Method for evaluating similarity of vehicle energy consumption test cycles

Abstract

The application discloses a vehicle energy consumption test working condition similarity evaluation method, comprising the following steps: obtaining reference working condition data v1(t) and test working condition data v2(t); calculating reference working condition vehicle driving force F1(t) under the reference working condition data and test working condition vehicle driving force F2(t) under the test working condition data according to the reference working condition data v1(t) and the test working condition data v2(t); calculating the relative deviation of vehicle excitation between the reference working condition and the test working condition according to the reference working condition data, the reference working condition vehicle driving force F1(t) and the test working condition vehicle driving force F2(t), and calculating the similarity of vehicle excitation between the reference working condition and the test working condition according to the relative deviation of vehicle excitation. The application can comprehensively and accurately evaluate the similarity between the automobile energy consumption test working condition and the actual driving working condition of the user or different working conditions, and is helpful to improve the efficiency of the automobile energy consumption test and the accuracy of the analysis and evaluation of the automobile energy consumption test results.

Description

Technical Field

[0001] This invention relates to the field of vehicle driving condition generation technology, and in particular to a method for evaluating the similarity of vehicle energy consumption test conditions. Background Technology

[0002] This section provides only background information relevant to this disclosure and is not necessarily prior art.

[0003] The driving cycle, also known as the vehicle driving cycle, describes the speed-time curve of a vehicle. It describes the most representative driving characteristics of a certain type of vehicle on a certain road or in a certain area. It is the basis for vehicle energy consumption / emission testing methods and limit standards, as well as the most basic basis for vehicle development and evaluation. It is an important common basic technology in the automotive industry.

[0004] Currently, there are various typical driving conditions worldwide, such as the New European Driving Cycle (NEDC), the World Light Vehicle Test Cycle (WLTC), and the China Automotive Test Cycle (CATC).

[0005] However, a single typical driving condition is difficult to represent the complex and ever-changing actual driving situation. The energy consumption of a car optimized and calibrated based on a public and fixed single typical driving condition often differs significantly from the energy consumption of actual users. Existing technologies construct new energy consumption test conditions that closely resemble actual driving conditions in order to comprehensively and accurately evaluate vehicle energy consumption. However, how to accurately evaluate the similarity between the newly generated energy consumption test conditions and the actual driving conditions of users is a problem that needs to be solved. Summary of the Invention

[0006] The purpose of this invention is to at least address the problem that the legal certification results of vehicles optimized and calibrated based on a single typical driving condition differ significantly from actual data. This purpose is achieved through the following technical solution:

[0007] The first aspect of the present invention proposes a vehicle energy consumption test condition similarity evaluation method, which includes: acquiring reference test condition data v1(t) and test test condition data v2(t);

[0008] Based on the reference operating condition data v1(t) and the test operating condition data v2(t), calculate the reference operating condition vehicle driving force F1(t) under the reference operating condition data and the test operating condition vehicle driving force F2(t) under the test operating condition data, respectively.

[0009] The reference operating condition energy requirement per unit distance of the vehicle is calculated based on the reference operating condition data and the reference operating condition vehicle driving force F1(t). and reference operating condition braking energy recovery space Calculate the energy requirement per unit distance of the vehicle under the test conditions based on the test condition data and the vehicle's driving force F2(t) under the test conditions. and test condition braking energy recovery space

[0010] Based on the energy demand of the reference operating condition Extract the excitation measure applied to the vehicle per unit distance by the reference operating condition data under traction conditions to overcome road resistance. The excitation applied to a vehicle to overcome air resistance The excitation applied to the vehicle to overcome acceleration resistance Based on the energy requirements of the test conditions Extract the excitation measure applied to the vehicle per unit distance to overcome road resistance under the test conditions during traction. The excitation applied to a vehicle to overcome air resistance The excitation applied to the vehicle to overcome acceleration resistance

[0011] Braking energy recovery space per unit distance based on the aforementioned reference operating conditions Extract the reference operating condition data under braking conditions to measure the road resistance on vehicle excitation per unit distance. Measurement of the effect of air resistance on vehicle excitation Measurement of vehicle excitation by acceleration resistance Based on the braking energy requirement per unit distance under the aforementioned test conditions Extract the test condition data under braking conditions to measure the road resistance on vehicle excitation per unit distance. Measurement of the effect of air resistance on vehicle excitation Measurement of vehicle excitation by acceleration resistance

[0012] The relative deviation of the vehicle excitation between the reference condition and the test condition is calculated using formulas (1) to (6);

[0013]

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] The similarity of vehicle excitation between the reference condition and the test condition is calculated using formula (7) based on the relative deviation of the vehicle excitation.

[0021] In some embodiments of the present invention, the vehicle energy consumption test condition similarity evaluation method further includes:

[0022] By setting an idle speed range, encoding interval, and step size, speed state encoding and acceleration state encoding are performed on the reference operating condition data and the test operating condition data according to the idle speed range, encoding interval, and step size, respectively, to obtain one-dimensional state encoded data of the reference operating condition. One-dimensional state coding data of test conditions

[0023] Based on the one-dimensional state coding data of the reference working condition Establish a two-dimensional frequency distribution matrix of velocity-acceleration reference conditions, based on the one-dimensional state coding data of the test conditions. Establish a two-dimensional frequency distribution matrix for the velocity-acceleration test conditions;

[0024] Calculate the velocity-acceleration two-dimensional probability distribution matrix of the reference working condition based on the velocity-acceleration two-dimensional frequency distribution matrix of the reference working condition, and calculate the velocity-acceleration two-dimensional probability distribution matrix of the test working condition based on the velocity-acceleration two-dimensional frequency distribution matrix of the test working condition;

[0025] Calculate the cosine similarity between the velocity-acceleration two-dimensional probability distribution matrices of the reference and test conditions based on the velocity-acceleration two-dimensional probability distribution matrices of the reference and test conditions.

[0026]

[0027] Among them, A k The k-th component represents the velocity-acceleration two-dimensional probability distribution matrix of the test condition after being arranged in one dimension.

[0028] B k The k-th component of the velocity-acceleration two-dimensional probability distribution matrix of the reference working condition is represented by a one-dimensional arrangement; m2 represents the acceleration state number, and m1 represents the velocity state number.

[0029] In some embodiments of the present invention, the vehicle energy consumption test condition similarity evaluation method further includes:

[0030] According to the reference working condition coding data and test condition coding data Establish the Markov state transition matrix P1 for the reference condition and the Markov state transition matrix P2 for the test condition, respectively, for the two parameters of velocity and acceleration.

[0031] Calculate the cosine similarity of the Markov state transition probability matrices between the reference condition data and the test condition data based on the Markov state transition matrix P1 and the test condition Markov state transition matrix P2.

[0032]

[0033] Where N represents the number of states in the Markov state transition matrix of the test condition and the reference condition, and the size of the Markov state transition matrix is ​​N×N, and N=m1×m2;

[0034] C k This represents the k-th component of the Markov state transition matrix P2 under the test condition after being arranged in one dimension.

[0035] D k This represents the k-th component of the Markov state transition matrix P1 for the reference operating condition after being arranged in one dimension.

