An Optimization Method for Parking and Charging Strategies of Range-Extended Electric Vehicles Based on MOMSA Algorithm

Abstract

This invention relates to the field of automotive energy management technology, specifically disclosing a method for optimizing the parking and charging strategy of range-extended electric vehicles based on the MOMSA algorithm. The method includes establishing a power equivalent circuit model, constructing a battery aging model, and a multi-objective optimization function. The optimization method comprises the following steps: Step 1: Initialize the population; Step 2: Simulate the charging process; Step 3: Calculate the fitness of the population and determine non-dominated solutions; Step 4: Select a position vector based on the congestion distance CD; Step 5: Calculate the fitness value of the updated mantis position; Step 6: Determine new non-dominated solutions in the population and eliminate all dominated solutions; Step 7: Calculate the CD of each Pareto archive member; Step 8: Check if the iteration requirements are met; if not, return to Step 2; Step 9: Select the most suitable charging current. This invention's charging strategy optimization method enables a smoother battery charging process, improving battery life and charging efficiency.

Description

Technical Field

[0001] This invention relates to the field of automotive energy management technology, and in particular to an optimization method for parking and charging strategies of range-extended hybrid electric vehicles based on the MOMSA algorithm. Background Technology

[0002] New energy vehicles, especially extended-range hybrid electric vehicles (E-RHEVs), are gradually becoming an important direction for the future development of the automotive industry due to their combined fuel economy and environmental advantages. E-RHEVs combine the advantages of traditional gasoline vehicles and electric vehicles, extending the vehicle's driving range by generating electricity from the engine to charge the battery. However, as a crucial component of E-RHEVs, the charging strategy of the battery directly affects the vehicle's performance, energy efficiency, and battery lifespan. With the increasing popularity of electric vehicles, charging technology, as a key technology to ensure the normal and continuous operation of the power battery, is becoming increasingly important. Currently, the long charging process of electric vehicles reduces their practicality and consumer acceptance. Excessively high charging current during charging can also cause a rapid rise in battery temperature, threatening battery safety and significantly shortening battery life. Energy loss occurs during battery charging and discharging; inappropriate charging strategies can significantly reduce charging efficiency and lead to more energy waste. Battery charging strategies have a significant impact on battery performance, charging safety, and efficiency, making battery charging control a research hotspot in the field of electric vehicle power batteries.

[0003] Most existing range-extended hybrid electric vehicle charging strategies employ traditional constant current-constant voltage (CC-CV) charging methods, constant current charging, and constant voltage charging. While these methods have advantages such as simple design, low cost, and ease of implementation, they also present several problems and drawbacks in practical applications.

[0004] The most commonly used constant current and constant voltage charging strategy has a low initial current, which fails to reach the maximum charging current that the lithium battery can withstand, resulting in longer charging time and increasing the risk of lithium side reactions, which can affect battery life. In addition, maintaining a high current for a long time during the constant current phase may cause the battery to overheat, increasing safety hazards; while in the constant voltage phase, the current gradually decreases to a lower value, and the charging speed slows down significantly, resulting in an overall longer charging time and affecting user experience.

[0005] For constant current charging strategies, the initial low current results in a longer charging time; the high current in the middle stage leads to high energy consumption and low charging efficiency, and overcharging is prone to occur in the later stage, affecting battery life. During constant current charging, excessively high charging current may accelerate battery aging, causing capacity decay and performance degradation, thereby shortening battery life.

[0006] Constant voltage charging strategies are characterized by long charging times and high initial current. This can easily affect battery lifespan. Rapid battery degradation not only increases maintenance costs for electric vehicle users but also seriously impacts the long-term reliability of the vehicle.

[0007] Furthermore, existing charging strategies lack dynamic optimization and fail to fully consider the impact of external conditions such as battery health and ambient temperature. This results in a lack of flexibility and intelligence in the charging process, making it impossible to achieve optimal charging efficiency and battery protection under various operating conditions. Therefore, optimizing charging strategies, balancing charging speed and battery life, and improving the intelligence level of the charging process are urgent problems to be solved. Summary of the Invention

[0008] The present invention aims to solve at least one of the technical problems mentioned above, and provides a parking charging strategy optimization method for range-extended hybrid electric vehicles based on the MOMSA algorithm, which can make the battery charging process more stable and improve battery life and charging efficiency.

