Multi-standard decision-making method for solving charging strategy selection problem

Abstract

The invention discloses a multi-standard decision-making method for solving a charging strategy selection problem. The method comprises the following steps: carrying out grade evaluation on an electric vehicle charging strategy according to the influence of an electric vehicle charging station on the aspects of economic operation cost, environmental influence, social influence, operation cost and service quality evaluation; on the basis of comparison and evaluation results of electric vehicle charging strategies, a fuzzy best-worst method is adopted, subjective fuzziness during comparison of different criteria or options is captured according to weight levels of different standards, and weights of the standards are determined again through paired comparison; an alternative scheme fuzzy sorting method is provided, available charging strategies of an electric vehicle system are evaluated and sorted, and the method helps a policy maker to use a reliable decision-making tool to compare and evaluate possible charging options / alternative schemes based on various related standards in practice so as to make an intelligent decision.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicles and relates to a multi-criteria decision-making method for solving the problem of charging strategy selection. Background Art

[0002] The transportation industry is a major source of global greenhouse gas emissions, and reducing these emissions is an important part of the response to climate change. According to the Intergovernmental Panel on Climate Change, approximately 14% of global greenhouse gas emissions come from the transportation sector. Road transportation is the largest emitter in this area, followed by air and sea transportation. The adoption of electric buses in urban areas is a promising solution for reducing greenhouse gas emissions in the transportation sector and mitigating the impact of climate change. Electric buses produce zero tailpipe emissions and can be powered by renewable energy, making them a sustainable and environmentally friendly alternative to traditional diesel or gasoline buses. In addition, electric buses have lower operating costs, can improve air quality, and reduce the health hazards caused by air pollution. However, the transition to electric buses requires significant investment in charging infrastructure as well as supportive policies and regulatory measures.

[0003] A key step in the electrification of the bus network is to select the best charging strategy from multiple options, such as overnight (slow) charging and opportunity (fast) charging systems. This step has become increasingly important for public transportation agencies as the demand for environmentally friendly transportation increases and electric buses become more prevalent in the public transportation system. In urban electric vehicle operations, these two common charging strategies each have their own advantages and disadvantages, such as the cost of each option. Battery cost: The opportunity charging system requires a smaller on-board battery, thus reducing the battery cost; Planning work: The opportunity charging system requires more complex planning to set up multiple charging stations along the bus route; Charging infrastructure cost: The opportunity charging system requires a higher infrastructure cost, which is related to the installation of high-power (fast) chargers and the acquisition of land at multiple locations throughout the city; Impact of battery weight: The overnight charging system requires a heavier battery pack, thus increasing the energy consumption of the electric vehicle; Electricity cost: The overnight charging system can take advantage of cheaper off-peak electricity rates at night; Service delay: The overnight charging system can charge the electric vehicle when it is not in service, reducing the risk of operational delays caused by charging idle time during daily operations.

[0004] As the above comparison shows, these advantages and disadvantages present operators with a challenging trade-off situation, and choosing the best strategy is a challenging task because each option has its own advantages and disadvantages. To effectively solve this problem, policymakers need to consider multiple factors (with different dimensions) simultaneously, which requires managing a multi-criteria decision-making problem. Therefore, this Summary of the Invention

[0005] To solve the above problems, the technical solution adopted by the present invention is: a multi-criteria decision-making method for solving the problem of charging strategy selection, including the following steps:

[0006] Evaluate the charging strategies of electric vehicles according to their impacts on the economic operation cost, environmental impact, social impact, operation cost, and service quality assessment of electric vehicle charging stations;

[0007] Based on the results of comparing and evaluating the charging strategies of electric vehicles, the fuzzy best-worst method is used. According to the weight levels of different criteria, the subjective fuzziness when comparing different criteria or options is captured, and the weights of the criteria are re-determined through pairwise comparison;

[0008] Based on the alternative fuzzy ranking method, evaluate and rank the available charging strategies of the electric vehicle system.

