Transmission device for new energy vehicle and gear shifting decision-making method

Abstract

The invention discloses a transmission device for a new energy vehicle and a gear shifting decision method.The transmission device comprises a driving motor, an input shaft, an output shaft, a clutch assembly, a first input gear, a second input gear, a first output gear and a second output gear; identifying the driving style of the driver; constructing a Markov chain prediction model to predict a future vehicle speed condition to obtain a predicted working condition; based on a fuzzy control strategy, fuzzy rules and mapping relations related to driving styles, predicted working conditions and gear coefficients are established; and a vehicle speed signal and a gear signal are collected, a self-adaptive adjustment strategy is established according to the gear coefficient, the pedal opening rate and the like, and a target gear is obtained. According to the method, the Markov chain and the fuzzy control strategy are combined, accurate prediction of the vehicle driving working condition is achieved, the gear shifting decision is made by integrating driving style recognition, and the economical efficiency and the dynamic property of the vehicle are improved.

Description

Technical Field

[0001] This invention relates to a transmission device and shift decision method for new energy vehicles, belonging to the field of vehicle powertrain control. Background Technology

[0002] In vehicle transmission systems, gear shifting decisions are a core technology that has a crucial impact on vehicle power, fuel economy, and environmental adaptability. Currently, most traditional gear shifting decision methods are based on fixed shift curves or rules, making it difficult to adapt to complex and ever-changing actual driving conditions. In automatic transmissions, vehicles need to acquire a large amount of information from sensors and quickly make reasonable gear shifting decisions to ensure driving safety and smoothness—something traditional gear shifting decision methods struggle to accomplish.

[0003] Therefore, combining driver driving style and predicted vehicle operating conditions to make shift decisions has important practical significance and application value. It can effectively solve problems such as poor adaptability of shift decision methods in the technology and improve the overall performance of the vehicle and the driving experience. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention provides a gear shifting device and decision-making method based on driving style and predicted operating conditions, which fully considers the influence of driving style and predicted operating conditions and performs fuzzy control on gear position based on fuzzy control strategy.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0006] A transmission device and shifting decision method for new energy vehicles includes a drive motor, an input shaft, an output shaft, a clutch assembly, a first input gear, a second input gear, a first output gear, and a second output gear. The shifting device includes two gears. The structural features of the shifting device are as follows: the input shaft is sequentially equipped with bearing A, bearing B, a first input gear, a first clutch driven plate, a clutch driving plate, a second clutch driven plate, bearing C, a second input gear, and bearing D from one end to the other; the output shaft is sequentially equipped with bearing E, a first output gear, a second output gear, and bearing F from one end to the other; the inner rings of bearings A, B, C, and D are mounted on the input shaft through shaft holes; the inner rings of bearings E and F are mounted on the output shaft through shaft holes; bearings A and D... The outer rings of bearings E and F are mounted on the housing through shaft holes. The clutch assembly consists of a first clutch driven plate, a second clutch driven plate, and a clutch driving plate. The first clutch driven plate is fixedly connected to the first input gear, the second clutch driven plate is fixedly connected to the second input gear, and the clutch driving plate is fixedly connected to the input shaft. The first input gear meshes with the first output gear, and the second input gear meshes with the second output gear. An angle sensor is used to detect the pedal opening. The control unit collects the speed of the drive motor and output shaft, the gear status, and receives the pedal opening signal detected by the angle sensor in real time. The control unit controls the disengagement and engagement of the clutch by controlling the hydraulic unit to charge and discharge oil, thereby achieving gear shifting. The drive motor, angle sensor, and hydraulic unit are all connected to the control unit.

[0007] The shifting device has two gears. In one gear, the power transmission route is as follows: when the clutch driving plate and the first clutch driven plate are engaged, the drive motor sequentially drives the input shaft, the clutch driving plate, the first clutch driven plate, the first input gear, the first output gear, and the output shaft.

[0008] The shifting device has two gears. In the second gear, the power transmission route is as follows: when the clutch driving plate and the second clutch driven plate are engaged, the drive motor sequentially drives the input shaft, the clutch driving plate, the second clutch driven plate, the second input gear, the second output gear, and the output shaft.

[0009] A shift decision method based on predicted operating conditions and driving style, characterized by the following specific steps:

[0010] Step 1: Based on the fuzzy control strategy, the driving style is identified by combining vehicle impact intensity, pedal opening rate, and pedal opening, and divided into "mild", "standard", and "aggressive" types.

[0011] Step 2: Construct the probability output matrix of the Markov chain model to predict the vehicle speed in the next 2 seconds and obtain the predicted driving conditions;

[0012] Step 3: Construct a fuzzy controller based on the fuzzy control strategy, taking the driving style and predicted operating conditions as inputs, and outputting the required gear coefficient;

[0013] Step 4: Construct an adaptive adjustment strategy based on the gear ratio, pedal opening rate, and vehicle acceleration to determine the target gear.

