A parameter matching method for electric drive system of new energy vehicles

Abstract

The present invention provides a parameter matching method for an electric drive system of a new energy vehicle. The method comprises the following steps: preliminarily determining vehicle parameters according to vehicle model positioning and vehicle power and economy indicators; calculating motor peak power according to vehicle power indicator requirements; calculating a reducer transmission ratio range according to the vehicle's maximum design speed and maximum gradeability requirements at a certain speed; selecting vehicle power and economy indicators, and assigning weights to the performance indicators according to design requirements and standards; constructing an AVL Cruise simulation model of the vehicle and solving the model; extracting power and economy performance indicator results of the vehicle simulation model under different design variables; setting vehicle power and economy performance indicator thresholds; calculating composite weights of the performance indicators, and quantitatively analyzing and calculating vehicle simulation results; calculating a comprehensive performance matrix of the vehicle according to the above method, obtaining comprehensive performance levels of the vehicle with different design parameters, and calculating an optimal matching solution for the vehicle.

Description

Technical Field

[0001] The present invention relates to the field of pure electric vehicles, and more specifically, to a parameter matching method for an electric drive system of a new energy vehicle. Background Art

[0002] As a core component of new energy vehicles, the electric drive system primarily converts the high speed and low torque of the electric motor into speed and torque levels that meet driving requirements. New energy vehicle electric drive systems have diverse configurations, significant electromechanical coupling, and complex integrated structures, making gear ratio matching challenging.

[0003] Matching vehicle transmission ratios based on traditional decoupling methods or engineering experience results in significant errors, meeting only partial performance requirements and failing to achieve optimal overall vehicle performance (power and economy). With the increasing number of power sources and power output in new energy vehicles, the overall performance requirements for vehicles are becoming increasingly demanding. Traditional transmission ratio matching methods are no longer able to meet these requirements. The overall transmission ratio of the electric drive system significantly impacts the power (acceleration time from 0 to 100 km / h, maximum speed, maximum gradeability, etc.) and economy (electricity consumption per 100 km) of new energy vehicles. A suitable transmission ratio can significantly improve a vehicle's energy efficiency, reduce failure rates, and enhance its overall performance and service life.

[0004] The electric drive system transmission ratio matching method based on fuzzy mathematics and vehicle performance adopts fuzzy mathematics theory and comprehensively considers the power and economy of the vehicle. It can greatly shorten the transmission ratio matching time, improve the transmission ratio matching accuracy, and make up for the shortcomings of the current transmission ratio matching of the electric drive system of new energy vehicles. It has strong engineering application value. Summary of the Invention

[0005] In response to the above-mentioned defects or improvement needs of the existing technology, the present invention provides a parameter matching method for the electric drive system of a new energy vehicle. Compared with the traditional design method, this method simplifies the design steps, shortens the design cycle, and can obtain the matching parameters with the best comprehensive performance in terms of power and economy.

[0006] In order to achieve the above technical features, the purpose of the present invention is achieved as follows: a method for matching parameters of an electric drive system of a new energy vehicle comprises the following steps:

[0007] Step 1: Determine vehicle parameters:

[0008] Preliminary determination of vehicle parameters based on vehicle model positioning and vehicle power and economy indicators;

[0009] Step 2, drive motor selection:

[0010] Calculate the peak power of the motor according to the requirements of the vehicle's dynamic performance indicators;

[0011] Step 3, calculate the reducer transmission ratio:

[0012] Calculate the speed ratio range of the reducer based on the vehicle's maximum design speed and the maximum climbing grade requirement at a certain speed;

[0013] Step 4: Determine the design variables:

[0014] Select the design variables. If there are multiple design variables, all permutations and combinations of the design variables need to be determined.

