CN-122014845-A - Transmission device for new energy vehicle and gear shifting decision method

CN122014845ACN 122014845 ACN122014845 ACN 122014845ACN-122014845-A

Abstract

The invention discloses a transmission device for a new energy vehicle and a gear shifting decision method, wherein the transmission box 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; the method comprises the steps of constructing a Markov chain prediction model to predict future vehicle speed conditions to obtain a prediction working condition, establishing a fuzzy rule and a mapping relation related to driving style, the prediction working condition and gear coefficients based on a fuzzy control strategy, collecting vehicle speed signals and gear signals, and establishing a self-adaptive adjustment strategy according to the gear coefficients, pedal opening rate and the like to obtain a target gear. According to the invention, through combining the Markov chain and the fuzzy control strategy, the accurate prediction of the running condition of the vehicle is realized, the driving style is integrated to identify and make a gear shifting decision, and the economical efficiency and the dynamic property of the vehicle are improved.

Inventors

LIU JINGANG
Yan Xiaqi
ZHANG GUANGJIE
LI YUNLONG
BU LEI
ZHANG DAQING
ZHANG WEI

Assignees

湘潭大学

Dates

Publication Date: 20260512
Application Date: 20250917

Claims (7)

1. The utility model provides a transmission and decision method of shifting for new forms of energy automobile-used includes driving motor, input shaft, output shaft, clutch assembly, first input gear, second input gear, first output gear, second output gear, and gearshift includes two keeps off position, its characterized in that: The input shaft is sequentially provided with a bearing A, a bearing B, a first input gear, a first clutch driven plate, a clutch driving plate, a second clutch driven plate, a bearing C, a second input gear and a bearing D from the head end to the tail end, and the output shaft is sequentially provided with a bearing E, a first output gear, a second output gear and a bearing F from the head end to the tail end; the clutch assembly consists of a first clutch driven plate, a second clutch driven plate and a clutch driving plate, wherein the first clutch driven plate is fixedly connected with a first input gear, the second clutch driven plate is fixedly connected with a second input gear, the clutch driving plate is fixedly connected with the input shaft, the first input gear is meshed with a first output gear, the second input gear is meshed with a second output gear, an angle sensor is used for detecting pedal opening, and a control unit is used for acquiring the rotation speed and the gear state of a driving motor and the output shaft in real time and receiving pedal opening signals detected by the angle sensor; when the clutch driving plate is connected with the first clutch driven plate, the driving 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 two gears of the gear shifting device are in a second gear power transmission route, wherein when a second clutch of a clutch driving plate is driven and engaged, a driving motor sequentially drives an input shaft, a clutch driving plate, a second clutch driven plate, a second input gear, a second output gear and an output shaft.
2. A new energy vehicle gear shifting decision method applied to the first claim, which is characterized by comprising the following specific steps: the method comprises the steps that firstly, driving styles are identified based on a fuzzy control strategy in combination with vehicle impact degree, pedal opening rate and pedal opening, and the driving styles are divided into mild types, standard types and aggressive types; Step two, constructing a Markov chain model probability output matrix, and predicting the speed of the vehicle for 2s in the future to obtain a predicted working condition; Step three, constructing a fuzzy controller based on a fuzzy control strategy, and taking the driving style and the predicted working condition as inputs and outputting to obtain a required gear coefficient; and fourthly, constructing a self-adaptive adjustment strategy based on the gear coefficient, the pedal opening rate and the vehicle acceleration, and determining a target gear.
3. The gear shifting decision method of the new energy vehicle according to claim 2, wherein the specific implementation manner of the step is as follows: (1) The vehicle impact degree analysis comprises the following steps of selecting time T as an identification period after the vehicle runs, collecting vehicle acceleration data in real time, and calculating instantaneous impact degree J i and impact degree analysis coefficient R: wherein: SD J is the standard deviation of the impact degree, eta is the impact degree coefficient, and 0.5 is taken as the impact degree average absolute value; (2) The pedal opening rate analysis adopts a Z-score algorithm, and specifically comprises the following steps of collecting pedal opening rate of a vehicle for a period of time when the vehicle normally runs as sample data, calculating a reference mean value mu 0 and a standard deviation sigma 0 , selecting time T as an identification period, collecting vehicle pedal opening rate v i in real time, and calculating a value Z after normalizing the pedal opening rate: Wherein mu 0 is a sample data pedal opening rate average value, sigma 0 is a sample data pedal opening rate standard deviation, eta ' is a deviation coefficient, and 0.3 is taken; (3) The pedal opening analysis specifically comprises the following steps of collecting pedal opening degree d in real time, wherein d is a general value (0% and 100%); (4) Constructing a fuzzy controller taking the impact degree, the pedal opening rate and the pedal opening of the vehicle as inputs and the driving style as outputs; (5) Dividing the input fuzzy variable vehicle impact degree into three fuzzy sets of small, medium and large, wherein the value range is (0, 1), and the driving style membership function adopts a triangle; (6) Dividing the input fuzzy variable pedal opening standardization value |z| into three fuzzy sets of less, middle and more, wherein the value range is (0, 1), and the prediction working condition membership function adopts a triangle; (7) Dividing the opening of the input fuzzy variable pedal into three fuzzy sets of small, medium and large, wherein the value range is (0, 1), and the driving style membership function adopts Gaussian; (8) Dividing the driving style of the output fuzzy variable into three types of mild type, normal type and aggressive type, wherein the value range is {1,2,3}, and the gear shift coefficient belongs to a single-point type of a function; (9) And constructing a fuzzy rule base, and reasoning to obtain an accurate value of a corresponding output variable by combining the membership function of the input variable with the fuzzy rule base, so that the type of the driving style can be judged.