[0014] The specific implementation method of step one is as follows:

[0015] (1) The vehicle impact analysis steps are as follows: After the vehicle starts running, time T is selected as the identification period, and vehicle acceleration data is collected in real time to calculate the instantaneous impact J. i Impact factor R:

[0016]

[0017] In the formula: SD is the average absolute value of the impact. J Let be the standard deviation of the impact intensity, and η be the impact intensity coefficient, which is taken as 0.5.

[0018] (2) The pedal opening rate analysis adopts the Z-score algorithm, and the specific steps are as follows: collect the pedal opening rate of the vehicle during a normal driving period as sample data, calculate the reference mean μ0 and standard deviation σ0; select time T as the recognition period, and collect the vehicle pedal opening rate v in real time. i Calculate the normalized value z of the pedal opening rate:

[0019]

[0020] Where: μ0 is the mean of the pedal opening rate in the sample data, σ0 is the standard deviation of the pedal opening rate in the sample data, and η ’ The deviation coefficient is set to 0.3.

[0021] (3) The specific steps of the pedal opening analysis are as follows: Real-time acquisition of pedal opening degree d, where d is usually taken as (0%, 100%).

[0022] (4) Construct a fuzzy controller with vehicle impact intensity, pedal opening rate and pedal opening as inputs and driving style as output;

[0023] (5) The input fuzzy variable vehicle impact degree is divided into three fuzzy sets: “small”, “medium”, and “large”, with a value range of (0, 1). The driving style membership function adopts a triangle.

[0024] (6) The standardized value of the input fuzzy variable pedal opening |z| is divided into three fuzzy sets: “few”, “medium”, and “many”, with a value range of (0, 1). The membership function of the predicted working condition adopts a triangle.

[0025] (7) Divide the input fuzzy variable pedal opening into three fuzzy sets: “small”, “medium”, and “large”, with a value range of (0, 1). The driving style membership function adopts Gaussian type.

[0026] (8) The output fuzzy variable driving style is divided into three types: "mild", "normal" and "aggressive", with a value range of {1, 2, 3}. The shift coefficient membership function is a single-point type.

[0027] (9) Construct a fuzzy rule base, wherein the fuzzy rules are as follows:

[0028]

[0029] (10) By combining the membership function of the input variable with the fuzzy rule base, the precise value of the corresponding output variable can be obtained through reasoning, and the type of driving style can be determined.

[0030] The specific implementation method of step two is as follows:

[0031] (1) Based on the speed and acceleration of the vehicle in a certain period of time under 10 typical driving cycle conditions such as INDIA_URBAN_SAMPLE, UDDS, WVUSUB, MANHATTAN, NurembergR36, NYCC, WVUCITY, HWFET, NREL2VAIL, and US06_HWY, a Markov probability output matrix is ​​constructed.

[0032] (2) The Markov probability output matrix is ​​divided into four stages, all of which are first-order Markov probability output matrices.

[0033] (3) Real-time vehicle speed collection, and the current vehicle speed V t Input the first-stage Markov probability output matrix to obtain the acceleration a from t to t+0.5s. t～t+0.5s According to V = V t +aΔt gives the vehicle speed V at time t+0.5s. t+0.5s ;

[0034] (4) The vehicle speed V t+0.5s Input the second-stage Markov probability output matrix to obtain the acceleration at from t+0.5s to t+1s. +0.5s～t+1s According to V = V t +aΔt gives the vehicle speed V at time t+1s. t+1s ;

[0035] (5) The vehicle speed V t+1sInput the Markov probability output matrix of the third stage to obtain the acceleration a from t+1s to t+1.5s. t+1s～t+1.5s According to V = V t +aΔt gives the vehicle speed V at time t+1.5s. t+1.5s ;

[0036] (6) The vehicle speed V t+1.5s Input the Markov probability output matrix of the third stage to obtain the acceleration a from t+1.5s to t+2s. t+1.5s～t+2s According to V = V t +aΔt gives the vehicle speed V at time t+2s. t+2s The predicted operating conditions are obtained.

[0037] The specific implementation method for constructing the Markov probability output matrix is ​​as follows:

[0038] (1) Discretize the vehicle speed and acceleration curves of the sample data and analyze them using a clustering algorithm. Discretize the sample vehicle speed curves into N intervals from smallest to largest, with the vehicle speed unit being m / s, and the value of each interval being v. i :

[0039] v i ={v1,v2,v3,...,v N}

[0040] In the formula, N takes the value of 30;

[0041] The acceleration curve corresponding to the vehicle speed is discretized into M intervals from smallest to largest, with the acceleration unit taken as m / s². 2 The value of each interval is a. j :

[0042] a j ={a1,a2,a3,...,a M}

[0043] In the formula, M takes the value of 30;

[0044] (2) Calculate the acceleration probability distribution matrix corresponding to different vehicle speeds based on the discretized sample data, wherein the element p in the probability distribution matrix... ij :

[0045] p ij =p{a(t+n)=a j |v(t+n-0.5)=v i}

[0046] i, j=1,2,...,m; n=0.5,1,...,L PH ;

[0047] In the formula, m is the number of states, n is the time point in the prediction time domain, and L PH To predict the length of the time domain;

[0048] (3) Input the real-time vehicle speed V t At that time, calculate the first-stage Markov probability output matrix, input the vehicle speed at time t under the same state according to the sample data, find the corresponding acceleration and calculate the probability distribution matrix of the acceleration corresponding to different vehicle speeds from t to t+0.5s, and obtain the first-stage Markov probability output matrix.

