A shifting system and method for commercial vehicles that takes into account NVH characteristics

Abstract

This invention provides a shifting system and method for commercial vehicles that considers NVH (Noise, Vibration, and Harshness) characteristics, relating to the field of commercial vehicle technology. The system includes an engine bench testing module that obtains engine torque and fuel characteristic data on a test bench; a parameter estimation module that estimates drag coefficient and rolling resistance coefficient using a discrete Kalman filter equation to solve for the speed-drag curve; a sound pressure testing module that measures the changes in cab sound pressure with vehicle speed, acceleration, and gradeability on a rotary drum test bench; a sound pressure prediction module that uses data obtained from the testing module to build a neural network model and predict the cab sound pressure value; a GPS system that transmits roadside geographic information to the shifting system; and a shifting control module that combines power performance, economy, and sound pressure to determine weighting coefficients, calculates the optimal shift point, and controls the shifting through the shifting mechanism. This invention avoids complex and cumbersome testing procedures, improving work efficiency, and uses actual operating condition data as input, thus improving the accuracy of the model.

Description

Technical Field

[0001] This invention relates to the field of commercial vehicle technology, and more specifically to a shifting system and method for commercial vehicles that takes into account NVH characteristics. Background Technology

[0002] With the rapid development of the automotive industry, people's focus has gradually shifted from performance indicators such as power and handling stability to ride comfort. Therefore, the NVH (noise, vibration, harshness) performance of vehicles has become an important indicator.

[0003] The study of NVH characteristics is not only applicable to the development process of new automotive products, but also to the research on improving the ride comfort of existing models. This involves modeling and analyzing a specific system or assembly of a vehicle to identify the factors that have the greatest impact on ride comfort, and improving ride comfort by improving the vibration conditions of the excitation source (amplitude reduction or frequency shifting) or controlling the transmission of vibration noise from the excitation source to the vehicle interior.

[0004] Noise, Vibration, and Harshness (NVH) is an abbreviation for the same substance used in automotive manufacturing. It's a comprehensive measure of automotive manufacturing quality, and its impact on the user's experience is the most direct and immediate. NVH is a major concern for international automotive manufacturers and parts suppliers. Statistics show that approximately one-third of vehicle malfunctions are related to NVH issues, and companies allocate nearly 20% of their R&D budget to addressing these problems.

[0005] Passenger cars typically have five or six forward gears, and their NVH (noise, vibration, and harshness) characteristics are well-tuned during the R&D phase. Heavy-duty trucks usually have around ten gears, or even more. More gears increase the opportunities for the engine to deliver higher power, improving acceleration and hill-climbing ability; they also increase the probability of the engine operating in a low-fuel-consumption range, reducing fuel consumption. However, NVH development in commercial vehicles started later, and their operating conditions are inferior to those of passenger cars, most notably during hill climbing. Commercial vehicle engines have a lower reserve power ratio than passenger cars, making them more prone to insufficient power, especially when climbing hills under heavy loads. The cab is often accompanied by a roaring acceleration sound, which affects the working condition of components and is detrimental to the driver's physical and mental health. Summary of the Invention

[0006] This invention provides a shifting method for commercial vehicles that takes into account NVH characteristics. Based on power performance and fuel economy, the sound pressure level on the right ear side of the driver's seat is used as an NVH evaluation index for subsequent shifting optimization, so as to improve the NVH characteristics of the vehicle during the shifting process.

[0007] The system includes: an engine bench test module, a parameter estimation module, a sound pressure test module, a sound pressure prediction module, a geographic information prediction module, and a gear shift control module;

[0008] The engine bench test module is used to obtain engine torque and fuel characteristic data on the test bench.

[0009] The parameter estimation module is used to estimate the drag coefficient and rolling resistance coefficient using the discrete Kalman filter equation, and to solve the speed-drag curve.

[0010] The sound pressure testing module is used to test multiple sets of test data on the relationship between the sound pressure in the cab and vehicle speed, acceleration, and gradeability on a rotary drum test bench.

[0011] The sound pressure prediction module establishes a neural network model based on multiple sets of test data from the sound pressure testing module to predict the sound pressure value in the cab.

