A big data power matching method based on wheel side working condition characteristics

Abstract

The present invention relates to a big data power matching method based on wheel side working condition characteristics, comprising the following steps: step one: collecting driving data of market vehicles, including vehicle speed, rotation speed and torque; and pre-processing the driving data to obtain a time-driving data table; step two: extracting wheel side working condition characteristics according to the time-driving data table, including a vehicle speed-wheel side power matrix under acceleration working condition, deceleration working condition and uniform speed working condition; step three: according to the vehicle speed-wheel side power matrix, integrating power configuration parameters, rotation speed theoretical calculation formula, gear matching theoretical method and torque theoretical calculation formula, reversely deriving engine working condition, and obtaining economic parameters and dynamic parameters; step four: normalizing the economic parameters and the dynamic parameters, calculating weights of the economic parameters and the dynamic parameters, calculating comprehensive indicators, ranking the comprehensive indicators, and selecting the optimal power configuration according to the comprehensive indicators.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a big data power matching method based on wheel side working condition characteristics. Background Art

[0002] Powertrain parameter matching is one of the important means of automobile energy conservation. Matching the engine economic circle with market operating conditions can achieve energy conservation to the greatest extent. For heavy-duty commercial vehicles, low energy consumption can not only reduce emissions but also reduce customer costs. Therefore, it is of great significance to study the most adaptable powertrain matching based on wheel-side operating condition big data. Summary of the invention

[0003] The purpose of the present invention is to provide a big data power matching method based on wheel side working condition characteristics in view of the deficiencies in the prior art.

[0004] The present invention is achieved by adopting the following technical solutions:

[0005] A big data power matching method based on wheel side working condition characteristics comprises the following steps:

[0006] Step 1: Collect driving data of market vehicles, including vehicle speed, rotation speed, and torque; and pre-process the driving data to obtain a time-driving data table;

[0007] Step 2: extracting wheel side working condition characteristics according to the time-driving data table, including: vehicle speed-wheel side power matrix under acceleration working condition, deceleration working condition and uniform speed working condition;

[0008] Step 3: According to the vehicle speed-wheel power matrix, the power configuration parameters, the speed theoretical calculation formula, the gear matching theoretical method and the torque theoretical calculation formula are integrated to reversely derive the engine operating conditions and obtain the economic parameters and power parameters;

[0009] Step 4: Normalize the economic parameters and the dynamic parameters, calculate the weights of the economic parameters and the dynamic parameters, calculate the comprehensive index, rank the comprehensive index, and select the optimal power configuration according to the comprehensive index.

[0010] As a further description of the invention, the step 1 specifically includes:

[0011] Use sensors to collect vehicle speed, rotation speed, and torque, and process them;

[0012] Based on the time column of the speed, a linear interpolation method is used to align the timestamps of the torque and vehicle speed with the timestamp of the speed, thereby obtaining a time-driving data table.

[0013] As a further explanation of the invention, the step 2 specifically includes:

[0014] According to the time-driving data table, the acceleration value at the time point is calculated by the speed values ​​of two adjacent points, and the acceleration condition threshold and the deceleration condition threshold are set. If the acceleration value of the current point is less than the deceleration condition threshold, the current point is in the deceleration condition. If the acceleration value of the current point is greater than the acceleration condition threshold, the current point is in the acceleration condition, otherwise it is in the uniform speed condition; and the wheel power is obtained according to the wheel power theoretical calculation formula, thereby obtaining the vehicle speed-wheel power matrix; wherein, the wheel power theoretical calculation formula is:

[0015] ;

[0016] In the formula, is the wheel power, is the torque, is the engine speed.