[0049] (4) To calculate the second-stage Markov probability output matrix, the vehicle speed at time t to t+0.5s must first be calculated using the first-stage Markov probability output matrix. After inputting the vehicle speed at time t+0.5s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+0.5 to t+1s is calculated to obtain the second-stage Markov probability output matrix.

[0050] (5) To calculate the third-stage Markov probability output matrix, the vehicle speed at time t+0.5 to t+1s must first be calculated using the second-stage Markov probability output matrix. After inputting the vehicle speed at time t+1s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+1 to t+1.5s is calculated to obtain the third-stage Markov probability output matrix.

[0051] (6) To calculate the fourth stage Markov probability output matrix, the vehicle speed at time t+1 to t+1.5s must first be calculated using the third stage Markov probability output matrix. After inputting the vehicle speed at time t+1.5s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+1.5 to t+2s is calculated to obtain the fourth stage Markov probability output matrix.

[0052] The specific implementation method of step three is as follows:

[0053] (1) Construct a fuzzy controller with driving style and predicted working conditions as inputs and shift coefficient as output;

[0054] (2) The input fuzzy variable driving style is divided into three sets: “mild”, “normal” and “aggressive”, with a value range of {1, 2, 3}. The driving style membership function adopts a single-point type.

[0055] (3) The input fuzzy variable prediction conditions are divided into three fuzzy sets: “low speed”, “medium speed”, and “high speed”, with a value range of [0, 100] m / s. The prediction condition membership function adopts a triangle.

[0056] (4) The output fuzzy variable shift coefficient is divided into three fuzzy sets: “low”, “medium”, and “high”, with a value range of [1, 2]. The shift coefficient membership function adopts a triangle.

[0057] (5) Construct a fuzzy rule base, wherein the fuzzy rules are as follows:

[0058]

[0059] (6) By combining the membership function of the input variable with the fuzzy rule base, the fuzzy value of the corresponding output variable is obtained through reasoning; the fuzzy value of the output variable is defuzzified by the centroid method, and the fuzzy output variable is converted into a specific value.

[0060] The specific implementation method of step four is as follows:

[0061] (1) When the gear ratio is less than 1.3 and the pedal opening rate is greater than 20%, the vehicle is in reverse, starting, uphill, or deceleration state, and is switched to first gear; when the pedal opening rate is less than 20%, the vehicle is in low-speed cruising state, and the current gear remains unchanged.

[0062] (2) When the gear ratio is greater than 1.7 and the pedal opening rate is greater than 20%, the vehicle is in acceleration mode and switches to second gear; when the pedal opening rate is less than 20%, the vehicle is in high-speed cruising mode and keeps the current gear unchanged.

[0063] (3) When the gear ratio is greater than 1.3 and less than 1.7 and the pedal opening rate is less than 20%, the vehicle is in a constant speed state and the current gear remains unchanged; when the pedal opening rate is greater than 20%, the vehicle is in an accelerating state when the vehicle acceleration is greater than 0 and switches to second gear; when the vehicle acceleration is less than 0, the vehicle is in a decelerating state and switches to first gear.

[0064] The beneficial effects of this invention are: by combining driving style and operating condition prediction, this invention dynamically adjusts the shift timing and gear position through fuzzy control and adaptive strategies, which can adapt to different complex operating conditions and driver driving habits, maintain the real-time performance and robustness of the system, and improve the vehicle's environmental adaptability. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of a transmission device for new energy vehicles according to the present invention (the schematic diagram of the transmission device for new energy vehicles is shown below). Figure 1 (as shown);

[0066] Figure 2 This is a schematic diagram of the low-gear power transmission route of the transmission device for new energy vehicles according to the present invention (as shown in the schematic diagram of the low-gear power transmission route of the transmission device for new energy vehicles). Figure 2 (as shown);

[0067] Figure 3 This is a schematic diagram of the high-end power transmission route of the transmission device for new energy vehicles according to the present invention (as shown in the schematic diagram of the high-end power transmission route of the transmission device for new energy vehicles). Figure 3 (as shown);

[0068] Figure 4 This is a 3D diagram of the transmission device for new energy vehicles according to the present invention (the 3D diagram of the transmission device for new energy vehicles is shown below). Figure 4 (as shown);

