Extended-range automobile parking charging strategy optimization method based on MOMSA algorithm

Abstract

The invention relates to the technical field of automobile energy management, and particularly discloses an extended-range automobile parking charging strategy optimization method based on an MOMSA algorithm, which comprises the steps of establishing a dynamic equivalent circuit model, constructing a battery aging model and a multi-objective optimization function, and comprises the following steps: step 1, initializing a population; 2, simulating a charging process; 3, calculating the fitness of the population and determining a non-dominated solution; 4, selecting a position vector according to the crowding distance CD; 5, calculating the fitness value of the updated mantis position; 6, new non-dominated solutions in the population are determined, and all dominated solutions are eliminated; step 7, calculating the CD of each Pareto file member; 8, checking whether iteration requirements are met or not, and if not, returning to the step 2; and step 9, selecting the most suitable charging current. According to the charging strategy optimization method, the battery charging process can be more stable, the service life of the battery is prolonged, and the charging efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile energy management, and in particular to a method for optimizing a parking charging strategy of a range-extended hybrid electric vehicle based on a MOMSA algorithm. Background Art

[0002] New energy vehicles, especially extended-range hybrid electric vehicles (E-RHEV, Extended-Range Hybrid Electric Vehicle), have gradually become an important direction for the development of the future automobile industry because of their fuel economy and environmental protection advantages. Extended-range hybrid electric vehicles combine the advantages of traditional fuel vehicles and electric vehicles, and use the engine to generate electricity to charge the battery, thereby extending the vehicle's driving range. However, as an important part of extended-range hybrid electric vehicles, the battery's charging strategy directly affects the vehicle's performance, energy efficiency and battery life. With the popularization of electric vehicles, charging technology has become increasingly important as a key technology to ensure the normal and continuous operation of power batteries. At present, the long charging process of electric vehicles will reduce the practicality and consumer acceptance of electric vehicles. Excessive charging current during the charging process will also cause the battery temperature to rise rapidly, threatening battery safety and significantly damaging battery life. A certain amount of energy loss will occur during the battery charging and discharging process. Inappropriate charging strategies will significantly reduce charging efficiency and bring more energy waste. Battery charging strategies have a significant impact on battery performance, charging safety and efficiency. Battery charging control is a research hotspot in the field of electric vehicle power batteries.

[0003] Most of the existing extended-range hybrid electric vehicle charging strategies adopt the traditional constant current constant voltage (CC-CV) charging method, constant current charging and constant voltage charging. Although these methods have the advantages of simple design, low cost and easy implementation, they have several problems and disadvantages in practical applications.

[0004] The most commonly used constant current and constant voltage charging strategy has a low current in the initial stage, which fails to reach the maximum charging current that the lithium battery can withstand, resulting in a longer charging time and easily causing lithium side reactions, which will affect the battery life. In addition, this strategy may cause the battery to heat up and increase safety risks by maintaining a large current for a long time during the constant current stage; while in the constant voltage stage, the current gradually decreases to a smaller value, and the charging speed is significantly slower, resulting in a longer overall charging time and affecting the user experience.

[0005] For the constant current charging strategy, the initial current is small, resulting in a long charging time; the mid-term current is large, energy consumption is high, and charging efficiency is low, and it is easy to overcharge in the later stage, affecting the battery life. During the constant current charging process, excessive charging current may accelerate battery aging, causing capacity attenuation and performance degradation, thereby shortening the battery life.

[0006] The constant voltage charging strategy has the characteristics of long charging time and large current in the initial charging stage. This can easily affect the service life of the battery. The rapid attenuation of battery life not only increases the maintenance cost for electric vehicle users, but also seriously affects the long-term reliability of the vehicle.

