KR101470120B1 - Method for maching driver's propensity to drive - Google Patents
Method for maching driver's propensity to drive Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B60W2540/00—Input parameters relating to occupants
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- B60W2540/30—Driving style
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- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
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Abstract
본 발명은 엔진 아이들 회전수 제어방법에 관한 것으로, 특정한 차량 운전자에 의해 운전되는 차량에서 평균속도가 포함된 다수의 운전정보들이 수집되어 뉴럴 알고리즘으로 적어도 1가지 이상의 학습운전성향등급으로 분류되며, 상기 학습운전성향등급이 기 설정된 설정운전성향등급과 일치 여부가 판단된 후, 서로가 일치될 때, 차량 운전자의 학습운전성향등급과 일치하는 상기 기 설정운전성향등급에 맞춰 차량의 엔진의 아이들 회전수가 제어됨으로써 차량 운전자의 다양한 운전성향에 맞춰줄 수 있고, 특히 엔진 아이들 시 연비 성능을 보다 향상시킬 수 있는 특징을 갖는다.The present invention relates to a method of controlling an engine idle speed, in which a plurality of operation information including an average speed is collected in a vehicle operated by a specific vehicle driver and classified into at least one learning operation tendency class by a neural algorithm, When it is determined whether or not the learning operation tendency class matches the predetermined set operation tendency class and there is a match between the idle rotation number of the engine of the vehicle It can be adjusted to various driving tendencies of the driver of the vehicle, and in particular, is capable of further improving the fuel efficiency in the idling state of the engine.
Description
본 발명은 차량의 아이들 회전수가 운전자의 운전성향에 따라 다단계로 선택됨으로써 운전자의 드라이빙 즐거움이 크게 향상되고, 더불어 운전자 성향에 따라 초기 기동 구동력의 증대와 차량의 연비도 증대될 수 있는 운전자 성향 자동 매칭 방법에 관한 것이다.
The present invention is characterized in that the number of idle revolutions of the vehicle is selected in multiple stages according to the driving propensity of the driver so that the driving pleasure of the driver is greatly improved and the initial driver's driving force is increased and the fuel consumption of the vehicle is increased according to the driver's propensity. ≪ / RTI >
일반적으로 엔진의 아이들 운전시에는 차량의 운전 및 차량에 필요한 기능이 안정적으로 발휘될 수 있는 목표 아이들 회전수가 고려되고, 이를 이용하여 엔진 아이들이 수행된다.Generally, during idling of the engine, the target idling speed, in which the functions of the vehicle and the vehicle can be stably exhibited, is considered, and the engine idling is performed using the target idling speed.
일례로, 목표 아이들 회전수에 따라 상대적으로 아이들 회전수가 낮으면 엔진의 연비가 향상될 수 있고, 이로부터 아이들 운전의 빈도수가 높은 시가지 주행조건에서 연비저감의 효과가 상당히 크게 된다.For example, if the idle speed is relatively low in accordance with the target idle speed, the fuel efficiency of the engine can be improved. Thus, the effect of reducing the fuel consumption is considerably increased under the urban driving conditions where the frequency of idling is high.
그러나, 아이들 회전수가 낮게 되면, 냉방 부하가 요구될 경우 엔진의 회전수가 저하될 수 있고, 특히 급격한 부하 변동을 발생하는 차량의 급가속 시 기동성이 저하되거나 심할 경우 엔진 스톨(Engine Stall)이 발생될 수 있다.However, if the idle speed is lowered, the rotation speed of the engine may be lowered when a cooling load is required. In particular, when the sudden acceleration of the vehicle causing sudden load fluctuation decreases the maneuverability or the engine stall occurs .
상기와 같이 엔진 아이들 시 발생될 수 있는 엔진의 회전수 저하나 급가속 시 기동성 저하나 엔진 스톨(Engine Stall)과 같은 현상들은 아이들 회전수(Idle rpm)가 가변적으로 제어됨으로써 해소될 수 있다.As described above, phenomena such as a decrease in the number of revolutions of the engine or a sudden acceleration of the engine during engine idling or an engine stall can be solved by variably controlling the idle rpm.
그러므로, 가변 아이들 회전수 제어는 일반화되고 있는 추세이다.Therefore, variable idle speed control is becoming a trend.
도1은 이러한 종래의 일반적인 엔진의 아이들 회전수 제어 방법의 일예를 나타낸다.Fig. 1 shows an example of a method for controlling the idle speed of such a conventional conventional engine.