[0036] In some embodiments of the present invention, the vehicle energy consumption test condition similarity evaluation method further includes:

[0037] Based on the cosine similarity of the velocity-acceleration two-dimensional probability distribution matrix The cosine similarity of the Markov state transition probability matrix Similarity to the aforementioned vehicle excitation Calculate the overall similarity φ between the reference working condition and the test working condition;

[0038]

[0039] Wherein, ω1, ω2, and ω3 are proportionality coefficients, all of which are greater than zero, and ω1 + ω2 + ω3 = 1.

[0040] In some embodiments of the present invention, the reference operating condition energy requirement per unit distance of the vehicle is calculated based on the reference operating condition data v1(t) and the reference operating condition vehicle driving force F1(t). and reference operating condition braking energy recovery space Calculate the energy requirement per unit distance of the vehicle under the test conditions based on the test condition data and the vehicle's driving force F2(t) under the test conditions. and test condition braking energy recovery space The steps include:

[0041] Calculate the driving force F(t) of the vehicle;

[0042]

[0043] In the formula, m is the vehicle mass, g is the acceleration due to gravity, f is the rolling resistance coefficient, i is the road gradient, and C is the acceleration due to gravity. D Where A is the air resistance coefficient, ρ is the frontal area, δ is the air density, and δ is the rotational mass conversion factor.

[0044] When the vehicle is not idling, the traction region t∈τ is divided according to F(t)>0. tr Based on F(t)<0, the region is divided into braking regions t∈τb. r ;

[0045] Calculate the energy demand per unit distance of the vehicle based on F(t)>0 and formula (8).

[0046]

[0047] Substitute the reference operating condition data v1(t) and the reference operating condition vehicle driving force F1(t) into equation (8) to obtain the energy requirement per unit distance under the reference operating condition. Substitute the test condition data v2(t) and the test condition vehicle driving force F2(t) into equation (8) to obtain the energy requirement per unit distance under the test condition.

[0048] The braking energy recovery space per unit distance of the vehicle is calculated based on F(t)<0 and formula (9).

[0049]

[0050] Substituting the reference operating condition data v1(t) and the reference operating condition vehicle driving force F1(t) into equation (9) yields the braking energy recovery space per unit distance under the reference operating condition. Substituting the test condition data v2(t) and the test condition vehicle driving force F2(t) into equation (9) yields the braking energy recovery space per unit distance under the test condition.

[0051] in, These represent the energy required per unit distance to overcome road resistance, air resistance, and acceleration resistance, respectively. These represent road resistance, air resistance, and acceleration resistance, respectively, with x representing the space provided for brake energy recovery per unit distance. tot This represents the total distance traveled.

[0052] In some embodiments of the present invention, the energy demand based on the reference operating condition is... Extract the reference operating condition data under traction conditions to measure the vehicle excitation per unit distance caused by overcoming road resistance. A measure of the excitation on a vehicle to overcome air resistance. Measurement of vehicle excitation in relation to overcoming acceleration resistance The steps include:

[0053] Calculate according to formulas (10), (11) and (12) respectively.

[0054]

[0055]

[0056]

[0057] Extract the portion of formulas (10), (11), and (12) that is only relevant to the reference working condition, and use it as a measure of the traction region of the reference working condition on the vehicle excitation. Use formulas (13), (14), and (15) to express and calculate the excitation of the reference working condition. and

[0058]

[0059]

[0060]

[0061] Specifically, the measurement of vehicle excitation in the traction region under the test condition is calculated. and The steps are the same as those for the reference operating condition.

[0062] In some embodiments of the present invention, the braking energy recovery space according to the reference operating condition Extract the reference operating condition data under braking conditions to measure the road resistance on vehicle excitation per unit distance. A measure of the excitation of vehicle by air resistance Measurement of vehicle excitation by acceleration resistance The steps include:

[0063] Calculate using formulas (16), (17), and (18) respectively.

[0064]

[0065]

[0066]

[0067] Extract the portion of formulas (16), (17), and (18) that is only relevant to the reference operating condition, and use it as a measure of the vehicle excitation of the braking zone of the reference operating condition. Use formulas (19), (20), and (21) to express and calculate it.

[0068]

[0069]

[0070]

[0071] Specifically, the measurement of vehicle excitation by the braking zone under the test condition is calculated. The steps are the same as those for the reference operating condition.

[0072] In some embodiments of the present invention, the step of setting an idle speed range, a coding interval, and a step size, and performing state coding on the reference operating condition data according to the idle speed range, the coding interval, and the step size to obtain one-dimensional state-coded data S of the reference operating condition. 1t The steps include:

[0073] Calculate the acceleration a1(t) corresponding to each time t based on the velocity v1(t) of the reference working condition data;

[0074] Set the idle speed range [0, V] idle ), acceleration idle range (-A) idle A idle ), speed encoding range [0, V max ], Acceleration coding interval [A min A max Set the velocity encoding step size Δv and the acceleration encoding step size Δa.

[0075] The velocity and acceleration of the reference operating condition data are respectively state encoded to obtain the velocity state code V. t and acceleration state coding A t ;

[0076]

[0077]

[0078] Encode the velocity state V t and acceleration state coding A t Convert to one-dimensional state coding

[0079]

[0080]

[0081] floor indicates rounding to negative infinity;

[0082] Specifically, the one-dimensional state code data of the test condition is calculated. The steps and calculations of the reference working condition one-dimensional state coding data The steps are the same.

[0083] In some embodiments of the present invention, the data is encoded according to the reference operating condition. The steps to establish the reference condition Markov state transition matrix P1 for the velocity-acceleration dual parameters include:

[0084] Based on the one-dimensional state encoding of velocity-acceleration Statistical state S i to state S j The transfer frequency f ij Create a state transition frequency matrix F of size N×N;

[0085]

[0086] The state S is estimated using the Kneser-Ney smoothing algorithm. i to state S j The transition probability p ij ;

[0087] Based on the transition probability p ij Construct a two-parameter Markov state transition matrix P1 of size N×N using maximum likelihood;

[0088] N = m1 × m2 is the number of one-dimensional states, p ij State S i to state S jThe transition probability;

[0089]

[0090] N ij State S i to state S j The transition frequency; N i For being in state S i frequency,

[0091] The steps of calculating the Markov state transition matrix P2 for the test condition and calculating the Markov state transition matrix P for the reference condition are as follows: i The steps are the same.

[0092] In some embodiments of the present invention, ω1 = ω2 = ω3 = 1 / 3.

[0093] According to the energy consumption test condition similarity evaluation method proposed in this invention, the relative deviation of the vehicle excitation between the reference condition and the test condition is calculated, and the similarity of the vehicle excitation between the reference condition and the test condition is calculated using the relative deviation of the vehicle excitation. This allows for an accurate assessment of the similarity of energy consumption between reference and test conditions, thereby improving the efficiency of energy consumption testing and the accuracy of analysis and evaluation using test conditions. Attached Figure Description

[0094] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0095] Figure 1 A flowchart illustrating a vehicle energy consumption test condition similarity evaluation method according to some embodiments of the present invention is shown.

[0096] Figure 2 A schematic diagram of the two-dimensional probability distribution of velocity-acceleration in WLTC is shown.

[0097] Figure 3 A schematic diagram of the velocity-acceleration two-dimensional probability distribution of CLTC-P is shown.

[0098] Figure 4 A schematic diagram of the three-dimensional plot of the Markov state transition probability matrix of WLTC is shown.

