristic during the charging and discharging process, the voltage may not be consistent after the equalization is turned off. Moreover, the voltage cannot reflect the battery's power state very well, and does not play a role in improving the inconsistency between battery packs. Summary of the invention

[0004] The purpose of the present invention is to provide a series battery pack balancing control strategy taking aging into consideration, so as to solve the problem of battery pack capacity degradation and performance degradation caused by inconsistent capacity of battery pack monomers due to aging and inconsistent SOC resulting in inconsistent battery pack power.

[0005] The technical solution to achieve the purpose of the present invention is: a series battery pack balancing control strategy considering aging, comprising the following steps:

[0006] Step 1: obtain the capacity of each battery in the battery pack through a capacity test experiment, obtain the OCV-SOC curve and internal resistance through an HPPC test experiment, and obtain the SOC of each battery by searching the OCV-SOC curve;

[0007] Step 2: Analyze the current power state of the battery pack using the obtained battery state information; use a loss leveling method for the battery packs in the first power state, and use a predictive power leveling method for the battery packs in the second power state;

[0008] Step 3: Write the equalization power transfer equation and the equalization process loss calculation formula for the battery pack in the first power state; for the battery pack in the second power state, determine the equalization time, and write the temperature rise equation and transfer power equation in the equalization process;

[0009] Step 4: Using genetic algorithms and combining balancing constraints, the battery packs in the first power state are optimized with the goal of maintaining consistent power after balancing and minimizing loss during balancing, and the balancing power and balancing time are determined; for the battery packs in the second power state, the balancing power is optimized with the goal of minimizing temperature rise and battery pack power difference during each balancing process, and the balancing power during each balancing process is determined.

[0010] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned equalization control strategy for the series battery group considering aging are implemented.

[0011] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned equalization control strategy for a series battery pack considering aging.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention provides a balancing control strategy for a series battery pack that takes aging into consideration, and can select different balancing methods according to the current power status of the aged battery pack, thereby ensuring the safety of batteries that are severely aged during the balancing process. The strategy is simple, practical, and has universal applicability. (2) The balancing control strategy proposed in the present invention can achieve the simultaneous discharge of the single cells in the battery pack, maximize the use of the available capacity of the battery pack, and extend the service life of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of the balanced control strategy of the present invention.

[0014] Figure 2 This is a diagram of the battery capacity test experiment.

[0015] Figure 3 HPPC test voltage diagram for batteries.

[0016] Figure 4 This is the OCV-SOC diagram of the identified battery.

[0017] Figure 5 This is the battery internal resistance-SOC diagram obtained by identification. DETAILED DESCRIPTION

[0018] like Figure 1 As shown, a series battery pack balancing control strategy considering aging includes the following steps:

[0019] Step 1: Obtain the capacity, SOC and internal resistance of each battery in the battery pack through characteristic test experiments;

[0020] Step 2: Analyze the current power state of the battery pack using the obtained battery status information. Use the loss leveling method for the battery packs in the first power state, and use the predictive power leveling method for the battery packs in the second power state;

[0021] Step 3: Write the equalization power transfer equation and the loss calculation formula for the equalization process for the battery pack in the first power state. For the battery pack in the second power state, determine the equalization time, and write the temperature rise equation and transfer power equation during the equalization process.

[0022] Step 4: Using genetic algorithms and combined with balancing constraints, the battery packs in the first power state are optimized with the goal of maintaining consistent power after balancing and minimizing loss during balancing, and the balancing power and balancing time are determined; for the battery packs in the second power state, the balancing power is optimized with the goal of minimizing temperature rise and battery pack power difference during each balancing process, and the balancing power during each balancing process is determined.

[0023] Before performing balanced optimization, the characteristics of the cells in the battery pack are first tested to obtain the capacity, SOC, and internal resistance of each cell. Then, the state of the battery pack at this time is divided into the first type of power state and the second type of power state according to the proposed classification formula. In the third step, different optimization goals and calculation formulas for each optimization goal are proposed for the two different power states. Finally, a genetic algorithm is used for balanced optimization to obtain the corresponding balanced transfer power.