[0007] In addition, the existing charging strategy lacks dynamic optimization and fails to fully consider the impact of external conditions such as battery health status and ambient temperature, resulting in a lack of flexibility and intelligence in the charging process, and an inability to achieve optimal charging efficiency and battery protection under various working conditions. Therefore, how to optimize the charging strategy, balance charging speed and battery life, and improve the intelligence level of the charging process is an urgent problem to be solved. Summary of the invention

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

[0009] In order to achieve the above object, the technical solution adopted by the present invention is: a method for optimizing the parking charging strategy of a range-extended hybrid electric vehicle based on the MOMSA algorithm, comprising:

[0010] For the extended-range hybrid vehicle power battery, an internal resistance R-int model is designed, which includes a resistor and a voltage source, and the power P of the power battery pack is established through Kirchhoff's law and current law. bat , open circuit voltage U oc and internal resistance R int The equivalent circuit model of:

[0011]

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

[0013] Building a battery aging model:

[0014]

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

[0016] A multi-objective function is established, and the optimal charging current Pareto solution set is obtained through the MOMSA algorithm. The most suitable charging current is selected after clustering analysis of the solution set based on the K-means clustering algorithm, and a multi-objective optimization function with charging time and battery life as the target is established:

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

[0018] Where, T tot is the charging time during the charging process, Q loss For battery life loss;

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

[0020] Step 1: Initialize the population. Randomly generate the initial mantis population in the search space. Each individual represents a potential solution, so that it is distributed as evenly as possible in the entire 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, bring the initialized mantis into the equivalent circuit model and battery aging model calculation to determine the voltage, SOC, Q loss , determine whether the SOC meets the preset SOC ch , and switch to the next constant current charging stage, and then determine whether the condition is met SOC = 90%. If so, switch to constant voltage charging, and stop charging when SOC = 92%;

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

[0023] Step 4: Calculate the crowding distance CD of each Pareto profile 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: Determine the new non-dominated solutions in the population and save them to the Pareto archive, and eliminate all dominated solutions in the Pareto archive;

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

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

[0028] Step 9: Perform cluster analysis on the final Pareto solution set based on the K-means clustering algorithm and select the most appropriate charging current.

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

[0030]

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

[0032] Preferably, a severity factor σ is set to quantify the aging effect of the battery in actual working conditions, and the severity factor σ is defined as:

[0033]

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

[0035]

[0036] In the formula, Q cyc,EOL Q is defined as the battery capacity loss caused by cycling to EOL based on engineering experience. cyc,EOL 20%;

[0037] Relative to the rated line condition, the effective ampere-hour throughput based on the severity factor is:

[0038]

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

[0040] Preferably, the calculation expression of CD is as follows:

[0041]

[0042] In the formula, and They 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, wherein the constant current charging stage is divided into four stages: SOC ch is the switching condition in the constant current stage, SOC ch =[SOC ch1 ,SOC ch2 ,SOC ch3 ,SOC ch4 ], SOC ch1 ,SOC ch2 ,SOC ch3 ,SOC ch4 They represent the switching condition values ​​corresponding to the four stages respectively, and the intervals between the four stages are equal.

[0044] The beneficial effect is: compared with the prior art, the extended-range vehicle parking charging strategy optimization method based on the MOMSA algorithm of the present invention makes the battery charging process smoother by gradually reducing the constant current charging in five stages, and further improves the charging efficiency and battery life by optimizing the optimal charging current through the intelligent optimization algorithm, thereby effectively improving the charging performance of the extended-range hybrid vehicle and promoting the popularization and development of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

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

[0047] Figure 2 Schematic diagram of the non-dominated sorting process;

[0048] Figure 3 Schematic diagram of crowding distance CD mechanism;

[0049] Figure 4 Schematic diagram of the charging strategy optimization algorithm flow. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 may be directly on the other component or there may also be a centered component. When a component is considered to be "connected" to another component, it may be directly connected to the other component or there may be a centered component at the same time. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a centered component at the same time. When a component is referred to as being "set in the middle", it is not just set in the middle position, as long as it is not set at both ends within the range defined by 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 those generally understood by technicians in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0052] The present application discloses a method for optimizing a parking charging strategy for a range-extended hybrid electric vehicle based on a MOMSA algorithm, comprising:

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

[0054]

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

[0056] Constructing a battery aging model. The present invention mainly considers the battery power cycle aging during vehicle operation. The battery aging model is expressed as follows:

[0057]

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

[0059]

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

[0061] The actual working conditions of the battery are complex and changeable. In order to quantify the aging effect in actual working conditions, the severity factor σ is defined as:

[0062]

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

[0064]

[0065] In the formula, Q cyc,EOL Q is defined as the battery capacity loss caused by cycling to EOL based on engineering experience. cyc,EOL is 20%.