[0012] The geographic information prediction module transmits the geographic information of the road ahead to the gear shifting system based on the GPS system;

[0013] The shift control module determines the weighting coefficients based on three indicators: power performance, economy, and sound pressure, calculates the shift points, and performs shift control through the shift mechanism.

[0014] It should be further noted that the engine bench test module represents the engine model based on the engine's steady-state characteristics. Analyzing engine torque Engine speed and throttle opening The correspondence between them.

[0015] It should be further noted that the engine bench test module also measures the engine speed based on a constant throttle opening. With engine torque The relationship data is then fitted using a quadratic curve to represent the relationship data as follows: ,in, , , represents the fitting coefficient.

[0016] It should be further noted that the discrete Kalman filter equations involved in the parameter estimation module are as follows:

[0017] set up for Constantly under system interference Estimated state under influence, process equation description:

[0018] (1)

[0019] The observation equation is:

[0020] (2)

[0021] in, for Time's up The transition matrix at time step, For the system in Interference at any moment; For the observed values, Interference during observation System noise sequence The variance matrix, To observe the noise sequence The variance matrix.

[0022] It should be further explained that, Predicted state Satisfying equation (1), the observation If equation (2) is satisfied, then:

[0023] (3)

[0024] for The covariance matrix at time t is calculated using the following formula:

[0025] (4)

[0026] Among them, the filter gain matrix for:

[0027] (5)

[0028] State estimation for:

[0029] (6)

[0030] covariance Updated to:

[0031] (7)

[0032] For a given initial value and ,according to Observation of time Recursive calculation State estimation at time 1 ;

[0033] The drag coefficient is then calculated using the following method. and rolling resistance coefficient :

[0034] The formula for calculating the longitudinal dynamics equation of the whole vehicle is shown below:

[0035] (8)

[0036] in, The mechanical efficiency of the transmission system. For engine torque, This refers to the gear ratio of the transmission. The transmission ratio of the main reducer. For vehicle speed, This refers to the drag coefficient;

[0037] Construct the Kalman state equations based on the longitudinal dynamics of vehicle movement;

[0038] The state variable is vehicle speed. drag coefficient Rolling resistance coefficient ,but The observed variable is vehicle speed, i.e. ;

[0039] according to Observation of time Recursive calculation State estimation at time 1 ,get drag coefficient at any time and rolling resistance coefficient .

[0040] It should be further explained that the sound pressure testing module measures the sound pressure at the driver's right ear in the cab using a sound pressure sensor. In addition to applying rolling resistance and wind resistance equivalents to the drum, it also applies slope resistance as an equivalent alternative, recording the cab sound pressure as a function of speed, acceleration, and gradient. By analyzing the changing data and adjusting the throttle opening, multiple sets of test data can be obtained.

[0041] It should be further explained that the sound pressure prediction module establishes a neural network model based on multiple sets of test data obtained from the sound pressure testing module, and uses a GA-BP neural network to integrate velocity, acceleration, and gradient. The results were correlated with the sound pressure in the driver's cab, and a sound pressure prediction model was established.

[0042] It should be further explained that the geographic information prediction module uses the GPS system to transmit the geographic information of the road ahead to the shifting system. The shifting control module obtains the slope length and gradient parameters, and obtains the subsequent shifting strategy based on the observation distance S.

[0043] This invention also provides a gear shifting method for commercial vehicles that takes into account NVH characteristics, the method comprising:

[0044] Step 1: Determine the upper limit of sound pressure level for each speed of the commercial vehicle. With lower limit ;

[0045] in, Selected as the standard for ordinary vehicles, Selected as a standard for high-end cars;

[0046] Step 2: Calculate the sound pressure value based on vehicle speed and acceleration, combined with the sound pressure prediction model. ;

[0047] Step 3: Calculate the sound pressure sensitivity parameters corresponding to each rotational speed. ,Right now:

[0048] (9)

[0049] Step 4: Combine the GPS system to provide the road geographic information ahead of the vehicle to the shift control system, taking the current driving position as the origin.