[0009] Furthermore: Based on the results of comparing and evaluating the charging strategies of electric vehicles, the process of using the fuzzy best-worst method to re-determine the weights of the criteria according to the weight levels of different criteria and capturing the subjective fuzziness when comparing different criteria or options through pairwise comparison is as follows:

[0010] The fuzzy best-worst method is a vector-based decision-making method. By pairwise comparison, the weights of the criteria (x 1 、x 2 ......、x i ) are estimated, a set of evaluation criteria is formulated, denoted as (a 1 、a 2 ,...,a n ), the best or most important and the worst or least important items are determined, and a fuzzy "best-others" vector is created to reflect the preference of the most important or best criterion for all other criteria. This vector is expressed as Equation (1):

[0011]

[0012] Where: The value of reflects the preference of the best criterion relative to criterion j, The value of is equal to (1,1,1); the linguistic expression is converted into a fuzzy number;

[0013] Calculate the best fuzzy value of the criterion weights. The fuzzy weights are calculated by solving a non-linear optimization model, which contains an objective function aimed at minimizing the maximum absolute difference between the fuzzy weights obtained from completely consistent comparison and the current weights;

[0014]

[0015] Among them,

[0016]

[0017] The consistency ratio is used to evaluate the consistency and accuracy of calculating weights;

[0018] When is the case, the fuzzy comparison is considered to be completely consistent; when is the case, the inconsistency rate will increase; when and both equal is the case, the inconsistency rate is the highest.

[0019] Among them, among them refers to the preference of the best criterion over the worst criterion,

[0020] is the optimization coefficient, which satisfies Equation (3):

[0021]

[0022] The consistency ratio is:

[0023]

[0024] where CI represents the consistency index, which varies according to the importance of the criteria.

[0025] Furthermore: The process of evaluating and ranking the available charging strategies of an electric vehicle system by the proposed alternative-based fuzzy ranking method is as follows:

[0026] Create a summary fuzzy decision matrix. For a set of criteria (c1, c2,..., cm), the fuzzy linguistic scale is provided, and the evaluations are represented in matrix form as Equation (5):

[0027]

[0028] where represents the fuzzy value calculated using the fuzzy linguistic scale;

[0029] Equation (6) is the fuzzy Heronian operator, which is used to aggregate k fuzzy decision matrices into a matrix

[0030]

[0031] where represents the average fuzzy number, and p, q ≥ 0 represents the set of non-negative numbers;

[0032] Convert the elements of the aggregated decision matrix into a standard interval, set the ideal and non-ideal values for each criterion, and use and Indicates that its function is to define a function for each alternative, as shown in formula (7), which maps the intervals in the aggregated decision matrix to a new interval [n 1 , n b :

[0033]

[0034] where n 1 and n b are the ratios of the ideal value to the anti-ideal value, and represents the fuzzy value of alternative i for criterion j in the aggregated decision matrix, and the result is the criterion judgment matrix:

[0035] Furthermore: The ratio of the ideal value to the anti-ideal value is at least 6:1.

[0036] Furthermore: The elements in the criterion judgment matrix are normalized:

[0037]

[0038] where A represents the arithmetic mean of n 1 and n b , and H represents their harmonic mean.

[0039] The fuzzy criterion function for each alternative is calculated, and the alternative with the highest fuzzy criterion function value is considered the best alternative.

[0040] Furthermore: The expression of the fuzzy criterion function is as follows:

[0041]

[0042] A multi-criteria decision-making method for solving the charging strategy selection problem provided by the present invention attempts to treat the charging strategy selection of an electric vehicle system as a multi-criteria decision-making problem, helping policymakers use reliable decision-making tools to compare and evaluate possible charging options / alternatives based on various relevant criteria in practice to make informed decisions.

[0043] For users or enterprises that need to consider charging strategies, a series of comprehensive criteria are first proposed, including five aspects: economy, environment, society, operation, and service quality; secondly, a fuzzy best-worst method is designed to determine the weights of the criteria; thirdly, an alternative fuzzy ranking method is proposed for evaluating and ranking the available charging strategies of an electric vehicle system, including overnight charging and opportunity charging strategies; finally, the evaluation results are extended by testing other alternative ranking methods (including fuzzy TOPSIS and fuzzy EDAS), and the achievements of various methods in solving the problem are compared. Description of the Drawings

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 is the logic diagram of the method of this application;

[0046] Figure 2 is the logic diagram of the relevant criteria for EB charging strategy selection. Specific embodiments

[0047] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail the present invention.

[0048] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will combine 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 some embodiments of the present invention, rather than all embodiments. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0049] Figure 1 is the logic diagram of the method of this application;

[0050] Figure 2 is the logic diagram of the relevant criteria for EB charging strategy selection.

