Fuzzy adaptive multi-point operation mode control method for range extender

Abstract

The invention is suitable for the energy management technology of an extended-range electric vehicle, and provides a range extender fuzzy adaptive multi-point operation mode control method, which comprises the following steps: optimizing an APU working point offline based on a dynamic programming algorithm, and defining a comprehensive performance evaluation index; dividing an APU working area based on a demand power probability density function, and calculating a power and torque interval; designing a mixed point-linear energy management strategy, and dynamically adjusting a threshold parameter in combination with a fuzzy logic controller; and performing multi-objective optimization on the parameters through a multi-objective backbone particle swarm optimization method, and realizing quantitative decision based on a Pareto solution set. The problems that in the prior art, the APU is low in high-load working condition efficiency, complex in control logic, insufficient in consideration of battery life factors and the like are solved, the energy consumption economy and emission performance of the whole vehicle are remarkably improved, and the service life of the battery is remarkably prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy management of range-extended electric vehicles, and particularly relates to a fuzzy adaptive multi-point operation mode control method for a range extender. Background Technique

[0002] With the increasing global energy crisis and environmental protection requirements, range-extended electric vehicles (R-EEVs), as a new energy vehicle type with high energy efficiency, the optimization of their energy management strategies has become a research hotspot. Range-extended electric vehicles achieve the decoupling of power generation and driving through an auxiliary power unit (APU). One of its core technologies is the optimization of the working mode of the APU system. In the current industry, energy management strategies mainly focus on the driving mode of the power system, the control of the APU working area, and the energy distribution strategy. However, the existing technologies still face problems such as high energy consumption, insufficient emission control, and rapid battery life attenuation.

[0003] In the prior art, the energy management strategies of range-extended electric vehicles mostly adopt fixed-point control (CPCS), power tracking control (PFCS), or multi-working point management strategies. The fixed-point control strategy operates the APU at the optimal fuel consumption point of the engine. Although it is simple to implement, it causes frequent charging and discharging of the battery, accelerating the capacity attenuation; the power tracking control strategy adjusts the APU speed to follow the target power, which has high requirements for dynamic response and does not consider the battery life; the multi-working point management strategy combines the fixed-point and tracking modes, but requires complex parameter optimization and mode switching logic. Summary of the Invention

[0004] The purpose of the present invention is to provide a fuzzy adaptive multi-point operation mode control method for a range extender, aiming to solve the technical problems existing in the prior art determined in the background technique.

[0005] The present invention is implemented as follows. A fuzzy adaptive multi-point operation mode control method for a range extender, the method includes: Step 1: Based on the dynamic programming algorithm, with the goal of minimizing the comprehensive evaluation function of APU energy consumption-emission, optimize the working points of the APU offline to obtain the optimal working curve under the APU energy consumption-emission characteristics. Based on this "optimal working curve", a comprehensive performance evaluation index is defined. The function considers the oil-electric conversion efficiency, the comprehensive emission index, and the battery capacity loss rate.

[0006] Step 2: Based on the basic research framework and ideas of multi-objective optimization problems, design the working mode of the APU system, conduct multi-objective optimization analysis on the key parameters of the APU power generation working area and control strategy. The optimization object is the number of APU working speed intervals and the power range corresponding to this speed.

[0007] Step 3: Combining the characteristics of good power followability and high energy efficiency of the APU system at multiple operating points, a hybrid point-line energy management strategy ( ) is developed. In addition, two types of APU multi-point strategies with different threshold parameter types are set to explore the influence of different types of APU system multi-point operating mode switching threshold parameters on the control performance and optimize the analysis.

[0008] Step 4: Based on the results of the co-simulation test, analyze the optimization results under different weight coefficients for the final quantitative decision-making based on the Pareto solution set. Finally, compare the performance results of several APU operating modes designed in the study horizontally, laying a research foundation for the design and development of subsequent intelligent energy management strategies.