[0017] As a further explanation of the invention, the step 3 of reversely obtaining the engine operating condition specifically includes:

[0018] Obtain power configuration parameters, including engine parameters, transmission ratio, final reducer ratio, engine speed, shift schedule curve, transmission efficiency curve, and accessory power consumption curve;

[0019] Fitting the vehicle speed-wheel power matrix into vehicle speed-wheel power-operating condition characteristic data, and inputting the power configuration parameters;

[0020] According to the speed theoretical calculation formula, the speed of the operating point at different gears can be inferred in reverse; among them, the speed theoretical calculation formula is:

[0021] ;

[0022] In the formula, is the vehicle speed, is the engine speed, is the tire radius, is the transmission ratio, is the main reducer transmission ratio;

[0023] The gear matching theory method is adopted, that is, the speed of each operating point at different gears is selected under the conditions of the upper and lower limits of the gear shift speed and the boundary conditions of the engine external characteristics under the acceleration condition, deceleration condition and uniform speed condition;

[0024] The current torque is calculated based on the torque theory calculation formula, which is the reverse calculation of the wheel power theory calculation formula.

[0025] As a further explanation of the invention, in step 3, the step of obtaining economic parameters specifically includes:

[0026] According to the current speed and torque, the current fuel consumption rate is obtained by looking up the engine performance parameter table, and the fuel consumption per 100 kilometers is obtained according to the theoretical calculation formula of fuel consumption per 100 kilometers, and the fuel consumption per 100 kilometers is used as the economic parameter; among which, the theoretical calculation formula of fuel consumption per 100 kilometers is:

[0027] / 3600;

[0028] In the formula, is the vehicle speed at a certain moment, for duration;

[0029] / 3600 / b i / 0.855 / 1000;

[0030] In the formula, is power, For duration, is the fuel consumption rate;

[0031] ;

[0032] In the formula, Fuel consumption per 100 kilometers, For mileage, For fuel consumption.

[0033] As a further explanation of the invention, in step 3, the step of obtaining dynamic parameters specifically includes:

[0034] The high power ratio is obtained according to the high power operating point, and the high power ratio is used as the dynamic parameter. The high power ratio calculation formula is:

[0035] ;

[0036] In the formula, is the number of cells with power greater than 90% of the peak power, is the total operating condition points.

[0037] As a further explanation of the invention, in step 4, the maximum and minimum method is used to normalize the economic parameters and the dynamic parameters, and the formula is:

[0038] ;

[0039] In the formula, is the original value, is the normalized value.

[0040] As a further explanation of the invention, in the step 4, weights a and b are set for the normalized economic value and the normalized power value respectively, and the comprehensive index of each power configuration is obtained, and the power configuration with the smallest comprehensive index is selected as the optimal power configuration; the formula is:

[0041] score=a +b ;

[0042] In the formula, score is a comprehensive indicator. is the normalized economic value; is the normalized dynamic value.

[0043] Compared with the prior art, the present invention has the following beneficial technical effects:

[0044] The present invention obtains wheel-side operating characteristics through market vehicle driving big data and combines them with power configuration parameters to reversely obtain engine operating points, economic parameters and power parameters, thereby achieving optimal power configuration recommendations and realizing power matching quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of the big data power matching method based on wheel side working condition characteristics of the present invention. DETAILED DESCRIPTION

[0046] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0047] like Figure 1 As shown, an embodiment of the present invention provides a big data power matching method based on wheel side working condition characteristics, comprising the following steps:

[0048] Step 1: Collect driving data of market vehicles, including vehicle speed, rotation speed, and torque; and pre-process the driving data to obtain a time-driving data table;

[0049] Step 2: Extract wheel-side operating condition characteristics based on the time-driving data table, including: vehicle speed-wheel-side power matrix under acceleration, deceleration and uniform speed conditions;

[0050] Step 3: According to the vehicle speed-wheel power matrix, the power configuration parameters, speed theoretical calculation formula, gear matching theoretical method and torque theoretical calculation formula are integrated to reversely derive the engine working conditions and obtain the economic parameters and power parameters;

[0051] Step 4: Normalize the economic parameters and power parameters, calculate the weights of the economic parameters and power parameters, calculate the comprehensive indicators, rank the comprehensive indicators, and select the optimal power configuration based on the comprehensive indicators.