[0051] A multi-criteria decision-making method for solving the problem of charging strategy selection includes the following steps:

[0052] S1: Evaluate the charging strategies of electric vehicles according to their impacts on the economic operation cost, environmental impact, social impact, operation cost and service quality evaluation of electric vehicle charging stations;

[0053] S2: Based on the results of comparing and evaluating the charging strategies of electric vehicles, adopt the fuzzy best-worst method, capture the subjective fuzziness when comparing different criteria or options according to the weight levels of different criteria, and re-determine the weights of the criteria through pairwise comparison;

[0054] S3: Evaluate and rank the available charging strategies for the electric vehicle system based on the alternative fuzzy ranking method.

[0055] The available charging strategies include overnight charging and opportunity charging strategies.

[0056] The steps S1 / S2 / S3 are executed sequentially;

[0057] For users or enterprises that need to consider charging strategies, a high-resolution multi-dimensional framework is provided in the form of a multi-dimensional control and management problem, which is used to compare and evaluate possible electric vehicle charging strategies according to the criteria in five aspects: economy, environment, society, operation, and service quality. The specific process is as follows:

[0058] A high-resolution multi-dimensional framework is provided in the form of a multi-dimensional control and management problem. The framework includes articles on charging strategies, and their texts and tables are carefully reviewed to extract relevant criteria; it includes a multi-criteria decision-making premise, which provides a more organized perspective to discover and classify new criteria according to these frameworks.

[0059] Standard analysis and extraction: In practice, charging strategies are affected by multiple factors, such as economic operating costs, environmental impacts, social impacts, operating costs, and service quality evaluation. There are a total of 25 specific criteria under these five factors, as shown in Table 1;

[0060]

[0061] Above, R is the scoring matrix of the criteria, where represents the jth criterion in the ith factor; there are a total of five factors, and the value of m is 5.

[0062] The comprehensive score C of each alternative can be calculated by the following formula:

[0063]

[0064] where w ij represents the social benefit of the jth criterion in the ith factor.

[0065] Rank all the criteria according to the comprehensive score C, and the ranking levels are shown in Table 2.

[0066] The evaluation criteria for economic operating costs specifically include: battery cost, infrastructure cost, and operating cost; among them, the battery cost refers to the price of the battery pack; the infrastructure cost refers to the cost of purchasing charging equipment and charging stations and installing chargers; the operating cost refers to the cost of regular inspections, repairs, or replacements of vehicle components and charging equipment.

[0067] Due to the large capital investment required for the electrification of urban bus networks, economic factors have a significant impact on it. In terms of selecting the optimal type of electric vehicle charging infrastructure, the economic aspects of the environment and biology have been studied in the literature. By considering the costs associated with purchasing, operating, maintaining, and building the infrastructure, the technical and financial performance of electric vehicles is evaluated. The results show that optimizing the charging infrastructure and battery size according to operating constraints can significantly reduce the ownership cost of an electric vehicle fleet. Secondly, the ecological aspects of electric vehicles in a medium-sized city are evaluated. After a comprehensive background analysis, the life cycle cost difference between electric buses and diesel buses is calculated based on the selected parameters. Finally, two types of lithium-ion batteries and different charging strategies are evaluated: charging in the garage, charging at the terminal, and charging at the line site. An integer linear programming model for the economic operating cost is proposed to simulate the transition problem of electric vehicles in order to formulate an electric vehicle replacement plan. The model takes into account the acquisition and operating costs, demand charges, and investment in charging infrastructure. The specific model is as follows:

[0068] The decision variables are: x it The number of purchased (integer) of the i-th type of electric vehicle in the t-th year; y it The number of operated (integer) of the i-th type of electric vehicle in the t-th year; w t The investment in charging infrastructure in the t-th year (continuous variable). The parameters are: The acquisition cost of the i-th type of electric vehicle; The operating cost of the i-th type of electric vehicle; D t The demand for electric vehicles in the t-th year; The investment in charging infrastructure required for electric vehicles in the t-th year.

