CN113627721B - Analytic hierarchy process-based electric vehicle energy system operation mode analysis optimization method - Google Patents
Analytic hierarchy process-based electric vehicle energy system operation mode analysis optimization method Download PDFInfo
- Publication number
- CN113627721B CN113627721B CN202110751529.7A CN202110751529A CN113627721B CN 113627721 B CN113627721 B CN 113627721B CN 202110751529 A CN202110751529 A CN 202110751529A CN 113627721 B CN113627721 B CN 113627721B
- Authority
- CN
- China
- Prior art keywords
- energy system
- layer
- electric automobile
- index
- operation mode
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Mathematical Physics (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Mathematical Optimization (AREA)
- Quality & Reliability (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Pure & Applied Mathematics (AREA)
- Algebra (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses an electric vehicle energy system operation mode analysis optimization method based on an analytic hierarchy process. The method mainly comprises the following steps: constructing relevant technical indexes of an electric automobile energy system as a criterion layer comprises the following steps: speed performance index, acceleration performance index, brake performance index, comfort level index and endurance index; constructing an electric automobile energy system operation mode model as a scheme layer; selecting an optimal electric vehicle energy system operation mode as a target layer; constructing a pair comparison matrix of the criterion layer to the target layer; constructing a quantitative analysis evaluation function of each index of the scheme layer for the criterion layer, constructing a pair comparison matrix of each index of the scheme layer when the scheme layer is aligned, and respectively carrying out consistency test on the matrixes; the method is used for analyzing the running mode of the energy system of the electric automobile and giving out the optimization direction of the mode. The model of the invention is based on analytic hierarchy process, and provides personalized energy system operation scheme through weight adjustment.
Description
Technical Field
The invention relates to the field of energy systems of electric automobiles, in particular to an energy system operation mode analysis and optimization method based on an analytic hierarchy process.
Background
Electric Vehicles (BEV) are a class of vehicles using on-board power sources as energy sources, which are key points for relieving the contradiction between the current severe energy crisis and the increasing traffic demands, and are also important directions for the development of the current and future automobile industries. However, there are many drawbacks of electric vehicles, such as endurance, charging, and stability of electric vehicles. At present, a great deal of research is being conducted on how to improve the energy utilization rate of the electric automobile, how to improve the power performance of the electric automobile and the like at home and abroad, and certain achievements are achieved.
However, improving a certain performance index of an electric automobile often sacrifices other performances, for example, improving the renewable braking proportion of the electric automobile can effectively recover braking energy and improve the endurance of the automobile, but the braking response time is prolonged, and the stability is reduced; for example, the acceleration capability of the automobile is improved, the starting energy consumption of the electric automobile is increased, the cruising ability is reduced and the like due to the fact that the hundred kilometers of acceleration time is reduced. On the other hand, the requirements of different users for various indexes of the electric automobile are greatly different, and the differences of factors such as various purposes, environments, habits and the like determine that a fixed running scheme is difficult to meet different use groups.
Therefore, while improving the performance of various aspects of electric vehicles, it is also necessary to analyze the various performances of electric vehicles and propose a reasonable energy distribution scheme. The invention provides an electric vehicle energy system operation mode analysis method based on an analytic hierarchy process, which can carry out scientific and reasonable comprehensive evaluation on various energy system operation modes, can obtain an optimization method on the basis, provides scientific basis for the design and improvement of the electric vehicle energy system operation mode, and can also provide a targeted energy system operation mode reference scheme according to different requirements.
Disclosure of Invention
The invention aims to provide an analysis and optimization method for an energy system operation mode of an electric automobile based on an analytic hierarchy process, which is used for analyzing various performances of the electric automobile and evaluating the energy system operation mode of the electric automobile, so that reasonable and scientific scheme selection results and scheme improvement ideas are obtained, a barrel effect in the energy system operation mode of the electric automobile is avoided, and theoretical guidance is provided for the optimal operation of the energy system of the electric automobile.