4. The gear shifting decision method of the new energy vehicle according to claim 2, wherein the specific implementation manner of the step two is as follows: (1) Based on the speed and acceleration of the vehicle in a certain period of 10 typical driving cycle working conditions such as INDIA_URBAN_ SAMPLE, UDDS, WVUSUB, MANHATTAN, nurembergR36, NYCC, WVUCITY, HWFET, NREL2VAIL, US06_HWY and the like as sample data, constructing a Markov probability output matrix; (2) The Markov probability output matrix is divided into four stages and is a first-order Markov probability output matrix; (3) The vehicle speed is collected in real time, the current vehicle speed V t is input into a first-stage Markov probability output matrix, the acceleration a t～t+0.5s of t-t+0.5s is obtained, and the vehicle speed V t+0.5s at the moment of t+0.5s is obtained according to V=V t +aDeltat; (4) Inputting the vehicle speed V t+0.5s into a second-stage Markov probability output matrix to obtain acceleration a t+0.5s～t+1s of t+0.5s-t+1s, and obtaining the vehicle speed V t+1s at the moment of t+1s according to V=V t +aDeltat; (5) Inputting the vehicle speed V t+1s into a Markov probability output matrix of a third stage to obtain acceleration a t+1s～t+1.5s of t+1s-t+1.5s, and obtaining a vehicle speed V t+1.5s at the moment of t+1.5s according to V=V t +aDeltat; (6) And inputting the vehicle speed V t+1.5s into a Markov probability output matrix of a third stage to obtain the acceleration a t+1.5s～t+2s of t+1.5s-t+2s, and obtaining the vehicle speed V t+2s at the moment of t+2s according to V=V t +aDeltat to obtain the predicted working condition.
5. The new energy vehicle gear shifting decision method according to claim 4, wherein the specific implementation mode of the markov probability output matrix is as follows: (1) Discretizing a speed and acceleration curve of the sample data, and analyzing according to a cluster analysis algorithm. The sample vehicle speed curve is discretized into N sections from small to large, the vehicle speed unit is m/s, and the numerical value of each section is v i : v i ＝{v 1 ,v 2 ,v 3 ,...,v N } wherein, the value of N is 30; The acceleration curve corresponding to the vehicle speed is discretized into M sections from small to large, the acceleration unit is M/s 2 , and the value of each section is a j : a j ＝{a 1 ,a 2 ,a 3 ,...,a M } wherein M has a value of 30; (2) Calculating acceleration probability distribution matrixes corresponding to different vehicle speeds according to the discretized sample data, wherein an element p ij in the probability distribution matrixes: 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 ; wherein m is a state number, n is a time point in a prediction time domain, and L PH is the length of the prediction time domain; (3) When the real-time vehicle speed V t is input, calculating a first-stage Markov probability output matrix, inputting the vehicle speed at the moment t under the same state according to sample data, finding out corresponding acceleration, and calculating a probability distribution matrix of acceleration corresponding to different vehicle speeds at the time of t-t+0.5s to obtain the first-stage Markov probability output matrix; (4) Calculating a second-stage Markov probability output matrix, namely calculating the vehicle speed at the moment of t-t+0.5s through the first-stage Markov probability output matrix, inputting the vehicle speed at the moment of t+0.5s according to sample data, and then calculating an acceleration probability distribution matrix corresponding to different vehicle speeds at the moment of t+0.5-t+1s to obtain the second-stage Markov probability output matrix; (5) Calculating a third-stage Markov probability output matrix, namely calculating the vehicle speed at the moment of t+0.5-t+1s through the second-stage Markov probability output matrix, inputting the vehicle speed at the moment of t+1s according to sample data, and then calculating acceleration probability distribution matrixes corresponding to different vehicle speeds at the moment of t+1-t+1.5s to obtain the third-stage Markov probability output matrix; (6) And calculating a fourth-stage Markov probability output matrix, namely calculating the vehicle speed at the moment of t+1-t+1.5s through the third-stage Markov probability output matrix, inputting the vehicle speed at the moment of t+1.5s according to sample data, and then calculating an acceleration probability distribution matrix corresponding to different vehicle speeds at the moment of t+1.5-t+2s to obtain the fourth-stage Markov probability output matrix.
6. The gear shifting decision method of the new energy vehicle according to claim 2, wherein the third specific implementation mode is as follows: (2) Constructing a fuzzy controller taking driving style and predicted working condition as input and gear shifting coefficient as output; (3) Dividing the driving style of the input fuzzy variable into three sets of a mild type, a normal type and an aggressive type, wherein the value range is {1,2 and 3}, and the driving style membership function adopts a single-point type; (4) Dividing the input fuzzy variable prediction working condition into three fuzzy sets of low speed, medium speed and high speed, wherein the value range is [0,100] m/s, and the prediction working condition membership function adopts a triangle; (5) Dividing the output fuzzy variable shift coefficient into three fuzzy sets of low, medium and high, wherein the value range is [1,2], and the shift coefficient membership function adopts triangle; (6) Establishing a fuzzy rule base, reasoning to obtain fuzzy values of corresponding output variables by combining membership functions of the input variables with the fuzzy rule base, and performing defuzzification treatment on the fuzzy values of the output variables by a gravity center method to convert the fuzzy output variables into specific numerical values.
7. The gear shifting decision method of the new energy vehicle according to claim 2, wherein the fourth embodiment is as follows: (1) When the speed of the pedal opening is less than 20%, the vehicle is in a low-speed cruising state, and the current gear is kept unchanged; (2) When the pedal opening rate is less than 20%, the vehicle is in a high-speed cruising state, and the current gear is kept unchanged; (3) When the gear coefficient is larger than 1.3 and smaller than 1.7 and the pedal opening rate is smaller than 20%, the vehicle is in a constant speed state, the current gear is kept unchanged, when the pedal opening rate is larger than 20%, the vehicle is in an acceleration state and is switched to a second gear when the vehicle acceleration is larger than 0, and when the vehicle acceleration is smaller than 0 and is in a deceleration state and is switched to a first gear.

Description

Transmission device for new energy vehicle and gear shifting decision method Technical Field The invention relates to a transmission device for a new energy vehicle and a gear shifting decision method, and belongs to the field of vehicle power assembly control. Background In a vehicle transmission system, gear shifting decision is taken as a core technology, and has key influence on vehicle dynamic property, fuel economy and environmental adaptability. At present, most of traditional gear shifting decision methods are based on fixed gear shifting curves or rules, complex and changeable actual driving conditions are difficult to fit, and in automatic gear shifting, vehicles need to acquire a large amount of information according to sensors to quickly make reasonable gear shifting decisions so as to ensure the driving safety and smoothness, so that the traditional gear shifting decision methods are difficult to be adequate. Therefore, the gear shifting decision is carried out by combining the driving style of the driver and the predicted working condition of the vehicle, so that the gear shifting decision has important practical significance and application value, the problems of poor adaptability and the like of a gear shifting decision method in the technology can be effectively solved, and the comprehensive performance and driving experience of the vehicle are improved. Disclosure of Invention Aiming at the problems in the prior art, the invention provides a gear shifting device and a decision method based on driving style and predicted working conditions, which fully consider the influences of the driving style and the predicted working conditions and perform fuzzy control on gears based on a fuzzy control strategy. The technical scheme for solving the technical problems is as follows: The utility model provides a transmission and decision method of shifting for new forms of energy automobile-used includes driving motor, input shaft, output shaft, clutch assembly, first input gear, second input gear, first output gear, second output gear, and gearshift includes two keeps off the position, gearshift structural feature is: the input shaft is sequentially provided with a bearing A, a bearing B, a first input gear, a first clutch driven plate, a clutch driving plate, a second clutch driven plate, a bearing C, a second input gear and a bearing D from the head end to the tail end, and the output shaft is sequentially provided with a bearing E, a first output gear, a second output gear and a bearing F from the head end to the tail end; the clutch assembly consists of a first clutch driven plate, a second clutch driven plate and a clutch driving plate, wherein the first clutch driven plate is fixedly connected with a first input gear, the second clutch driven plate is fixedly connected with a second input gear, the clutch driving plate is fixedly connected with the input shaft, the first input gear is meshed with a first output gear, the second input gear is meshed with a second output gear, an angle sensor is used for detecting pedal opening, and a control unit is used for acquiring the rotation speed and the gear state of a driving motor and the output shaft in real time and receiving pedal opening signals detected by the angle sensor; when the clutch driving plate is connected with the first clutch driven plate, the driving 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 two gears of the gear shifting device are in a second gear power transmission route, wherein when the clutch driving plate is connected with the second clutch driven plate, the driving 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. A gear shifting decision method based on predicted working conditions and driving styles is characterized by comprising the following specific steps: the method comprises the steps that firstly, driving styles are identified based on a fuzzy control strategy in combination with vehicle impact degree, pedal opening rate and pedal opening, and the driving styles are divided into mild types, standard types and aggressive types; Step two, constructing a Markov chain model probability output matrix, and predicting the speed of the vehicle for 2s in the future to obtain a predicted working condition; Step three, constructing a fuzzy controller based on a fuzzy control strategy, and taking the driving style and the predicted working condition as inputs and outputting to obtain a required gear coefficient; and fourthly, constructing a self-adaptive adjustment strategy based on the gear coefficient, the pedal opening rate and the vehicle acceleration, and determining a target gear. An embodiment of the step is as follows: (1) The vehicle impact degree analysis comprises the following s