[0009] To achieve the above objectives, the technical solution adopted by this invention is: a parking and charging strategy optimization method for range-extended hybrid electric vehicles based on the MOMSA algorithm, comprising:

[0010] For the power battery of a range-extended hybrid electric vehicle, an internal resistance R-int model is designed. This model includes a resistor and a voltage source. Using Kirchhoff's laws and current law, the power consumption P of the power battery pack is established. bat Open circuit voltage U oc and internal resistance R int Equivalent circuit model:

[0011]

[0012] In the formula, I bat Q is the battery current. bat Q represents the nominal capacity of the battery. loss ΔSOC represents the total capacity loss caused by battery aging, and ΔSOC represents the instantaneous rate of change of the SOC of the power battery.

[0013] Constructing a battery aging model:

[0014]

[0015] In the formula, α and β are constant terms; E a The activation energy of the battery; C rate The battery charge / discharge rate is η; the compensation coefficient is Z; the power law factor is R. gas T is the gas constant; K Ambient temperature; Ah is the throughput in ampere-hours;

[0016] A multi-objective function is established. The optimal Pareto solution set for the charging current is obtained through the MOMSA algorithm. After cluster analysis of the solution set using the K-means clustering algorithm, the most suitable charging current is selected. A multi-objective optimization function is established with charging time and battery life as the objectives.

[0017] Min J={T tot (T K ,I batt ),Q loss (T K ,I batt )}

[0018] In the formula, T tot Q represents the charging time during the charging process. loss This results in a loss of battery life.

[0019] The charging strategy optimization algorithm process includes the following steps:

[0020] Step 1: Initialize the population. Randomly generate an initial mantis population in the search space. Each individual represents a potential solution, which is distributed as evenly as possible throughout the space and stored in a matrix. At the same time, set upper and lower limits and the maximum iteration value.

[0021] Step 2: Simulate the charging process by inputting the initialized mantis into the equivalent circuit model and battery aging model to determine the voltage, SOC, and Q. loss Determine whether the SOC meets the preset SOC. ch Then switch to the next constant current charging stage, and then determine whether the condition of SOC = 90% is met. If it is met, switch to constant voltage charging, and stop charging when SOC = 92%.

[0022] Step 3: Calculate the fitness of each mantis and sort them to determine the non-dominated solution of the initial population and save it to the Pareto archive;

[0023] Step 4: Calculate the crowding distance CD for each Pareto archive particle, and select a position vector based on CD;

[0024] Step 5: Update the position of each mantis according to the position vector, and calculate the fitness value of the updated mantis position;

[0025] Step 6: Identify new non-dominated solutions in the population and save them to the Pareto archive, and remove all dominated solutions from the Pareto archive;

[0026] Step 7: Calculate the CD for each Pareto file member and delete as many as possible based on the file size and minimum crowding distance value;

[0027] Step 8: Check if the iteration requirements are met. If not, return to step 2.

[0028] Step 9: After performing cluster analysis on the final Pareto solution set using the K-means clustering algorithm, select the most suitable charging current.

[0029] Preferably, when the capacity loss of the power battery reaches 20%, the battery life is considered to have ended. The rated life of the battery can be determined by the total amount of electricity flowing through the battery at the end of its life under rated operating conditions. Therefore, the total ampere-hour throughput that can be passed during the battery's life cycle is expressed as:

[0030]

[0031] In the formula, I c,nom This represents the battery current under calibrated conditions; EOL represents the end of battery life.

[0032] Preferably, a severity factor σ is set to quantify the aging effect of the battery under actual operating conditions. The severity factor σ is defined as follows:

[0033]

[0034] In the formula, SOC nom C rate，nom ,T K,nom These represent the battery charge, charge / discharge rate, and ambient temperature under standard test conditions; Ah nom For in SOC nom =0.5, C rate，nom =1.5C,T K,nom =298.15K under standard test conditions, total ampere-hour throughput to EOL; Ah cyc The total ampere-hour throughput corresponding to the actual working conditions, Ah nom and Ah cyc The value can be represented as:

[0035]

[0036] In the formula, Q cyc,EOL Based on engineering experience, Q is defined as the battery capacity loss caused by cycling to end-of-life (EOL). cyc,EOL It is 20%;

[0037] The effective ampere-hour throughput, relative to the rated row conditions, is obtained based on the severity factor as follows:

[0038]

[0039] In the formula, Ah effThe effective charge flowing through the battery is used to characterize the depletion of the effective cycle life due to internal charge exchange within the battery.