[0024] In this embodiment, by obtaining the capacity, SOC and internal resistance of the series battery pack cells, the battery pack's charge state is divided into a first charge state and a second charge state according to the proposed classification formula, and the calculation formulas for the optimization targets under different states are listed. By combining different optimization targets and genetic algorithms, a balancing control strategy that meets the balancing optimization target is implemented by optimizing the balancing charge.

[0025] Furthermore, in step 1 of this embodiment, the capacity, SOC and internal resistance of the battery pack monomer can be obtained according to the following steps. The capacity test is specifically as follows:

[0026] Step 1, at room temperature, each cell in the battery pack is first charged at a constant current of 0.5C to the upper cut-off voltage of the battery, and then switched to a constant upper cut-off voltage to charge until the charging current is less than 0.02C, and then charging is stopped;

[0027] Step 2, let stand for 2 hours;

[0028] Step 3, discharge at a constant current of 0.5C to the lower cut-off voltage of the battery;

[0029] Step 4, using the discharge amount of each battery at this time as the capacity of each battery;

[0030] The HPPC test is as follows:

[0031] Step 5, at room temperature, each cell in the battery pack is first charged at a constant current of 0.5C to the upper cut-off voltage of the battery, and then switched to charging at a constant upper cut-off voltage until the charging current is less than 0.02C, and then charging is stopped;

[0032] Step 6, let stand for 2 hours;

[0033] Step 7, 1C constant current discharge for 10s, let stand for 40s, 0.75C constant current charge for 10s, let stand for 40s;

[0034] Step 8, discharge the battery at a constant current of 0.5C for 12 minutes, that is, discharge 10% of the power;

[0035] Step 9, let stand for 2 hours;

[0036] Step 10, repeating steps 7 to 9 until the battery is discharged to the lower cut-off voltage;

[0037] Step 11, using parameter identification, the OCV-SOC curve and ohmic internal resistance of each battery can be obtained;

[0038] Step 12, the initial SOC of each battery can be obtained by searching the OCV-SOC curve;

[0039] Furthermore, in step 2 of this embodiment, the power status of the battery pack can be distinguished according to the following steps, specifically:

[0040] Using the capacity and SOC of each battery cell in the current state obtained in step 1, the average power of the current battery pack can be calculated. At this time, the method of distinguishing the first and second power states is to compare the average power of the battery pack and the capacity of the smallest battery cell in the battery pack. When the average power of the battery pack is less than or equal to the minimum capacity, the battery pack at this time is classified as the first power state; if it is greater, it is classified as the second power state.

[0041] The specific formula is expressed as:

[0042]

[0043] Where n is the number of batteries in the battery pack, C is the battery cell capacity, SOC is the battery state of charge, i and j represent a certain battery, minC j Indicates the minimum capacity of a battery cell.

[0044] The reason for using "predictive power balancing" for battery packs in the second power state is that when the average power of the battery pack is greater than the minimum capacity of the battery pack single cell, direct balancing operation at this time cannot balance the power of the battery pack, and will cause overcharge or over-discharge of a battery in the battery pack. Therefore, it is necessary to discharge the battery pack first. When the average power of the battery pack is less than the minimum capacity of the battery pack single cell, balancing operation can balance the power of the battery pack and ensure that the battery power is in the normal working range.

[0045] The specific method is to first discharge the battery pack. When the average power of the battery pack is less than the capacity of the smallest battery cell in the battery pack, start balancing. The specific formula is as follows:

[0046]

[0047] Among them, i dis is the discharge current, t dis is the discharge time.

[0048] Furthermore, in step 3 of this embodiment, the loss and transfer power of the balancing process, the temperature rise and power difference of the balancing process can be calculated according to the following formula, which is specifically:

[0049] The energy transfer equation of the battery pack balancing process in the first type of power state is as follows:

[0050]

[0051] Among them, ΔQ i_cell Represents the amount of electricity transferred from the battery pack to the i-th battery cell, ΔQ i_pack Represents the amount of electricity transferred from the i-th battery to each battery in the battery pack, ΔQ i_bal Represents the amount of power required to transfer when balancing the i-th battery.