[0015] Step 5: Set the evaluation dimensions:

[0016] Select vehicle power and economy indicators and assign weights to performance indicators according to design requirements;

[0017] Step 6: Construction and calculation of vehicle simulation model:

[0018] Use AVLCruise to build vehicle component models and the entire vehicle model, build the vehicle control strategy based on Simulink, and finally complete the calculation task setting and calculation result post-processing in AVLCruise;

[0019] Step 7: extract the power and economy index results of the vehicle simulation model under different design variables;

[0020] Step 8: Set the evaluation indicators and their thresholds:

[0021] Set thresholds for vehicle power and economy performance indicators;

[0022] Step 9, calculate the composite weight:

[0023] A method based on the combination of entropy weight and weight is used to calculate the composite weight of performance indicators, and quantitative analysis and calculation of vehicle simulation results are performed;

[0024] Step 10: Comprehensive vehicle performance evaluation:

[0025] According to step 9, the comprehensive performance matrix of the vehicle is calculated. The maximum membership principle is used to obtain the comprehensive performance level of the vehicle under different design parameters. The optimal matching solution of the vehicle under the design parameters is obtained through quantitative analysis and calculation.

[0026] The vehicle parameters in step 1 include curb weight, fully loaded weight, frontal area, drag coefficient, rolling damping coefficient, total speed ratio, mechanical transmission efficiency, wheelbase and tire size.

[0027] The vehicle power and economy indicators described in step 1 include acceleration time from 0 to 100 km / h, acceleration time from 40 km / h to 80 km / h, acceleration time from 80 km / h to 120 km / h, maximum speed, maximum climbing grade and power consumption per 100 km, with weights of 20%, 5%, 5%, 20%, 20% and 30% respectively.

[0028] The peak power of the motor in step 2 should meet the following requirements:

[0029] P max ≥max(P max1 ,P max2 ,P max3 )(1)

[0030] Where, P max is the peak power of the motor, P max1 、P max2 and P max3 They are the power required for maximum vehicle speed, maximum gradeability, and acceleration performance respectively;

[0031] The car is at maximum speed U amax When driving on a good road, affected by rolling resistance and air resistance, the power balance equation is:

[0032]

[0033] Where η T is the transmission efficiency, m is the curb mass, g is the acceleration of gravity, f is the rolling resistance coefficient, C D is the drag coefficient, A is the frontal area;

[0034] When a car climbs the maximum gradient on a good road at a constant speed and maintains a constant speed during the climb without the influence of acceleration resistance, its power balance equation is:

[0035]

[0036] Where α is the climbing angle, u i is the climbing speed;

[0037] The empirical formula for the power required for the acceleration process of an electric vehicle starting from a standstill is:

[0038]

[0039] Where δ is the rotation mass conversion coefficient, u 100 is the vehicle speed, t 100 is the acceleration time of 0-100km / h, and dt is the iterative step length of the design process.

[0040] The calculation formula for the upper limit of the vehicle speed reducer transmission ratio in step 3 is:

[0041]

[0042] The formula for calculating the lower limit of the vehicle reducer transmission ratio is:

[0043]

[0044] Where, T max is the maximum torque output by the motor at the corresponding vehicle speed, r is the tire radius, i g is the transmission ratio, i o is the main reducer speed ratio, n max The maximum speed of the motor.

[0045] The AVL Cruise vehicle model includes the vehicle frame, electric drive assembly, control strategy and vehicle Data Bus signal connection;

[0046] The whole vehicle simulation model is calculated using the AVLCruise batch calculation method.

[0047] When the design variables in step 7 are multiple variable combinations, the number of models that need to be calculated is the number of permutations and combinations of the variables;

[0048] If the number of design variables is n1 and the number of candidate values ​​for each design variable is n2, then the total number of permutations and combinations is

[0049] The specific process of step 8 is as follows:

[0050] The acceleration time from 40 km / h to 80 km / h (A1), the time from 80 km / h to 120 km / h (A3), the maximum climbing grade (A4), the maximum speed (A5), and the power consumption per 100 km (A6) are selected as vehicle power and economy indicators;

[0051] The vehicle performance matrix is ​​expressed as:

[0052] T=[A1,A2,A3,A4,A5,A6](7)

[0053] According to the performance data of new energy vehicles, the performance indicators of the vehicle are divided into five levels: very low V vl , low V l , General V n , High V h and very high V vh , select the thresholds of six performance indicators at different levels.