[0040] Preferably, the expression for calculating CD is as follows:

[0041]

[0042] In the formula, and Let $\mathbf{j}$ and $\mathbf{j}$ represent the maximum and minimum values ​​of the $j$-th (j = 1, 2) objective function, respectively.

[0043] Preferably, in step 2, the battery charging strategy includes a constant current charging stage and a constant voltage charging stage. The constant current charging stage is further divided into four phases, including the State of Charge (SOC) phase. ch For the switching conditions of the constant current phase, SOC ch =[SOC ch1 SOC ch2 SOC ch3 SOC ch4 SOC ch1 SOC ch2 SOC ch3 SOC ch4 These represent the switching condition values ​​corresponding to the four stages, with equal intervals between the four stages.

[0044] The beneficial effects are as follows: Compared with the prior art, the parking charging strategy optimization method for range-extended electric vehicles based on the MOMSA algorithm of the present invention makes the battery charging process more stable by gradually reducing the constant current charging in five stages, and further improves charging efficiency and battery life by optimizing the optimal charging current through intelligent optimization algorithm, thereby effectively improving the charging performance of range-extended hybrid electric vehicles and promoting the popularization and development of new energy vehicles. Attached Figure Description

[0045] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0046] Figure 1 A schematic diagram of the R-int equivalent circuit model of the battery;

[0047] Figure 2 This is a schematic diagram of a non-dominated sorting process;

[0048] Figure 3 A schematic diagram of the crowding distance CD mechanism;

[0049] Figure 4 A schematic diagram of the algorithm flow for optimizing the charging strategy. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] It should be noted that when a component is referred to as being "fixed to" another component, it can be directly on the other component or there may be an intermediate component. When a component is referred to as being "connected to" another component, it can be directly connected to the other component or there may be an intermediate component. When a component is referred to as being "set on" another component, it can be directly set on the other component or there may be an intermediate component. When a component is referred to as being "set in the middle," it is not necessarily set in the exact center, as long as it is not set within the area defined by both ends being in the middle. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0052] This application discloses an optimization method for parking and charging strategies of range-extended hybrid electric vehicles based on the MOMSA algorithm, including:

[0053] For the power battery of a range-extended hybrid electric vehicle, an internal resistance R-int model is designed, which includes a resistor and a voltage source, such as... Figure 1 As shown, the power P of the power battery pack is established using Kirchhoff's laws and the current law. bat Open circuit voltage U oc and internal resistance R int Equivalent circuit model:

[0054]

[0055] In the formula, I bat Q represents the battery current (negative for charging, positive for discharging). bat Q represents the nominal capacity of the battery. loss ΔSOC represents the total capacity loss caused by battery aging, and ΔSOC represents the instantaneous rate of change of the SOC of the power battery.

[0056] A battery aging model is constructed. This invention primarily considers the battery's power cycle aging during vehicle operation. The battery aging model is represented as follows:

[0057]

[0058] In the formula, α and β are constant terms; E a The activation energy of the battery; C rate The battery charge / discharge rate is η; the compensation coefficient is Z; the power law factor is R. gas T is the gas constant; K Here, represents ambient temperature; Ah represents ampere-hour throughput; the battery is considered to have reached the end of its life when its capacity loss reaches 20%. The rated lifespan of a battery can be determined by the total amount of electricity flowing through it at the end of its life (EOL) under rated operating conditions. Therefore, the total ampere-hour throughput that can pass through the battery during its lifespan is expressed as:

[0059]

[0060] In the formula, I c,nom This represents the battery current under calibrated conditions; EOL represents the end of battery life.

[0061] The actual operating conditions of batteries are complex and varied. To quantify the aging effect under actual operating conditions, the severity factor σ is defined as:

[0062]

[0063] In the formula, SOC nom C rate，nom ,T K,nom These represent the battery charge, charge / discharge rate, and ambient temperature under standard test conditions; Ah nom For in SOC nom =0.5, C rate，nom =1.5C,T K,nom =298.15K under standard test conditions, total ampere-hour throughput to EOL; Ah cyc The total ampere-hour throughput corresponding to the actual working conditions, Ah nom and Ah cyc The value can be represented as:

[0064]

[0065] In the formula, Q cyc,EOL Based on engineering experience, Q is defined as the battery capacity loss caused by cycling to end-of-life (EOL). cyc,EOL It is 20%.