The technical scheme of the invention is as follows: an electric automobile energy system operation mode analysis optimization method based on an analytic hierarchy process comprises the following steps:
step 1: constructing relevant technical indexes of an electric automobile energy system as a criterion layer comprises the following steps: speed performance index, acceleration performance index, brake performance index, comfort level index and endurance index;
step 2: constructing an electric automobile energy system operation mode model as a scheme layer;
step 3: taking the optimal operation mode of the energy system of the electric vehicle as a target layer, and constructing an analytic hierarchy model of the operation mode of the energy system of the electric vehicle according to the analytic hierarchy process AHP by combining the step 1 and the step 2;
step 4: constructing a pair comparison matrix A of the criterion layer to the target layer according to the 1-9 comparison scale;
step 5: calculating a weight vector w according to the matrix A, and carrying out consistency test on the matrix A;
step 6: constructing a quantitative analysis evaluation function of each index of the scheme layer relative to the criterion layer and constructing a pairwise comparison matrix B of each index of the scheme layer when the scheme layer is aligned i (i＝1、2、3、4、5)；
Step 7: according to matrix B i Calculate the corresponding weight vector W i (i=1, 2, 3, 4, 5), and for matrix B i Respectively carrying out consistency test;
step 8: outputting a comparison weight vector w' of the scheme layer to realize the analysis of the running mode of the energy system of the electric automobile;
step 9: and (5) combining the step 5, the step 8 and the constraint condition, and providing an optimization direction of an operation mode of the energy system of the electric automobile.
The beneficial effects of the invention are as follows: the energy system operation mode evaluation optimization model is established on the basis of an analytic hierarchy process, and various operation modes can be evaluated, selected and optimized scientifically and reasonably by reasonably establishing criterion indexes: (1) The operation mode of the energy system of the electric automobile can be scientifically and reasonably selected. (2) The optimization direction can be provided on the basis of the original operation scheme, and theoretical basis is provided for the improvement of the operation mode of the energy system of the electric automobile. (3) According to different requirements, the invention can provide a personalized energy system operation scheme through adjustment of the weight.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
fig. 2 is a hierarchical structure diagram of a specific embodiment of an analysis and optimization method for an energy system operation mode of an electric vehicle.
Detailed description of the preferred embodiments
In order to describe the technical scheme disclosed by the invention in detail, the method for analyzing and optimizing the operation mode of the energy system of the electric automobile is described in detail below with reference to the accompanying drawings and specific embodiments, and the protection scope of the invention includes but is not limited to the following embodiments.
An electric automobile energy system operation mode analysis optimization method based on analytic hierarchy process, as shown in figure 1, comprises the following detailed steps:
step 1: constructing relevant technical indexes of an electric automobile energy system as a criterion layer, wherein the related indexes comprise: speed performance index I v Acceleration performance index I a Index of brake Performance I b Comfort index I c Endurance index I e The specific construction method is as follows:
1) Speed performance index I v : the functional expression is as follows:
wherein: v (V) max_l The maximum allowable speed of an electric automobile under the control of an energy system is set; v (V) max Is the theoretical maximum speed which can be achieved by a certain electric automobile.
2) Acceleration performance index I a : the functional expression is as follows:
wherein: t is t min_l The shortest hundred kilometer acceleration time of a certain electric automobile under the control of an energy system, namely the time for accelerating the automobile from rest to 100 km/h; t is t min The theoretical shortest hundred kilometer acceleration time which can be achieved for a certain electric automobile.
3) Brake performance index I b : the functional expression is as follows:
wherein: f (F) m F is the magnitude of mechanical braking force during pure mechanical braking m ' is the magnitude of mechanical braking force during hybrid braking, F e N is the magnitude of regenerative braking force during hybrid braking m For braking effectiveness of mechanical braking, n e Is the braking efficiency of the regenerative braking.
4) Comfort indexI c : the function expression is established based on the electric automobile energy system as follows:
wherein: p (P) max_l The maximum output power sum of the air conditioning system and the low-voltage system of a certain electric automobile under the control of the energy system; p (P) max The maximum output power of the air conditioning system and the low-voltage system which can be provided for an electric automobile.
5) Endurance index I e : the functional expression is as follows:
wherein: m is M max_l The maximum endurance mileage of a certain electric automobile under the control of an energy system; m is M max Is the theoretical maximum endurance mileage which can be achieved by a certain electric automobile.
Step 2: an electric automobile energy system operation mode model is built as a scheme layer, and the built model comprises the following steps: comfort optimization model M c Energy-saving optimization model M e Performance optimization model M p These three schemes all have different function values for the five criteria indexes set forth in step 1, which are used to establish the pairwise alignment in step 6.