[0054] The specific process of step 9 is as follows:

[0055] For a multi-state system consisting of five states and six performance indicators, the performance indicator matrix is: Y = [y ij ] 6×5 , then the information entropy calculation formula of the j-th indicator is:

[0056]

[0057] Where, p ij It represents the proportion of the i-th state index under the j-th index, and its calculation formula is:

[0058] The entropy weight w of the j-th indicator Hj Expressed as:

[0059]

[0060] Entropy weight matrix W H It can be calculated by equation (9):

[0061] W H =[w H1 ,w H2 ,w H3 ,w H4 ,w H5 ](10)

[0062] The engineering weights are:

[0063] W Z =[w Z1 ,w Z2 ,w Z3 ,w Z4 ,w Z5 ](11)

[0064] Combining the entropy weight matrix and engineering weight, the calculation formula of the composite weight matrix W is:

[0065] W=[w1,w2,w3,w4,w5](11)

[0066] in,

[0067] The specific process of step 10 is as follows:

[0068] There are six performance index matrices that can be expressed as:

[0069] X=[X1,X2,…,X6] (12)

[0070] The comprehensive performance of the vehicle is divided into five different performance levels, from low to high: V vl 、V l 、V n 、Vh and V vh , the matrix is ​​represented as:

[0071] V level =[V vl ,V l ,V n ,V h ,V vh ] (13)

[0072] In order to truly obtain the single-factor performance set, triangular fuzzy numbers and semi-trapezoidal fuzzy numbers are used to analyze the initial performance index matrix. The calculation formula of the performance set of the k-th design scheme is:

[0073]

[0074] Where, Indicates affiliation, which is factor X in performance indicator system X i About V level Medium performance level V j Affiliation, affiliation Calculated by the following formula:

[0075]

[0076] By combining the single-factor performance matrix and the composite weight, the vehicle comprehensive performance level matrix is ​​obtained, as shown in the following formula (16):

[0077]

[0078] in, is a fuzzy operator, k=1,2,…,5.

[0079] In general, compared with the prior art, the above technical solution conceived by the present invention provides a parameter matching method for an electric drive system of a new energy vehicle, which has the following beneficial effects:

[0080] 1. This method can comprehensively consider the vehicle's power and economy, and assign weights to design indicators according to design requirements. It does not require complex theoretical calculations and can perform qualitative and quantitative analysis of different design schemes.

[0081] 2. This method only requires a rough estimate of the range of the preliminary design parameters to accurately obtain the optimal design parameters according to the steps of the present invention, which greatly reduces the steps of parameter matching of the electric drive system of new energy vehicles and shortens the development cycle of new energy vehicles.

[0082] 3. This method is applicable to parameter matching of various types of vehicle drive systems and has the characteristics of wide applicability and simple operation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The present invention will be further described below with reference to the accompanying drawings and examples.

[0084] Figure 1 Fuzzy triangular membership function diagram for comprehensive performance evaluation.

[0085] Figure 2 It is the relationship between the comprehensive performance membership of the vehicle and the transmission ratio at the highest level.

[0086] Figure 3 It is the relationship between the comprehensive performance membership of the vehicle and the transmission ratio. DETAILED DESCRIPTION

[0087] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0088] Example 1:

[0089] See also Figure 1-3 , a parameter matching method for an electric drive system of a new energy vehicle, comprising the following steps:

[0090] Step 1: Determine vehicle parameters: Preliminarily determine vehicle parameters based on vehicle model positioning and vehicle power and economy indicators;

[0091] The vehicle parameters include curb weight, fully loaded mass, frontal area, drag coefficient, rolling damping coefficient, total speed ratio, mechanical transmission efficiency, wheelbase and tire size;

[0092] The vehicle's power and economy indicators include acceleration time from 0 to 100 km / h, acceleration time from 40 km / h to 80 km / h, acceleration time from 80 km / h to 120 km / h, maximum speed, maximum climbing grade and power consumption per 100 km.