[0066] Relative to the rated line condition, the effective ampere-hour throughput based on the severity factor is:

[0067]

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

[0069] Establish a multi-objective function. Charging time and battery life loss are two conflicting goals that cannot reach the optimal value at the same time. In order to obtain the most balanced charging current, a multi-objective optimization function with charging time and battery life as the goals is established. The optimal charging current Pareto solution set is obtained through the MOMSA algorithm, and the most appropriate charging current is selected after clustering analysis of the solution set based on 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] Where, T tot is the charging time during the charging process, Q loss Loss of battery life.

[0072] The MOMSA algorithm needs to be combined with the elite non-dominated sorting method to estimate the Pareto optimal solution. The non-dominated sorting process is composed of Figure 2 In addition, MOMSA adopts the crowding distance (CD) mechanism to enhance the coverage of the optimal solution for all targets, as shown in Figure 3 The calculation expression of CD is as follows:

[0073]

[0074] In the formula, and They represent the maximum and minimum values ​​of the j-th (j=1,2) objective function respectively.

[0075] Based on the construction of the above model and function, this application takes each multi-segment constant current and constant voltage charging curve as a particle and takes the constant current value as the 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 the initial mantis population in the search space. Each individual represents a potential solution, so that it is distributed as evenly as possible in the entire 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, bring the initialized mantis into the equivalent circuit model and battery aging model calculation to determine the voltage, SOC, Q loss , determine whether the SOC meets the preset SOC ch , and switch to the next constant current charging stage, and then determine whether the condition meets SOC = 90%. If it meets the condition, switch to constant voltage charging, and stop charging when SOC = 92%. The battery charging strategy includes constant current charging stage and constant voltage charging stage. Among them, the constant current charging stage is divided into four stages, SOC ch is the switching condition in the constant current stage, SOC ch =[SOC ch1 ,SOC ch2 ,SOC ch3 ,SOC ch4 ], SOC ch1 ,SOC ch2 ,SOC ch3 ,SOC ch4 They represent the switching condition values ​​corresponding to the four stages respectively. The intervals of 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 SOC is 0.1, the interval value of 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, determine the non-dominated solution of the initial population and save it in the Pareto archive;

[0079] Step 4: Calculate the crowding distance CD of each Pareto profile 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: Determine the new non-dominated solutions in the population and save them to the Pareto archive, and eliminate all dominated solutions in the Pareto archive;

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

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

[0084] Step 9: Perform cluster analysis on the final Pareto solution set based on the K-means clustering algorithm and select the most appropriate charging current.

[0085] The following is an example of specific charging optimization. When the initial SOC is 20%, the charging strategy optimization algorithm process includes:

[0086] Step 1: Initialize the population

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

[0088] Step 2: Simulate the charging process

[0089] The initialized mantis individual is brought into the equivalent circuit model and battery aging model for calculation. At this time, since the initial SOC is only 20%, after charging starts, the voltage and other related parameters during the charging process will be obtained in real time. As charging progresses, it is constantly judged whether the SOC meets the preset SOC. ch . When the initial SOC is 20%, charging continues until the switching conditions are met, and then it switches to the next constant current charging stage. When the SOC reaches 90%, it switches to the constant voltage charging stage according to the established strategy, and charging stops when the SOC reaches 92%. The entire process is carried out strictly in accordance with the set charging stage switching rules to simulate a complete charging process.