[0052] Step 1 specifically includes:

[0053] Use sensors to collect vehicle speed, rotation speed, and torque, and process them;

[0054] Based on the time column of the speed, a linear interpolation method is used to align the timestamps of the torque and vehicle speed with the timestamp of the speed, thereby obtaining a time-driving data table.

[0055] Step 2 specifically includes:

[0056] According to the time-driving data table, the acceleration value at the time point is calculated through the speed values ​​of two adjacent points, and the acceleration condition threshold and the deceleration condition threshold are set. If the acceleration value of the current point is less than the deceleration condition threshold, the current point is in the deceleration condition. If the acceleration value of the current point is greater than the acceleration condition threshold, the current point is in the acceleration condition, otherwise it is in the uniform speed condition; and the wheel power is obtained according to the wheel power theoretical calculation formula, thereby obtaining the vehicle speed-wheel power matrix; wherein, the wheel power theoretical calculation formula is:

[0057] ;

[0058] In the formula, is the wheel power kW, is the torque N·m, is the engine speed r / min.

[0059] Taking the vehicle speed range of 0~120km / h, with an interval of 5km / h, and the wheel power range of -600~400kW, with an interval of 50kW as an example, the following vehicle speed-wheel power matrix is ​​obtained; this matrix is ​​used to represent the distribution of market wheel characteristics.

[0060] ;

[0061] in, Indicates the speed range is ~ The power range is ~ The frequency ratio of , satisfies:

[0062] .

[0063] Step 3, reverse acquisition of engine operating conditions specifically includes:

[0064] Obtain power configuration parameters, including engine parameters, transmission ratio, final reducer ratio, engine speed, shift schedule curve, transmission efficiency curve, and accessory power consumption curve;

[0065] Fit the vehicle speed-power matrix into vehicle speed-wheel power-operating condition characteristic data, and input power configuration parameters;

[0066] According to the speed theoretical calculation formula, the speed of the operating point at different gears can be inferred in reverse; among them, the speed theoretical calculation formula is:

[0067] The gear matching theory method is adopted, that is, the speed of each operating point at different gears is selected under the conditions of the upper and lower limits of the gear shift speed and the boundary conditions of the engine external characteristics under the acceleration condition, deceleration condition and uniform speed condition;

[0068] ;

[0069] In the formula, is the vehicle speed km / h, is the engine speed r / min, is the tire radius m, is the transmission ratio, is the main reducer transmission ratio;

[0070] The current torque is calculated based on the torque theory calculation formula, which is the reverse calculation of the wheel power theory calculation formula.

[0071] In this embodiment, since the amount of driving data is too large and the analysis efficiency is low, in order to retain the wheel side working condition characteristics and keep the number within a controllable range, the overall characteristic distribution is generated according to the distribution ratio. , thus fitting into the discrete distribution points of vehicle speed-wheel power-operating condition:

[0072] ;

[0073] in, Indicates the vehicle speed at a certain moment. Expressed as wheel power, Indicates uniform speed condition, Indicates deceleration condition. Indicates acceleration condition;

[0074] In this embodiment, the operating point is deduced based on the speed theoretical calculation formula The speed at each gear position, the operating point is expanded to ,in, Indicates the speed at a certain moment, Indicates duration;

[0075] In this embodiment, according to the current speed meeting the above two conditions, there may be multiple gears that meet the requirements. When the deceleration condition is in progress, a higher gear is selected; when the acceleration condition is in progress, the lowest gear is selected; when the uniform speed condition is in progress, a gear is randomly selected; at this time, the working condition point is expanded to ;in, Indicates gear position.