[0093] Step 2: Drive motor selection: Calculate the peak power of the motor based on the vehicle's dynamic performance requirements;

[0094] The peak power of the motor should meet the following requirements:

[0095] P max ≥max(P max1 ,P max2 ,P max3 )(1)

[0096] Where, P max is the peak power of the motor, P max1 、P max2 and P max3 They are the power required for maximum vehicle speed, maximum gradeability, and acceleration performance respectively;

[0097] The car is at maximum speed U amaxWhen driving on a good road, affected by rolling resistance and air resistance, the power balance equation is:

[0098]

[0099] Where η T is the transmission efficiency, m is the curb mass, g is the acceleration of gravity, f is the rolling resistance coefficient, C D is the drag coefficient, A is the frontal area;

[0100] When a car climbs the maximum gradient on a good road at a constant speed and maintains a constant speed during the climb without the influence of acceleration resistance, its power balance equation is:

[0101]

[0102] Where α is the climbing angle, u i is the climbing speed;

[0103] The empirical formula for the power required for the acceleration process of an electric vehicle starting from a standstill is:

[0104]

[0105] Where δ is the rotation mass conversion coefficient, u 100 is the vehicle speed, t 100 is the acceleration time of 0-100km / h, and dt is the iterative step length of the design process.

[0106] Step 3: Calculate the speed ratio of the reducer: Calculate the speed ratio range of the reducer based on the maximum design speed of the vehicle and the maximum climbing grade requirement at a certain speed;

[0107] The formula for calculating the upper limit of the vehicle reducer transmission ratio is:

[0108]

[0109] The formula for calculating the lower limit of the vehicle reducer transmission ratio is:

[0110]

[0111] Where, T max is the maximum torque output by the motor at the corresponding vehicle speed, r is the tire radius, i g is the transmission ratio, i o is the main reducer transmission ratio, n max The maximum speed of the motor.

[0112] Step 4, determine the design variables: select the design variables. If there are multiple design variables, all permutations and combinations of the design variables need to be determined.

[0113] Step 5: Set the evaluation dimensions: Select vehicle power and economy indicators and assign weights to the performance indicators based on design requirements; the specific performance indicators are shown in Table 1.

[0114] Table 1 Vehicle dynamics and economic performance indicators

[0115]

[0116]

[0117] Step 6: Build and calculate the whole vehicle simulation model: Use AVL Cruise to build the whole vehicle component model and the whole vehicle model, build the whole vehicle control strategy based on Simulink, and finally complete the calculation task setting and calculation result post-processing in AVL Cruise. The AVL Cruise whole vehicle model includes the whole vehicle frame, electric drive assembly, control strategy and whole vehicle Data Bus signal connection. The whole vehicle simulation model calculation adopts the AVL Cruise batch calculation method.

[0118] The AVLCruise mentioned above is a vehicle economy and power simulation software that can be used to build vehicle simulation models and conduct performance analysis.

[0119] Step 7: extract the power and economy index results of the vehicle simulation model under different design variables;

[0120] When the design variable is a combination of multiple variables, the number of models that need to be calculated is the number of permutations and combinations of the variables; if the number of design variables is n1 and the number of candidate values ​​for each design variable is n2, then the total number of permutations and combinations is

[0121] Step 8: Set evaluation indicators and their thresholds: Set vehicle power and economy performance indicator thresholds;

[0122] The acceleration time from 40 km / h to 80 km / h (A1), the time from 80 km / h to 120 km / h (A3), the maximum climbing grade (A4), the maximum speed (A5), and the power consumption per 100 km (A6) are selected as vehicle power and economy indicators;

[0123] The vehicle performance matrix is ​​expressed as:

[0124] T=[A1,A2,A3,A4,A5,A6](7)

[0125] According to the performance data of new energy vehicles, the performance indicators of the vehicle are divided into five levels: very low V vl , low V l , General V n , High V h and very high Vvh , select the thresholds of six performance indicators at different levels.

[0126] Step 9, calculate composite weight: use a method based on the combination of entropy weight and weight to calculate the composite weight of the performance index, and perform quantitative analysis and calculation on the vehicle simulation results;

[0127] For a multi-state system consisting of five states and six performance indicators, the performance indicator matrix is: Y = [y ij ] 6×5 , then the information entropy calculation formula of the j-th indicator is:

[0128]

[0129] Where, p ij It represents the proportion of the i-th state index under the j-th index, and its calculation formula is:

[0130] The entropy weight w of the j-th indicator Hj Expressed as:

[0131]

[0132] Entropy weight matrix W H It can be calculated by equation (9):