[0009] The beneficial effects of the present invention are as follows: 1. By offline optimizing the APU operating curve through the dynamic programming algorithm, combining the oil-electric conversion efficiency, comprehensive emission index, and battery capacity loss rate model, the fuel consumption and waste gas emissions (such as CO, CH, NO x ) are significantly reduced by 15% - 20%.

[0010] 2. Introduce the battery capacity attenuation rate model, optimize the charge and discharge strategy, reduce the damage to the battery caused by frequent charge and discharge, and the experimental data shows that the battery life is extended by about 30%.

[0011] 3. Based on the fuzzy logic controller, dynamically adjust the threshold parameters, real-time identify the driving style and road condition characteristics, and the mode switching response error is less than 5%, adapting to complex working condition changes.

[0012] 4. Use the BB-MOPSO algorithm to perform multi-objective optimization on the threshold parameters, combine the Pareto solution set for quantitative decision-making, achieve the balance of energy consumption, emissions, and battery life, and the comprehensive performance is improved by more than 25%.

[0013] 5. The hybrid point-line strategy divides the power region through the probability density function, reduces the complexity of dynamic coordination control, and reduces the calibration workload by 50%. Description of the Drawings

[0014] Figure 1 It is a flowchart for offline obtaining the multi-objective optimal curve of the APU based on the DP algorithm; Figure 2 It is a multi-objective optimization result diagram of the optimal curve of the APU system based on the DP algorithm; Figure 3 It is a schematic diagram of the working area division of the APU under the demand power probability density function; Figure 4 It is for the APU system working mode switching and implementation schematic diagram; Figure 5Schematic diagram of parameter adjustment based on a fuzzy controller; Figure 6 Membership function and output surface diagram of the fuzzy controller; Figure 7 Distribution diagram of Pareto optimal solutions in the target space; Figure 8 Based on Performance comparison diagram of the control strategy; Figure 9 For different Performance result diagram. Specific implementation mode

[0015] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] A control method for a range extender fuzzy adaptive multi-point operation mode, the method comprising: Step 1: Based on the Dynamic Programming (DP) algorithm, with the minimum APU energy consumption-emission comprehensive evaluation function as the objective, offline optimize the operating points of the APU to obtain the optimal operating curve under the APU energy consumption-emission characteristics. Based on this "optimal operating curve", a comprehensive performance evaluation index is defined, and the function considers the oil-electric conversion efficiency, the comprehensive emission index and the battery capacity loss rate.

[0017] Define the oil-electric conversion loss rate of the APU system ( ) as follows: ; In the formula, is the power generation efficiency of the generator, is the efficiency between the fuel and the effective power of the engine, is the calorific value of gasoline, which is 4.6×10 7 J / kg. defines the energy transfer loss rate of the APU system, and the smaller the value, the better the fuel economy of the APU.

[0018] Define the emission characteristic functions of CO, CH, NO x gases as , and Calculate as follows: ; The APU comprehensive exhaust gas emission characteristic function Ecom considers the conventional exhaust gases (CO, CH and NO x), the calculation is as follows: ; In the formula, , and are the gas emission results of CO, CH, and NO x gas emissions, , and are the gas emission characteristic functions of CO, CH, and NO x gas emissions, , , are respectively , and weight coefficients, . defines the comprehensive emission value of the APU system, The smaller the value, the better the exhaust gas quality.

[0019] Establish an objective function representing the comprehensive energy consumption - emission performance of the APU system (APU Comprehensive Evaluation Index, Icom_APU), and the evaluation index is calculated as follows: ; In the formula, and are respectively the weight coefficients of the objective function. Compared with energy consumption, due to the catalytic conversion of the three - way catalytic converter, the relationship between the final exhaust gas emission level and the APU working area is weakened. Therefore, is less than . In this method, .

[0020] Battery capacity attenuation rate The model is as follows: ; In the formula, is the depth of discharge, , is the cumulative capacity of the battery, is the battery current, is the total cycle time, is the number of cycles, , and are fitting parameters.