도시된 바와 같이, 종래에는 엔진의 아이들 회전수를 상승시키는 제어로써, 운전자의 의도에 따라 버튼 또는 임의로 고안된 장치를 작동(S12)시킴으로써, ON-OFF 조작에 의한 아이들 회전수의 결정(S11 or S13)이 수동의 형태로 구현되었다.As shown in the drawing, in the prior art, by operating a button or an arbitrarily designed device in accordance with the intention of the driver (S12) as the control for increasing the idle speed of the engine, the determination of the idle speed by the ON- ) Was implemented in the form of a manual.
하지만, 이러한 방식에서는 운전자에 의한 아이들 회전수 변경이 이루어지려면, 운전자가 버튼 입력 또는 회전수 변경을 위한 특정 동작을 수행함으로써 불편을 초래할 수밖에 없고, 특히 아이들 회전수 변경을 위해서는 이러한 동작이 반복적으로 요구되는 블편이 있게 된다.However, in this method, in order to change the idle speed by the driver, the driver must incur inconvenience by performing a specific operation for changing the button input or the number of revolutions. In particular, in order to change the idle speed, .
뿐만 아니라, 종래의 아이들 회전수 변경에서는 운전자가 선택할 수 있는 경우가 ON-OFF에 의한 2 가지 모드에 국한됨으로써 구현 가능한 모드가 운전자의 운전 성향과 맞지 않더라도 운전자에게는 선택여지가 없고, 특히 운전자의 운전 성향이 반영되지 못함으로써 차량의 연비 또는 초기 기동 성능을 저하시킬 수도 있게 된다.In addition, in the conventional change of the idle revolution speed, the driver can select two modes by ON-OFF, so that there is no choice for the driver even if the mode that can be implemented does not match the driving propensity of the driver, It is possible that the fuel economy or the initial start performance of the vehicle may be deteriorated.
이에, 상기와 같은 점을 감안하여 발명된 본 발명은 운전자의 운전성향을 학습한 뉴럴 네트워크(NEURAL NETWORK)제어로 운전자의 운전성향이 기반되어 다단계로 제어됨으로써 운전자의 드라이빙 즐거움을 크게 향상하고, 더불어 초기 기동 구동력의 증대에도 운전자 성향에 맞춰 최적으로 대응하면서도 차량의 연비도 증대할 수 있는 운전자 성향 자동 매칭 방법을 제공하는 것을 목적으로 한다.
In view of the above, the present invention, which has been developed in view of the above, has a neural network (NEURAL NETWORK) control that learns the driving behavior of the driver, and is controlled in multiple stages based on the driving behavior of the driver, thereby greatly improving the driving pleasure of the driver, It is another object of the present invention to provide a driver-oriented automatic matching method capable of increasing the fuel consumption of a vehicle while optimally adapting to the driver tendency even in the increase of the initial startup driving force.
상기와 같은 목적을 달성하기 위한 본 발명의 운전자 성향 자동 매칭 방법에 따르면, 차량 운전자에 의해 운전되는 차량에서 적어도 평균속도가 포함된 다수의 운전정보들이 수집되는 정보수집단계와, 상기 다수의 운전정보들 각각이 뉴럴 알고리즘에 의해 최적화되어, 상기 차량 운전자의 운전성향으로 분석된 후, 적어도 한 가지 이상의 학습운전성향등급으로 분류되어 학습되는 운전성향 학습단계와, 상기 차량 운전자의 학습운전성향등급이 설정운전성향등급과 매칭되는지 비교 판단되는 운전성향 판단단계와, 상기 학습운전성향등급이 상기 설정운전성향등급과 일치되면, 상기 학습운전성향등급이 상기 설정운전성향등급으로 수정되는 운전성향결정단계와, 상기 설정운전성향등급에 맞춰 상기 차량의 엔진의 아이들 회전수가 제어되는 운전성향 적용단계로 이루어지는 운전자 성향 자동 매칭 방법이 제공된다.According to an aspect of the present invention, there is provided an automatic vehicle driver matching method comprising: an information collecting step of collecting a plurality of operation information including at least an average speed in a vehicle operated by a vehicle driver; Each of which is optimized by a neural algorithm, is analyzed as an operation propensity of the vehicle driver, and then classified into at least one or more learning operation tendency classes and learned; Determining whether the learning operation tendency class is equal to the driving tendency class; comparing the learning activity tendency class with the setting operation tendency class; Wherein the idling speed of the engine of the vehicle is controlled in accordance with the set operation propensity level Driver tendency comprising the application step is provided with the automatic matching.