[0099] Figure 5 A schematic diagram of the three-dimensional plot of the Markov state transition probability matrix of CLTC-P is shown.

[0100] Figure 6 A flowchart illustrating a vehicle energy consumption test condition similarity evaluation method according to some embodiments of the present invention is shown. Detailed Implementation

[0101] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0102] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0103] Although terms such as first, second, third, etc., may be used in this document to describe multiple elements, components, regions, layers, and / or segments, these elements, components, regions, layers, and / or segments should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or segment from another. Unless the context clearly indicates otherwise, terms such as "first," "second," and other numerical terms used herein do not imply order or sequence. Therefore, the first element, component, region, layer, or segment discussed below may be referred to as the second element, component, region, layer, or segment without departing from the teachings of the exemplary embodiments.

[0104] For ease of description, spatial relative terms may be used in the text to describe the relationship of one element or feature relative to another element or feature, as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "below," "above," "over," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure. For example, if the device in the figure is flipped, an element described as "below other elements or features" or "below other elements or features" would subsequently be oriented as "above other elements or features" or "above other elements or features." Therefore, the example term "below" can include both upper and lower orientations. The device may be otherwise oriented (rotated 90 degrees or in other directions), and the spatial relative descriptors used in the text will be interpreted accordingly.

[0105] like Figure 1 and Figure 2 As shown, according to an embodiment of the present invention, a method for evaluating the similarity of vehicle energy consumption test conditions is proposed. The method includes the following steps:

[0106] Step 1: Obtain reference operating condition data v1(t) and test operating condition data v2(t);

[0107] Step 2: Calculate the reference driving force F1(t) under the reference driving condition data and the test driving force F2(t) under the test driving condition data based on the reference driving condition data v1(t) and the test driving condition data v2(t), respectively.

[0108] Step 3: Calculate the reference operating condition energy requirement per unit distance of the vehicle based on the reference operating condition data and the reference operating condition vehicle driving force F1(t). and reference operating condition braking energy recovery space Calculate the energy requirement per unit distance of the vehicle under test conditions based on the test condition data and the vehicle's driving force F2(t). and test condition braking energy recovery space

[0109] Step 4: Based on the energy requirements of the reference operating condition Extract the excitation measure applied to the vehicle per unit distance to overcome road resistance from the reference operating condition data under traction conditions. The excitation applied to a vehicle to overcome air resistance The excitation applied to the vehicle to overcome acceleration resistance Based on the energy requirements of the test conditions Extract the test condition data under traction conditions, specifically the excitation measure applied to the vehicle per unit distance to overcome road resistance. The excitation applied to a vehicle to overcome air resistance The excitation applied to the vehicle to overcome acceleration resistance

[0110] Step 5: Braking energy recovery space per unit distance based on reference operating conditions Extract the reference operating condition data under braking conditions to measure the vehicle excitation per unit distance of road resistance. A measure of the excitation of vehicle by air resistance Measurement of vehicle excitation by acceleration resistance Based on the energy requirement per unit distance under test conditions Extract the test condition data under braking conditions to measure the vehicle excitation per unit distance caused by road resistance. A measure of the excitation of vehicle by air resistance Measurement of vehicle excitation by acceleration resistance

[0111] Step 6: Calculate the relative deviation of vehicle excitation between the reference condition and the test condition using formulas (1) to (6), and calculate the similarity of vehicle excitation between the reference condition and the test condition using formula (7) based on the relative deviation of vehicle excitation.

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119] Energy consumption comparability refers to the fact that for the same vehicle, the energy consumption is the same or similar under different driving conditions.

[0120] According to the energy consumption test condition similarity evaluation method proposed in this invention, the relative deviation of the vehicle excitation between the reference condition and the test condition is calculated, and the similarity of the vehicle excitation between the reference condition and the test condition is calculated using the relative deviation of the vehicle excitation. This allows for an accurate assessment of the similarity of energy consumption between reference and test conditions, thereby improving the efficiency of energy consumption testing and the accuracy of analysis and evaluation using test conditions. It also helps to narrow the gap between actual energy consumption and test results, and promotes the upgrading of vehicle economic development and evaluation methods.

[0121] In step 1, the reference driving condition data v1(t) includes: actual driving condition data collected by the user based on various data acquisition devices such as GPS, CAN bus, or OBD; or typical driving conditions such as the New European Driving Cycle (NEDC), the World Light Vehicle Test Cycle (WLTC), and the China Automotive Test Cycle (CATC). The test condition is a new vehicle driving condition benchmarked against the reference condition.

[0122] In step 2: For the same vehicle under test, the driving force F(t) of the vehicle under the corresponding working condition is calculated using formula (8).

[0123]

[0124] In the formula, m is the vehicle mass; g is the acceleration due to gravity; f is the rolling resistance coefficient; i is the road gradient; C D ρ is the air resistance coefficient; A is the windward area; ρ is the air density; δ is the rotational mass conversion factor. If the road slope data is missing in the operating data, formula (8) can be simplified to formula (9).

[0125]

[0126] Substitute the reference working condition data v1(t) and the test working condition data v2(t) into equation (8) or equation (9) respectively to calculate the reference working condition vehicle driving force F1(t) under the reference working condition data and the test working condition vehicle driving force F2(t) under the test working condition data respectively.

[0127] In step 3: the traction zone and braking zone of the working condition are divided according to the following principles:

[0128] When the vehicle is in a non-idling state, F(t)>0 is the traction state point, and the region is divided into traction regions t∈τ. tr ;

[0129] When the vehicle is in a non-idling state, F(t)<0 is the braking state point, and the region is divided into braking regions t∈τ. br .

[0130] In the traction zone of the operating condition, the energy requirement per unit distance of the vehicle is calculated using formula (10).

[0131]

[0132] In the formula, x tot This represents the total distance traveled. Formulas (11), (12), and (13) are used to calculate the energy required per unit distance to overcome road resistance, air resistance, and acceleration resistance, respectively.

[0133]

[0134]

[0135]

[0136] In steps 4 and 5: extract the part that is only related to the working condition from formulas (11), (12), and (13) as a measure of the vehicle excitation of the traction area of ​​the working condition, and use formulas (14), (15), and (16) to express and calculate it.

[0137]

[0138]

[0139]

[0140] In the braking zone of the operating condition, the braking energy recovery space provided to the vehicle per unit distance is calculated using formula (17).

[0141]

[0142] In the formula, Formulas (18), (19), and (20) are used to calculate the road resistance, air resistance, and acceleration resistance, respectively, and the unit distance is the space provided by the vehicle for brake energy recovery.

[0143]

[0144]

[0145]

[0146] Substitute the reference working condition data v1(t) and the reference working condition vehicle driving force F1(t) into equations (10) to (20) in turn to calculate the energy demand per unit distance under the reference working condition. Braking energy recovery space per unit distance compared to reference operating conditions

[0147] Substitute the test condition data v2(t) and the test condition vehicle driving force F2(t) into equations (10) to (20) in turn to calculate the energy demand per unit distance under the test condition. Braking energy recovery space per unit distance under test conditions

[0148] Extract the parts related only to the working condition from formulas (18), (19), and (20) as a measure of the vehicle excitation of the braking area of ​​the working condition, and use formulas (21), (22), and (23) to express and calculate them.