[0052] The loss calculation formula of the battery pack balancing process in the first type of power state is as follows:

[0053]

[0054]

[0055] Among them, Q j_loss represents the power loss during the balancing of the jth battery, Q loss_all It represents the total power loss in the whole balancing process, η1 represents the efficiency of power transfer from battery cells to battery packs, and η2 represents the efficiency of power transfer from battery packs to battery cells.

[0056] The calculation method for the temperature rise of the battery pack in the second power state is as follows:

[0057] Using the Rint model, the terminal voltage equation of the lithium-ion battery is:

[0058] U t =OCV+IR

[0059] Among them U t is the battery terminal voltage, OCV is the battery open circuit voltage, I is the balancing current, and R is the battery ohmic internal resistance obtained in step 1.

[0060] The temperature rise in the kth equilibrium stage is:

[0061]

[0062] Where m is the battery mass, C is the battery heat capacity, T is the battery surface temperature, E is the battery open circuit voltage, h is the heat transfer coefficient, S is the battery surface area, T a is the ambient temperature;

[0063] Total temperature rise:

[0064]

[0065] Where T1 is the temperature rise of the first equilibrium, T k is the kth equilibrium temperature rise.

[0066] The calculation method of the battery pack charge difference in the second state of charge balancing process is as follows:

[0067] ΔQ=max(C i ×SOC i )-min(C i ×SOC i )

[0068] Among them C i and SOC i They are the capacity and state of charge of the battery respectively, and i represents a certain battery.

[0069] Furthermore, in step 4 of this embodiment, the balancing current of the battery packs in different power states can be optimized according to the following steps to achieve balancing optimization control of the aging battery packs, specifically:

[0070] Step 1: Determine the optimization objective function, fitness function and optimization conditions. For the objective function of the battery pack balancing optimization in the first type of power state:

[0071]

[0072] Among them, Q i is the power at the end of balancing the cells, is the average power of the battery cells in the battery pack, Q loss_all Indicates the total power loss during the entire balancing process.

[0073] The objective function for optimizing the balance of the battery pack in the second state of charge is:

[0074] J(x)=max(C i ×SOC i )-min(C i ×SOC i )+minT

[0075] where T is the equilibrium total temperature rise.

[0076] Fitness function expression:

[0077]

[0078] Where β is the reward factor and p(x) is the reward function.

[0079] The constraints of the optimization algorithm are as follows:

[0080] 1) Balanced temperature rise and balanced power balance: When the battery temperature is lower than the threshold, a current greater than the set threshold is used for balancing; when the current temperature is higher than or equal to the threshold, the current balancing current is reduced.

[0081] 2) Balancing voltage and current constraints: During the balancing process of each battery, the voltage and current should be kept within the maximum upper and lower limits allowed by the battery.

[0082] 3) Power constraint: The power should be kept within the set range during battery balancing.

[0083] 4) Battery temperature constraint: The battery temperature itself is not higher than the allowable temperature during the battery balancing process.

[0084] Step 2: Create an initial population. Use real number coding to encode the SOC and balanced power;

[0085] x=(SOC,Q)

[0086] Step 3, use the fitness function to calculate the fitness of each individual in the population;

[0087] Step 4: Design a selection function. Select individuals with high fitness to reproduce the next generation of population; the selection function is as follows:

[0088]

[0089] Step 5, selection of genetic operator. The crossover rate expression is as follows:

[0090]

[0091] where f max is the maximum fitness in the entire population, f avg is the average fitness of the entire population, f c ' is the fitness of the individual to be crossed.

[0092] The mutation rate expression is as follows:

[0093]

[0094] where f max is the maximum fitness in the entire population, f avg is the average fitness of the entire population, f m ' is the fitness of the individual to be mutated.