[0069] The minimum total cost including the acquisition cost, operating cost, and investment in charging infrastructure is:

[0070]

[0071] Evaluation criteria for environmental impact: including greenhouse gas emissions, energy consumption, environmental pollution after demolition, greenhouse gas emissions during battery production, water consumption during battery production, and ecological environment;

[0072] The impact of the batteries used in electric batteries on the environment has been studied from different aspects, such as greenhouse gas emissions, emissions during the power generation process, and water consumption during the battery production and recycling stages. The life cycle of battery-grade lithium carbonate and lithium hydroxide monohydrate produced from brine and sodium calcium ore has been analyzed. A life cycle assessment framework is proposed in the investigation to evaluate the environmental costs associated with electric vehicle battery packs;

[0073] Secondly, some models for estimating the energy consumption of electric vehicles are proposed, such as a model that combines a longitudinal dynamics model and digital elevation for planning the installation of electric vehicles. The specific model is as follows:

[0074] The energy consumption calculation formula is:

[0075]

[0076] The driving range calculation formula is:

[0077]

[0078] Among them, E is the energy consumption of the electric vehicle; D is the driving distance; U is the battery terminal voltage during vehicle operation; I is the battery terminal current during vehicle operation; E d is the energy from the power grid during charging; C is the energy consumption rate.

[0079] Regarding the environmental emissions of various charging methods, it has been reported that overnight charging using renewable energy during off-peak hours can reduce emissions and improve the environmental performance of the electric vehicle system, but it will also increase the demand for power distribution and energy storage infrastructure. Finally, due to the increased demand for the power grid, fast charging may lead to more generation emissions. The ability to utilize renewable energy plays a crucial role in the selection of charging strategies.

[0080] Evaluation criteria in terms of social impact: including job opportunities, fire risk, and the impact of charging infrastructure on surrounding residential areas. Among them, job opportunities refer to the employment opportunities (part-time and full-time) created by battery production and charging station construction; fire risk refers to the possibility and risk of fires occurring in batteries and garages / charging stations; the impact of charging infrastructure on surrounding residential areas refers to the impact of the electric vehicle charging process on residents' lives.

[0081] Society: The third type of extraction criterion is to review the social aspects of alternatives. Literature and interviews with experts on charging strategy selection indicate that social factors such as fire risk, the impact on residents' quality of life, and public perception are important considerations. Overnight charging can reduce noise pollution and improve the quality of life of residents near bus stops; fast charging will result in increased noise pollution and visual impact when installing charging infrastructure in public places. Another factor in this category is fire risk. The increasing use of electric vehicles has drawn people's attention to the safety issues brought about by potential fire risks, such as recent fire safety issues related to electric vehicles and thermal runaway and fire accidents of lithium-ion batteries. A fire test model was established based on a survey using hard-shell prismatic lithium iron phosphate batteries to study the fire characteristics and fire extinguishing methods of lithium-ion batteries used in electric vehicles. The fire thermal runaway formula in this model is:

[0082]

[0083] Among them, T d is the battery temperature, Q is the heat generated per unit time, m is the battery mass, c p is the specific heat capacity, T is the ambient temperature, and τ is the thermal time constant.

[0084] Evaluation criteria for operation: including vehicle capacity, energy monitoring, driving range, charging time, and scheduling complexity. Among them, vehicle capacity refers to the passenger capacity (depending on the internal design of the vehicle); energy monitoring refers to the monitoring of the energy level (remaining energy) during operation; driving range refers to the distance (in kilometers) that the vehicle can travel on a single battery charge; charging time refers to the time required to fully charge the battery; and scheduling complexity refers to the computational complexity of planning work.

[0085] In terms of operation: A series of operation standards, such as the uniqueness of electric minibuses (such as range anxiety and battery charging limitations), fundamentally understanding the factors affecting the operation of electric minibuses is of great practical significance for the electrification of public transportation systems. First, in this regard, it is studied how the limited driving range of electric vehicles affects vehicle operation under the charging strategy at the vehicle factory. Since the driving range and charging duration of electric vehicles are affected by the characteristics of their on-board battery packs (type, capacity, voltage, price, and lifespan), the evaluation of vehicle battery performance has been the subject of many studies and also provides clear insights into this issue. A method for selecting the best lithium-ion battery for electric vehicles is proposed, using weights, which is a multi-criteria decision-making method. Second, the impact of cold weather on lithium-ion batteries is discussed, as cold weather can cause capacity / power degradation of lithium-ion battery technology. Third, the life cycle assessment of lithium-ion batteries throughout their life cycle is reviewed, and the framework, types, criteria, methods, and technical difficulties of life cycle assessment are introduced. Finally, the steps of manufacturing, using, reusing, and material recycling of lithium-ion batteries are studied.

[0086] Service quality evaluation criteria: including crowding level, travel time, and reliability; among them, crowding level refers to the degree of crowding inside the vehicle (affecting the travel comfort of users); travel time refers to the round-trip time (considering charging events during operation); and reliability refers to the normality of the service from the user's perspective.