[0133] W H =[w H1 ,w H2 ,w H3 ,w H4 ,w H5 ](10)

[0134] The engineering weights are:

[0135] W Z =[w Z1 ,w Z2 ,w Z3 ,w Z4 ,w Z5 ](11)

[0136] Combining the entropy weight matrix and engineering weight, the calculation formula of the composite weight matrix W is:

[0137] W=[w1,w2,w3,w4,w5](11)

[0138] in,

[0139] Step 10, comprehensive vehicle performance evaluation: Calculate the comprehensive vehicle performance matrix according to step 9, and use the maximum membership principle to obtain the comprehensive performance level of the vehicle under different design parameters. Quantitatively analyze and calculate the optimal matching solution for the vehicle under the design parameters.

[0140] There are six performance index matrices that can be expressed as:

[0141] X=[X1,X2,…,X6] (12)

[0142] The comprehensive performance of the vehicle is divided into five different performance levels, from low to high: V vl 、V l 、V n 、V h and V vh , the matrix is ​​represented as:

[0143] V level =[V vl ,V l ,V n ,V h ,V vh ] (13)

[0144] In order to truly obtain the single-factor performance set, triangular fuzzy numbers and semi-trapezoidal fuzzy numbers are used to analyze the initial performance index matrix. The calculation formula of the performance set of the k-th design scheme is:

[0145]

[0146] Where, Indicates affiliation, which is factor X in performance indicator system X i About V level Medium performance level V j Affiliation, affiliation It is obtained by calculation through the following formula (15). The graphical expression of the fuzzy triangular membership function of the vehicle comprehensive performance level is shown in the attached Figure 1 shown.

[0147]

[0148] By combining the single-factor performance matrix and the composite weight, the vehicle comprehensive performance level matrix is ​​obtained, as shown in the following formula (16):

[0149]

[0150] in, is a fuzzy operator, k=1,2,…,5.

[0151] Finally, the comprehensive performance matrix of the vehicle is obtained according to the above method. The maximum membership principle is adopted to determine the comprehensive performance level of the vehicle under different design parameters. The design scheme with the largest membership in the higher level is given priority, and finally the optimal matching scheme of the vehicle under the design parameters is obtained.

[0152] Example 2:

[0153] See also Figures 1 to 3 , a new energy vehicle electric drive system parameter matching method is used to match the transmission ratio of a new energy vehicle electric drive assembly. The main steps are as follows:

[0154] Step 1: Preliminary selection of transmission ratio based on vehicle parameters.

[0155] Based on the vehicle parameters, a full-vehicle simulation model was built using AVLCruise. A preliminary transmission ratio was selected within the range of [4.0, 9.5]. This ratio range was divided into 12 groups, resulting in 12 sets of vehicle simulation data for the ratios [4.0, 4.5, …, 9.5].

[0156] Step 2: Perform AVL Cruise simulation calculation on the selected transmission ratio to obtain vehicle dynamics and economy simulation data.

[0157] The comprehensive performance of this new energy vehicle can be quantitatively calculated from six dimensions: first, dynamic performance, including acceleration time from 0 to 100 km / m (s), acceleration time from 40 to 80 km / m (s), acceleration time from 80 to 120 km / m (s), maximum gradeability (%), and maximum speed (km / s); second, economic performance, namely, WLTC power consumption per 100 kilometers (km / 100km), as shown in Table 2. Weights are assigned to each dimension, as shown in Table 2.

[0158] Table 2 Vehicle dynamics and economic performance indicators

[0159]

[0160] Based on new energy vehicle performance data and design experience, vehicle performance indicators are now divided into five levels: very low, low, average, high, and very high. The thresholds for vehicle performance at different levels are shown in Table 3.