[0021] As Figure 1 shown, in the cost function of the DP algorithm, respectively set , and As a penalty function, the three optimized curves obtained for different optimization objectives can be used as the basis for evaluating and comparing the effectiveness of real-time control strategies.

[0022] The state equation of the APU system model is expressed as follows: ; In the formula, represents the state vector of the system, represents the control variables (speed increment and torque increment ), and the parameter can be calculated through the following formula: ; To reduce the dimension of the optimization problem, select the operating point of the APU in the interval, and increase the required power of the APU from to . The output power of the battery can be determined according to the required power. The detailed state equations of the power battery and the APU system are described as follows: ; The optimization objective is to locate the optimal control variables ( , ) to minimize the cost function, as follows: ; In the formula, represents the cumulative number of times of power increase.

[0023] To ensure the safe and reasonable operation of the APU, the speed and torque of the APU need to satisfy the following inequality constraints: ; The multi-objective offline optimization results of the optimal working curve of the APU system based on the DP algorithm are as Figure 2 shown.

[0024] On a certain optimal working curve of the APU obtained, when the output power is determined, the working point of the APU is uniquely determined. Working along this curve, parameters such as the fuel consumption per unit time, the instantaneous oil-electricity conversion loss rate, and the instantaneous comprehensive emission index are functions of its output power.

[0025] Step 2: Based on the basic research framework and ideas of the multi-objective optimization (Multi-objective optimization, MOO) problem, design the working mode of the APU system, conduct multi-objective optimization analysis on the power generation working area and key parameters of the control strategy of the APU. The optimization objects are the number of APU working speed intervals and the power range corresponding to this speed.

[0026] Calculate the probability density function of the required power through the data of typical driving cycle conditions (such as CCDC). It is: ; In the formula, F p (p) is the power cumulative distribution function, and p(t) is the probability density function of the required power.

[0027] The principle of dividing the APU working area under the probability density function of the required power is as Figure 3 shown: Evenly divide the required power interval into (Number of Constant speed operatingpoint, Ncsop) parts. Each part can correspond to an interval where the working point of the APU is located. The upper and lower limits of the power corresponding to the power interval are calculated as follows: ; Actually, at the working point (working rotational speed ), the upper limit of the power and the lower limit of the power are: ; ; In the formula: is the maximum power, is the minimum power, is the coefficient power fluctuation margin coefficient; Furthermore, the torque interval is obtained: ; ; In the formula, and are the upper limit and lower limit of the torque at the working rotational speed n i respectively.

[0028] The power coverage size at this rotational speed working point can be obtained as: ; At , the power fluctuation margin coefficient is calculated as: ; The switching threshold value between two adjacent working points is set to satisfy the calculation: ; ; Finally, the target value of the APU operating point is calculated as follows: ; ; ; In the formula, is the real-time power value of the APU system, is the real-time rotational speed value of the APU system, is the real-time torque value of the APU system.

[0029] Step 3: On this basis, combining the characteristics of good power followability and high energy efficiency of the APU system, a hybrid point-line energy management strategy ( ) is developed. In addition, two different threshold parameter types of APU multi-point strategies are set to explore the influence of different APU system multi-point operation mode switching threshold parameter types on the control performance and optimize the analysis.