[0050] Step 5, the vehicle acceleration is calculated using the following formula:

[0051] (10)

[0052] Step 6: Determine the control variables and state variables and their constraints;

[0053] Define the state variable as The current gear is Assuming the highest gear is 16, the gear to be shifted into is... The gear position constraint is represented as:

[0054] (11)

[0055] Vehicle speed is also related to the current gear, that is , The lowest speed in the current gear. This represents the maximum speed in the current gear.

[0056] The control variable is the gear position. , indicating the first Phase-specific gear selection decisions;

[0057] Step 7: Determine the optimization objective.

[0058] It should be further noted that step 7 also includes:

[0059] Select time As a dynamic indicator, As an economic indicator, Sound pressure level;

[0060] For a segment starting from The destination is The total distance is The journey, the time required for vehicle travel for:

[0061] (13)

[0062] Fuel consumption during the trip for:

[0063] (14)

[0064] Sound pressure sensitive parameters within this range points for:

[0065] (15)

[0066] Divide the predicted distance into Segment, length of each segment The above indicators are as follows:

[0067] (16)

[0068] Optimization metrics Represented as:

[0069] (17)

[0070] in , , The weighting coefficients for power performance, economy, and sound pressure characteristics are initially set to 1 / 3. The weighting coefficients are then adjusted to obtain shift strategies with different characteristics. Under a certain throttle, the optimal shift point for the corresponding vehicle speed is found through optimization, and the shift mechanism controls the shift.

[0071] As can be seen from the above technical solutions, the present invention has the following advantages:

[0072] The commercial vehicle shifting system and method considering NVH characteristics provided by this invention are based on the discrete Kalman algorithm to calculate the drag coefficient and rolling resistance coefficient, solve the speed-drag curve, avoid complex and cumbersome test procedures, improve work efficiency, and improve the accuracy of the model by using actual working condition data as input.

[0073] (2) The present invention also introduces a sound pressure sensitive parameter. The sound pressure characteristics were evaluated and combined with power performance and fuel economy to obtain different shift control strategies through weighted coefficients. Based on power performance and fuel economy, the sound pressure level at the driver's right ear was used as an NVH evaluation index for subsequent shift optimization, in order to improve the NVH characteristics during vehicle shifting. Attached Figure Description

[0074] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 Example diagram of a commercial vehicle gear shifting system taking NVH characteristics into account;

[0076] Figure 2 This is a schematic diagram of the Kalman filtering recursion process of the present invention;

[0077] Figure 3 This is a schematic diagram illustrating the working principle of the GA-BP neural network of the present invention.

[0078] Figure 4 This is a broken-line diagram illustrating the sound pressure limit value of the driver's cab according to the present invention. Detailed Implementation

[0079] like Figure 1 This is an example diagram of a commercial vehicle shifting system that takes into account NVH characteristics, provided by the present invention. The example diagram of the commercial vehicle shifting system is for the purpose of illustrating the basic concept of the present invention. Therefore, the diagram only shows the modules that are relevant to the present invention and not the number and function of the modules in actual implementation. In actual implementation, the function, quantity and role of each module can be arbitrarily changed, and the function and purpose of the modules may also be more complex.

[0080] Commercial vehicle gear shifting systems that consider NVH (Noise, Vibration, and Harshness) characteristics involve both hardware and software technologies. Hardware technologies typically include sound pressure level sensors, dedicated AI chips, GPS positioning modules, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. Software technologies primarily include computer vision techniques, speech processing, machine learning / deep learning, and programming languages. Programming languages ​​include, but are not limited to, object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar languages.

[0081] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0082] Please see Figures 1 to 4 The diagram shown is a schematic of a commercial vehicle shifting system that takes into account NVH characteristics in a specific embodiment. The system includes: an engine bench test module, a parameter estimation module, a sound pressure test module, a sound pressure prediction module, a geographic information prediction module, and a shifting control module.