[0076] Step 3: Obtaining economic parameters specifically includes:

[0077] According to the current speed and torque, the current fuel consumption rate is obtained by looking up the engine performance parameter table, and the fuel consumption per 100 kilometers is obtained according to the theoretical calculation formula of fuel consumption per 100 kilometers, and the fuel consumption per 100 kilometers is used as the economic parameter; among which, the theoretical calculation formula of fuel consumption per 100 kilometers is:

[0078] / 3600;

[0079] In the formula, is the vehicle speed, for duration;

[0080] / 3600 / b i / 0.855 / 1000;

[0081] In the formula, is power, For duration, is the fuel consumption rate;

[0082] ;

[0083] In the formula ， Fuel consumption per 100 kilometers, For mileage, For fuel consumption.

[0084] In this embodiment, the operating point is expanded to:

[0085] ;

[0086] in, is the fuel consumption rate, Indicates the total number of operating points. Generally, for algorithm efficiency, 30000≤ N ≤50000.

[0087] In step 3, the dynamic parameter acquisition step specifically includes:

[0088] The high power ratio is obtained according to the high power operating point, and the high power ratio is used as the dynamic parameter. The high power ratio is calculated as follows: the number of wheel side power greater than 90% of the wheel side peak power / total number of operating points; the calculation formula is:

[0089] ;

[0090] In the formula, is the number of cells with power greater than 90% of the peak power, is the total operating condition points.

[0091] In step 4, the maximum and minimum method is used to normalize the economic parameters and dynamic parameters. The formula is:

[0092] ;

[0093] In the formula, is the original value, is the normalized value.

[0094] In step 4, weights a and b are set for the normalized economic value and the normalized power value respectively to obtain the comprehensive index of each power configuration, and the power configuration with the smallest comprehensive index is selected as the optimal power configuration; the formula is:

[0095] score=a +b

[0096] In the formula, score is a comprehensive indicator. is the normalized economic value; is the normalized dynamic value.

[0097] In this embodiment, when selecting appropriate power configuration parameters, different market segments have different requirements for economy and power. Therefore, when considering the economy and power indicators, different weights are set to meet the requirements of different market segments. The economy and power are weighted separately, and each is assigned a value of 0.5. A power configuration-comprehensive indicator table is prepared, as shown in the table:

[0098] Table 1 Power configuration-comprehensive index table

[0099] ;

[0100] The three normalized economic values ​​in the above table are calculated as follows: (31-31) / (35-31) = 0; (32-31) / (35-31) = 0.25; (35-31) / (35-31)=1; the three normalized dynamic values ​​in the above table are calculated as follows: (25-10) / (25-10)=1; (20-10) / (25-10)=0.67; (10-10) / (25-10)=0; the three comprehensive indexes in the above table are calculated as follows: 0×0.5+1×0.5=0.5; 0.25×0.5+0.67×0.5=0.46; 1×0.5+0×0.5=0.5;

[0101] As mentioned above, for economic evaluation, the lower the fuel consumption, the better, so the smaller the normalized economic value, the better; for dynamic evaluation, high-power conditions often indicate poor dynamic performance, so the smaller the normalized dynamic value, the better; that is, the smaller the comprehensive index, the better, so choose the power configuration with the smallest comprehensive index.