[0090] Step 3: Calculate fitness and sort

[0091] The fitness value corresponding to each mantis individual is calculated according to the multi-objective optimization function (which comprehensively considers the two objectives of charging time and battery life loss). Then, the elite non-dominated sorting method is used to sort these individuals. For example, there are mantis individuals A and B. If individual A is not inferior to individual B in all objectives (such as shorter charging time, less battery life loss, etc.), and is better than individual B in at least one objective, it means that individual A dominates individual B. Through such comparison and judgment, the solutions in the entire population are divided into different non-dominated frontiers, and then the non-dominated solutions in the initial population are extracted and saved in the Pareto archive. These non-dominated solutions represent relatively better charging strategies at the current stage.

[0092] Step 4: Calculate the crowding distance CD and select the position vector

[0093] For each particle saved in the Pareto archive, calculate its crowding distance CD. In each non-dominated front, sort according to the size of the crowding distance CD. Prioritize those solutions with larger CD values, because a larger crowding distance means that the position of the solution in the front is relatively "sparse" and more representative. If there are multiple solutions with the same CD, a solution can be randomly selected from them, and such screening can further streamline and optimize the set of alternative charging strategies.

[0094] Step 5: Update position and calculate fitness

[0095] The positions of each mantis are updated according to the corresponding position vectors. This update operation will change the parameters related to the charging strategies they represent (charging current). Then, the battery charging is simulated again, and the fitness values ​​corresponding to the updated mantis positions are calculated to further evaluate whether these updated charging strategies are better.

[0096] Step 6: Determine the new non-dominated solution and archive it

[0097] Determine the new non-dominated solutions that appear after the population has been updated and other operations, and save these new non-dominated solutions to the Pareto archive. At the same time, carefully check the Pareto archive to eliminate all dominated solutions to ensure that the archive retains relatively better charging strategies and avoid redundant or poor strategy options in the archive.

[0098] Step 7: Adjust the Pareto Profile Size

[0099] Recalculate the CD of each Pareto file member, and delete as many solutions as possible according to the existing size of the file and the set minimum crowding distance value. The specific method is to continuously select and delete those solutions with the smallest CD value, and repeat this operation until the size of the Pareto file meets the pre-set requirements, so that the number of solutions in the file is in a reasonable and more representative range.

[0100] Step 8: Check iteration requirements

[0101] Check whether the current number of iterations meets the iteration requirements set at the beginning. If it has not reached the set number of iterations, return to step 2 and continue 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] When 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 classified into one category, and then the most appropriate charging current can be 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 in terms of charging time and battery life can be selected.

[0104] The above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be included in the scope of the technical solution of the present invention.