[0069] Figure 5 This is a three-dimensional diagram of the input shaft system of the transmission device for new energy vehicles according to the present invention (the three-dimensional diagram of the input shaft system of the transmission device for new energy vehicles is shown below). Figure 5 (as shown); Figure 6 This is a three-dimensional diagram of the output shaft system of the transmission device for new energy vehicles according to the present invention (the three-dimensional diagram of the output shaft system of the transmission device for new energy vehicles is shown below). Figure 6 (as shown); Figure 7 This is a schematic diagram of the fuzzy control membership function of the shifting strategy of the present invention (as shown in the schematic diagram of the fuzzy control membership function of the shifting strategy). Figure 7 (as shown); Figure 8 This is a flowchart illustrating a shifting strategy method based on predicted operating conditions and driving style according to the present invention (a flowchart illustrating a shifting strategy method based on predicted operating conditions and driving style is shown below). Figure 8 (As shown).

[0070] Figure 1 In the middle: 1-Input shaft, 2-Output shaft, 3-Bearing A, 4-Bearing B, 5-Bearing C, 6-Bearing D, 7-Bearing E, 8-Bearing F, 9-First input gear, 10-Second input gear, 11-First output gear, 12-Second output gear, 13-Clutch assembly, 14-First clutch driven plate, 15-Clutch driving plate, 16-Second clutch driven plate, 17-Drive motor, 18-Angle sensor, 19-Hydraulic unit, 20-Control unit, 21-Housing housing. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0072] like Figure 1As shown, a gear shifting device based on predicted operating conditions and driving style includes: a drive motor (17), an input shaft (1), an output shaft (2), a clutch assembly (13), a first input gear (9), a second input gear (10), a first output gear (11), and a second output gear (12). The gear shifting device includes two gears. The structural features of the gear shifting device are: the input shaft (1) is provided with bearing A (3), bearing B (4), first input gear (9), first clutch driven plate (14), and clutch driving plate (15) sequentially from the first end to the last end. 5) The second clutch driven plate (16), bearing C (5), second input gear (10), and bearing D (6) are arranged sequentially from the beginning to the end on the output shaft (2), including bearing E (7), first output gear (11), second output gear (12), and bearing F (8); the inner rings of bearings A (3), B (4), C (5), and D (6) are assembled on the input shaft (1) through shaft holes, and the inner rings of bearings E (7) and F (8) are assembled on the output shaft (2) through shaft holes. The outer rings of bearings E (7) and F (8) are mounted on the housing (21) through shaft holes. The clutch assembly (13) consists of a first clutch driven plate (14), a second clutch driven plate (16), and a clutch driving plate (15). The first clutch driven plate (14) is fixedly connected to the first input gear (9), the second clutch driven plate (16) is fixedly connected to the second input gear (10), and the clutch driving plate (15) is fixedly connected to the input shaft (1). The first input gear (9) meshes with the first output gear (11), and the second... The input gear (10) meshes with the second output gear (12); the angle sensor (18) is used to detect the pedal opening degree; the control unit (20) collects the speed and gear status of the drive motor (17) and the output shaft (2) in real time and receives the pedal opening degree signal detected by the angle sensor (18); the control unit (20) controls the disengagement and engagement of the clutch assembly (13) by controlling the filling and discharging of the hydraulic unit (19) to achieve gear shifting; wherein the drive motor (17), the angle sensor (18), and the hydraulic unit (19) are respectively connected to the control unit (20);

[0073] The shifting device has two gears. In one gear, the power transmission route is as follows: when the clutch driving plate (15) engages with the first clutch driven plate (14), the drive motor (17) sequentially drives the input shaft (1), the clutch driving plate (15), the first clutch driven plate (14), the first input gear (9), the first output gear (10), and the output shaft (2).

[0074] The shifting device has two gears. The power transmission route for the second gear is as follows: when the clutch driving plate (15) and the second clutch driven plate (16) are engaged, the drive motor (17) sequentially drives the input shaft (1), the clutch driving plate (15), the second clutch driven plate (16), the second input gear (10), the second output gear (12), and the output shaft (2).

[0075] like Figure 3 As shown, a shift decision method based on predicted operating conditions and driving style is characterized by the following specific steps:

[0076] Step 1: Based on the fuzzy control strategy, the driving style is identified by combining vehicle impact intensity, pedal opening rate, and pedal opening, and divided into "mild", "standard", and "aggressive" types.

[0077] Step 2: Construct the probability output matrix of the Markov chain model to predict the vehicle speed in the next 2 seconds and obtain the predicted driving conditions;

[0078] Step 3: Construct a fuzzy controller based on the fuzzy control strategy, taking the driving style and predicted operating conditions as inputs, and outputting the required gear coefficient;

[0079] Step 4: Construct an adaptive adjustment strategy based on the gear ratio, pedal opening rate, and vehicle acceleration to determine the target gear.