[0095] Step 6: Repeat steps 3 to 5. The genetic algorithm continuously optimizes the balanced electricity and finds a set of solutions that maximize the fitness function value under the current number of iterations until the following convergence conditions are met at the same time, completing the balanced optimization control:

[0096] (a)min J(x i ) <m

[0097] Among them, J(x i ) is the minimum value of the objective function after the i-th iteration, and m is a constant

[0098] (b) After the i-th iteration, the standard deviation of the fitness function F is not less than n, where n is a constant.

[0099] The present invention is simulated and explained below by taking a ternary lithium battery as an example.

[0100] Simulation Example

[0101] Three battery cells with a rated capacity of 2.5Ah were selected, and a standard cycle charge and discharge was first performed according to the manual provided by the manufacturer. The specific process is as follows.

[0102] Step 1: At room temperature, each cell in the battery pack is first charged at a constant current of 0.5C to the upper cut-off voltage of the battery, and then switched to a constant upper cut-off voltage to charge until the charging current is less than 0.02C, and then charging is stopped;

[0103] Step 2: Let stand for 2 hours;

[0104] Step 3: Discharge at a constant current of 0.5C to the lower cut-off voltage of the battery;

[0105] The discharge amount of each battery at this time is taken as the capacity of each battery. The relationship between the capacity and time during the discharge process is shown in the figure below. Figure 2 shown.

[0106] Then, the three batteries were subjected to HPPC test respectively. The HPPC test voltage diagram is as follows: Figure 3 The specific process is as follows.

[0107] Step 1: At room temperature, each cell in the battery pack is first charged at a constant current of 0.5C to the upper cut-off voltage of the battery, and then switched to a constant upper cut-off voltage to charge until the charging current is less than 0.02C, and then charging is stopped;

[0108] Step 2: Let stand for 2 hours;

[0109] Step 3: 1C constant current discharge for 10s, let stand for 40s, 0.75C constant current charge for 10s, let stand for 40s;

[0110] Step 4: Discharge the battery at a constant current of 0.5C for 12 minutes, that is, discharge 10% of the power;

[0111] Step 5: Let stand for 2 hours;

[0112] Step 6: Repeat steps 1-7 to 1-9 until the battery is discharged to the lower cut-off voltage;

[0113] The OCV-SOC curve and ohmic internal resistance curve of each battery can be obtained by parameter identification, which are Figure 4 and Figure 5 .

[0114] It can be obtained that the capacities of the three batteries are 2.35Ah, 2.48Ah and 2.28Ah, the initial SOCs are 61.7%, 67.7% and 68.4%, and the internal resistances are 35.6mΩ, 36.5mΩ and 35.2mΩ.

[0115] First, compare the average battery capacity and the minimum capacity of each battery cell.

[0116]

[0117] The calculation results show that the average power of the battery pack is less than the minimum capacity of the battery pack, indicating that the battery pack is in the first power state. The "loss leveling" method is used for balanced optimization control. Figure 1 The algorithm flow chart is given for balanced optimization. First, the genetic algorithm population is initialized, the balanced power and SOC are encoded into chromosomes using real number coding, and the fitness function and objective function are set.

[0118] The power transfer equation and loss calculation formula are used to calculate the power after balancing and the balance loss respectively. The results are input into the fitness function to obtain the fitness value.

[0119] By judging the convergence conditions, if the current optimization result meets the optimal balanced transfer power, otherwise the balanced power is updated through genetic algorithm iteration, and the balanced power and balanced loss are recalculated.

[0120] The obtained equalization result with less loss is shown in Table 1.

[0121] Table 1

[0122]

[0123] It can be seen that the power of each battery after balancing is 1.555Ah, and the balancing loss of this balancing is 0.024Ah. It can be seen from the balancing results that the third battery has no balancing operation, and the power of each cell in the battery pack can be balanced only by balancing the first two batteries, so the loss is also low. The battery pack capacity before balancing is 1.453Ah, and the battery pack capacity after balancing is 1.555Ah. Therefore, the proposed balancing method can increase the battery pack capacity and extend the battery pack service life.