[0149]

[0150]

[0151]

[0152] Energy demand per unit distance under reference operating conditions Substitute these values ​​sequentially into equations (11) through (16) to calculate the measure of the vehicle excitation per unit distance from overcoming road resistance based on the reference operating conditions. A measure of the excitation on a vehicle to overcome air resistance. Measurement of vehicle excitation in relation to overcoming acceleration resistance

[0153] The energy demand per unit distance under test conditions will be tested. Substitute these values ​​sequentially into equations (11) through (16) to calculate the measure of the vehicle excitation caused by overcoming road resistance per unit distance under test conditions. A measure of the excitation on a vehicle to overcome air resistance. Measurement of vehicle excitation in relation to overcoming acceleration resistance

[0154] The braking energy recovery space per unit distance under reference operating conditions Substitute into equations (18) to (23) to calculate the measure of the road resistance on the vehicle excitation per unit distance using reference operating data. A measure of the excitation of vehicle by air resistance Measurement of vehicle excitation by acceleration resistance

[0155] The braking energy requirement per unit distance will be determined based on the test conditions. Substituting into equations (18) to (23) allows for the calculation of the road resistance's effect on vehicle excitation per unit distance under braking conditions. A measure of the excitation of vehicle by air resistance Measurement of vehicle excitation by acceleration resistance

[0156] In step 6: use formulas (1)-(7) to calculate the relative deviation of vehicle excitation by two different operating conditions v1(t) and v2(t), and use formula (30) to calculate the similarity of vehicle excitation by two different operating conditions v1(t) and v2(t).

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164] According to the energy consumption test condition similarity evaluation method proposed in this invention, the relative deviation of the vehicle excitation between the reference condition and the test condition is calculated, and the similarity of the vehicle excitation between the reference condition and the test condition is calculated using the relative deviation of the vehicle excitation. This allows for an accurate assessment of the similarity of energy consumption between reference and test conditions, thereby improving the efficiency of energy consumption testing and the accuracy of analysis and evaluation using test conditions.

[0165] Furthermore, the energy consumption test condition similarity evaluation method also includes the following steps:

[0166] Step 2.1: Set the idle speed range, encoding interval, and step size. Based on the idle speed range, encoding interval, and step size, perform speed state encoding and acceleration state encoding on the reference operating condition data and test operating condition data respectively to obtain one-dimensional state encoding data of the reference operating condition. One-dimensional state coding data of test conditions

[0167] Step 2.2: Based on the one-dimensional state coding data of the reference working condition Establish a two-dimensional frequency distribution matrix for velocity-acceleration reference conditions, based on one-dimensional state-coded data from the test conditions. Establish a two-dimensional frequency distribution matrix for the velocity-acceleration test conditions;

[0168] Step 2.3: Calculate the velocity-acceleration two-dimensional probability distribution matrix of the reference working condition based on the velocity-acceleration two-dimensional frequency distribution matrix of the reference working condition, and calculate the velocity-acceleration two-dimensional probability distribution matrix of the test working condition based on the velocity-acceleration two-dimensional frequency distribution matrix of the test working condition;

[0169] Step 2.4: Calculate the cosine similarity of the velocity-acceleration two-dimensional probability distribution matrices between the reference and test condition data based on the reference and test condition two-dimensional probability distribution matrices.

[0170]

[0171] Among them, A k This represents the k-th component after the two-dimensional probability distribution matrix of the test condition is arranged in one dimension.

[0172] B k The k-th component is the one-dimensional arrangement of the two-dimensional probability distribution matrix of the reference working condition; m2 represents the acceleration state number, and m1 represents the velocity state number.

[0173] In step 2.1: Convert the units of velocity v(t) for different operating conditions to m / s, and use formula (25) to calculate the acceleration a(t) corresponding to each time t, in m / s. 2 .

[0174]

[0175] Set the idle speed range [0, V] idle ), acceleration idle range (-A) idle A idle ), speed encoding range [0, V max ], Acceleration coding interval [A min A max Set the speed encoding step size Δv and the acceleration encoding step size Δa to ensure that the encoding range includes all operating condition data, and the idling range is less than one step size. Preferably, the speed is in the range of [0, 0.02 m / s) and the acceleration is in the range of (-0.01 m / s). 2 0.01m / s 2 Even at this time, the vehicle is still considered to be idling, with a speed coding step size of 1 m / s and an acceleration coding step size of 0.2 m / s. 2 .

[0176] Speed ​​and acceleration are respectively coded into states. The speed value in the idle range is coded as speed state 1, and the speed state code V is calculated using formula (26). tThe acceleration value in the idle range is encoded as acceleration state 1, and the acceleration state code A is calculated using formula (27). t In the formula, floor represents rounding to negative infinity.

[0177]

[0178]

[0179] The two-dimensional state encoding of velocity-acceleration (V) is performed using formula (28). t A t Convert to one-dimensional state code S t In formula (28), m2 represents the acceleration state number and m1 represents the velocity state number, which are calculated using formula (29) and formula (30) respectively.

[0180] S t =A t +(V t -1)×m2 (28)

[0181]

[0182]

[0183] In this step, the reference operating condition data v1(t) and the test operating condition data v2(t) are substituted into equations (31) to (36) respectively to calculate the one-dimensional state coding data of the reference operating condition. One-dimensional state coding data of test conditions

[0184] In step 2.2, the statistical velocity state is V. t The acceleration state is A t By calculating the frequency of the velocity-acceleration two-dimensional frequency distribution matrix, and then dividing the frequency distribution matrix by the total operating time T, we obtain a velocity-acceleration two-dimensional probability distribution matrix of size m1×m2.

[0185] In step 2.3, the cosine similarity of the velocity-acceleration two-dimensional probability distribution matrices between the reference operating condition data and the test operating condition data is calculated based on the reference operating condition two-dimensional probability distribution matrix and the test operating condition two-dimensional probability distribution matrix.

[0186]

[0187] Among them, A k This represents the k-th component of the two-dimensional probability distribution matrix of the test condition after being arranged in one dimension;

[0188] B kThis represents the k-th component after the two-dimensional probability distribution matrix of the reference working condition is arranged in one dimension.

[0189] Furthermore, the energy consumption test condition similarity evaluation method also includes the following steps:

[0190] Step 3.1: Based on the reference operating condition coding data and test condition coding data Establish the Markov state transition matrix P1 for the reference condition and the Markov state transition matrix P2 for the test condition, respectively, for the two parameters of velocity and acceleration.

[0191] Step 3.2: Calculate the cosine similarity of the Markov state transition probability matrices between the reference operating condition data and the test operating condition data based on the reference operating condition Markov state transition matrix P1 and the test operating condition Markov state transition matrix P2.

[0192]

[0193] Among them, C k This represents the k-th component of the Markov state transition matrix P2 under the test condition after being arranged in one dimension.

[0194] D k This represents the k-th component of the Markov state transition matrix P1 for the reference operating condition after being arranged in one dimension.

[0195] N represents the number of states in the Markov state transition matrix for the test condition and the reference condition. The size of the Markov state transition matrix is ​​N×N, and N=m1×m2.

[0196] In step 3.1, the Markov state transition probability matrix is ​​calculated using the maximum likelihood estimation principle, as shown in formula (32).

[0197]

[0198] In the formula, N = m1 × m2 is the number of one-dimensional states; p ij State S i to state S j The transition probability is calculated using formula (33).