[0083] Specifically, this embodiment uses the engine's steady-state characteristics to represent the engine model, namely, the engine torque. (Nm) and engine speed (rpm), throttle opening Correspondence of (%) Taking a certain throttle opening as an example, with the throttle opening constant, the measured engine speed... With torque The relationship between the changes is expressed by fitting the obtained experimental data with a quadratic curve as follows: ,in, , , The fitting coefficients are used. By adjusting the throttle opening and using this fitting method, the engine bench test module obtains the engine torque characteristic curves under different throttle openings. Similarly, the fuel characteristic curves of the engine under different throttle openings are obtained.

[0084] The parameter estimation module estimates the drag coefficient and rolling resistance coefficient using the discrete Kalman filter equation and solves the speed-drag curve.

[0085] According to an embodiment of this application, let for Constantly under system interference Estimated state under influence, process equation description:

[0086] (18)

[0087] The observation equation is:

[0088] (19)

[0089] in, for Time's up The transition matrix at time step, For the system in Interference at any moment; For the observed values, Interference during observation System noise sequence The variance matrix, To observe the noise sequence The variance matrix.

[0090] Predicted state Satisfying equation (1), the observation If equation (2) is satisfied, then:

[0091] (20)

[0092] for The covariance prediction at time t is:

[0093] (twenty one)

[0094] The filter gain is:

[0095] (twenty two)

[0096] State estimation for:

[0097] (twenty three)

[0098] Covariance prediction:

[0099] (twenty four)

[0100] For a given initial value and ,according to Observation of time Then it can be calculated recursively. State estimation at time 1 The recursive process can be used... Figure 2 describe.

[0101] For a given initial value and ,according to Observation of time Then it can be calculated recursively. State estimation at time 1 The Kalman filter recursive process can be used... Figure 2 describe.

[0102] The longitudinal dynamic equation of the vehicle is shown below, where, The mechanical efficiency of the transmission system. For engine torque, This refers to the gear ratio of the transmission. The transmission ratio of the main reducer. For vehicle speed, The drag coefficient is the air density, generally... =1.2258kg / m3, This is the rolling resistance coefficient.

[0103] (25)

[0104] The Kalman state equations are constructed based on the longitudinal dynamics of the vehicle. The state variable is vehicle speed. drag coefficient Rolling resistance coefficient ,but The observed variable is vehicle speed, i.e. .

[0105] according to Observation of time Then it can be calculated recursively. State estimation at time 1 You can get drag coefficient at any time and rolling resistance coefficient .

[0106] In this embodiment, the sound pressure test module replaces the resistance applied to the drum with slope resistance and adjusts different throttle openings to record the change data of sound pressure in the cab with speed and acceleration.

[0107] The sound pressure prediction module obtains data from the test module to establish a neural network model. It uses the GA genetic algorithm and BP neural network to correlate the test results of speed and acceleration with the sound pressure in the cab and establish a sound pressure prediction model.

[0108] The geographic information prediction module can use the GPS system to transmit the geographic information ahead of the road to the shifting system, predict the road conditions, obtain parameters such as slope length and gradient, and solve the optimal shifting strategy based on the observable distance S.

[0109] The shift control module of this invention establishes sound pressure sensitive parameters. Calculate the sound pressure index The optimal shift point is calculated by weighting the performance and economy, and then shifting is controlled by the shifting mechanism.

[0110] Specifically, the shift control module mainly consists of the following steps:

[0111] Step 1: Determine the upper limit of sound pressure level for each speed of the commercial vehicle. With lower limit ;

[0112] Step 2: Calculate the sound pressure value based on vehicle speed and acceleration, combined with the sound pressure prediction model. ;

[0113] Step 3: Calculate the sound pressure sensitivity parameters corresponding to each rotational speed. ,Right now:

[0114] (26)

[0115] Step 4: Combine the GPS system to provide the road geographic information ahead of the vehicle to the shift control system, taking the current driving position as the origin.

[0116] Step 5, system modeling: The clutch is simplified as a power transmission switch, ignoring its engagement characteristics and energy losses. Vehicle acceleration can be described by the following formula:

[0117] (27)

[0118] in, The mechanical efficiency of the transmission system. For engine torque, This refers to the gear ratio of the transmission. The transmission ratio of the main reducer. For vehicle speed, The drag coefficient is the air density, generally... =1.2258kg / m3, This is the rolling resistance coefficient.