[0066] The effective ampere-hour throughput, relative to the rated row conditions, is obtained based on the severity factor as follows:

[0067]

[0068] In the formula, Ah eff The effective charge flowing through the battery is used to characterize the depletion of the effective cycle life due to internal charge exchange within the battery.

[0069] A multi-objective function is established. Charging time and battery life loss are two conflicting objectives that cannot be simultaneously optimized. To obtain the most balanced charging current, a multi-objective optimization function is established with charging time and battery life as the objectives. The optimal Pareto solution set for the charging current is obtained using the MOMSA algorithm, and the most suitable charging current is selected after cluster analysis of the solution set using the K-means clustering algorithm. The multi-objective optimization problem can be expressed as:

[0070] Min J={T tot (T K ,I batt ),Q loss (T K ,I batt )}

[0071] In the formula, T tot Q represents the charging time during the charging process. loss This results in a loss of battery life.

[0072] The MOMSA algorithm requires the use of an elite non-dominated sorting method to estimate the Pareto optimal solution. The non-dominated sorting process is as follows: Figure 2 As shown. Furthermore, MOMSA employs a crowding distance (CD) mechanism to enhance the coverage of the optimal solution for all targets, such as... Figure 3 As shown. The expression for calculating CD is as follows:

[0073]

[0074] In the formula, and Let $\mathbf{j}$ and $\mathbf{j}$ represent the maximum and minimum values ​​of the $j$-th (j = 1, 2) objective function, respectively.

[0075] Based on the above model and function construction, this application treats each multi-segment constant current and constant voltage charging curve as a particle, and uses the magnitude of the constant current value as an optimization variable, such as... Figure 4 As shown, the specific charging strategy optimization algorithm process includes the following steps:

[0076] Step 1: Initialize the population. Randomly generate an initial mantis population in the search space. Each individual represents a potential solution, which is distributed as evenly as possible throughout the space and stored in a matrix. At the same time, set upper and lower limits and the maximum iteration value.

[0077] Step 2: Simulate the charging process by inputting the initialized mantis into the equivalent circuit model and battery aging model to determine the voltage, SOC, and Q. loss Determine whether the SOC meets the preset SOC. ch Then, it switches to the next constant current charging stage, and then determines whether the condition of SOC = 90% is met. If it is met, it switches to constant voltage charging, and charging stops when SOC = 92%. The battery charging strategy includes a constant current charging stage and a constant voltage charging stage. The constant current charging stage is divided into four phases, SOC... ch For the switching conditions of the constant current phase, SOC ch =[SOC ch1 SOC ch2 SOC ch3 SOC ch4 SOC ch1 SOC ch2 SOC ch3 SOC ch4 These represent the switching condition values ​​corresponding to the four stages. The intervals between the four stages are equal. When the maximum SOCch of the constant current switching stage is set to 0.9 and the minimum SOCmin of the battery is 0.1, the interval between the four stages is (0.9-0.1) / 4 = 0.2, that is, SOCch = [0.3, 0.5, 0.7, 0.9].

[0078] Step 3: Calculate the fitness of each mantis and sort them to determine the non-dominated solution of the initial population and save it to the Pareto archive;

[0079] Step 4: Calculate the crowding distance CD for each Pareto archive particle, and select a position vector based on CD;

[0080] Step 5: Update the position of each mantis according to the position vector, and calculate the fitness value of the updated mantis position;

[0081] Step 6: Identify new non-dominated solutions in the population and save them to the Pareto archive, and remove all dominated solutions from the Pareto archive;

[0082] Step 7: Calculate the CD for each Pareto file member and delete as many as possible based on the file size and minimum crowding distance value;

[0083] Step 8: Check if the iteration requirements are met. If not, return to step 2.

[0084] Step 9: After performing cluster analysis on the final Pareto solution set using the K-means clustering algorithm, select the most suitable charging current.

[0085] The following example illustrates the charging optimization process when the initial SOC is 20%. The charging strategy optimization algorithm includes the following steps:

[0086] Step 1: Initialize the population

[0087] First, an initial mantis population is randomly generated in the search space. For example, the upper and lower limits of the search space are set (this depends on the reasonable range of values ​​for specific charging-related parameters, such as the minimum and maximum charging current), as well as the maximum number of iterations (assuming it is set to 100 iterations). Each generated mantis represents a potential charging strategy solution, and these individuals are distributed as evenly as possible throughout the defined space. They are then stored in the corresponding matrix, ready for subsequent processing.