[0161] Table 3 Thresholds of various performance indicators at different performance levels

[0162] level <![CDATA[A1(s)]]> <![CDATA[A2(s)]]> <![CDATA[A3(s)]]> <![CDATA[A4(%)]]> <![CDATA[A5(km / h)]]> <![CDATA[A6(kWh / 100km)]]> Very low 7 3 3.2 30 150 15 Low 6 2.7 3 40 180 14 generally 5 2.4 2.8 50 210 13 high 4 2.1 2.6 60 240 12 Very high 3 1.8 2.4 70 270 11

[0163] The vehicle parameters and the 12 pre-selected transmission ratios were input into the established Cruise vehicle model to calculate the specific values ​​of the vehicle's 0-100 km / m acceleration time (A1), 40-80 km / m acceleration time (A2), 80-120 km / m acceleration time (A3), maximum gradeability (A4), maximum speed (A5), and power consumption per 100 kilometers (A6) under the pre-selected transmission ratios.

[0164] Step 3: After standardizing and weighting the simulation data, the simulation results are quantitatively calculated using fuzzy mathematics and the principle of maximum membership to obtain the comprehensive performance level of the vehicle under different total transmission ratios, as shown in Table 4. The calculation results of the comprehensive performance of the vehicle under different transmission ratios at the highest level are as follows: Figure 2 As shown in the figure, the optimal transmission ratio of the car falls between [6.5, 7.5], so the transmission ratio can be selected based on this result.

[0165] Table 4 Vehicle comprehensive performance matrix

[0166]

[0167]

[0168] Step 4: Narrow the range according to the transmission ratio and repeat steps 1 to 3 to obtain the vehicle comprehensive performance matrix shown in Table 5.

[0169] Table 5 Vehicle comprehensive performance matrix

[0170]

[0171] Using fuzzy mathematics methods, we can obtain the fuzzy matrix of vehicle performance under different transmission ratios. Based on the maximum membership principle, we can obtain the comprehensive performance level of the vehicle under different total transmission ratios. Since the comprehensive performance level of the vehicle is "very high" when the total transmission ratio is in the range of [6.5,7.2], we only need to select the transmission ratio corresponding to the maximum membership. Figure 3 It can be seen that the optimal transmission ratio of the electric drive assembly of this new energy vehicle is 6.9.

Claims

1. A parameter matching method for an electric drive system of a new energy vehicle, characterized in that: The following steps are involved: Step 1: Determine vehicle parameters: Preliminarily determine vehicle parameters based on vehicle model positioning and vehicle power and economy indicators; Step 2: Drive motor selection: Calculate the peak power of the motor based on vehicle power indicator requirements; Step 3: Calculate reducer transmission ratio: Calculate the reducer transmission ratio range based on the vehicle's maximum design speed and the maximum climbing grade requirement at a certain speed; Step 4: Determine design variables: Select design variables. If there are multiple design variables, all permutations and combinations of design variables must be determined; Step 5: Set evaluation dimensions: Select vehicle power and economy indicators, and assign weights to performance indicators based on design requirements; Step 6: Build and calculate vehicle simulation model: Use AVLCruise to build vehicle component models and vehicle model, build vehicle control strategy based on Simulink, and finally complete calculation task setting and calculation result post-processing in AVLCruise; Step 7: Extract the power and economy indicator results of the vehicle simulation model under different design variables; Step 8: Set evaluation indicators and their thresholds: Set vehicle power and economy performance indicator thresholds; Step 9, Calculate Composite Weight: Calculate the composite weight of the performance indicators using a method based on a combination of entropy weight and weight, and perform quantitative analysis and calculation on the vehicle simulation results. Step 10, Vehicle Comprehensive Performance Evaluation: Calculate the vehicle comprehensive performance matrix according to step 9, and use the maximum membership principle to obtain the comprehensive performance level of the vehicle under different design parameters. Quantitatively analyze and calculate the optimal matching solution for the vehicle under the design parameters. The empirical formula for the power required for the acceleration process of an electric vehicle starting from a standstill is: （4） Where, δ is the rotation mass conversion factor, is the vehicle speed, is the acceleration time to 100 kilometers per hour, d t is the iterative step length of the design process; The specific process of step 10 is as follows: There are six performance index matrices that can be expressed as: X=[X1,X2,…,X6](12) The comprehensive performance of the vehicle is divided into five different performance levels, from low to high: 、 、 、 and , the matrix is ​​represented as: （13） In order to truly obtain the single factor performance set, triangular fuzzy numbers and semi-trapezoidal fuzzy numbers are used to analyze the initial performance index matrix. k The performance set of a design solution is calculated as follows: （14） Where, Indicates affiliation and is a performance indicator system Medium Factors about Medium performance level Affiliation, affiliation Calculated by the following formula: Combining the single factor performance matrix and the composite weight, the vehicle comprehensive performance level matrix is ​​obtained, as shown in the following formula (16): （16） in, is the fuzzy operator, .