[0119] Step 6: Determine the control variables and state variables, and their constraints. The state variables are... The current gear is Assuming the highest gear is 16, the gear to be shifted into is... Gear state constraints can be represented as:

[0120] (28)

[0121] Vehicle speed is also related to the current gear, that is , The lowest speed in the current gear. This represents the maximum speed in the current gear.

[0122] The control variable is the gear position. , for the first Gear selection decision at each stage. In situations where skipping gears is not possible, The value range is -1, 0, 1.

[0123] The control variable is also constrained by the current gear position. Similar to the gear constraint principle, if the current gear is the highest, it cannot be shifted up; if the current gear is the lowest, it cannot be shifted down. That is:

[0124] (29)

[0125] Step 7, Define the optimization objective. Select a time period. As a dynamic indicator, As an economic indicator, This is a sound pressure level indicator.

[0126] For a segment starting from The destination is The total distance is The journey, the time required for vehicle travel for:

[0127] (30)

[0128] Fuel consumption during the trip for:

[0129] (31)

[0130] Sound pressure sensitive parameters within this range points for:

[0131] (32)

[0132] Divide the predicted distance into Segment, length of each segment The above indicators are as follows:

[0133] (33)

[0134] Optimization metrics Represented as:

[0135] (34)

[0136] in Is the final number The cost function value of the stage. It is the cost function value at each stage of the calculation process. , , The weighting coefficients for power performance, economy, and sound pressure level are initially set at 1 / 3, and can be adjusted later to obtain different shift strategies with different characteristics. Under a certain throttle, the optimal shift point for the corresponding vehicle speed is found through optimization, and finally, the shift mechanism controls the shift.

[0137] Thus, based on the above system, the shifting strategy of the present invention, which takes into account NVH characteristics, is implemented. On the basis of power performance and fuel economy, the sound pressure on the right ear side of the driver is used as an NVH evaluation index for subsequent shifting optimization, so as to improve the NVH characteristics of the vehicle during the shifting process.

[0138] The following are embodiments of the commercial vehicle shifting method considering NVH characteristics provided in this disclosure. This method and the commercial vehicle shifting system considering NVH characteristics in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the commercial vehicle shifting method considering NVH characteristics, please refer to the embodiments of the commercial vehicle shifting system considering NVH characteristics described above.

[0139] Taking a commercial vehicle as the analysis object, its matching clutch has 16 gears. Bench tests were conducted on the matching engine to obtain engine torque characteristic data and fuel consumption characteristic data.

[0140] Step 1: Engine torque can be expressed as... ,in, (Nm) represents engine torque. (rpm) refers to the engine speed. (%) represents the throttle opening. .

[0141] The experiment was conducted at a fixed throttle opening to obtain... The curve obtained at the corresponding throttle opening is fitted with a quadratic curve, and expressed as: ,in, , , The fitting coefficients are used. Using this fitting method, engine load characteristic curves under different throttle openings are obtained.

[0142] Similarly, a quadratic fitting curve of the fuel characteristics obtained from bench tests of the vehicle engine is obtained. ,in, , , The fitting coefficients are used. Using this fitting method, engine fuel characteristic curves under different throttle openings are obtained.

[0143] Step 2: Estimate the drag coefficient and rolling resistance coefficient using the discrete Kalman filter equation, and solve for the speed-drag curve;

[0144] First, the Kalman equation of state is constructed based on the longitudinal dynamics of the vehicle. According to my country's highway design standards, the gradient angle of normal roads is not large. , , where i is the road slope (%). According to vehicle longitudinal dynamics, vehicle acceleration can be expressed as...

[0145] (35)

[0146] in, The mechanical efficiency of the transmission system. For engine torque, This refers to the gear ratio of the transmission. The transmission ratio of the main reducer. For vehicle speed, The drag coefficient is the air density, generally... =1.2258kg / m3, This is the rolling resistance coefficient.