[0088] Step 2: Simulate the charging process

[0089] The initialized mantis individual is then input into the equivalent circuit model and battery aging model for calculation. Since the initial SOC is only 20%, after charging begins, relevant parameters such as voltage are acquired in real time. As charging progresses, it is continuously checked whether the SOC meets the preset SOC. ch With an initial SOC of 20%, charging continues until the switching conditions are met, at which point it switches to the next constant current charging stage. Subsequently, when the SOC reaches 90%, it switches to the constant voltage charging stage according to a predetermined strategy, until charging stops when the SOC reaches 92%. The entire process strictly follows the set charging stage switching rules, simulating a complete charging process.

[0090] Step 3: Calculate fitness and sort

[0091] The fitness value of each mantis individual is calculated based on a multi-objective optimization function (which comprehensively considers both charging time and battery life loss). Then, these individuals are ranked using an elite non-dominated ranking method. For example, given mantis individuals A and B, if individual A is not inferior to individual B in all objectives (such as shorter charging time and less battery life loss), and is superior to individual B in at least one objective, then individual A dominates individual B. Through this comparison and judgment, the solutions in the entire population are divided into different non-dominated frontiers. Then, the non-dominated solutions from the initial population are extracted and saved in the Pareto archive. These non-dominated solutions represent the relatively optimal charging strategies at the current stage.

[0092] Step 4: Calculate the congestion distance CD and select the location vector.

[0093] For each particle stored in the Pareto archive, its crowding distance CD is calculated. Within each non-dominated front, solutions are sorted according to the magnitude of their crowding distance CD. Solutions with larger CD values ​​are preferred because a larger crowding distance means that the solution is located in a relatively "sparse" and more representative position within its respective front. If multiple solutions have the same CD, one solution can be randomly selected. This filtering process further refines and optimizes the set of alternative charging strategies.

[0094] Step 5: Update position and calculate fitness

[0095] The positions of each mantis individual are updated based on their corresponding position vectors. This update operation changes the parameters (charging current magnitude) of the charging strategy they represent. Then, battery charging is simulated again, and the fitness value corresponding to the updated mantis positions is calculated to further evaluate whether these updated charging strategies are better.

[0096] Step 6: Identify and archive new non-dominated solutions.

[0097] Identify new non-dominated solutions that emerge after position updates and other operations, and save these new non-dominated solutions to the Pareto archive. Simultaneously, carefully examine the Pareto archive to eliminate all dominated solutions, ensuring that only relatively better charging strategies are retained, and avoiding redundant or inferior strategy options.

[0098] Step 7: Adjust Pareto file size

[0099] Recalculate the CD (Crowd Decomposition) of each Pareto file member, and delete as many solutions as possible based on the current file size and the set minimum congestion distance. Specifically, continuously select and delete solutions with the minimum CD value, repeating this operation until the Pareto file size meets the preset requirements, bringing the number of solutions in the file to a reasonable and more representative range.

[0100] Step 8: Check iteration requirements

[0101] Check if the current number of iterations meets the initially set iteration requirement. If the set number of iterations has not been reached, return to step 2 and continue with the next round of simulated charging, fitness calculation, and other operations to continuously optimize the charging strategy.

[0102] Step 9: Cluster analysis and selection of appropriate charging current

[0103] Once the iteration requirements are met, the K-means clustering algorithm is used to perform cluster analysis on the final Pareto solution set. Through clustering, similar charging strategies can be grouped into one category, and then the most suitable charging current is selected from these categories as the final optimized charging strategy for the initial SOC of 20%. For example, the charging current value corresponding to the cluster center that performs best considering factors such as charging time and battery life can be chosen.

[0104] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the technical solutions of the present invention.