[0102] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A big data power matching method based on wheel side working condition characteristics, characterized in that: The following steps are involved: Step 1: Collect driving data of market vehicles, including vehicle speed, rotation speed, and torque; and pre-process the driving data to obtain a time-driving data table; Step 2: extracting wheel side working condition characteristics according to the time-driving data table, including: vehicle speed-wheel side power matrix under acceleration working condition, deceleration working condition and uniform speed working condition; Step 3: According to the vehicle speed-wheel power matrix, the power configuration parameters, the speed theoretical calculation formula, the gear matching theoretical method and the torque theoretical calculation formula are integrated to reversely derive the engine operating conditions and obtain the economic parameters and power parameters; Step 4: normalizing the economic parameter and the dynamic parameter, calculating the weights of the economic parameter and the dynamic parameter, calculating a comprehensive index, ranking the comprehensive index, and selecting the optimal power configuration according to the comprehensive index; The step three of reversely obtaining the engine operating condition specifically includes: Obtain power configuration parameters, including engine parameters, transmission ratio, final reducer ratio, engine speed, shift schedule curve, transmission efficiency curve, and accessory power consumption curve; Fitting the vehicle speed-wheel power matrix into vehicle speed-wheel power-operating condition characteristic data, and inputting the power configuration parameters; According to the speed theoretical calculation formula, the speed of the operating point at different gears can be inferred in reverse; among them, the speed theoretical calculation formula is: ； In the formula, is the vehicle speed, is the engine speed, is the tire radius, is the transmission ratio, is the main reducer transmission ratio; The gear matching theory method is adopted, that is, the speed of each operating point at different gears is selected under the conditions of the upper and lower limits of the gear shift speed and the boundary conditions of the engine external characteristics under the acceleration condition, deceleration condition and uniform speed condition; The current torque can be calculated by reverse calculation of the theoretical calculation formula of torque, i.e. the theoretical calculation formula of wheel power. In the step 3, the step of obtaining the dynamic parameters specifically includes: The high power ratio is obtained according to the high power operating point, and the high power ratio is used as the dynamic parameter. The high power ratio calculation formula is: ； In the formula, is the number of cells with power greater than 90% of the peak power, is the total operating condition points.

2. The big data power matching method based on wheel side working condition characteristics according to claim 1 is characterized in that: The step 1 specifically includes: Use sensors to collect vehicle speed, rotation speed, and torque, and process them; Based on the time column of the speed, a linear interpolation method is used to align the timestamps of the torque and vehicle speed with the timestamp of the speed, thereby obtaining a time-driving data table.

3. The big data power matching method based on wheel side working condition characteristics according to claim 2 is characterized in that: The step 2 specifically includes: According to the time-driving data table, the acceleration value at the time point is calculated by the speed values ​​of two adjacent points, and the acceleration condition threshold and the deceleration condition threshold are set. If the acceleration value of the current point is less than the deceleration condition threshold, the current point is in the deceleration condition. If the acceleration value of the current point is greater than the acceleration condition threshold, the current point is in the acceleration condition, otherwise it is in the uniform speed condition; and the wheel power is obtained according to the wheel power theoretical calculation formula, thereby obtaining the vehicle speed-wheel power matrix; wherein, the wheel power theoretical calculation formula is: ； In the formula, is the wheel power, is the torque, is the engine speed.

4. The big data power matching method based on wheel side working condition characteristics according to claim 3 is characterized in that: In step 3, the economic parameter acquisition step specifically includes: According to the current speed and torque, the current fuel consumption rate is obtained by looking up the engine performance parameter table, and the fuel consumption per 100 kilometers is obtained according to the theoretical calculation formula of fuel consumption per 100 kilometers, and the fuel consumption per 100 kilometers is used as the economic parameter; among which, the theoretical calculation formula of fuel consumption per 100 kilometers is: / 3600； In the formula, is the vehicle speed at a certain moment, for duration; / 3600 / b i / 0.855 / 1000； In the formula, is power, For duration, is the fuel consumption rate; ； In the formula, Fuel consumption per 100 kilometers, For mileage, For fuel consumption.

5. The big data power matching method based on wheel side working condition characteristics according to claim 4 is characterized in that: In the step 4, the maximum and minimum value method is used to normalize the economic parameters and the dynamic parameters, and the formula is: ； In the formula, is the original value, is the normalized value.

6. The big data power matching method based on wheel side working condition characteristics according to claim 5 is characterized in that: In the step 4, weights a and b are set for the normalized economic value and the normalized power value respectively to obtain the comprehensive index of each power configuration, and the power configuration with the smallest comprehensive index is selected as the optimal power configuration; the formula is: score=a +b ; In the formula, score is a comprehensive indicator. is the normalized economic value; is the normalized dynamic value.

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