Claims

1. A balancing control strategy for a series battery pack considering aging, characterized in that: The steps include: Step 1: obtain the capacity of each battery in the battery pack through a capacity test experiment, obtain the OCV-SOC curve and internal resistance through an HPPC test experiment, and obtain the SOC of each battery by searching the OCV-SOC curve; Step 2: Analyze the current power state of the battery pack using the obtained battery state information; use a loss leveling method for the battery packs in the first power state, and use a predictive power leveling method for the battery packs in the second power state; specifically: According to the capacity and SOC of each cell in the current state of the battery pack, the average capacity of the current battery pack is obtained; at this time, the method of distinguishing the first type of capacity state and the second type of capacity state is to compare the average capacity of the battery pack and the capacity of the smallest capacity battery cell in the battery pack; when the average capacity of the battery pack is less than or equal to the minimum capacity, the battery pack at this time is classified as the first type of capacity state; if it is greater, it is classified as the second type of capacity state; The specific formula is expressed as: Where n is the number of batteries in the battery pack, C is the battery cell capacity, SOC is the battery state of charge, i and j represent a certain battery, minC j Indicates the minimum capacity of a battery pack; For the battery packs in the second type of power state, the predictive power balancing method is used. First, the battery pack is discharged. When the average power of the battery pack is less than the capacity of the smallest battery cell in the battery pack, balancing begins. The specific formula is as follows: Among them, i dis is the discharge current, t dis is the discharge time; Step 3: Write the equalization power transfer equation and the equalization process loss calculation formula for the battery pack in the first power state; for the battery pack in the second power state, determine the equalization time, write the temperature rise equation and transfer power equation in the equalization process; the details are as follows: The balanced power transfer equation of the battery pack in the first power state is as follows: Among them, ΔQ i_cell Represents the amount of electricity transferred from the battery pack to the i-th battery cell, ΔQ i_pack Represents the amount of electricity transferred from the i-th battery to each battery in the battery pack, ΔQ i_bal Represents the amount of power required to transfer when balancing the i-th battery; The loss calculation formula of the battery pack balancing process in the first type of power state is as follows: Among them, Q j_loss represents the power loss during the balancing of the jth battery, Q loss_all It represents the total power loss in the whole balancing process, η1 represents the efficiency of power transfer from battery cells to battery packs, and η2 represents the efficiency of power transfer from battery packs to battery cells; The calculation method for the temperature rise of the battery pack in the second power state is as follows: Using the Rint model, the terminal voltage equation of the lithium-ion battery is: U t =E+IR (5) Among them U t is the battery terminal voltage, E is the battery open circuit voltage, I is the balancing current, and R is the battery ohmic internal resistance obtained in step 1; The temperature rise in the kth equilibrium stage is: Where m is the battery mass, C is the battery heat capacity, T is the battery surface temperature, and h is the heat transfer coefficient. S is the battery surface area, T a is the ambient temperature; Total temperature rise: Where T1 is the temperature rise of the first equilibrium, ΔT k is the kth equilibrium temperature rise; The transfer power equation of the battery pack balancing process in the second type of power state is: ΔQ=max(C i ×SOC i )-min(C i ×SOC i ) (8) Among them C i and SOC i They are the capacity and state of charge of the battery respectively, and i represents a certain battery; Step 4: Using genetic algorithm and combining the balancing restriction factors, the battery pack in the first power state is optimized with the goal of keeping the power consistent after balancing and minimizing the loss during the balancing process, and the balancing power and balancing time are determined; the battery pack in the second power state is optimized with the goal of minimizing the temperature rise and the battery pack power difference during each balancing process, and the balancing power during each balancing process is determined; the details are as follows: The objective function for optimizing the balance of the battery pack in the first state of charge is: Among them, Q i is the power at the end of balancing the cells, is the average power of the battery cells in the battery pack, Q loss_all Indicates the total power loss during the entire balancing process; The objective function for optimizing the balance of the battery pack in the second state of charge is: J(x)=max(C i ×SOC i )-min(C i ×SOC i )+minT (10) Where T is the equilibrium total temperature rise; Fitness function expression: Where β is the reward factor, p(x) is the reward function; The specific steps of using genetic algorithm are as follows: 1) Create an initial population; encode the SOC and balanced power using real number encoding; x=(SOC,Q) (12) 2) Use the fitness function to calculate the fitness of each individual in the population; 3) Design a selection function; select individuals with high fitness to reproduce the next generation of population; the selection function is as follows: 4) Selection of genetic operators; the crossover rate expression is as follows: where f max is the maximum fitness in the entire population, f avg is the average fitness of the entire population, f c ' is the fitness of the individual to be crossed; The mutation rate expression is as follows: where f max is the maximum fitness in the entire population, f avg is the average fitness of the entire population, f m ' is the fitness of the individual to be mutated; 5) Repeat steps 2) to 4), the genetic algorithm continuously optimizes the balanced power and finds a set of solutions that maximize the fitness function value under the current number of iterations until the following convergence conditions are met at the same time, completing the balanced optimization control: (a)minJ(x i ) <m Among them, J(x i ) is the minimum value of the objective function after the i-th iteration, and m is a constant; (b) After the i-th iteration, the standard deviation of the fitness function F is not less than n, where n is a constant.