Step 3: and (3) taking the optimal operation mode of the energy system of the electric vehicle as a target layer, and constructing an analysis-by-layer model of the operation mode of the energy system of the electric vehicle according to the AHP of the analysis-by-layer method by combining the step (1) and the step (2), wherein the analysis-by-layer model is shown in figure 2.
As can be seen from the figure, the content of the target layer is to select the optimal operation mode of the energy system of the electric automobile, and is placed on the first layer, which is one of the purposes to be achieved by the invention; criterion layer consists of speed performance index I v Acceleration performance index I a Index of brake Performance I b Comfort index I c Endurance index I e Five parallel judgment indexes are formed and arranged on the second layer, and are five judgment criteria which are also the main basis of the invention; the scheme layer comprises three schemes of a comfortable optimization mode model, an energy-saving optimization mode model and a performance optimization mode model, and is also three different schemes provided by the embodiment of the invention.
Step 4: and constructing a pair comparison matrix A of the criterion layer to the target layer according to the 1-9 comparison scale. Wherein, the meaning of the comparison scale of 1-9 is: when comparing two criteria I i 、I j The comparison scale uses the scale a shown in Table 1 when the importance of the target layer is relative to that of the target layer ij The representation is performed:
TABLE 1
Thus, the criterion layer-to-target layer pair-wise comparison matrix a can be constructed according to:
wherein a is ij Is I i 、I j The construction method is shown in table 1 with respect to the importance of the target layer.
Step 5: and calculating the weight vector w according to the matrix A, and carrying out consistency check on the matrix A. The expression of the weight vector w corresponding to the pair comparison matrix a of the criterion layer to the target layer is as follows:
w＝[w 1 ,w 2 ,w 3 ,w 4 ,w 5 ] T ，
wherein w is 1 Weights, w, representing speed performance indicators 2 Weight, w, representing acceleration performance index 3 Weights, w, representing brake performance indicators 4 Weights representing comfort indicators, w 5 And the weight of the endurance index is represented. The corresponding calculation formula is as follows:
A·w＝λ max ·w，
wherein lambda is max And w is the feature vector which corresponds to the feature root and is subjected to normalization processing, namely the weight vector.
In addition, the consistency check formula is:
wherein lambda is max For the maximum feature root of matrix A, n is the matrix order, CI is the consistency index, RI is the random consistency index, and the specific value is related to the matrix order and can be obtained through table lookup, as shown in Table 2. CR is a consistency ratio, and if CR is less than 0.1, the matrix is considered to have consistency, otherwise, the matrix is considered to have no consistency, and the construction of the pair comparison matrix is needed to be carried out again until consistency test is met. The weight vector calculation method and the consistency check method in the step 7 are the same as the corresponding methods in the step.
TABLE 2
Through step 4 and step 5, the pair comparison matrix a and the weight vector thereof in this embodiment can be constructed as shown in table 3:
TABLE 3 Table 3
The consistency ratio cr=0.0089 < 0.1 of matrix a in this example satisfies the consistency check.
Step 6: the quantized analysis evaluation function of each index of the criterion layer is constructed by the scheme layer, and the quantized analysis evaluation function value of each index of the criterion layer can be calculated by using each performance index function value of each scheme in the step 1 as the quantized analysis evaluation function.
Second, build scheme layer versus criterion layerPaired comparison matrix B of each index i (i=1, 2, 3, 4, 5), wherein matrix B 1 ,B 2 ,B 3 ,B 4 ,B 5 Sequentially corresponding to criterion index I v ,I a ,I b ,I c ,I e And (3) constructing: matrix B i The quantitative analysis and evaluation function ratio after normalization treatment can be directly assigned, and approximate assignment can be performed according to 1-9 comparison scales on the basis. In order to simplify the calculation step and more intuitively reflect the comparison relation of various indexes, the embodiment adopts a quantitative analysis evaluation function ratio assignment method to construct a pair comparison matrix B of various indexes of a scheme layer alignment i See step 7 for specific results.
The quantitative analysis evaluation function values selected in this example are shown in table 4:
TABLE 4 Table 4
Step 7: according to step 6, the embodiment uses the quantitative analysis evaluation function ratio assignment method to construct a pairwise comparison matrix B of the indexes of the scheme layer alignment i . For example, for matrix B 1 (corresponding criterion index I) v ) A certain element b of ij The specific calculation formula of (2) is shown as follows:
wherein b is ij For matrix B 1 Wherein I and j represent the rank value of the element, I v_i Quantifying function value for speed performance index of I row corresponding scheme, I v_j And quantifying the function value for the speed performance index of the j-column corresponding scheme.