[0147] Establish the state equation for driving on a slope, with vehicle speed as the state variable. drag coefficient Rolling resistance coefficient ,but The vehicle load is unknown but constant, and the gradient, according to road construction standards, is a slowly varying quantity, without any abrupt changes; therefore, the time derivatives of both are approximately zero. Taking the differential, we have:

[0148] (36)

[0149] exist Discretizing the system using the forward Euler method within time intervals, the state equations of the system can be written as:

[0150] (37)

[0151] If the system observation is vehicle speed, then the system measurement equation is:

[0152] (38)

[0153] The system equations can be written as:

[0154] (39)

[0155] Thus, the system equations and observation equations of equations (1) and (2) in the Kalman filter principle are designed, and can be expressed as:

[0156] (40)

[0157] The EKF algorithm mainly consists of two computational processes: observation update and time update. The time update equation is written as:

[0158] (41)

[0159] in, These are the prior estimates of the state variables; This is the optimal estimate of the state variables at the previous moment; The prior error covariance; The error covariance of the previous time step; The solution process for the Jacobian matrix is ​​as follows:

[0160] (42)

[0161] This method can effectively identify the drag coefficient and rolling resistance coefficient in a short time, which is more beneficial for real-time identification when the vehicle is in motion.

[0162] Step 3: By applying resistance to the drum as an equivalent substitute for slope resistance and adjusting different throttle openings, record the changes in cab sound pressure with speed and acceleration;

[0163] A drum test was conducted on the entire vehicle. A sound pressure sensor was placed on the right side of the driver's seat. The resistance calculated in step two was applied in real time on the drum test bench. Under the condition of 100% throttle opening, additional resistance was added to the drum test bench, which was equivalent to the gradient resistance, and the equivalent gradeability was calculated. The driver's cab noise was tested, and the throttle opening and gear position were adjusted to initially obtain 100 sets of sample data.

[0164] Step 4: Combine velocity and acceleration to evaluate the sound pressure level.

[0165] A GA-BP neural network model is established, then trained with data and the relevant neuron weight coefficients are set. A three-layer neural network structure is determined, along with the weights and thresholds of the initial BP neural network structure. The initial values ​​are encoded using the GA algorithm to generate an initial population. The fitness function and individual fitness are calculated. If the requirement of 200 iterations is met, the weights and thresholds are output; otherwise, mutations occur, creating new species, and the fitness function and individual fitness are recalculated. The network is trained using the BP algorithm to achieve an accuracy of 10. -4 Thus, a BP neural network based on a genetic algorithm was obtained.

[0166] Prior to analysis, 100 sets of cab noise samples at different vehicle speeds and accelerations were collected. 80 sets of these samples were used for neural network training, and 20 sets were used to test the error of the established model. The sound pressure results were then input into the GA-BP neural network model to obtain the sound pressure prediction model. The process is as follows: Figure 3 As shown.

[0167] Step 5: The geographic information prediction module uses the GPS system to transmit the geographic information of the road ahead to the gear shifting system, predicts the road conditions, obtains parameters such as slope length and gradient, and solves the optimal gear shifting strategy based on the observable distance S.

[0168] Step Six: Shift Strategy Optimization Module, which establishes sound pressure sensitivity parameters. Calculate the sound pressure index The optimal shift point is calculated by weighting the performance and economy, and then shifting is controlled by the shifting mechanism.

[0169] The gear shift control mainly consists of the following steps:

[0170] (1) Determine the upper limit of sound pressure for each speed of commercial vehicle. With lower limit ,like Figure 4 As shown;

[0171] (2) Calculate the sound pressure value based on vehicle speed and acceleration, combined with the sound pressure prediction model. ;

[0172] (3) Calculate the sound pressure sensitivity parameters corresponding to each rotational speed. ,Right now:

[0173] (43)

[0174] (4) Combine the GPS system to provide the road geographic information ahead of the vehicle to the shift control system, with the current driving position as the origin;

[0175] (5) System modeling: The clutch is simplified as a power transmission switch, and its engagement characteristics and energy losses are not considered. Vehicle acceleration can be described by the following formula:

[0176] (44)

[0177] in, The mechanical efficiency of the transmission system. For engine torque, This refers to the gear ratio of the transmission. The transmission ratio of the main reducer. For vehicle speed, The drag coefficient is the air density, generally... =1.2258kg / m3, This is the rolling resistance coefficient.