[0080] The specific implementation of step one is as follows: (1) The vehicle impact analysis steps are as follows: After the vehicle runs, time T is selected as the identification period, and vehicle acceleration data is collected in real time to calculate the instantaneous impact J. i Impact factor R:

[0081]

[0082] In the formula: SD is the average absolute value of the impact. J Let be the standard deviation of the impact intensity, and η be the impact intensity coefficient, which is taken as 0.5.

[0083] (2) The pedal opening rate analysis adopts the Z-score algorithm, and the specific steps are as follows: collect the pedal opening rate of the vehicle during a normal driving period as sample data, calculate the reference mean μ0 and standard deviation σ0; select time T as the recognition period, and collect the vehicle pedal opening rate v in real time. i Calculate the normalized value z of the pedal opening rate:

[0084]

[0085] Where: μ0 is the mean of the pedal opening rate in the sample data, σ0 is the standard deviation of the pedal opening rate in the sample data, and η’ The deviation coefficient is set to 0.3.

[0086] (3) The specific steps of the pedal opening analysis are as follows: Real-time acquisition of pedal opening degree d, where d is usually taken as (0%, 100%).

[0087] (4) Construct a fuzzy controller with vehicle impact intensity, pedal opening rate and pedal opening as inputs and driving style as output;

[0088] (5) The input fuzzy variable vehicle impact degree is divided into three fuzzy sets: “small”, “medium”, and “large”, with a value range of (0, 1). The driving style membership function adopts a triangle.

[0089] (6) The standardized value of the input fuzzy variable pedal opening |z| is divided into three fuzzy sets: “few”, “medium”, and “many”, with a value range of (0, 1). The membership function of the predicted working condition adopts a triangle.

[0090] (7) Divide the input fuzzy variable pedal opening into three fuzzy sets: “small”, “medium”, and “large”, with a value range of (0, 1). The driving style membership function adopts Gaussian type.

[0091] (8) The output fuzzy variable driving style is divided into three types: "mild", "normal" and "aggressive", with a value range of {1, 2, 3}. The shift coefficient membership function is a single-point type.

[0092] (9) Construct a fuzzy rule base, wherein the fuzzy rules are as follows:

[0093]

[0094] (10) By combining the membership function of the input variable with the fuzzy rule base, the precise value of the corresponding output variable can be obtained through reasoning, and the type of driving style can be determined.

[0095] The specific implementation method of step two is as follows:

[0096] (7) Based on the speed and acceleration of the vehicle in a certain period of time under 10 typical driving cycle conditions such as INDIA_URBAN_SAMPLE, UDDS, WVUSUB, MANHATTAN, NurembergR36, NYCC, WVUCITY, HWFET, NREL2VAIL, and US06_HWY, a Markov probability output matrix is ​​constructed.

[0097] (8) The Markov probability output matrix is ​​divided into four stages, all of which are first-order Markov probability output matrices.

[0098] (9) Real-time vehicle speed collection, and the current vehicle speed V t Input the first-stage Markov probability output matrix to obtain the acceleration a from t to t+0.5s. t～t+0.5s According to V = V t +aΔt gives the vehicle speed V at time t+0.5s. t+0.5s ;

[0099] (10) The vehicle speed V t+0.5s Input the second-stage Markov probability output matrix to obtain the acceleration a from t+0.5s to t+1s. t+0.5s～t+1s According to V = V t +aΔt gives the vehicle speed V at time t+1s. t+1s ;

[0100] (11) The vehicle speed V t+1s Input the Markov probability output matrix of the third stage to obtain the acceleration a from t+1s to t+1.5s. t+1s～t+1.5s According to V = V t +aΔt gives the vehicle speed V at time t+1.5s. t+1.5s ;

[0101] (12) The vehicle speed V t+1.5s Input the Markov probability output matrix of the third stage to obtain the acceleration a from t+1.5s to t+2s. t+1.5s～t+2s According to V = V t +aΔt gives the vehicle speed V at time t+2s. t+2s The predicted operating conditions are obtained.

[0102] The specific implementation method for constructing the Markov probability output matrix is ​​as follows:

[0103] (1) Discretize the vehicle speed and acceleration curves of the sample data and analyze them using a clustering algorithm. Discretize the sample vehicle speed curves into N intervals from smallest to largest, with the vehicle speed unit being m / s, and the value of each interval being v. i :

[0104] v i ={v1,v2,v3,...,v N}

[0105] In the formula, N takes the value of 30;

[0106] The acceleration curve corresponding to the vehicle speed is discretized into M intervals from smallest to largest, with the acceleration unit taken as m / s². 2 The value of each interval is a. j :

[0107] a j ={a1,a2,a3,...,a M}

[0108] In the formula, M takes the value of 30;

[0109] (2) Calculate the acceleration probability distribution matrix corresponding to different vehicle speeds based on the discretized sample data, wherein the element p in the probability distribution matrix... ij :

[0110] p ij =p{a(t+n)=a j |v(t+n-0.5)=v i}

[0111] i, j=1,2,...,m; n=0.5,1,...,L PH ;

[0112] In the formula, m is the number of states, n is the time point in the prediction time domain, and L PH To predict the length of the time domain;

[0113] (3) Input the real-time vehicle speed V t At that time, calculate the first-stage Markov probability output matrix, input the vehicle speed at time t under the same state according to the sample data, find the corresponding acceleration and calculate the probability distribution matrix of the acceleration corresponding to different vehicle speeds from t to t+0.5s, and obtain the first-stage Markov probability output matrix.