[0087] Service quality: The last category of criteria specifically targets service quality (including congestion level, travel time, and reliability), measuring the service quality of each vehicle. First, from the user's perspective, the performance of substitutes is considered. Since the charging time required by charging facilities affects the travel time and normal operation of electric vehicles. For example, an overnight charging system has the potential to reduce the risk of operational delays caused by charging idle time. Second, a new method is proposed to balance the economy and ecology of using electric vehicles in order to achieve environmental protection and the economic operation of buses. The charging time of electric vehicles is optimized as a function of carbon dioxide emissions. Considering different charging schemes, the electricity cost affects the charging time of electric buses during service, thus affecting the travel time. Third, the views in the literature and among experts also vary. Regarding the reliability of charging strategies, some studies have pointed out that overnight charging can also improve the reliability of the electric bus system for passengers by reducing the demand for backup energy generators and increasing the availability of buses during peak hours. Finally, rapid charging can reduce the need for long stops and increase the flexibility of bus routes, thereby improving the reliability and convenience of the electric vehicle system.

[0088] Based on the results of comparing and evaluating electric vehicle charging strategies, the fuzzy best-worst method is adopted. According to the weight levels of different criteria, the subjective fuzziness when comparing different criteria or options is captured, and the weights of the criteria are re-determined through pairwise comparisons.

[0089] The fuzzy best-worst method is a vector-based multi-criteria decision-making method that estimates the criteria weights (x 1 , x 2 ......, x i ) through pairwise comparisons, formulates a set of evaluation criteria, denoted as (a 1 , a 2 ,..., a n ), determines the best or most important and the worst or least important items, creates a fuzzy "best-others" vector that reflects the preference of the most important or best criterion for all other criteria. This vector is expressed as Equation (5):

[0090]

[0091] Where: The value of reflects the preference of the best criterion relative to criterion j, The value of equals (1, 1, 1); the linguistic expressions are converted into fuzzy numbers;

[0092] Calculate the optimal fuzzy value of the criterion weights. The fuzzy weights are calculated by solving a non-linear optimization model that contains an objective function aiming to minimize the maximum absolute difference between the fuzzy weights obtained from fully consistent comparisons and the current weights;

[0093]

[0094] Among them,

[0095]

[0096] The consistency ratio (CR) is used to evaluate the consistency and accuracy of calculating weights. When , the fuzzy comparison is considered to be completely consistent, where refers to the preference of the best criterion over the worst criterion; when , the inconsistency rate increases; when and both equal , the inconsistency rate is the highest.

[0097] Among them,

[0098] is the optimization coefficient, which satisfies Equation (7):

[0099]

[0100] The consistency ratio is:

[0101]

[0102] where CI represents the consistency index, which varies according to the importance of the criteria.

[0103] Furthermore, the process of evaluating and ranking the available charging strategies of an electric vehicle system by the proposed alternative fuzzy ranking method is as follows:

[0104] Create a summary fuzzy decision matrix. Regarding the fuzzy linguistic scale of a set of criteria (c1, c2,..., cm), the provided evaluations are represented in matrix form as Equation (8):

[0105]

[0106] Among them, represents the fuzzy value calculated using the fuzzy linguistic scale;

[0107] Formula (10) is the fuzzy Heron operator, which is used to aggregate k fuzzy decision matrices into matrix

[0108]

[0109] where represents the average fuzzy number, and p, q ≥ 0 represents the set of non-negative numbers;

[0110] The elements of the aggregated decision matrix are transformed into a standard interval, and ideal and non-ideal values are set for each criterion. Using and to represent, the role is to define a function for each alternative, as shown in formula (11), mapping the interval in the aggregated decision matrix to a new interval [n 1 , n b :

[0111]

[0112] where n 1 and n b are the ratios of the ideal value to the anti-ideal value, and represents the fuzzy value of alternative i for criterion j in the aggregated decision matrix. The result is the criterion judgment matrix:

[0113] The said ideal value and anti-ideal value have a ratio of at least 6:1.

[0114] Normalize the elements in the criterion judgment matrix:

[0115]

[0116] where A represents the arithmetic mean of n 1 and n b , and H represents their harmonic mean.