상기 정보수집단계에서 상기 다수의 운전정보들에는 상기 차량의 감가속도와, 쓰로틀 포지션 센서 변화량과, 엑셀 포지션 센서 변화량과, 브레이크 작동량의 변화 및 주기 및 선행차량과의 평균거리 중의 어느 하나 이상이 더 포함되는 것을 특징으로 한다.In the information collection step, the plurality of operation information includes at least one of a depreciation rate of the vehicle, a throttle position sensor change amount, an excel position sensor change amount, a change in brake operation amount, and an average distance between the cycle and the preceding vehicle Is further included.
상기 운전성향 학습단계에서, 상기 학습운전성향등급은 스포츠 변속모드와, 상기 스포츠 변속모드에 비해 상대적으로 부드러운 운전성향과, 상기 스포츠 변속모드에 비해 상대적으로 거친 운전성향의 3 가지 모드로 구분되는 것을 특징으로 한다.In the driving tendency learning step, the learning operation tendency class is classified into a sport shift mode, a smooth driving tendency relatively as compared with the sport shift mode, and an operation tendency relatively rough compared to the sport shift mode .
상기 학습운전성향등급은 상기 스포츠 변속모드인 제3등급을 기준으로, 상기 스포츠 변속모드에 비해 부드러운 운전성향은 제1등급과, 제2등급의 두 단계로 구분되고, 상기 스포츠 변속모드에 비해 거친 운전성향은 제4등급과, 제5등급의 두 단계로 구분됨으로써, 제1등급 내지 제5등급으로 된 것을 특징으로 한다.Wherein the learning operation tendency class is classified into a first grade and a second grade based on a third grade that is the sports shift mode and a smooth operation tendency that is lower than the sport shift mode in comparison with the sport shift mode, The driving tendency is classified into the first to fifth grades by being divided into the fourth grade and the fifth grade.
상기 제3등급의 기준은 상기 스포츠 변속모드의 전환이 이루어지려는 시점인 것을 특징으로 한다.And the third grade reference is a time point when the sport shift mode is to be switched.
상기 제1등급과 상기 제2등급은 상기 제3등급의 엔진 회전수에 일정 회전수가 차감되어 결정된 것을 특징으로 한다.And the first and second grades are determined by subtracting a predetermined number of revolutions from the engine revolution number of the third grade.
상기 제4등급과 상기 제5등급은 상기 제3등급의 엔진 회전수에 일정 회전수가 더해져 결정되는 것을 특징으로 한다.And the fourth and fifth grades are determined by adding a predetermined number of revolutions to the engine revolution number of the third grade.
상기 제3등급에 더해지거나 차감되는 일정 회전수는 50 rpm인 것을 특징으로 한다.And the predetermined number of revolutions added to or subtracted from the third grade is 50 rpm.
상기 운전성향 적용단계에서, 상기 차량의 엔진 제어는 엔진의 마진 토크(Margin Torque)가 고려된 회전수로 산출되는 것을 특징으로 한다.In the driving propensity application step, the engine control of the vehicle is calculated by the number of revolutions in consideration of the margin torque of the engine.
상기 마진 토크(Margin Torque)는 상기 엔진의 스톨 토크(Stall Torque)에 일정한 마진 값(Margin Value)을 합산하여 산출된 것을 특징으로 한다.
The marginal torque is calculated by summing a certain margin value with a stall torque of the engine.
이러한 본 발명에 따른 운전자 성향 자동 매칭 방법에 의하면, 뉴럴 네트워크(Neural Network) 제어를 통해 학습된 운전자의 운전성향에 따라서 엔진의 아이들 회전수를 다단계로 제어함으로써, 차량 운전자의 다양한 운전성향에 맞춰줄 수 있고, 특히 엔진 아이들 시 연비 성능을 보다 향상시킬 수 있는 효과가 있다.According to the driver tendency automatic matching method of the present invention, the idle speed of the engine is controlled in multiple stages in accordance with the driving tendency of the driver learned through the neural network control, There is an effect that the fuel efficiency of the engine idling can be further improved.
또한, 본 발명에 따른 엔진 아이들 회전수 제어방법에 의하면, 아이들 회전수 결정시 차량의 스톨 토크(Stall Torque)와 마진 토크(Margin Torque)를 적용함으로써, 엔진의 스톨이 방지될 수 있다.
According to the engine idle speed control method of the present invention, the stall of the engine can be prevented by applying the stall torque and the marginal torque of the vehicle when determining the idle speed.