[0114] (4) To calculate the second-stage Markov probability output matrix, the vehicle speed at time t to t+0.5s must first be calculated using the first-stage Markov probability output matrix. After inputting the vehicle speed at time t+0.5s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+0.5 to t+1s is calculated to obtain the second-stage Markov probability output matrix.

[0115] (5) To calculate the third-stage Markov probability output matrix, the vehicle speed at time t+0.5 to t+1s must first be calculated using the second-stage Markov probability output matrix. After inputting the vehicle speed at time t+1s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+1 to t+1.5s is calculated to obtain the third-stage Markov probability output matrix.

[0116] (6) To calculate the fourth stage Markov probability output matrix, the vehicle speed at time t+1 to t+1.5s must first be calculated using the third stage Markov probability output matrix. After inputting the vehicle speed at time t+1.5s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+1.5 to t+2s is calculated to obtain the fourth stage Markov probability output matrix.

[0117] The specific implementation method of step three is as follows:

[0118] (7) Construct a fuzzy controller with driving style and predicted working conditions as inputs and shift coefficient as output;

[0119] (8) Divide the input fuzzy variable driving style into three sets: “mild”, “normal” and “aggressive”, with a value range of {1, 2, 3}. The driving style membership function adopts a single-point type.

[0120] (9) The input fuzzy variable prediction condition is divided into three fuzzy sets: "low speed," "medium speed," and "high speed," with a value range of [0, 100] m / s. The prediction condition membership function adopts a triangle, such as... Figure 2 As shown;

[0121] (10) The output fuzzy variable shift coefficient is divided into three fuzzy sets: "low", "medium", and "high", with a value range of [1, 2]. The membership function of the shift coefficient adopts a triangle, such as... Figure 2 As shown;

[0122] (11) Construct a fuzzy rule base, wherein the fuzzy rules are as follows:

[0123]

[0124] (12) By combining the membership function of the input variable with the fuzzy rule base, the fuzzy value of the corresponding output variable is obtained through reasoning; the fuzzy value of the output variable is defuzzified by the centroid method, and the fuzzy output variable is converted into a specific value.

[0125] The specific implementation method of step four is as follows:

[0126] (1) When the gear ratio is less than 1.3 and the pedal opening rate is greater than 20%, the vehicle is in reverse, starting, uphill, or deceleration state, and is switched to first gear; when the pedal opening rate is less than 20%, the vehicle is in low-speed cruising state, and the current gear remains unchanged.

[0127] (2) When the gear ratio is greater than 1.7 and the pedal opening rate is greater than 20%, the vehicle is in acceleration mode and switches to second gear; when the pedal opening rate is less than 20%, the vehicle is in high-speed cruising mode and keeps the current gear unchanged.

[0128] (3) When the gear ratio is greater than 1.3 and less than 1.7 and the pedal opening rate is less than 20%, the vehicle is in a constant speed state and the current gear remains unchanged; when the pedal opening rate is greater than 20%, the vehicle is in an accelerating state when the vehicle acceleration is greater than 0 and switches to second gear; when the vehicle acceleration is less than 0, the vehicle is in a decelerating state and switches to first gear.

Claims

1. A transmission device and shifting decision method for new energy vehicles includes a drive motor, an input shaft, an output shaft, a clutch assembly, a first input gear, a second input gear, a first output gear, and a second output gear. The shifting device includes two gears, characterized in that: The input shaft, from beginning to end, is equipped with bearings A and B, a first input gear, a first clutch driven plate, a clutch driving plate, a second clutch driven plate, bearing C, a second input gear, and bearing D. The output shaft, from beginning to end, is equipped with bearing E, a first output gear, a second output gear, and bearing F. The inner rings of bearings A, B, C, and D are mounted on the input shaft through shaft holes. The inner rings of bearings E and F are mounted on the output shaft through shaft holes. The outer rings of bearings A, D, E, and F are mounted on the housing through shaft holes. The clutch assembly consists of a first clutch driven plate, a second clutch driven plate, and a clutch... The clutch consists of a drive plate and a drive plate. The first clutch driven plate is fixedly connected to the first input gear, the second clutch driven plate is fixedly connected to the second input gear, and the clutch drive plate is fixedly connected to the input shaft. The first input gear meshes with the first output gear, and the second input gear meshes with the second output gear. An angle sensor is used to detect the pedal opening. The control unit collects the speed of the drive motor and output shaft, the gear status, and receives the pedal opening signal detected by the angle sensor in real time. The control unit controls the hydraulic unit to charge and discharge oil, and further controls the disengagement and engagement of the clutch to achieve gear shifting. The drive motor, angle sensor, and hydraulic unit are respectively connected to the control unit. The shifting device has two gears. In one gear, the power transmission route is as follows: when the clutch driving plate and the first clutch driven plate are engaged, the drive motor sequentially drives the input shaft, the clutch driving plate, the first clutch driven plate, the first input gear, the first output gear, and the output shaft. The shifting device has two gears. In the second gear, the power transmission route is as follows: when the clutch driving plate is engaged and the second clutch driven plate is disengaged, the drive motor sequentially drives the input shaft, the clutch driving plate, the second clutch driven plate, the second input gear, the second output gear, and the output shaft.