2. A parameter matching method for electric drive system of new energy vehicle according to claim 1, characterized in that: The vehicle parameters in step 1 include curb weight, fully loaded weight, frontal area, drag coefficient, rolling damping coefficient, total speed ratio, mechanical transmission efficiency, wheelbase and tire size.

3. The parameter matching method for electric drive system of new energy vehicle according to claim 1, characterized in that: The vehicle power and economy indicators described in step 1 include acceleration time from 0 to 100 km / h, acceleration time from 40 km / h to 80 km / h, acceleration time from 80 km / h to 120 km / h, maximum speed, maximum climbing grade and power consumption per 100 km, with weights of 20%, 5%, 5%, 20%, 20% and 30% respectively.

4. The parameter matching method for electric drive system of new energy vehicle according to claim 1, characterized in that: The peak power of the motor in step 2 should meet the following requirements: （1） Where, is the peak power of the motor, 、 and They are the power required for maximum vehicle speed, maximum gradeability, and acceleration performance respectively; Car at maximum speed When driving on a good road, affected by rolling resistance and air resistance, the power balance equation is: （2） Where, is the transmission efficiency, m is the curb mass, g is the acceleration due to gravity, f is the rolling resistance coefficient, C D is the drag coefficient, A is the windward area; When a car climbs the maximum gradient on a good road at a constant speed and maintains a constant speed during the climb without the influence of acceleration resistance, its power balance equation is: （3） Where, α is the climbing angle, is the climbing speed.

5. The parameter matching method for electric drive system of new energy vehicle according to claim 1, characterized in that: The calculation formula for the upper limit of the vehicle speed reducer transmission ratio in step 3 is: （5） The formula for calculating the lower limit of the vehicle reducer transmission ratio is: （6） Where, is the maximum torque output by the motor at the corresponding vehicle speed, r is the tire radius, i g is the transmission ratio, i o is the main reducer rotation ratio, n max The maximum speed of the motor.

6. The parameter matching method for electric drive system of new energy vehicle according to claim 1, characterized in that: The AVL Cruise vehicle model includes the vehicle frame, electric drive assembly, control strategy and vehicle Data Bus signal connection; The whole vehicle simulation model is calculated using the AVLCruise batch calculation method.

7. The parameter matching method for electric drive system of new energy vehicle according to claim 1, characterized in that: When the design variables in step 7 are multiple variable combinations, the number of models that need to be calculated is the number of permutations and combinations of the variables; If the number of design variables is , the number of candidate values ​​for each design variable is , then the total number of permutations and combinations is .

8. The parameter matching method for electric drive system of new energy vehicle according to claim 1, characterized in that: The specific process of step 8 is as follows: Select the acceleration time from 0 to 100 km / h , time taken to accelerate from 40 km / h to 80 km / h , Time taken to accelerate from 80 km / h to 120 km / h , maximum climbing grade , maximum speed and power consumption per 100 kilometers As an indicator of vehicle power and economy; The vehicle performance matrix is ​​expressed as: （7） According to the performance data of new energy vehicles, the performance indicators of the vehicles are divided into five levels: very low ,Low ,generally ,high and very high , select the thresholds of six performance indicators at different levels.

9. The parameter matching method for electric drive system of new energy vehicle according to claim 1, characterized in that: The specific process of step 9 is as follows: For a multi-state system consisting of five states and six performance indicators, the performance indicator matrix is: , then j The information entropy calculation formula of an indicator is: （8） Where, , Indicates in j Under the indicator i The proportion of the state indicators is calculated as follows: ; No. j Entropy weight of each indicator Expressed as: （9） Entropy weight matrix W H It can be calculated by equation (9): （10） The engineering weights are: （11） Combining the entropy weight matrix and engineering weight, the calculation formula of the composite weight matrix W is: （11） in, ( j =1,2,…,5).

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