2. The balancing control strategy for series-connected battery packs considering aging according to claim 1, characterized in that: The capacity, SOC and internal resistance of each battery are different. The capacity test in step 1 is as follows: Step 1-1, at room temperature, each cell in the battery pack is first charged at a constant current of 0.5C to the upper cut-off voltage of the battery, and then switched to a constant upper cut-off voltage to charge until the charging current is less than 0.02C, and then charging is stopped; Step 1-2, let stand for 2 hours; Step 1-3, discharge at a constant current of 0.5C to the lower cut-off voltage of the battery; Step 1-4, the discharge amount of each battery at this time is used as the capacity of each battery; The HPPC test is as follows: Step 1-5, at room temperature, each cell in the battery pack is first charged at a constant current of 0.5C to the upper cut-off voltage of the battery, and then switched to a constant upper cut-off voltage to charge until the charging current is less than 0.02C, and then charging is stopped; Steps 1-6, let stand for 2 hours; Steps 1-7: 1C constant current discharge for 10s, rest for 40s, 0.75C constant current charge for 10s, rest for 40s; Step 1-8, discharge the battery at a constant current of 0.5C for 12 minutes, that is, discharge 10% of the power; Steps 1-9, let stand for 2 hours; Step 1-10, repeat steps 1-7 to 1-9 until the battery is discharged to the lower cut-off voltage; Step 1-11, using a parameter identification program to obtain the OCV-SOC curve and ohmic internal resistance of each battery; Step 1-12, obtain the initial SOC of each battery by looking up the OCV-SOC curve.

3. The balancing control strategy for series-connected battery packs considering aging according to claim 1, characterized in that: The balancing time of the battery pack in the second state of charge is fixed each time.

4. The balancing control strategy for series-connected battery packs considering aging according to claim 1, characterized in that: The constraints of the optimization algorithm are as follows: 1) Balanced temperature rise and balanced power balance: When the battery temperature is lower than the temperature threshold, a current greater than the set current threshold is used for balancing; when the battery temperature is greater than or equal to the temperature threshold, the current balancing current is reduced; 2) Balancing voltage and current constraints: During the balancing process of each battery, the voltage and current should be kept within the maximum upper and lower limits allowed by the battery; 3) Power constraint: The power should be kept within the set range during battery balancing; 4) Battery temperature constraint: The battery temperature itself is not higher than the allowable temperature during the battery balancing process.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the processor implements the steps of the balancing control strategy for the series battery group considering aging as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the balancing control strategy for the series battery group considering aging as described in any one of claims 1 to 4 are implemented.

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