[0030] N csop = 1 + The line working mode is a hybrid working mode based on the advantages of the single point and the optimal curve. The control objective is to keep the APU within the high-efficiency range. The APU system N csop = 1 + The switching and implementation principle of the line working mode is as Figure 4 shown: The APU power is divided into four power zones according to the demand power probability density function, namely Zone 1 - Zone 4 (A1 - A4), as shown in a of Figure 4 . The corresponding probability values of A1 - A4 are ω(A1) - ω(A4). Geometrically, ω(A1) - ω(A4) represents the area value enclosed by the zone. The corresponding four zones are also clearly divided in the Map diagram, as shown in Figure 4 . P sop_1 is the boundary power between Zone 1 and Zone 2 and is also the maximum power point of the demand power. In the charge depletion - extended range (CD - EV + CS - Blend) mode, when the real-time SoC value of the battery (SoC act ) is lower than the preset threshold value SoC 0_cs , that is, SoC act < SoC 0_cs , the APU starts. If the demand power is less than the point-line switching threshold value (Threshold power - medium, P thp_m ), that is, P req < P thp_m , the APU system is set to work at the fixed point position (Stable operation point - optimum, P sop_op ). It should be noted that this position is the maximum usage probability value in the statistical sense, rather than the working point with the highest frequency, Pthp_m is the boundary power between Region 2 and Region 3, and the threshold boundary coefficient k th_m is determined as follows: where P osp_l is the lower limit of the power adjustment region, and P osp_h is the upper limit of the power adjustment region, and P osp_h represents the minimum value of the APU operating region in the APU linear operating mode, and can also be understood as the upper limit value of the APU fixed-point operating power adjustment region.

[0031] When the required power is reached, the APU generates electricity at the fixed point (constant speed "long strip region") position and the excess energy is used to charge the battery, while the insufficient part is supplemented by battery discharge.

[0032] When is reached, the APU operates on the optimal efficiency curve. The optimal can change the distribution of the APU operating points: , the fixed-point operating area of the APU is the largest, and when ( ), power tracking control is performed; , the fixed-point operating area of the APU is the smallest, and when ( ), power tracking control is performed; , use the value to adjust the fixed-point operating power range and the tracking operating power range. As an adjustment coefficient, needs to satisfy the same charge-discharge ratio of the battery during the participation process. That is to say, the proportion of the battery participating in charge and discharge should have the same probability. ω (A1)- ω (A4) The relationship between them can be expressed as: ; After simplification, we get: ; It can be seen that , The relationship between them can be calculated by the following formula: ; In order to make adapt to different driving conditions, an adaptive controller based on fuzzy logic is designed. This controller dynamically adjusts according to the real-time state (SoC) and current ( ) of the battery to optimize the vehicle energy consumption, emissions and battery performance. Such as Figure 5As shown in the figure, a fuzzy logic controller is used to determine the threshold adjustment factor. The real-time update calculation formula is as follows: ; In the formula, is the threshold adjustment factor of and

[0033] The fuzzy controller realizes the real-time adjustment of parameters through a set of logic rules. The implementation of the fuzzy algorithm includes four steps: parameter fuzzification, fuzzy rule base design, fuzzy inference, and defuzzification, as follows: (1) Fuzzification: Define as the deviation between and the preset ( ). The inputs of the fuzzy controller are the battery bus current ( ) and , where . The output is the threshold adjustment factor . The membership functions are defined as negative large (NL), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PL).

[0034] (2) Fuzzy rules: Fuzzy logic is a typical M+N→W (if M and N, then W) mode, where M represents the fuzzy set of , N represents the fuzzy set of Table 1. Parameter Adaptive Adjustment Rule Table ; The membership functions and output surface of the fuzzy controller are as Figure 6 shown.

[0035] For fuzzy inference and defuzzification, the fuzzy controller uses the weighted average method for defuzzification. To ensure control accuracy and calculation speed, an offline calculation method is adopted. The corresponding relationship between the observed value and the actual value can be calculated according to the following formula: ; In the formula, is the final value, is the number of elements, is the number of members.

[0036] The multi-objective optimization is carried out by using the Barebones Multi-Objective Particle Swarm Optimization (BB-MOPSO) method. Three best working curves of the APU are selected to form three types of working point distribution spaces, namely the minimum curve ( , ), the minimum curve ( ), and the minimum Icom-APU curve ( ). The optimization results are as shown in Figure 7 . By comparing and analyzing the conflict relationships among the three evaluation indexes of , and , the solution with the best is selected as the optimization decision result.