Claims

1. A method for optimizing parking charging strategy of extended-range hybrid electric vehicles based on MOMSA algorithm, characterized in that, Comprise: For the extended range hybrid electric vehicle power battery, a internal resistance R-int model is designed, which contains a resistance and a voltage source, and through Kirchhoff's law and current law, the equivalent circuit model of power battery pack is established: power P bat , open circuit voltage U oc and internal resistance R int ​ where I is the battery current, Q is the battery state of charge (SOC), and t is the time. bat where I is the battery current, Q is the battery state of charge (SOC), and t is the time. bat where I is the battery current, Q is the battery state of charge (SOC), and t is the time. loss where I is the battery current, Q is the battery state of charge (SOC), and t is the time. Build battery aging model: In the formula, α, β are constant terms; E a is the activation energy of the battery; C rate is the charge-discharge rate of the battery; η is a compensation factor; Z is a power-law factor; R gas is the gas constant; T K is the ambient temperature; Ah is the ampere-hour throughput; Establish multi-objective function, get the optimal charging current Pareto solution set by MOMSA algorithm, and select the most suitable charging current after clustering analysis of the solution set by K-means clustering algorithm, establish multi-objective optimization function with charging time and battery life as target: Min J = {T tot (T K ,I bat ),Q loss (T K ,I bat )} where T tot is the charging time during charging, Q loss is the battery life loss; The charging strategy optimization algorithm process includes the following steps: Step 1: initialize the population, randomly generate the initial mantis population in the search space, each individual represents a potential solution, make it uniformly distributed in the whole space, and store it in the matrix, at the same time, set the upper and lower limit, the maximum iteration value; Step 2: simulate the charging process, bring the initialized mantis into the equivalent circuit model and battery aging model calculation, get voltage, SOC and Q loss , determine whether the SOC meets the preset SOC ch , SOC ch is the switching condition of constant current stage, if the switching condition is met, switch to the next constant current charging stage, when SOC=90%, switch to constant voltage charging, and stop charging when SOC=92%. Step 3: calculate the fitness of each mantis according to the multi-objective optimization function, and sort them combined with the elite non-dominated sorting method, if solution A is not worse than B in all targets, and at least one target is better than B, then solution A dominates solution B, through this way, the solutions in the population are divided into different non-dominated front, determine the non-dominated solution of the initial population and save it to the Pareto file; Step 4: calculate the crowding distance CD of each Pareto file particle, for each non-dominated front, sort according to the crowding distance CD, prefer to choose the solution with larger CD, if there are multiple solutions with the same CD, you can randomly select one of them; Step 5: update the position of each mantis according to the position vector, calculate the fitness value of the updated mantis position; Step 6: determine the new non-dominated solution in the population and save it to the Pareto file, and eliminate all dominated solutions in the Pareto file; Step 7: calculate the CD of each member of the Pareto file, and delete according to the file size and the minimum crowding distance value, constantly select and delete the solution with the minimum CD value until the size of the Pareto file meets the requirements; Step 8: check if the iteration requirement is met, if not, return to step 2; Step 9: select the most suitable charging current after clustering analysis of the final Pareto solution set by K-means clustering algorithm.

2. The method of claim 1, wherein the method is based on a MOMSA algorithm. When the capacity loss of power battery reaches 20%, it is considered that the battery life is over, the rated life of the battery can be determined by the total electric quantity flowing through the battery when the life is over under the rated operating condition, therefore, the total ampere-hour throughput in the life cycle of the battery is expressed as: where I c,nom is the battery current under the calibration condition; EOL is the time of the end of battery life.

3. The method of claim 1, wherein the method is based on a MOMSA algorithm. The severity factor σ is set to quantify the aging effect of the battery in the actual working condition, and the severity factor σ is defined as: where SOC nom , C rate，nom , and T K,nom are the battery charge, the charge-discharge rate, and the ambient temperature under the standard test condition, respectively; Ah nom is the total ampere-hour throughput to EOL under the standard test condition of SOC nom = 0.5, C rate，nom = 1.5C, and T K,nom = 298.15 K; Ah cyc is the total ampere-hour throughput corresponding to the actual working condition; and the values of Ah nom and Ah cyc can be expressed as: where Q cyc,EOL is the battery capacity loss due to cycling to EOL, defined as Q cyc,EOL is 20%; Compared with the rated operating condition, the effective ampere-hour throughput based on the severity factor is: wherein Ah eff is the effective amount of electricity flowing through the battery, used to characterize the effective cycle life depletion due to internal charge exchange within the battery.

4. The method of claim 1, wherein the method is based on a MOMSA algorithm. The calculation expression of CD is as follows: In the formula, and respectively represent the maximum and minimum values of the jth (j = 1, 2) objective function.

5. The method of claim 1, wherein the method is based on a MOMSA algorithm. The battery charging strategy in step 2 includes a constant current charging phase and a constant voltage charging phase, wherein the constant current charging phase is divided into four phases, and SOC ch is a switching condition for the constant current phase, and SOC ch = [SOC ch1 , SOC ch2 , SOC ch3 , SOC ch4 , SOC ch1 , SOC ch2 , SOC ch3 , SOC ch4 respectively represent the switching condition values corresponding to the four phases, and the intervals of the four phases are equal.

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