According to Table 4 and the above calculation formula, the weight vector calculation and consistency check method in step 5 are used to obtain the paired comparison matrix B of the layer to the criterion layer in the embodiment i Weight vector W of the system i As shown in tables 5 to 9, the correspondence ratio CR for each table is illustrated below the table:
TABLE 5
Cr= 0.00017 < 0.1 table 6
Cr=0.00026 < 0.1 table 7
Cr= 0.00069 < 0.1 table 8
CR＝0.00000＜0.1
TABLE 9
CR＝0.00034＜0.1
Step 8: from the calculation results of step 5 and step 7, the calculation results of the scheme layer to the criterion layer can be constructed as shown in table 10:
table 10
Will weight vector W i And sequentially performing matrix transverse combination to obtain a total weight matrix W of the 3X 5 scheme layer alignment rule layer. And the final comparison weight vector w' of the scheme layer can be obtained through the weight vector operation of the criterion layer on the target layer, whereinThe calculation formula is as follows:
w'＝W·w，
by combining the data in Table 10, the layer comparison weight vector w' of the present embodiment is calculated as [0.5179,0.6389,0.5307 ]] T . The weights are ordered according to the size, so that 0.6389 > 0.5307 > 0.5179 can be obtained, which shows that the priority order of the three energy system operation modes of the electric automobile in the embodiment is an energy-saving optimization mode M e Performance optimization model M p Comfort optimization mode M c 。
Step 9: according to step 5, the expression of the weight vector w corresponding to the pair comparison matrix a of the criterion layer to the target layer can be obtained as follows:
w＝[w 1 ,w 2 ,w 3 ,w 4 ,w 5 ] T ，
wherein w is 1 Weights, w, representing speed performance indicators 2 Weight, w, representing acceleration performance index 3 Weights, w, representing brake performance indicators 4 Weights representing comfort indicators, w 5 The weight of the endurance index is represented, and an objective function Ta of a simplified optimal electric vehicle energy system operation mode is constructed according to the weight value, wherein the calculation formula is as follows:
Ta＝max(w 1 ·I v +w 2 ·I a +w 3 ·I b +w 4 ·I c +w 5 ·I e )，
according to the weight vector w calculation result in step 5, the specific calculation formula of the objective function Ta of the operation mode of the optimal electric vehicle energy system in this embodiment is as follows:
Ta＝max(0.428·I v +0.199·I a +0.094·I b +0.217·I c +0.061·I e )，
and certain constraint conditions exist in and among the criterion layer indexes, such as total energy storage capacity limit, total power limit and the like of the electric automobile. These constraints can be obtained by modeling and solving to obtain a corresponding constraint equation, and the approximate constraint obtained by the embodiment with respect to a certain use background is shown in the following formula:
the approximate optimal solution can be obtained by a solving mode of a mathematical programming model to be I= [1,1,0.286,0.630,0.95 ]] T Contrast energy-efficient optimization mode M e Scheme weight value of [0.7,0.5,0.7,0.4,0.9 ]] T The optimization direction under the background of the application of the embodiment can be obtained as follows: in energy-saving optimization mode M e On the basis of the vehicle speed control system, the limitation on the speed performance and the acceleration performance is relaxed, the duty ratio of regenerative braking is further limited, and the whole cruising ability and the comfort are improved.
It is obvious that the above examples of the present invention are presented only for illustrating the processes of the present invention, and the implementation of the present invention is not limited by the above examples, and the present invention is within the scope of the present invention as long as the same or a slightly simple variation or modification is used.