2. A method for making gear shift decisions in a new energy vehicle as described in claim 1, characterized in that... The specific steps are as follows: Step 1: Based on the fuzzy control strategy, the driving style is identified by combining vehicle impact intensity, pedal opening rate, and pedal opening, and divided into "mild", "standard", and "aggressive" types. Step 2: Construct the probability output matrix of the Markov chain model to predict the vehicle speed in the next 2 seconds and obtain the predicted driving conditions; Step 3: Construct a fuzzy controller based on the fuzzy control strategy, taking the driving style and predicted operating conditions as inputs, and outputting the required gear coefficient; Step 4: Construct an adaptive adjustment strategy based on the gear ratio, pedal opening rate, and vehicle acceleration to determine the target gear.

3. The new energy vehicle shifting decision method according to claim 2, characterized in that... The specific implementation method of step one is as follows: (1) The vehicle impact analysis steps are as follows: After the vehicle starts running, time T is selected as the identification period, and vehicle acceleration data is collected in real time to calculate the instantaneous impact J. i Impact factor R: In the formula: SD is the average absolute value of the impact. J Let be the standard deviation of the impact intensity, and η be the impact intensity coefficient, which is taken as 0.

5. (2) The pedal opening rate analysis adopts the Z-score algorithm. The specific steps are as follows: collect the pedal opening rate of the vehicle during a period of normal driving as sample data, and calculate the reference mean μ0 and standard deviation σ0. The recognition period is selected as time T, and the vehicle pedal opening rate v is collected in real time. i Calculate the normalized value z of the pedal opening rate: Where: μ0 is the mean of the pedal opening rate in the sample data, σ0 is the standard deviation of the pedal opening rate in the sample data, and η ’ The deviation coefficient is set to 0.

3. (3) The specific steps of the pedal opening analysis are as follows: Real-time acquisition of pedal opening degree d, where d is usually taken as (0%, 100%). (4) Construct a fuzzy controller with vehicle impact intensity, pedal opening rate and pedal opening as inputs and driving style as output; (5) The input fuzzy variable vehicle impact degree is divided into three fuzzy sets: "small", "medium" and "large", with a value range of (0, 1). The driving style membership function adopts a triangle. (6) Divide the standardized value of the input fuzzy variable pedal opening |z| into three fuzzy sets: "few", "medium" and "many", with a value range of (0, 1). The membership function of the predicted working condition adopts a triangle. (7) Divide the input fuzzy variable pedal opening into three fuzzy sets: "small", "medium" and "large", with a value range of (0, 1). The driving style membership function adopts Gaussian type. (8) The output fuzzy variable driving style is divided into three types: "mild", "normal" and "aggressive", with a value range of {1, 2, 3}. The shift coefficient membership function is a single-point type. (9) Construct a fuzzy rule base. By combining the membership function of the input variable with the fuzzy rule base, the precise value of the corresponding output variable can be obtained through reasoning, thereby determining the type of driving style.

4. The new energy vehicle shifting decision method according to claim 2, characterized in that... The specific implementation method of step two is as follows: (1) Based on the speed and acceleration of the vehicle in a certain period of time under 10 typical driving cycle conditions such as INDIA_URBAN_SAMPLE, UDDS, WVUSUB, MANHATTAN, NurembergR36, NYCC, WVUCITY, HWFET, NREL2VAIL, and US06_HWY, a Markov probability output matrix is ​​constructed. (2) The Markov probability output matrix is ​​divided into four stages, all of which are first-order Markov probability output matrices. (3) Real-time vehicle speed collection, and the current vehicle speed V t Input the first-stage Markov probability output matrix to obtain the acceleration a from t to t+0.5s. t～t+0.5s According to V = V t +aΔt gives the vehicle speed V at time t+0.5s. t+0.5s ; (4) The vehicle speed V t+0.5s Input the second-stage Markov probability output matrix to obtain the acceleration a from t+0.5s to t+1s. t+0.5s～t+1s According to V = V t +aΔt gives the vehicle speed V at time t+1s. t+1s ; (5) The vehicle speed V t+1s Input the Markov probability output matrix of the third stage to obtain the acceleration a from t+1s to t+1.5s. t+1s～t+1.5s According to V = V t +aΔt gives the vehicle speed V at time t+1.5s. t+1.5s ; (6) The vehicle speed V t+1.5s Input the Markov probability output matrix of the third stage to obtain the acceleration a from t+1.5s to t+2s. t+1.5s～t+2s According to V = V t +aΔt gives the vehicle speed V at time t+2s. t+2s The predicted operating conditions are obtained.