[0178] (6) Determine the control variables and state variables and their constraints. The state variables are... The current gear is Assuming the highest gear is 16, the gear to be shifted into is... Gear state constraints can be represented as:

[0179] (45)

[0180] Vehicle speed is also related to the current gear, that is , The lowest speed in the current gear. This represents the maximum speed in the current gear.

[0181] The control variable is the gear position. , for the first Gear selection decision at each stage. In situations where skipping gears is not possible, The value range is -1, 0, and 1. The control variable is also constrained by the current gear position, similar to the gear constraint principle: if the current gear is the highest, it cannot be shifted up; if the current gear is the lowest, it cannot be shifted down. That is:

[0182] (46)

[0183] Define the optimization objective. Select a time period. As a dynamic indicator, As an economic indicator, This is a sound pressure level indicator.

[0184] For a segment starting from The destination is The total distance is The journey, the time required for vehicle travel for:

[0185] (47)

[0186] Fuel consumption during the trip for:

[0187] (48)

[0188] Sound pressure sensitive parameters within this range points for:

[0189] (49)

[0190] Divide the predicted distance into Segment, length of each segment The above indicators are as follows:

[0191] (50)

[0192] Optimization metrics Represented as:

[0193] (51)

[0194] in Is the final number The cost function value of the stage. It is the cost function value at each stage of the calculation process. , , The weighting coefficients for power performance, economy, and sound pressure level are initially set at 1 / 3, and can be adjusted later to obtain different shift strategies with different characteristics. Under a certain throttle, the optimal shift point for the corresponding vehicle speed is found through optimization, and finally the shift mechanism controls the shift.

[0195] The units and algorithm steps of the various examples described in the embodiments of the commercial vehicle shifting system and method considering NVH characteristics disclosed in this invention can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0196] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A shifting system for commercial vehicles that takes into account NVH characteristics, characterized in that, include: Engine bench test module, parameter estimation module, sound pressure test module, sound pressure prediction module, geographic information prediction module, and gear shift control module; The engine bench test module is used to obtain engine torque and fuel characteristic data on the test bench. The parameter estimation module is used to estimate the drag coefficient and rolling resistance coefficient using the discrete Kalman filter equation, and to solve the speed-drag curve. The sound pressure testing module is used to test multiple sets of test data on the relationship between the sound pressure in the cab and vehicle speed, acceleration, and gradeability on a rotary drum test bench. The sound pressure prediction module establishes a neural network model based on multiple sets of test data from the sound pressure testing module to predict the sound pressure value in the cab. The geographic information prediction module transmits the geographic information of the road ahead to the gear shifting system based on the GPS system; The shift control module determines the weighting coefficients based on three indicators: power performance, economy, and sound pressure, calculates the shift points, and performs shift control through the shift mechanism.

2. The commercial vehicle shifting system considering NVH characteristics according to claim 1, characterized in that, The engine bench test module represents the engine model based on the engine's steady-state characteristics. Analyzing engine torque Engine speed and throttle opening The correspondence between them.

3. The commercial vehicle shifting system considering NVH characteristics according to claim 2, characterized in that, The engine bench test module also measures the engine speed based on a constant throttle opening. With engine torque The relationship data is then fitted using a quadratic curve to represent the relationship data as follows: ,in, , , The fitting coefficients are denoted as .

4. The commercial vehicle shifting system considering NVH characteristics according to claim 1, characterized in that, The discrete Kalman filter equations involved in the parameter estimation module are shown below: set up for Constantly under system interference Estimated state under influence, process equation description: (1) The observation equation is: (2) in, for Time's up The transition matrix at time step, For the system in Interference at any moment; For the observed values, Interference during observation System noise sequence The variance matrix, To observe the noise sequence The variance matrix.