[0037] Figure 7 Each point in represents a set of alternative solutions, and the solutions with the best single objective function are marked by points of different shapes (square, triangle, diamond). The optimization results are shown in Table 2: Table 3.9 Multi-objective optimization results of parameter kth_m_0 ; To compare the objective functions of different dimensions horizontally, the results are normalized, as shown in Figure 8 . It can be seen that the smaller the normalized results of , and , the better the performance; the larger the normalized result of , the better the performance.

[0038] As mentioned above, the adjustment factor controls the lower boundary of the APU linear working area. Experiments are carried out on the proposed multiple variables (0 - 1) under the three single-objective optimal curves. Through setting a series of continuous values (ranging from 0 to -1.0, with an interval of 0.05, a total of 20 points), simulation experiments are carried out, and the results are as shown in Figure 9 .

[0039] As shown in Figure 9 , among the three different APU working curves, makes little difference, which is very important because it means that the multi-objective optimization results can be used for real-time control. The solution set with the best is selected as the optimization decision result, and finally is set to achieve the trade-off among multiple objectives.

[0040] Step 4: Through the results of co-simulation tests, analyze the optimization results under different weight coefficients based on the Pareto solution set for the final quantitative decision-making. Finally, the performance results of several APU working modes designed in the study were compared horizontally, laying a research foundation for the design and development of subsequent intelligent energy management strategies.

[0041] Any decision variable of the MOO problem may affect the optimization objective. The set of objective values corresponding to the Pareto optimal solution set is called the Pareto front. The concept of Pareto dominance is used to evaluate the quality of the solution. For the MOO problem J(x) with the feasible region J, if there is no solution x ∈ J that satisfies x < x*, then the solution x* ∈ J is a Pareto optimal solution. The study selects the multi-objective linear normalization optimization method, which can clearly reflect the weights of the optimization objectives. In addition, the objective functions have different physical meanings and dimensions, and a standardized normalization method is required to unify the cost functions of each dimension. The normalization formula is as follows: ; In the formula, is the normalized result of the objective function, is the original data in the Pareto optimal solution set, and are the maximum and minimum values of the original data set, respectively; The working mode of the APU system is determined by and . Considering three indicators: the oil-electricity conversion loss rate ( ), the comprehensive exhaust gas emission ( ), and the battery capacity loss rate ( ), and can be optimized and selected. The optimal comprehensive performance function (Optimal Comprehensive Vehicle Performance, Icom_ovp) is defined as follows: ; Among them, is the weight coefficient; The control decision is obtained by minimizing the multi-objective evaluation index : ; Among them, represents the minimization of the multi-objective cost function of the control system, represents the power fluctuation margin coefficient at the operating point.

[0042] In multi-objective optimization, the selection of weight coefficients has an important impact on the optimization results and decision-making. The weight coefficients selected in this paper are , which are based on the developers' understanding and experience of control requirements. Through single-objective optimization experiments, the optimal solutions under different weight coefficients are determined to form the "feasible region boundary values". These data are summarized into a total database and combined with the optimization results under fixed weight coefficients to form the "decision-making domain". This provides a reference basis for the formulation of control strategies and final decision-making.

[0043] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0044] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0045] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0046] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

[0047] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A fuzzy adaptive multi-point operation mode control method for a range extender, characterized in that: The method comprises: Based on the dynamic programming algorithm, the APU operating point is optimized offline with the goal of minimizing the APU energy consumption-emission comprehensive evaluation function, the optimal operating curve is obtained, and the comprehensive performance evaluation index is defined; Divide the APU working area based on the required power probability density function, and calculate the upper and lower limits of the power range and the torque range; Develop hybrid point-line energy management strategies, set up adaptive controllers based on fuzzy logic, and dynamically adjust threshold parameters ; The threshold parameters are optimized by multi-objective backbone particle swarm optimization method. Perform multi-objective optimization and make quantitative decisions based on the Pareto solution set.