Claims (3)
1. An electric automobile energy system operation mode analysis optimization method based on an analytic hierarchy process is characterized by comprising the following steps of: mainly comprises the following steps:
step 1: constructing relevant technical indexes of an electric automobile energy system as a criterion layer comprises the following steps: speed performance index, acceleration performance index, brake performance index, comfort level index and endurance index;
the method specifically comprises the following indexes:
1) Speed performance index I v : the functional expression is as follows:
wherein: v (V) max_l The maximum allowable speed of an electric automobile under the control of an energy system is set; v (V) max The theoretical maximum speed which can be achieved by a certain electric automobile;
2) Acceleration performance index I a : the functional expression is as follows:
wherein: t is t min_l The shortest hundred kilometer acceleration time of a certain electric automobile under the control of an energy system, namely the time for accelerating the automobile from rest to 100 km/h; t is t min The theoretical shortest hundred kilometer acceleration time which can be achieved for a certain electric automobile;
3) Brake performance index I b : the functional expression is as follows:
wherein: f (F) m F is the magnitude of mechanical braking force during pure mechanical braking m ' is the magnitude of mechanical braking force during hybrid braking, F e N is the magnitude of regenerative braking force during hybrid braking m For braking effectiveness of mechanical braking, n e Braking effectiveness for regenerative braking;
4) Comfort index I c : the function expression is established based on the electric automobile energy system as follows:
wherein: p (P) max_l The maximum output power sum of the air conditioning system and the low-voltage system of a certain electric automobile under the control of the energy system; p (P) max The sum of maximum output power of an air conditioning system and a low-voltage system which can be provided for a certain electric automobile;
5) Endurance index I e : the functional expression is as follows:
wherein: m is M max_l The maximum endurance mileage of a certain electric automobile under the control of an energy system; m is M max The theoretical maximum endurance mileage which can be achieved for a certain electric automobile;
step 2: the method for constructing the electric automobile energy system operation mode model as a scheme layer comprises the following steps: a comfortable optimization mode model, an energy-saving optimization mode model and a performance optimization mode model;
step 3: taking the optimal operation mode of the energy system of the electric vehicle as a target layer, and constructing an analytic hierarchy model of the operation mode of the energy system of the electric vehicle according to the analytic hierarchy process AHP by combining the step 1 and the step 2;
step 4: constructing a pair comparison matrix A of the criterion layer to the target layer according to the 1-9 comparison scale;
step 5: calculating a weight vector w according to the matrix A, and carrying out consistency test on the matrix A;
step 6: constructing quantitative analysis evaluation functions of each index of scheme layer to criterion layer and constructing a paired comparison matrix B of each index of the scheme layer alignment layer i ，i＝1、2、3、4、5；
Step 7: according to matrix B i Calculate the corresponding weight vector W i I=1, 2, 3, 4,5, and for matrix B i Respectively carrying out consistency test;
step 8: and outputting a comparison weight vector w 'of the scheme layer to realize the analysis of the running mode of the energy system of the electric automobile, wherein the comparison weight vector w' comprises the following specific steps:
will weight vector W i Sequentially performing matrix transverse combination to obtain a total weight matrix W of a 3×5 scheme layer alignment rule layer, and obtaining a final comparison weight vector W' of the scheme layer by performing weight vector operation on a target layer with a criterion layer, wherein the calculation formula is as follows:
w'＝W·w
step 9: and (5) providing an optimization direction of the running mode of the energy system of the electric automobile by combining the step 5, the step 8 and the constraint condition.
2. The analytic hierarchy process-based operation mode analysis optimization method of the electric vehicle energy system of claim 1, wherein the method comprises the following steps: the expression of the weight vector w corresponding to the pair comparison matrix a of the criterion layer to the target layer is as follows:
w＝[w 1 ,w 2 ,w 3 ,w 4 ,w 5 ] T ，
wherein w is 1 Weights, w, representing speed performance indicators 2 Weight, w, representing acceleration performance index 3 Weights, w, representing brake performance indicators 4 Weights representing comfort indicators, w 5 The weight of the endurance index is represented, and an objective function Ta of the running mode of the energy system of the optimal electric automobile is constructed according to the weight value, wherein the calculation formula is as follows:
Ta＝max(w 1 ·I v +w 2 ·I a +w 3 ·I b +w 4 ·I c +w 5 · I e )。