5. The commercial vehicle shifting system considering NVH characteristics according to claim 4, characterized in that, Predicted state Satisfying equation (1), the observation If equation (2) is satisfied, then: (3) for The covariance matrix at time t is calculated using the following formula: (4) Among them, the filter gain matrix for: (5) State estimation for: (6) covariance Updated to: (7) Given an initial value and ,according to Observation of time Recursive calculation State estimation at time 1 ; The drag coefficient is then calculated using the following method. and rolling resistance coefficient : The formula for calculating the longitudinal dynamics equation of the whole vehicle is shown below: (8) in, The mechanical efficiency of the transmission system. For engine torque, This refers to the gear ratio of the transmission. The transmission ratio of the main reducer. For vehicle speed, This refers to the drag coefficient; Construct the Kalman state equations based on the longitudinal dynamics of vehicle movement; The state variable is vehicle speed. drag coefficient Rolling resistance coefficient ,but The observed variable is vehicle speed, i.e. ; according to Observation of time Recursive calculation State estimation at time 1 ,get drag coefficient at any time and rolling resistance coefficient .

6. The commercial vehicle shifting system considering NVH characteristics according to claim 1, characterized in that, The sound pressure testing module measures the sound pressure at the driver's right ear in the cab using a sound pressure sensor. In addition to applying rolling resistance and wind resistance equivalents to the drum, it also applies slope resistance as an equivalent alternative, recording the cab sound pressure as a function of speed, acceleration, and gradient. By analyzing the changing data and adjusting the throttle opening, multiple sets of test data can be obtained.

7. The commercial vehicle shifting system considering NVH characteristics according to claim 1, characterized in that, The sound pressure prediction module establishes a neural network model based on multiple sets of test data obtained from the sound pressure testing module, and uses a GA-BP neural network to integrate velocity, acceleration, and gradient. The results were correlated with the sound pressure in the driver's cab, and a sound pressure prediction model was established.

8. The commercial vehicle shifting system considering NVH characteristics according to claim 1, characterized in that, The geographic information prediction module uses the GPS system to transmit the geographic information of the road ahead to the shifting system. The shifting control module obtains the slope length and gradient parameters, and obtains the subsequent shifting strategy based on the observation distance S.

9. A shifting method for commercial vehicles that takes into account NVH characteristics, characterized in that, The method employs a commercial vehicle shifting system considering NVH characteristics as described in any one of claims 1 to 8; the method includes: Step 1: Determine the upper limit of sound pressure level for each speed of the commercial vehicle. With lower limit ; in, Selected as the standard for ordinary vehicles, Selected as a standard for high-end cars; Step 2: Calculate the sound pressure value based on vehicle speed and acceleration, combined with the sound pressure prediction model. ; Step 3: Calculate the sound pressure sensitivity parameters corresponding to each rotational speed. ,Right now: (9) Step 4: Combine the GPS system to provide the road geographic information ahead of the vehicle to the shift control system, taking the current driving position as the origin. Step 5, the vehicle acceleration is calculated using the following formula: (10) Step 6: Determine the control variables and state variables and their constraints; Define the state variable as The current gear is Assuming the highest gear is 16, the gear to be shifted into is... The gear position constraint is represented as: (11) Vehicle speed is also related to the current gear, that is , The lowest speed in the current gear. This represents the maximum speed in the current gear. The control variable is the gear position. , indicating the first Phase-specific gear selection decisions; Step 7: Determine the optimization objective.

10. The commercial vehicle gear shifting method considering NVH characteristics according to claim 1, characterized in that, Step 7 also includes: Select time As a dynamic indicator, As an economic indicator, Sound pressure level; For a segment starting from The destination is The total distance is The journey, the time required for vehicle travel for: (13) Fuel consumption during the trip for: (14) Sound pressure sensitive parameters within this range points for: (15) Divide the predicted distance into Segment, length of each segment The above indicators are as follows: (16) Optimization metrics Represented as: (17) in , , The weighting coefficients for power performance, economy, and sound pressure characteristics are initially set to 1 / 3. The weighting coefficients are then adjusted to obtain shift strategies with different characteristics. Under a certain throttle, the optimal shift point for the corresponding vehicle speed is found through optimization, and the shift mechanism controls the shift.

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