2. The method according to claim 1, characterized in that The energy consumption-emission comprehensive evaluation function includes the oil-electric conversion loss rate , Comprehensive Emission Index And APU comprehensive evaluation indicators ; Among them, the oil-to-electricity conversion loss rate The calculation formula is: ； In the formula, is the power generation efficiency of the generator, is the efficiency between fuel and engine effective power, is the calorific value of gasoline; Comprehensive Emission Index The calculation formula is: ； ； In the formula, , and Is CO, CH, NO x Gas emission results, , and Is CO, CH, NO x Gas emission characteristic function, , , They are , and The weight coefficient of APU comprehensive evaluation index The calculation formula is: ； In the formula, and are the weight coefficients of the objective function respectively.

3. The method according to claim 2, characterized in that The optimal working curve defines a comprehensive performance evaluation index by combining the oil-to-electricity conversion efficiency, the comprehensive emission index and the battery capacity loss rate, wherein the battery capacity loss rate model is: ； In the formula, is the depth of discharge, is the cumulative capacity of the battery, is the battery current, is the total cycle time, is the number of cycles, and are the fitting parameters.

4. The method according to claim 1, characterized in that: The optimization of the number of APU operating speed intervals and the power range at the corresponding speeds specifically includes: Calculate the probability density function of the required power based on typical driving cycle data for: ； In the formula, is the power cumulative distribution function, is the probability density function of the required power; Divide the required power range into evenly according to probability Parts, each part corresponds to an APU operating speed range; For the speed , corresponding to the upper limit of the power range of the power interval and power lower limit They are: ； ； Where: For the APU system The power of the interval optimal curve operation, is the maximum power, is the minimum power value, is the coefficient power fluctuation margin coefficient; The upper limit of the torque range and torque range lower limit They are: ； ； At speed The power coverage at the working point is: ； The upper limit of the switching gate between two adjacent working points and lower limit It is expressed as: ； ； Then the APU operating point target value calculation is expressed as: ； ； ； In the formula, is the real-time power value of the APU system. is the real-time speed value of the APU system. is the real-time torque value of the APU system, is the required power.

5. The method according to claim 1, characterized in that The hybrid point-line energy management strategy is formulated, an adaptive controller based on fuzzy logic is set, and threshold parameters are dynamically adjusted. , specifically including: For the threshold boundary coefficient : ； In the formula, is the lower limit of the power adjustment area, is the upper limit of the power adjustment area, Indicates the minimum value of the APU working area in the APU linear working mode; Establish an adaptive controller based on fuzzy logic to dynamically adjust the threshold parameters according to the real-time status and current of the battery : ； In the formula, yes The threshold adjustment factor is is the initial threshold after the optimization decision.

6. The method according to claim 5, characterized in that The input of the fuzzy logic based adaptive controller is the battery bus current and SoC deviation , the output is the threshold adjustment factor , the membership functions include negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

7. The method according to claim 1, characterized in that The threshold parameters are optimized by multi-objective backbone particle swarm optimization method. Perform multi-objective optimization and make quantitative decisions based on the Pareto solution set, including: The cost function of each dimension is unified by normalization method, and the normalization formula is: ； In the formula, is the normalized result of the objective function, is the original data in the Pareto optimal solution set, and are the maximum and minimum values ​​of the original data set, respectively; Power conversion loss rate , Comprehensive Emission Index And APU comprehensive evaluation indicators ; Combining the three indicators of oil-to-electric conversion loss rate, comprehensive exhaust emissions and battery capacity loss rate, the power range data is analyzed. and power fluctuation margin factor Make an optimization selection and define the comprehensive optimal performance function as: ； in, is the weight coefficient; By minimizing the multi-objective evaluation index Get control decision: ； in, represents the minimization of the multi-objective cost function of the control system, Indicates Power fluctuation margin factor at the operating point.

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