[0117] Calculate the fuzzy criterion function for each alternative using formula (13). The alternative with the highest fuzzy criterion function value is considered the best alternative:

[0118]

[0119] The method also includes expanding the evaluation results by testing other alternative ranking methods (including fuzzy TOPSIS and fuzzy EDAS), so as to compare the achievements obtained by various methods in solving problems.

[0120] The scores of two alternatives, namely overnight charging and opportunity charging, in each criterion are shown in Table 1:

[0121] Table 1 Scores of two alternatives in each criterion

[0122]

[0123] It can be clearly seen from the results shown in Table 1 that the scores of each criterion are different. Arranged according to the level, the focus can be accurately found according to this ranking.

[0124] Table 2 Descriptions and scoring levels of all criteria

[0125]

[0126]

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-criteria decision-making method for solving the problem of charging strategy selection, characterized by: The following steps are involved: Evaluate the EV charging strategy based on the economic operating costs, environmental impact, social impact, operating costs and service quality of EV charging stations; Based on the results of comparing and evaluating the charging strategies of electric vehicles, the fuzzy best-worst method is used to capture the subjective ambiguity when comparing different criteria or options according to the weight levels of different criteria, and the weights of the criteria are re-determined through pairwise comparisons; Based on the fuzzy ranking method of alternatives, the available charging strategies for electric vehicle systems are evaluated and ranked.

2. A multi-criteria decision-making method for solving the charging strategy selection problem according to claim 1, characterized in that: Based on the results of comparing and evaluating the charging strategies for electric vehicles, the fuzzy best-worst method is used to capture the subjective ambiguity when comparing different criteria or options according to the weight levels of different criteria. The process of re-determining the weights of the criteria through pairwise comparison is as follows: The fuzzy best-worst method is a vector-based decision-making method that estimates the standard weights (x1, x2, ..., x i ), formulate a set of evaluation criteria, denoted as (a1, a2, ..., a n ), determine the best or most important and the worst or least important items, and create a fuzzy "best-other" vector that reflects the preference of the most important or best criterion over all other criteria. The vector is expressed as formula (1): in: The value of reflects the preference of the best criterion over criterion j, The value of is (1,1,1); the language expression is converted into fuzzy numbers; Calculate the optimal fuzzy value of the standard weight, the fuzzy weight is calculated by solving a nonlinear optimization model, which contains an objective function to minimize the maximum absolute difference between the fuzzy weight obtained by the exact comparison and the current weight; in, The consistency ratio was used to assess the consistency and accuracy of the calculated weights; when When , the fuzzy comparison is considered to be completely consistent; when When and are equal to The inconsistency rate is highest when . Among them, refers to the preference for the best standard over the worst standard, is the optimization coefficient, which satisfies formula (3): The consistency ratio is: Where CI stands for consistency index, which varies according to the importance of the criterion.

3. A multi-criteria decision-making method for solving the charging strategy selection problem according to claim 1, characterized in that: The process of evaluating and ranking the available charging strategies for electric vehicle systems based on the alternative fuzzy ranking method is as follows: Create a summary fuzzy decision matrix, about a set of fuzzy language scales of standards (c1, c2, ..., cm), and provide an evaluation in the form of a matrix as formula (5): in, represents the fuzzy value calculated using the fuzzy linguistic scale; Formula (6) is the fuzzy Heron operator, which is used to aggregate k fuzzy decision matrices into a matrix in represents the average fuzzy number, p,q≥0 represents a non-negative number set; The elements of the decision matrix are summarized into standard intervals, and ideal and non-ideal values ​​are set for each standard. and It is used to define a function for each alternative, as shown in formula (7), which maps the interval in the summary decision matrix to the new interval [n1,n b ]: Among them, n1 and n b is the ratio of the ideal value to the anti-ideal value, represents the fuzzy value of alternative i for criterion j in the summary decision matrix, and the result is the standard decision matrix:

4. A multi-criteria decision-making method for solving the charging strategy selection problem according to claim 1, characterized in that: The ratio of the ideal value to the anti-ideal value is at least 6:

1.

5. A multi-criteria decision-making method for solving the charging strategy selection problem according to claim 4, characterized in that: Normalize the elements in the standard decision matrix: Where A represents n1 and n b , and H represents their harmonic mean. The fuzzy criterion function of each alternative is calculated, and the alternative with the highest fuzzy criterion function value is considered the best alternative.

6. A multi-criteria decision-making method for solving the charging strategy selection problem according to claim 5, characterized in that: The expression of the fuzzy standard function is as follows:

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