5. The new energy vehicle shifting decision method according to claim 4, characterized in that... The specific implementation method for constructing the Markov probability output matrix is ​​as follows: (1) Discretize the vehicle speed and acceleration curves of the sample data and analyze them using a clustering algorithm. Discretize the sample vehicle speed curves into N intervals from smallest to largest, with the vehicle speed unit being m / s, and the value of each interval being v. i : v i ={v1,v2,v3,...,v N } In the formula, N takes the value of 30; The acceleration curve corresponding to the vehicle speed is discretized into M intervals from smallest to largest, with the acceleration unit taken as m / s². 2 The value of each interval is a. j : a j ={a1,a2,a3,...,a M } In the formula, M takes the value of 30; (2) Calculate the acceleration probability distribution matrix corresponding to different vehicle speeds based on the discretized sample data, wherein the element p in the probability distribution matrix... ij : p ij =p{a(t+n)=a j |v(t+n-0.5)=v i } i,j=1,2,...,m;n=0.5,1,...,L PH ; In the formula, m is the number of states, n is the time point in the prediction time domain, and L PH To predict the length of the time domain; (3) Input the real-time vehicle speed V t At that time, calculate the first-stage Markov probability output matrix, input the vehicle speed at time t under the same state according to the sample data, find the corresponding acceleration and calculate the probability distribution matrix of the acceleration corresponding to different vehicle speeds from t to t+0.5s, and obtain the first-stage Markov probability output matrix. (4) To calculate the second-stage Markov probability output matrix, the vehicle speed at time t to t+0.5s must first be calculated using the first-stage Markov probability output matrix. After inputting the vehicle speed at time t+0.5s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+0.5 to t+1s is calculated to obtain the second-stage Markov probability output matrix. (5) To calculate the third-stage Markov probability output matrix, the vehicle speed at time t+0.5 to t+1s must first be calculated using the second-stage Markov probability output matrix. After inputting the vehicle speed at time t+1s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+1 to t+1.5s is calculated to obtain the third-stage Markov probability output matrix. (6) To calculate the fourth stage Markov probability output matrix, the vehicle speed at time t+1 to t+1.5s must first be calculated using the third stage Markov probability output matrix. After inputting the vehicle speed at time t+1.5s according to the sample data, the acceleration probability distribution matrix corresponding to different vehicle speeds at time t+1.5 to t+2s is calculated to obtain the fourth stage Markov probability output matrix.

6. The new energy vehicle shifting decision method according to claim 2, characterized in that... The specific implementation method of step three is as follows: (2) Construct a fuzzy controller with driving style and predicted working conditions as inputs and shift coefficient as output; (3) Divide the input fuzzy variable driving style into three sets: "mild", "normal" and "aggressive", with a value range of {1, 2, 3}. The driving style membership function adopts a single-point type. (4) The input fuzzy variable prediction conditions are divided into three fuzzy sets: "low speed", "medium speed" and "high speed", with a value range of [0, 100] m / s. The membership function of the prediction conditions adopts a triangle. (5) Divide the output fuzzy variable shift coefficient into three fuzzy sets: "low", "medium" and "high", with a value range of [1, 2]. The shift coefficient membership function adopts a triangle. (6) Construct a fuzzy rule base. By combining the membership function of the input variable with the fuzzy rule base, the fuzzy value of the corresponding output variable is obtained through reasoning. The fuzzy value of the output variable is defuzzified by the centroid method, and the fuzzy output variable is converted into a specific value.

7. The new energy vehicle shifting decision method according to claim 2, characterized in that... The specific implementation method of step four is as follows: (1) When the gear ratio is less than 1.3 and the pedal opening rate is greater than 20%, the vehicle is in reverse, starting, uphill, or deceleration state, and is switched to first gear; when the pedal opening rate is less than 20%, the vehicle is in low-speed cruising state, and the current gear remains unchanged. (2) When the gear ratio is greater than 1.7 and the pedal opening rate is greater than 20%, the vehicle is in acceleration mode and switches to second gear; when the pedal opening rate is less than 20%, the vehicle is in high-speed cruising mode and keeps the current gear unchanged. (3) When the gear ratio is greater than 1.3 and less than 1.7 and the pedal opening rate is less than 20%, the vehicle is in a constant speed state and the current gear remains unchanged; When the pedal opening rate is greater than 20%, the vehicle is accelerating when the vehicle acceleration is greater than 0, and the vehicle is shifted to second gear. When the vehicle acceleration is less than 0, the vehicle is decelerating, and the vehicle is shifted to first gear.

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