[0199]

[0200] In the formula, N ij State S i to state S j The transition frequency; N i For being in state S i frequency,

[0201] In step 3.2, the cosine similarity, i.e., the similarity coefficient, of the Markov state transition probability matrices of the two different operating conditions, reference operating condition data v1(t) and test operating condition data v2(t), is calculated using formula (34).

[0202]

[0203] Furthermore, the energy consumption test condition similarity evaluation method also includes the following steps:

[0204] Based on the cosine similarity of the velocity-acceleration two-dimensional probability distribution matrix The cosine similarity of the Markov state transition probability matrix Similarity to the aforementioned vehicle excitation Calculate the overall similarity φ between the reference working condition and the test working condition;

[0205]

[0206] In this embodiment, the operating characteristics, dynamic transfer patterns, and vehicle excitation are considered equally important. Therefore, the three similarities are assigned the same weights: ω1 = 1 / 3, ω2 = 1 / 3, and ω3 = 1 / 3. According to formula (33), the overall similarity between WLTC and CLTC-P is 0.62.

[0207] In an exemplary embodiment of the present invention, a method for evaluating the similarity of energy consumption test conditions is proposed, the method comprising the following steps:

[0208] Step 1: For different operating conditions, speed and acceleration are encoded according to the idling range, encoding range, and step size;

[0209] Step 2: Calculate the velocity-acceleration two-dimensional probability distribution matrix and similarity coefficient for different operating conditions based on the state code, which is used to evaluate the similarity of operating characteristics between different operating conditions;

[0210] Step 3: Calculate the Markov state transition probability matrix and similarity coefficient for different working conditions based on the state code, which are used to evaluate the similarity between the dynamic transition patterns of the working conditions.

[0211] Step 4: For the same vehicle under test, the traction and braking regions of the working conditions are divided using the vehicle's longitudinal dynamics equations.

[0212] Step 5: Calculate the excitation and relative deviation of the vehicle in the traction and braking regions respectively to evaluate the similarity of vehicle excitation under different operating conditions.

[0213] Step 6: Assign weights to the similarity of operating features, the similarity of dynamic transfer patterns, and the similarity of vehicle incentives, and calculate the comprehensive similarity of the vehicle energy consumption test conditions.

[0214] The specific process of step 1 is as follows:

[0215] Step 1.1: Convert the units of velocity v(t) for different operating conditions to m / s, and use formula (1) to calculate the acceleration a(t) at each time t, in m / s. 2 .

[0216]

[0217] Step 1.2: Set the idle speed range [0, V] idle ), acceleration idle range (-A) idle A idle ), speed encoding range [0, V max ], Acceleration coding interval [A min A max Set the speed encoding step size Δv and the acceleration encoding step size Δa to ensure that the encoding range includes all operating condition data, and the idling range is less than one step size. Preferably, the speed is in the range of [0, 0.02 m / s) and the acceleration is in the range of (-0.01 m / s). 2 0.01m / s 2 Even at this time, the vehicle is still considered to be idling, with a speed coding step size of 1 m / s and an acceleration coding step size of 0.2 m / s. 2 .

[0218] Step 1.3: Encode the speed and acceleration states separately. Encode the speed value in the idle range as speed state 1, and calculate the speed state code V using formula (2). t The acceleration value in the idle range is encoded as acceleration state 1, and the acceleration state code A is calculated using formula (3). t In the formula, floor represents rounding to negative infinity.

[0219]

[0220]

[0221] Step 1.4: Use formula (4) to encode the two-dimensional state of velocity-acceleration (V t A t Convert to one-dimensional state code S t In formula (4), m2 represents the acceleration state number and m1 represents the velocity state number, which are calculated using formula (5) and formula (6) respectively.

[0222] St =A t +(V t -1)×m2 (4)

[0223]

[0224]

[0225] The specific process of step 2 is as follows:

[0226] Step 2.1: Statistical speed status is V t The acceleration state is A t By calculating the frequency of the velocity-acceleration two-dimensional frequency distribution matrix, and then dividing the frequency distribution matrix by the total operating time T, we obtain a velocity-acceleration two-dimensional probability distribution matrix of size m1×m2.

[0227] Step 2.2: Use formula (7) to calculate v for two different operating conditions. The cosine similarity, i.e., the similarity coefficient, of the velocity-acceleration two-dimensional probability distribution matrix.

[0228]

[0229] In the formula, A k With B k Representing operating condition v and The k-th component after arranging the two-dimensional probability distribution matrix of velocity and acceleration in one dimension.

[0230] The specific process of step 3 is as follows:

[0231] Step 3.1: Calculate the Markov state transition probability matrix using the maximum likelihood estimation principle, as shown in formula (8).

[0232]

[0233] In the formula, N = m1 × m2 is the number of one-dimensional states; p ij State S i to state S j The transition probability is calculated using formula (9):

[0234]

[0235] In the formula, N ij State S i to state S j The transition frequency; N i For being in state S i frequency,

[0236] Step 3.2: Use formula (10) to calculate v for two different operating conditions. The cosine similarity, i.e., the similarity coefficient, of the Markov state transition probability matrix.

[0237]

[0238] In the formula, C k With D k Representing operating condition v and The k-th component of the Markov state transition probability matrix after one-dimensional permutation.

[0239] The specific process of step 4 is as follows:

[0240] Step 4.1: For the same vehicle under test, use formula (11) to calculate the driving force of the vehicle under the corresponding working conditions.

[0241]

[0242] In the formula, m is the vehicle mass; g is the acceleration due to gravity; f is the rolling resistance coefficient; i is the road gradient; C D ρ is the air resistance coefficient; A is the windward area; ρ is the air density; δ is the rotational mass conversion factor. If the road slope data is missing in the working condition data, formula (11) can be simplified to formula (12).

[0243]

[0244] Step 4.2: Divide the traction and braking zones of the working condition, according to the following principles:

[0245] When the vehicle is in a non-idling state, F(t)>0 is the traction state point, and the region is divided into traction regions t∈τ. tr ;

[0246] When the vehicle is in a non-idling state, F(t)<0 is the braking state point, and the region is divided into braking regions t∈τ. br .

[0247] The specific process of step 5 is as follows:

[0248] Step 5.1: In the traction zone of the working condition, use formula (13) to calculate the energy requirement per unit distance of the vehicle.

[0249]

[0250] In the formula, x tot This represents the total distance traveled. Formulas (14), (15), and (16) are used to calculate the energy required per unit distance to overcome road resistance, air resistance, and acceleration resistance, respectively.

[0251]

[0252]

[0253]

[0254] Step 5.2: Extract the part that is only related to the working condition from formulas (14), (15), and (16) as the measure of the vehicle excitation of the traction area of ​​the working condition, and use formulas (17), (18), and (19) to express and calculate it.

[0255]

[0256]

[0257]

[0258] Step 5.3: In the braking zone of the operating condition, use formula (20) to calculate the braking energy recovery space provided to the vehicle per unit distance under the operating condition.

[0259]

[0260] In the formula, Formulas (21), (22), and (23) are used to calculate the road resistance, air resistance, and acceleration resistance, respectively, and the unit distance is the space provided by the vehicle for brake energy recovery.

[0261]

[0262]

[0263]

[0264] Step 5.4: Extract the parts related only to the working condition from formulas (21), (22), and (23) as a measure of the vehicle excitation of the braking area of ​​the working condition, and express and calculate them using formulas (24), (25), and (26).

[0265]

[0266]

[0267]

[0268] Step 5.5: Use formulas (27)-(32) to calculate v for two different operating conditions. The relative deviation of vehicle excitation is calculated using formula (33) for two different operating conditions. Similarity of vehicle incentives.

[0269]

[0270]

[0271]

[0272]

[0273]

[0274]

[0275]

[0276] Step 6: Based on the different requirements for similarity in operational features, similarity in dynamic transfer patterns, and similarity in vehicle excitation, for... Weights ω1, ω2, and ω3 are assigned respectively, and formula (34) is used to calculate v for two different working conditions. The overall similarity.

[0277]

[0278] The beneficial effects of this invention are as follows:

[0279] This invention proposes a method for evaluating the similarity of vehicle energy consumption test conditions. Compared with traditional methods, such as measuring the similarity between driving conditions and actual user driving conditions or different conditions by comparing the relative deviations of certain feature values, this method proposes quantitative parameters and calculation methods for similarity measurement in three aspects: operating characteristics, dynamic transfer patterns, and vehicle excitation. Finally, it provides a comprehensive similarity score for vehicle energy consumption test conditions.

[0280] The operating characteristics of the working conditions were analyzed by calculating the two-dimensional probability distribution matrix of velocity-acceleration, and the similarity of the operating characteristics between different working conditions was quantified by using the cosine similarity measure.

[0281] The dynamic transition laws of the working conditions were analyzed by calculating the velocity-acceleration Markov state transition probability matrix, and the similarity of the dynamic transition laws between different working conditions was quantified by using the cosine similarity measure.

[0282] The traction and braking regions were defined as measures of vehicle excitation, and their relative deviation was calculated to quantify the similarity of vehicle excitation under different operating conditions.

[0283] Taking into account the similarity of operating characteristics, dynamic transfer patterns, and vehicle excitation, a comprehensive similarity test condition for vehicle energy consumption is proposed. This comprehensive and accurate evaluation of the similarity between different operating conditions helps improve the efficiency of vehicle energy consumption testing and provides strong support for the accurate analysis and evaluation of vehicle energy consumption.

[0284] The present invention will be further described below with reference to the embodiments and accompanying drawings. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way.

[0285] As attached Figure 6 As shown, this invention proposes a method for evaluating the similarity of vehicle energy consumption test conditions. First, the similarity of different conditions is evaluated from three aspects: operational characteristics, dynamic transition patterns, and vehicle excitation. The similarity of operational characteristics is evaluated by calculating the speed-acceleration two-dimensional probability distribution matrix and similarity coefficients. The similarity of dynamic transition patterns is evaluated by calculating the Markov state transition probability matrix and similarity coefficients. For the same vehicle under test, the similarity of vehicle excitation under different conditions is evaluated by calculating the excitation and relative deviation of the traction and braking regions under different conditions. Finally, considering the similarity of the three aspects comprehensively, the overall similarity of the vehicle energy consumption test conditions is obtained.

[0286] The following example, using the evaluation of the similarity between the World Light Vehicle Test Cycle (WLTC) and the China light-duty vehicle test cycle for passenger car (CLTC-P) as an example, provides a detailed description of the specific implementation of the present invention:

[0287] WLTC and CLTC-P represent speed time series, which is the vehicle speed per second, with the unit of vehicle speed being km / h.

[0288] Step 1: Encode the speed and acceleration status based on the idle speed range, encoding range, and step size of the operating condition data.

[0289] Step 1.1: Convert the unit of velocity v(t) from km / h to m / s, and use formula (1) to calculate the acceleration a(t) at each time t, in m / s. 2 .

[0290]

[0291] Step 1.2: Set the velocity to [0, 0.02 m / s) and the acceleration to (-0.01 m / s²). 2 0.01m / s 2 The vehicle is still considered to be idling at this time; the WLTC speed coding range is [0, 37 m / s], and the acceleration coding range is [-1.6 m / s²]. 2 1.8m / s 2 The velocity encoding range of CLTC-P is [0, 32 m / s], and the acceleration encoding range is [-2 m / s]. 2 2m / s 2 The velocity encoding step size is set to 1 m / s, and the acceleration encoding step size is set to 0.2 m / s. 2 ;

[0292] Step 1.3: Encode the speed and acceleration states separately. Encode the speed value in the idle range as speed state 1, and calculate the speed state code V using formula (2). t The acceleration value in the idle range is encoded as acceleration state 1, and the acceleration state code A is calculated using formula (3). t In the formula, floor represents rounding to negative infinity.

[0293]

[0294]

[0295] Step 1.4: Use formula (4) to encode the two-dimensional state of velocity-acceleration (V t A t Convert to one-dimensional state code S t In formula (4), m2 represents the acceleration state number and m1 represents the velocity state number, which are calculated using formula (5) and formula (6) respectively.

[0296] S t =A t +(V t -1)×m2 (4)

[0297]

[0298]

[0299] In this embodiment, according to formulas (5) and (6), the WLTC has 38 velocity states and 18 acceleration states; the CLTC-P has 33 velocity states and 21 acceleration states.

[0300] Step 2: Calculate the velocity-acceleration two-dimensional probability distribution matrix and similarity coefficient for different operating conditions based on the state code, which are used to evaluate the similarity of operating characteristics between different operating conditions.

[0301] Step 2.1: Statistical speed status is V t The acceleration state is A t The frequency of the velocity-acceleration two-dimensional frequency distribution matrix is ​​established. The frequency distribution matrix is ​​then divided by the total operating time T to obtain a velocity-acceleration two-dimensional probability distribution matrix of size n×m. In this embodiment, the velocity-acceleration two-dimensional probability distribution diagram of the WLTC is attached. Figure 2 As shown in the attached figure, the velocity-acceleration two-dimensional probability distribution diagram of CLTC-P is as follows. Figure 3 As shown.

[0302] Step 2.2: Use formula (7) to calculate v for two different operating conditions. The cosine similarity, i.e., the similarity coefficient, of the velocity-acceleration two-dimensional probability distribution matrix.

[0303]

[0304] In the formula, A k With B k Representing operating condition v and The k-th component of the velocity-acceleration two-dimensional probability distribution matrix after one-dimensional arrangement. In this embodiment, according to formula (7), the similarity of the running characteristics of WLTC and CLTC-P is 0.9515.

[0305] Step 3: Calculate the Markov state transition probability matrix and similarity coefficient for different working conditions based on the state code, which are used to evaluate the similarity between the dynamic transition patterns of the working conditions.

[0306] Step 3.1: Calculate the Markov state transition probability matrix using the maximum likelihood estimation principle, as shown in formula (8).

[0307]

[0308] In the formula, N = m1 × m2 is the number of one-dimensional states; p ij State S i to state S j The transition probability is calculated using formula (9):

[0309] In the formula, N ij State S i to state S j The transition frequency; N i For being in state S i frequency, In this embodiment, the three-dimensional diagram of the Markov state transition probability matrix of WLTC is shown in the attached figure. Figure 4 As shown in the attached figure, the three-dimensional diagram of the Markov state transition probability matrix of CLTC-P is as follows. Figure 5 As shown.

[0310] Step 3.2: Use formula (10) to calculate v for two different operating conditions. The cosine similarity, i.e., the similarity coefficient, of the Markov state transition probability matrix.

[0311]

[0312] In the formula, C k With D k Representing operating condition v and The k-th component of the Markov state transition probability matrix after one-dimensional permutation. In this embodiment, according to formula (10), the similarity between the dynamic transition laws of WLTC and CLTC-P is 0.2114.

[0313] Step 4: For the same vehicle under test, the traction and braking regions of the working conditions are divided using the vehicle's longitudinal dynamics equations.

[0314] In this embodiment, the vehicle parameters are derived from a specific pure electric vehicle model included in the CRUISE simulation software: the vehicle weight is 1200 kg, and the coasting resistance curve is F = 0.03399 × v. 2 +143.06.

[0315] Step 4.1: For the above vehicle parameters, use formula (11) to calculate the driving force of the vehicle under the corresponding working conditions.

[0316]

[0317] In the formula, m = 1200; mgf = 143.06; The apparent value is δ = 1.

[0318] Step 4.2: Divide the traction and braking zones of the working condition, according to the following principles:

[0319] When the vehicle is in a non-idling state, F(t)>0 is the traction state point, and the region is divided into traction regions t∈τ. tr ;

[0320] When the vehicle is in a non-idling state, F(t)<0 is the braking state point, and the region is divided into braking regions t∈τ. br .

[0321] Step 5: Calculate the excitation and relative deviation of the vehicle in the traction and braking regions respectively to evaluate the similarity of vehicle excitation under different operating conditions.

[0322] Step 5.1: In the traction zone of the working condition, use formula (12) to calculate the energy requirement per unit distance of the vehicle.

[0323]

[0324] In the formula, x tot This represents the total distance traveled. Formulas (13), (14), and (15) are used to calculate the energy required per unit distance to overcome road resistance, air resistance, and acceleration resistance, respectively.

[0325]

[0326]

[0327]

[0328] Step 5.2: Extract the part that is only related to the working condition from formulas (13), (14), and (15) as the measure of the vehicle excitation of the traction area of ​​the working condition, and use formulas (16), (17), and (18) to express and calculate it.

[0329]

[0330]

[0331]

[0332] Step 5.3: In the braking zone of the operating condition, use formula (19) to calculate the braking energy recovery space provided to the vehicle per unit distance under the operating condition.

[0333]

[0334] In the formula, Formulas (20), (21), and (22) are used to calculate the road resistance, air resistance, and acceleration resistance, respectively, and the unit distance is the space provided by the vehicle for brake energy recovery.

[0335]

[0336]

[0337]

[0338] Step 5.4: Extract the part that is only related to the working condition from formulas (20), (21), and (22) as a measure of the vehicle excitation of the braking area of ​​the working condition, and use formulas (23), (24), and (25) to express and calculate it.

[0339]

[0340]

[0341]

[0342] In this embodiment, according to formulas (16)-(18) and (23)-(25), the excitation of the vehicle by the traction and braking regions of WLTC and CLTC-P is shown in Table 1.

[0343] Table 1. Excitation of vehicles by WLTC and CLTC-P

[0344]

[0345] Step 5.5: Use formulas (26)-(31) to calculate v for two different operating conditions. The relative deviation of vehicle excitation is calculated using formula (32) for two different operating conditions. Similarity of vehicle incentives.

[0346]

[0347]

[0348]

[0349]

[0350]

[0351]

[0352]

[0353] In this embodiment, according to formula (32), the similarity of WLTC and CLTC-P for vehicle excitation is 0.70.

[0354] Step 6: Based on the different requirements for similarity in operational features, similarity in dynamic transfer patterns, and similarity in vehicle excitation, for... Weights ω1, ω2, and ω3 are assigned respectively, and formula (33) is used to calculate v for two different working conditions. The overall similarity.

[0355]

[0356] In this embodiment, the operating characteristics, dynamic transfer patterns, and vehicle excitation are considered equally important. Therefore, the three similarities are assigned the same weights: ω1 = 1 / 3, ω2 = 1 / 3, and ω3 = 1 / 3. According to formula (33), the overall similarity between WLTC and CLTC-P is 0.62.

[0357] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for evaluating the similarity of vehicle energy consumption test conditions, characterized in that, The vehicle energy consumption test condition similarity evaluation method includes: Obtain reference operating condition data v1(t) and test operating condition data v2(t); Based on the reference operating condition data v1(t) and the test operating condition data v2(t), calculate the reference operating condition vehicle driving force F1(t) and the test operating condition vehicle driving force F2(t) respectively. The reference operating condition energy requirement per unit distance of the vehicle is calculated based on the reference operating condition data and the reference operating condition vehicle driving force F1(t). and reference operating condition braking energy recovery space Calculate the energy requirement per unit distance of the vehicle under the test conditions based on the test condition data and the vehicle's driving force F2(t) under the test conditions. and test condition braking energy recovery space Based on the energy demand of the reference operating condition Extract the excitation measure applied to the vehicle per unit distance by the reference operating condition data under traction conditions to overcome road resistance. The excitation applied to a vehicle to overcome air resistance The excitation applied to the vehicle to overcome acceleration resistance Based on the energy requirements of the test conditions Extract the excitation measure applied to the vehicle per unit distance to overcome road resistance under the test conditions during traction. The excitation applied to a vehicle to overcome air resistance The excitation applied to the vehicle to overcome acceleration resistance According to the reference operating condition braking energy recovery space Extract the reference operating condition data under braking conditions to measure the road resistance on vehicle excitation per unit distance. Measurement of the effect of air resistance on vehicle excitation Measurement of vehicle excitation by acceleration resistance According to the braking energy requirement under the test conditions Extract the test condition data under braking conditions to measure the road resistance on vehicle excitation per unit distance. Measurement of the effect of air resistance on vehicle excitation Measurement of vehicle excitation by acceleration resistance The relative deviation of the vehicle excitation between the reference condition and the test condition is calculated using formulas (1) to (6); The similarity of vehicle excitation between the reference condition and the test condition is calculated using formula (7) based on the relative deviation of the vehicle excitation.

2. The energy consumption test condition similarity evaluation method according to claim 1, characterized in that, The vehicle energy consumption test condition similarity evaluation method also includes: By setting an idle speed range, encoding interval, and step size, speed state encoding and acceleration state encoding are performed on the reference operating condition data and the test operating condition data according to the idle speed range, encoding interval, and step size, respectively, to obtain one-dimensional state encoded data of the reference operating condition. One-dimensional state coding data of test conditions Based on the one-dimensional state coding data of the reference working condition Establish a two-dimensional frequency distribution matrix of velocity-acceleration reference conditions, based on the one-dimensional state coding data of the test conditions. Establish a two-dimensional frequency distribution matrix for the velocity-acceleration test conditions; Calculate the velocity-acceleration two-dimensional probability distribution matrix of the reference working condition based on the velocity-acceleration two-dimensional frequency distribution matrix of the reference working condition, and calculate the velocity-acceleration two-dimensional probability distribution matrix of the test working condition based on the velocity-acceleration two-dimensional frequency distribution matrix of the test working condition; Calculate the cosine similarity between the velocity-acceleration two-dimensional probability distribution matrices of the reference and test conditions based on the velocity-acceleration two-dimensional probability distribution matrices of the reference and test conditions. Among them, A k The k-th component represents the velocity-acceleration two-dimensional probability distribution matrix of the test condition after being arranged in one dimension. B k The k-th component of the velocity-acceleration two-dimensional probability distribution matrix of the reference working condition is represented by a one-dimensional arrangement; m2 represents the acceleration state number, and m1 represents the velocity state number.

3. The energy consumption test condition similarity evaluation method according to claim 2, characterized in that, The vehicle energy consumption test condition similarity evaluation method also includes: According to the reference working condition coding data and test condition coding data Establish the Markov state transition matrix P1 for the reference condition and the Markov state transition matrix P2 for the test condition, respectively, for the two parameters of velocity and acceleration. Calculate the cosine similarity of the Markov state transition probability matrices between the reference condition data and the test condition data based on the Markov state transition matrix P1 and the test condition Markov state transition matrix P2. Where N represents the number of states in the Markov state transition matrix of the test condition and the reference condition, and the size of the Markov state transition matrix is ​​N×N, and N=m1×m2; C k This represents the k-th component of the Markov state transition matrix P2 under the test condition after being arranged in one dimension. D k This represents the k-th component of the Markov state transition matrix P1 for the reference operating condition after being arranged in one dimension.

4. The energy consumption test condition similarity evaluation method according to claim 3, characterized in that, The vehicle energy consumption test condition similarity evaluation method also includes: Based on the cosine similarity of the velocity-acceleration two-dimensional probability distribution matrix The cosine similarity of the Markov state transition probability matrix Similarity to the aforementioned vehicle excitation Calculate the overall similarity φ between the reference working condition and the test working condition; Wherein, ω1, ω2, and ω3 are proportionality coefficients, all of which are greater than zero, and ω1 + ω2 + ω3 = 1.

5. The energy consumption test condition similarity evaluation method according to claim 1, characterized in that, The reference operating condition energy requirement per unit distance of the vehicle is calculated based on the reference operating condition data v1(t) and the reference operating condition vehicle driving force F1(t). and reference operating condition braking energy recovery space Calculate the energy requirement per unit distance of the vehicle under the test conditions based on the test condition data and the vehicle's driving force F2(t) under the test conditions. and test condition braking energy recovery space The steps include: Calculate the driving force F(t) of the vehicle; In the formula, m is the vehicle mass, g is the acceleration due to gravity, f is the rolling resistance coefficient, i is the road gradient, and C is the acceleration due to gravity. D Where A is the air resistance coefficient, ρ is the frontal area, δ is the air density, and δ is the rotational mass conversion factor. When the vehicle is not idling, the traction region t∈τ is divided according to F(t)>0. tr Based on F(t)<0, the region is divided into braking regions t∈τb. r ; Calculate the energy demand per unit distance of the vehicle based on F(t)>0 and formula (8). Substitute the reference operating condition data v1(t) and the reference operating condition vehicle driving force F1(t) into equation (8) to obtain the energy requirement per unit distance under the reference operating condition. Substitute the test condition data v2(t) and the test condition vehicle driving force F2(t) into equation (8) to obtain the energy requirement per unit distance under the test condition. The braking energy recovery space per unit distance of the vehicle is calculated based on F(t)<0 and formula (9). Substituting the reference operating condition data v1(t) and the reference operating condition vehicle driving force F1(t) into equation (9) yields the braking energy recovery space per unit distance under the reference operating condition. Substituting the test condition data v2(t) and the test condition vehicle driving force F2(t) into equation (9) yields the braking energy recovery space per unit distance under the test condition. in, These represent the energy required per unit distance to overcome road resistance, air resistance, and acceleration resistance, respectively. These represent road resistance, air resistance, and acceleration resistance, respectively, with x representing the space provided for brake energy recovery per unit distance. tot This represents the total distance traveled.

6. The energy consumption test condition similarity evaluation method according to claim 5, characterized in that, The energy requirement per unit distance based on the reference operating conditions Extract the reference operating condition data under traction conditions to measure the vehicle excitation per unit distance caused by overcoming road resistance. A measure of the excitation on a vehicle to overcome air resistance. Measurement of vehicle excitation in relation to overcoming acceleration resistance The steps include: Calculate according to formulas (10), (11) and (12) respectively. Extract the portion of formulas (10), (11), and (12) that is only relevant to the reference working condition, and use it as a measure of the traction region of the reference working condition on the vehicle excitation. Use formulas (13), (14), and (15) to express and calculate the excitation of the reference working condition. and Specifically, the measurement of vehicle excitation in the traction region under the test condition is calculated. and The steps are the same as those for the reference operating condition.

7. The energy consumption test condition similarity evaluation method according to claim 5, characterized in that, The braking energy recovery space based on the reference operating condition unit distance Extract the reference operating condition data under braking conditions to measure the road resistance on vehicle excitation per unit distance. A measure of the excitation of vehicle by air resistance Measurement of vehicle excitation by acceleration resistance The steps include: Calculate using formulas (16), (17), and (18) respectively. Extract the portion of formulas (16), (17), and (18) that is only relevant to the reference operating condition, and use it as a measure of the vehicle excitation of the braking zone of the reference operating condition. Use formulas (19), (20), and (21) to express and calculate it. Specifically, the measurement of vehicle excitation by the braking zone under the test condition is calculated. The steps are the same as those for the reference operating condition.

8. The energy consumption test condition similarity evaluation method according to claim 2, characterized in that, The idle speed range, encoding interval, and step size are set, and the reference operating condition data is state-coded according to the idle speed range, encoding interval, and step size to obtain one-dimensional state-coded data of the reference operating condition. The steps include: Calculate the acceleration a1(t) corresponding to each time t based on the velocity v1(t) of the reference working condition data; Set the idle speed range [0, V] idle ), acceleration idle range (-A) idle A idle ), speed encoding range [0, V max ], Acceleration coding interval [A min ,S max Set the velocity encoding step size Δv and the acceleration encoding step size Δa. The velocity and acceleration of the reference operating condition data are respectively state encoded to obtain the velocity state code V. 1t and acceleration state coding A t ; Encode the velocity state V 1t and acceleration state coding A 1t Convert to one-dimensional state code S 1t ; A 1t =S 1t +(V 1t -1)×m2 floor indicates rounding to negative infinity; Specifically, the one-dimensional state code data of the test condition is calculated. The steps and calculations of the reference working condition one-dimensional state coding data The steps are the same.

9. The energy consumption test condition similarity evaluation method according to claim 8, characterized in that, The data is encoded according to the reference working condition. The steps to establish the reference condition Markov state transition matrix P1 for the velocity-acceleration dual parameters include: Based on the one-dimensional state encoding of velocity-acceleration Statistical state S i to state S j The transfer frequency f ij Create a state transition frequency matrix F1 of size N×N; The state S is estimated using the Kneser-Ney smoothing algorithm. i to state S j The transition probability p ij ; Based on the transition probability p ij Construct a two-parameter Markov state transition matrix P1 of size N×N using maximum likelihood; N = m1 × m2 is the number of one-dimensional states, p ij State S i to state S j The transition probability; N ij State S i to state S j The transition frequency; N i For being in state S i frequency, The steps for calculating the Markov state transition matrix P2 for the test condition are the same as those for calculating the Markov state transition matrix P1 for the reference condition.

10. The energy consumption test condition similarity evaluation method according to claim 4, characterized in that, ω1=ω2=ω3=1 / 3.

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