Claims

1. A method for optimizing the parking charging strategy of a range-extended hybrid electric vehicle based on the MOMSA algorithm, characterized in that: include: For the extended-range hybrid vehicle power battery, an internal resistance R-int model is designed, which includes a resistor and a voltage source, and the power P of the power battery pack is established through Kirchhoff's law and current law. bat , open circuit voltage U oc and internal resistance R int The equivalent circuit model of: In the formula, I bat is the battery current, Q bat is the nominal capacity of the battery, Q loss is the total capacity loss caused by battery aging, ΔSOC is the instantaneous change rate of the power battery SOC; Building a battery aging model: In the formula, α and β are constant terms; E a is the activation energy of the battery; C rate is the battery charge and discharge rate; η is the compensation coefficient; Z is the power law factor; R gas is the gas constant; T K is the ambient temperature; Ah is the ampere-hour throughput; A multi-objective function is established, and the optimal charging current Pareto solution set is obtained through the MOMSA algorithm. The most suitable charging current is selected after clustering analysis of the solution set based on the K-means clustering algorithm, and a multi-objective optimization function with charging time and battery life as the target is established: Min J={T tot (T K ,I batt ),Q loss (T K ,I batt )} Where, T tot is the charging time during the charging process, Q loss For 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, so that it is distributed as evenly as possible in the entire space and stored in a matrix. At the same time, set upper and lower limits and the maximum iteration value. Step 2: Simulate the charging process, bring the initialized mantis into the equivalent circuit model and battery aging model calculation to obtain voltage, SOC and Q loss , determine whether the SOC meets the preset SOC ch If the switching conditions are met, it switches to the next constant current charging stage. When SOC = 90%, it switches to constant voltage charging, and charging stops when SOC = 92%; Step 3: Calculate the fitness of each mantis according to the multi-objective optimization function, and sort them in combination with the elite non-dominated sorting method. If solution A is not inferior to B in all objectives and is better than B in at least one objective, then solution A dominates solution B. In this way, the solutions in the population are divided into different non-dominated frontiers, and the non-dominated solutions of the initial population are determined and saved in the Pareto archive. Step 4: Calculate the crowding distance CD of each Pareto profile particle. For each non-dominated frontier, sort them according to the crowding distance CD, and give priority to solutions with larger CD. If there are multiple solutions with the same CD, one of them can be randomly selected. Step 5: Update the position of each mantis according to the position vector, and calculate the fitness value of the updated mantis position; Step 6: Determine the new non-dominated solutions in the population and save them to the Pareto archive, and eliminate all dominated solutions in the Pareto archive; Step 7: Calculate the CD of each Pareto archive member and delete as many as possible based on the archive size and the minimum crowding distance value, and continue to select solutions with the minimum CD value until the size of the Pareto archive meets the requirements; Step 8: Check whether the iteration requirements are met. If not, return to step 2; Step 9: Perform cluster analysis on the final Pareto solution set based on the K-means clustering algorithm and select the most appropriate charging current.

2. The method for optimizing the parking charging strategy of an extended-range vehicle based on the MOMSA algorithm according to claim 1, characterized in that: When the power battery capacity loss reaches 20%, the battery life is considered to have ended. The rated battery life can be determined by the total amount of electricity flowing through the battery at the end of life under rated operating conditions. Therefore, the total ampere-hour throughput that can be passed through the battery life cycle is expressed as: In the formula, I c,nom is the battery current under calibration conditions; EOL is the end of battery life.

3. The method for optimizing the parking charging strategy of an extended-range vehicle based on the MOMSA algorithm according to claim 1, characterized in that: The severity factor σ is set to quantify the aging effect of the battery under actual working conditions. The severity factor σ is defined as: In the formula, SOC nom ,C rate，nom ,T K,nom are the battery charge, charge and discharge rate and ambient temperature under standard test conditions; Ah nom For SOC nom =0.5, C rate，nom =1.5C, T K,nom = Total ampere-hour throughput to EOL under standard test conditions of 298.15K; Ah cyc is the total ampere-hour throughput corresponding to the actual working state, Ah nom and Ah cyc The value of can be expressed as: In the formula, Q cyc,EOL Q is defined as the battery capacity loss caused by cycling to EOL based on engineering experience. cyc,EOL 20%; Relative to the rated line condition, the effective ampere-hour throughput based on the severity factor is: In the formula, Ah eff It is the effective amount of electricity flowing through the battery and is used to characterize the depletion of effective cycle life due to charge exchange within the battery.

4. The method for optimizing the parking charging strategy of an extended-range vehicle based on the MOMSA algorithm according to claim 1, characterized in that: The calculation expression of CD is as follows: In the formula, and They represent the maximum and minimum values ​​of the j-th (j=1,2) objective function respectively.

5. The method for optimizing the parking charging strategy of an extended-range vehicle based on the MOMSA algorithm according to claim 1, characterized in that: In step 2, the battery charging strategy includes a constant current charging stage and a constant voltage charging stage, wherein the constant current charging stage is divided into four stages: SOC ch is the switching condition in the constant current stage, SOC ch =[SOC ch1 ,SOC ch2 ,SOC ch3 ,SOC ch4 ], SOC ch1 ,SOC ch2 ,SOC ch3 ,SOC ch4 They represent the switching condition values ​​corresponding to the four stages respectively, and the intervals between the four stages are equal.

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