3. the analytic hierarchy process-based operation mode analysis optimization method of the electric vehicle energy system of claim 1, wherein the method comprises the following steps: the sequences of step 1 and step 2 are interchanged, and the sequences of steps 4,5 and steps 6,7 are interchanged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110751529.7A CN113627721B (en) | 2021-07-02 | 2021-07-02 | Analytic hierarchy process-based electric vehicle energy system operation mode analysis optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110751529.7A CN113627721B (en) | 2021-07-02 | 2021-07-02 | Analytic hierarchy process-based electric vehicle energy system operation mode analysis optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113627721A CN113627721A (en) | 2021-11-09 |
| CN113627721B true CN113627721B (en) | 2023-07-18 |
Family
ID=78378889
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110751529.7A Active CN113627721B (en) | 2021-07-02 | 2021-07-02 | Analytic hierarchy process-based electric vehicle energy system operation mode analysis optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113627721B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978397A (en) * | 2019-04-01 | 2019-07-05 | 河海大学 | Integrated energy system based on analytic hierarchy process (AHP) optimizes operational mode appraisal procedure |
| CN112184008A (en) * | 2020-09-27 | 2021-01-05 | 科大国创云网科技有限公司 | Base station intelligent energy-saving model evaluation method and system based on analytic hierarchy process |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8907629B2 (en) * | 2012-02-28 | 2014-12-09 | Tesla Motors, Inc. | Electric vehicle battery lifetime optimization operational mode |
| CN106228305A (en) * | 2016-07-26 | 2016-12-14 | 中国葛洲坝集团电力有限责任公司 | A kind of appraisal procedure of Lightning stroke Protection Measures for Over-Head Lines |
-
2021
- 2021-07-02 CN CN202110751529.7A patent/CN113627721B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978397A (en) * | 2019-04-01 | 2019-07-05 | 河海大学 | Integrated energy system based on analytic hierarchy process (AHP) optimizes operational mode appraisal procedure |
| CN112184008A (en) * | 2020-09-27 | 2021-01-05 | 科大国创云网科技有限公司 | Base station intelligent energy-saving model evaluation method and system based on analytic hierarchy process |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113627721A (en) | 2021-11-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109733406B (en) | Pure electric vehicle driving strategy control method based on fuzzy control and dynamic planning | |
| Li et al. | Rule-based control strategy with novel parameters optimization using NSGA-II for power-split PHEV operation cost minimization | |
| Xu et al. | Energy flow modeling and real-time control design basing on mean values for maximizing driving mileage of a fuel cell bus | |
| Taherzadeh et al. | A comprehensive study of the parameters impacting the fuel economy of plug-in hybrid electric vehicles | |
| CN105868942A (en) | Ordered charging scheduling method for electric vehicle | |
| CN102991503A (en) | Method for controlling a vehicle | |
| Zhou et al. | A long‐term energy management strategy for fuel cell electric vehicles using reinforcement learning | |
| CN110210098B (en) | Transmission parameter matching method for extended range four-wheel drive hybrid electric vehicle | |
| Zhou et al. | Optimized fuzzy logic control strategy for parallel hybrid electric vehicle based on genetic algorithm | |
| Wang et al. | Control rules extraction and parameters optimization of energy management for bus series-parallel AMT hybrid powertrain | |
| CN114537087B (en) | Performance optimization method for integrated thermal management system of pure electric vehicle | |
| Bhattacharjee et al. | Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance | |
| CN112180280A (en) | Hybrid electric vehicle battery life optimization method considering battery health state | |
| Huang et al. | Research on hybrid ratio of fuel cell hybrid vehicle based on ADVISOR | |
| Lin et al. | Multi-objective optimized driving strategy of dual-motor EVs using NSGA-II as a case study and comparison of various intelligent algorithms | |
| CN113627721B (en) | Analytic hierarchy process-based electric vehicle energy system operation mode analysis optimization method | |
| Wu et al. | Multi-objective reinforcement learning-based energy management for fuel cell vehicles considering lifecycle costs | |
| Donateo et al. | A two-step optimisation method for the preliminary design of a hybrid electric vehicle | |
| Pan et al. | Driving cycle construction and combined driving cycle prediction for fuzzy energy management of electric vehicles | |
| CN111959491A (en) | Rule control parameter optimization method based on unified model of hybrid electric vehicle | |
| Liu et al. | Global Optimal Control Strategy of PHEV Based on Dynamic Programming | |
| Gao et al. | Control strategy for PHEB based on actual driving cycle with driving style characteristic | |
| CN115147244A (en) | Method for achieving wind curtailment and accommodation by considering charging load-electricity price response of electric automobile | |
| Xie et al. | Control strategy of hybrid power system for Fuel Cell Electric Vehicle based on neural network optimization | |
| Pan et al. | Fuzzy optimal energy management for battery electric vehicles concerning equivalent speed |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |