Custom unit working condition construction method combining user portraits and industry requirements

Abstract

The invention discloses a self-defined unit working condition construction method combining user portraits and industry requirements, which comprises the following steps: defining a new energy automobile characteristic working condition construction scheme based on'unit working conditions', defining internal and external parameters and constructing a state transition probability matrix; constructing a Markov chain, generating an initial curve, and adjusting external parameters of the initial curve to form a trend curve meeting user portraits and industry requirements; based on the trend curve, external characteristic parameters are set, through error distribution, characteristic parameter adjustment of the trend curve is carried out, characteristic parameter errors are calculated, through feedback adjustment, the trend curve is adjusted to be a custom unit working condition curve meeting industry requirements, and a system development interface is designed; through adjustment of options and parameters in an interface and selection of a trend curve, a custom unit working condition curve fragment combining a user portrait and industry requirements is formulated. And according to the required working condition characteristics, generating a self-defined unit working condition curve to form a unit working condition library and returning to the trend working condition library.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicle driving technology, and in particular to a method for constructing a custom unit operating condition combining user portraits and industry requirements. Background Art

[0002] Automobile driving conditions, also known as vehicle test cycles, refer to speed-time curves that describe the driving status of a car under specific conditions. These conditions are usually used to evaluate indicators such as automobile performance, energy consumption and emissions, and are an important area of ​​automobile testing technology. With the development of new energy vehicles, due to the vast territory of my country and the large differences in traffic conditions in various provinces and cities, the European NEDC driving conditions can no longer meet my country's requirements for new automobile driving conditions. Although CLTC (China Light Vehicle Test Cycle) has made improvements in simulating actual driving conditions, the test environment of CLTC is mainly based on laboratory settings, which makes it difficult to fully reproduce the complex and changeable actual road conditions. In addition, CLTC is also simplified in simulating driving behavior and cannot fully reflect the impact of different drivers' habits. Therefore, my country urgently needs to specify customized driving conditions for diverse vehicle conditions and traffic conditions.

[0003] The commonly used energy consumption evaluation characteristic operating conditions have defects when applied to new energy vehicles and pure electric vehicles, such as fixed and limited characteristic operating condition segments; they can only characterize vehicle operating condition characteristics with relatively fixed operating conditions; characteristic operating conditions based on standard driving cannot be flexibly modified; they cannot reflect the driving condition characteristics of drivers with different driving styles, etc. These problems will lead to a large deviation between the actual remaining mileage of passenger cars and the mileage prediction results, which is not conducive to vehicle companies to better develop vehicle energy management control strategies.

[0004] In the automotive industry and traffic engineering, the collection and analysis of driving data is crucial for vehicle performance evaluation. Vehicle performance evaluation requires accurate driving data to assess the performance of the vehicle under different road conditions in order to guide improvement and optimization of the design. However, there are indeed many challenges in the process of collecting driving data from real vehicles. First, cost is an important issue, involving expenses in terms of vehicles, manpower, equipment, etc., especially when large-scale data collection will increase exponentially. Secondly, real-vehicle collection requires a lot of time and manpower investment, especially when tracking and collecting data under different road conditions for a long time. In addition, road safety risks are also an issue that cannot be ignored. Real-vehicle collection often needs to be carried out on real roads, which poses the risk of traffic accidents and impacts on other road users, limiting the feasibility and safety of data collection. Summary of the invention

[0005] In view of the defects in the existing energy consumption evaluation characteristic conditions and the limitations of real vehicle driving data collection, the present invention provides a diversified and customized electric vehicle driving condition definition and construction method that combines user portraits and industry requirements. Its purpose is to propose a new energy vehicle characteristic condition construction plan based on "unit conditions" and generate a vehicle driving condition model that meets the requirements through user-defined parameters.

[0006] The purpose of the present invention is achieved through the following technical solutions.

[0007] A method for constructing a custom unit condition combining user portraits and industry requirements, comprising the following steps:

[0008] S1 defines the characteristic operating condition construction scheme of new energy vehicles based on "unit operating condition", defines internal and external parameters and constructs the state transition probability matrix;

[0009] S2 constructs a Markov chain, generates an initial curve, and adjusts the external parameters of the initial curve to form a trend curve that meets the user portrait and industry requirements to a certain extent;

[0010] Based on the trend curve, S3 sets external characteristic parameters according to customer and industry needs, adjusts the characteristic parameters of the trend curve through error allocation, calculates the characteristic parameter error, and continuously reduces the characteristic parameter error through feedback adjustment. The trend curve is adjusted to a custom unit operating curve that meets industry requirements, and a system development interface is designed. By adjusting the options and parameters in the interface and selecting the trend curve, a custom unit operating curve segment that combines user portraits and industry requirements is formulated.

[0011] S4 generates custom unit operating condition curves to form a unit operating condition library and returns the trend operating condition library based on customer needs and industry specifications and the required operating condition characteristics.

[0012] In S1, the operating condition segment in which the vehicle runs from an initial speed of 0 (km / h) until the speed reaches 0 (km / h) again is called a unit operating condition. Each unit operating condition has a corresponding physical meaning. Different attribute characteristics can be given to the unit operating condition through the speed curve and sample data during driving.

[0013] Preferably, in S1, the external parameters include: the duration of the operating condition segment T dura , maximum speed v max , maximum acceleration a max , Maximum deceleration a min , average speed v mean , acceleration time proportion P acc , Deceleration time proportion P dec .

[0014] Preferably, in S2, an initial curve is generated by using a Markov chain through internal and external parameters and a state transition probability matrix, and the initial curve has basic information of a custom unit operating curve. By adjusting external parameters, the matching degree of characteristic parameters with the characteristic parameters of the required customized custom unit operating curve is adjusted to form a form that is almost consistent with the final operating curve, and the curve trend shows a very high similarity, so it is named a trend curve.

[0015] Preferably, in S3, based on the trend curve, according to customer and industry needs, external characteristic parameters are set, and a speed compensation method based on error distribution is used to further optimize the driving condition curve, and the curve condition parameters are brought close to the custom characteristic parameters through speed compensation and acceleration compensation of the curve. The speed compensation method depends on:

[0016]

[0017] In the formula, ∑v dem is the expected total velocity, v mean is the expected average speed, T dura is the duration of the working condition segment, ∑v act is the actual speed sum, v i is the actual driving speed at time i, v gap is the sum of the speed differences.

[0018] Preferably, in S3, a custom driving unit operating condition system development interface is designed, and parameter input is added according to customer and industry requirements.

[0019] Preferably, in S4, according to customer and industry needs, all characteristic parameters of a required characteristic condition can be used as parameter input, the generated driving condition can be labeled based on different states, and systematically classified in the trend condition library and unit condition library according to different labeling methods.

[0020] Preferably, in S1, for different driving style requirements, the state transition probability matrix based on the Markov chain is changed. By adjusting the probability of the entire process, the curve style that adapts to user needs can be adjusted. Similarly, by adjusting the internal parameters, the trend style of the initial curve can be changed. The initial curve has certain physical constraints, including the maximum acceleration a max ≤10km / h, maximum deceleration a max ≥-10km / h.

[0021] The Markov property can be expressed as:

[0022] P(X n+1 =x n+1 |X1=x1,L,X n =xn )=P(X n+1 =x n+1 |X n =x n )

[0023] Where, X n is the state space; P(X n+1 =x n+1 |X n =x n ) is the state transition probability of the system from i to j.

[0024] Preferably, in S3, feedback adjustment is performed through error allocation, so that the characteristic parameters of the trend curve can be continuously approached to the characteristic parameters of the custom unit operating curve. The feedback adjustment method is:

[0025]

[0026] In the formula, i represents a characteristic parameter, Rerror i represents the actual and expected error rate of the feature parameter, Rerror represents the average error of the overall feature parameter, act i Indicates the actual value of the characteristic parameter, dem i Indicates the expected value of the feature parameter.

[0027] Preferably, in S3, the system development interface includes multiple menus, including: trend curve selection menu, external parameter input menu, probability matrix adjustment menu, and custom unit operating condition curve generation menu. According to the trend curve library, a driving style personalized matching data selection is formed, and a vehicle driving test, simulation and optimization tool integrating different user portraits is provided.

[0028] Preferably, in S4, the unit conditions with different characteristics (velocity distribution, acceleration distribution) under specific conditions are called unit condition groups, and the required unit condition groups can be customized according to user portraits and industry requirements. A variety of unit condition groups can be formulated for the same group of unit conditions, and the grouping of unit condition groups is only related to subjective intentions. All unit condition groups will form a unit condition library. Due to the characteristics of unit conditions, the unit condition library is reusable, suitable for a variety of conditions, and has practical engineering significance. Similarly, trend conditions and trend condition libraries have similar characteristics, but the grouping of trend condition libraries only puts forward certain requirements on curve trends. Customized unit condition curves that meet customer and industry needs can be imported into the "unit condition library" according to the operating condition characteristics, and can also be imported into the "trend condition library" according to its curve trend.

[0029] Compared with the prior art, the advantages of the present invention are: the present invention proposes a new energy vehicle construction plan based on "unit operating conditions", which has better adaptability when applied to new energy vehicles in view of the defects of commonly used energy consumption evaluation characteristic operating conditions.

[0030] The present invention generates custom feature conditions from scratch based on user portraits and industry needs. Based on this, a driving condition library can be further generated, solving the problems of high real vehicle collection costs, many uncertain factors, and data loss.

[0031] The custom unit operating condition constructed by the present invention is reusable. The operating condition curves recognized by customers and industry authorities are integrated into specific operating conditions with different characteristics in the form of unit operating condition library and trend operating condition library, which provides convenience and reference for subsequent operating condition reuse and has flexible and diverse application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a flow chart of a method for constructing a new energy vehicle driving condition simulation system that combines user portraits and industry requirements.

[0033] Figure 2 The figure is a flow chart of a speed compensation method in a preferred embodiment of the present invention.

[0034] Figure 3 This is a schematic diagram of the trend condition library described in an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram of the system development interface described in an embodiment of the present invention.

[0036] Figure 5 A custom unit operating condition schematic diagram combining user portraits and industry requirements constructed as described in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0038] Reference Figure 1-Figure 5 ,A method for constructing a custom unit operating condition curve fragment combining ,user portrait and industry requirements, includes the following steps:

[0039] S1 defines the characteristic operating condition construction scheme of new energy vehicles based on "unit operating condition", defines internal and external parameters and constructs the state transition probability matrix;

[0040] Specifically, the operating condition segment in which the vehicle runs from an initial speed of 0 (km / h) to a speed of 0 (km / h) again is called a unit operating condition. Each unit operating condition has corresponding physical meaning. Different attribute characteristics can be given to the unit operating condition through the speed curve and sample data during driving.

[0041] Unit conditions with different characteristics (velocity distribution, acceleration distribution) under specific conditions are called unit condition groups. According to user portraits and industry requirements, the required unit condition groups can be customized. Multiple unit condition groups can be formulated for the same group of unit conditions, and the grouping of unit condition groups is only related to subjective intentions. All unit condition groups will form a unit condition library. Due to the characteristics of unit conditions, the unit condition library is reusable, applicable to multiple conditions, and has practical engineering significance.

[0042] Specifically, the model external parameters, namely the characteristic parameters, are set: the duration of the working condition segment T dura , maximum speed v max , maximum acceleration a max , Maximum deceleration a min , average speed v mean , acceleration time proportion P acc , Deceleration time proportion P dec The calculation formulas for the above 7 typical characteristic parameters are as follows:

[0043] Maximum speed: v max =max{v i}

[0044] Average speed:

[0045] Maximum acceleration: a max =max{a i}

[0046] Maximum deceleration: a min =min{a i}

[0047] Acceleration time ratio:

[0048] Deceleration rate reduction ratio:

[0049] In the above formula, v i represents the actual speed at time i, a i is the actual acceleration from time i to time i+1, T acc is the time occupied by acceleration, T dec The time taken for deceleration.

[0050] Specifically, internal parameters include all non-characteristic parameters required to construct custom unit conditions.

[0051] This embodiment divides the states into accelerating driving, decelerating driving, constant speed driving and idling driving, and removes the speed-time curve of idling driving as the main object division principle in this embodiment as follows:

[0052]

[0053] Generate 0 to v according to external parameter constraints max The n velocity elements, with 0 as the initial state, the state transition probability P ij is the probability of transferring to the jth state in the next stage when the curve is in the i-th state in the current stage.

[0054] S2 constructs a Markov chain, generates an initial curve, and adjusts the external parameters of the initial curve to form a trend curve that meets the user portrait and industry requirements to a certain extent;

[0055] In this embodiment, the state transition probability matrix based on the Markov chain is changed for different driving style requirements. By adjusting the probability of the entire process, the curve style that adapts to user needs can be adjusted. Similarly, by adjusting the internal parameters, the trend style of the initial curve can be changed. The initial curve has certain physical constraints, including the maximum acceleration a max ≤10km / h, maximum deceleration a max ≥-10km / h.

[0056] The Markov property can be expressed as:

[0057] P(X n+1 =x n+1 |X1=x1,L,X n =x n )=P(X n+1 =x n+1 |X n =x n )

[0058] Where, X n is the state space; P(X n+1 =x n+1 |X n =x n ) is the state transition probability of the system from i to j.

[0059] S3 sets external characteristic parameters based on trend curves according to customer and industry needs, adjusts characteristic parameters of trend curves through error distribution, calculates characteristic parameter errors, continuously reduces characteristic parameter errors through feedback adjustment, adjusts trend curves to custom unit operating curves that meet industry requirements, and designs system development interfaces. Custom unit operating curve segments that combine user portraits and industry requirements are developed by adjusting options and parameters in the interface and selecting trend curves.

[0060] In this embodiment, based on the trend curve, according to customer and industry needs, external characteristic parameters are set, and the driving condition curve is further optimized through error distribution. By speed compensation and acceleration compensation of the curve, the curve condition parameters are close to the custom characteristic parameters. Figure 2 As shown, the speed compensation method depends on:

[0061]

[0062] In the formula, ∑v dem is the expected total velocity, v mean is the expected average speed, T dura is the duration of the working condition segment, ∑v act is the actual speed sum, v i is the actual driving speed at time i, v gap is the sum of the speed differences.

[0063] In this embodiment, feedback adjustment is performed through error distribution, so that the characteristic parameters of the trend curve can be continuously approached to the characteristic parameters of the custom unit operating curve. The feedback adjustment method is:

[0064]

[0065]

[0066] In the formula, i represents a characteristic parameter, Rerror i represents the actual and expected error rate of the feature parameter, Rerror represents the average error of the overall feature parameter, act i Indicates the actual value of the characteristic parameter, dem i Indicates the expected value of the feature parameter.

[0067] Speed ​​compensation and feedback adjustment bring actual parameters close to expected parameters, reduce parameter errors, and realize the construction of custom unit working conditions. Different trend curves have different characteristic parameter approaching effects. When the custom characteristic parameters cannot be reached, the trend curve is fed back for inspection.

[0068] The construction method system development interface is designed to increase parameter input according to customer and industry needs. In this embodiment, the parameter input includes: the working condition segment duration T dura , maximum speed v max , maximum acceleration a max , Maximum deceleration a min , average speed v mean , acceleration time proportion P acc , Deceleration time proportion P dec .

[0069] In this embodiment, the interface includes multiple menus, such as the development interface Figure 3 As shown, it includes: trend curve selection menu, external parameter input menu, state transition probability matrix adjustment menu, and custom unit operating condition curve generation menu. Based on the trend curve library, a driving style personalized matching data selection is formed, integrating automobile driving test, simulation and optimization tools for different user portraits.

[0070] By setting external parameters, selecting trend conditions from the trend condition library or randomly generating and adjusting the state transition probability matrix, custom unit conditions that meet user needs and industry requirements are generated.

[0071] S4 generates custom unit operating condition curves to form a unit operating condition library and returns the trend operating condition library based on customer needs and industry specifications and the required operating condition characteristics.

[0072] In this embodiment, according to customer and industry needs, all characteristic parameters of a required characteristic condition can be used as parameter input, the generated driving condition can be labeled based on different states, and systematically classified in the trend condition library and unit condition library according to different labeling methods.

[0073] Unit conditions with different characteristics (velocity distribution, acceleration distribution) under specific conditions are called unit condition groups. According to user portraits and industry requirements, the required unit condition groups can be customized. Multiple unit condition groups can be formulated for the same group of unit conditions, and the grouping of unit condition groups is only related to subjective intentions. All unit condition groups will form a unit condition library. Due to the characteristics of unit conditions, the unit condition library is reusable, suitable for a variety of conditions, and has practical engineering significance. Similarly, trend conditions and trend condition libraries have similar characteristics, but the grouping of trend condition libraries only puts forward certain requirements for curve trends. Customized unit condition curves that meet customer and industry needs can be imported into the "Unit Condition Library" according to the operating condition characteristics, and can also be imported into the "Trend Condition Library" according to its curve trend.

[0074] In summary, the present invention provides a method for defining and constructing a custom "unit operating condition" based on user portraits and industry requirements. The definition of the "unit operating condition" is aimed at the defects of the commonly used energy consumption evaluation characteristic operating conditions, and has better adaptability when applied to new energy vehicles. This construction method can flexibly generate various driving data in a controlled environment, avoiding the cost, time and safety risks in real vehicle collection, and at the same time providing efficient and reliable data support for vehicle performance evaluation, fuel efficiency optimization and transportation system design. Through this construction method, the efficiency and accuracy of data acquisition can be greatly improved, promoting technological development and application innovation in the automotive industry and transportation engineering fields. At the same time, this method has high reuse characteristics and flexible and changeable application scenarios.

[0075] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for constructing a custom unit working condition combining user portraits and industry requirements, the steps of which include: S1 defines the characteristic operating condition construction scheme of new energy vehicles based on "unit operating condition", defines internal and external parameters and constructs the state transition probability matrix; S2 constructs a Markov chain, generates an initial curve, and adjusts the external parameters of the initial curve to form a trend curve that meets user portraits and industry requirements; S3 is based on the trend curve and sets external characteristic parameters according to customer and industry needs. It adjusts the characteristic parameters of the trend curve through error allocation, calculates the characteristic parameter error, and continuously reduces the characteristic parameter error through feedback adjustment. It adjusts the trend curve to a custom unit operating condition curve that meets industry requirements, and designs a system development interface. By adjusting the options and parameters in the interface and selecting the trend curve, a custom unit operating condition curve segment that combines user portraits and industry requirements is formulated; S4 generates a custom unit operating condition curve to form a unit operating condition library based on customer needs and industry specifications and the required operating condition characteristics, and returns the trend operating condition library.

2. A method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 1, characterized in that: In S1, the operating condition segment in which the vehicle runs from an initial speed of 0 km / h until the speed reaches 0 km / h again is called a unit operating condition. Each unit operating condition has a corresponding physical meaning, and different attribute characteristics are given to the unit operating condition through the speed curve and sample data during driving.

3. A method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 1 or 2, characterized in that: In S1, the external parameters include: the duration of the working condition segment T dura , maximum speed v max , maximum acceleration a max , Maximum deceleration a min , average speed v mean , acceleration time proportion P acc , Deceleration time proportion P dec .

4. A method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 3, characterized in that: In S2, an initial curve is generated by using a Markov chain through internal and external parameters and a state transition probability matrix. The initial curve has basic information of a custom unit operating curve. By adjusting external parameters, the matching degree of some characteristic parameters with the characteristic parameters of the required customized custom unit operating curve is adjusted.

5. A method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 4, characterized in that: In S3, based on the trend curve, according to customer and industry needs, external characteristic parameters are set, and the driving condition curve is further optimized by using a speed compensation method based on analog PID control. By speed compensation and acceleration compensation of the curve, the curve condition parameters are brought close to the custom characteristic parameters; the speed compensation method depends on: In the formula, ∑v dem is the expected total velocity, v mean is the expected average speed, T dura is the duration of the working condition segment, ∑v act is the actual speed sum, v i is the actual driving speed at time i, v gap is the sum of the speed differences.

6. A method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 5, characterized in that: In S4, according to customer and industry needs, all characteristic parameters of a required characteristic condition are used as parameter input, the generated driving condition is labeled based on different states, and is systematically classified in the trend condition library and the unit condition library according to different labeling methods.

7. A method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 1, characterized in that: In S1, for different driving style requirements, the state transition probability matrix based on the Markov chain is changed; by adjusting the probability of the entire process, the curve style adapted to the user's needs is adjusted; similarly, by adjusting the internal parameters, the trend style of the initial curve is changed. The initial curve has certain physical constraints, including the maximum acceleration a max ≤10km / h, maximum deceleration a max ≥-10km / h; The Markov property can be expressed as: P(X n+1 =x n+1 |X1=x1,L,X n =x n )=P(X n+1 =x n+1 |X n =x n ) Where, X n is the state space; P(X n+1 =x n+ 1|X n =x n ) is the state transition probability of the system from i to j.

8. The method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 1, characterized in that: In S3, according to the simulated PID control, the characteristic parameters of the trend curve are continuously approached to the characteristic parameters of the custom unit operating curve through feedback adjustment; the feedback adjustment method is: In the formula, i represents a characteristic parameter, Rerror i represents the actual and expected error rate of the feature parameter, Rerror represents the average error of the overall feature parameter, act i Indicates the actual value of the characteristic parameter, dem i Indicates the expected value of the feature parameter.

9. A method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 1, characterized in that: In the S3, the menus of the system development interface include: a trend curve selection menu, an external parameter input menu, a probability matrix adjustment menu, and a custom unit operating condition curve generation menu; based on the trend curve library, a driving style personalized matching data selection is formed, and a car driving test, simulation and optimization tool integrating different user portraits is provided.

10. The method for constructing a custom unit working condition combining user portraits and industry requirements according to claim 1, characterized in that: In S4, unit operating conditions with different characteristics under specific operating conditions are called unit operating condition groups. The required unit operating condition groups are customized according to user portraits and industry requirements. Multiple unit operating condition groups are formulated for the same group of unit operating conditions, and the grouping of unit operating condition groups is only related to subjective intentions. All unit operating condition groups will form a unit operating condition library.

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