도1은 종래의 아이들 회전수 제어방법을 나타낸 순서도.
도2는 본 실시예에 의한 아이들 회전수 제어를 위한 구성을 나타낸 블록도.
도3은 본 실시예에 의한 아이들 회전수 제어의 흐름을 나타낸 순서도.
도4는 본 실시예에 의한 아이들 회전수 결정의 기준이 되는 기설정된 회전수 등급을 나타낸 표.
도5는 도4에 표시된 회전수 등급과 차량 성능에 대한 관계를 나타낸 그래프.1 is a flowchart showing a conventional idle speed control method.
2 is a block diagram showing a configuration for idle speed control according to this embodiment;
3 is a flowchart showing the flow of idle speed control according to the present embodiment.
FIG. 4 is a table showing a predetermined rotation rate grade serving as a reference of the idle rotation number determination according to the present embodiment.
5 is a graph showing the relation between the number of revolutions shown in FIG. 4 and vehicle performance.
이하 본 발명의 실시예를 첨부된 예시도면을 참조로 상세히 설명하며, 이러한 실시예는 일례로서 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자가 여러 가지 상이한 형태로 구현될 수 있으므로, 여기에서 설명하는 실시예에 한정되지 않는다.Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, which illustrate exemplary embodiments of the present invention. The present invention is not limited to these embodiments.
도2는 본 실시예에 의한 운전자 성향 자동 매칭 방법을 위한 기본 구성을 블록화하여 나타낸 것이다.2 is a block diagram showing a basic configuration for a driver tendency automatic matching method according to the present embodiment.
도시된 바와 같이, 본 발명에 의한 엔진 아이들 회전수 제어에 필요한 운전정보를 수집하기 위한 수단으로써, 차속센서(11)와, 가속도 센서(12)와, 쓰로틀 포지션 센서(13)와, 액셀 포지션 센서(14)와, 브레이크 센서(15) 및 차간 거리센서(16)가 구성된다.As shown in the figure, the
상기 차속센서(11)와, 가속도 센서(12)와, 쓰로틀 포지션 센서(13)와, 액셀 포지션 센서(14)와, 브레이크 센서(15) 및 차간 거리센서(16)에 의해 측정된 각각의 신호는 엔진 컨트롤 유닛(ECU; 20)으로 전송되어, 차량의 평균속도 및 감가속도와, 엔진의 쓰로틀 밸브 및 액셀 페달의 변화량과, 브레이크 페달의 작동 변화 및 그 주기와, 선행차량과의 평균 거리를 산출하여, 이를 통해 운전자의 운전성향등급을 학습하는데 사용한다.The
상기 엔진 컨트롤 유닛(ECU; 20)은 상기 운전정보를 분석하여 운전자의 운전성향을 학습하는 뉴럴 네트워크 제어부(21)를 포함하고, 상기 뉴럴 네트워크 제어부(21)에 의해 분석된 현재 운전성향을 매칭하여 엔진의 아이들 회전수(Idle rpm)를 결정할 수 있도록 하는 운전성향등급이 설정된 운전성향등급맵(22)을 포함한다.The engine control unit (ECU) 20 includes a neural
그리고, 상기 엔진 컨트롤 유닛(ECU; 20)은 엔진의 아이들 회전수(Idle rpm)를 산출하여 아이들 스피드 컨트롤(ISC) 구동부(30)를 제어하여 엔진의 아이들 회전수(Idle rpm)를 제어한다.The engine control unit (ECU) 20 calculates the idle speed of the engine and controls the idle speed control (ISC)
도3은 본 실시예에 의한 운전자 성향 자동 매칭 방법의 흐름을 나타낸 것이다.FIG. 3 shows a flow of a driver tendency automatic matching method according to the present embodiment.
도시된 바와 같이, 본 발명의 제어가 시작되면, 차량의 운전성향을 파악하기 위한 운전정보를 수집하는 정보수집단계가 이루어진다(S51).As shown in the figure, when the control of the present invention is started, an information collecting step of collecting driving information for understanding driving behavior of the vehicle is performed (S51).
상기 정보수집단계는 특정한 차량의 운전자에 의해 운영되는 차량에서 적어도 평균속도가 포함된 다수의 운전정보들이 수집되며, 상기 다수의 운전정보들에는 차량의 감가속도와, 쓰로틀 포지션 센서 변화량과, 엑셀 포지션 센서 변화량과, 브레이크 작동량의 변화 및 주기 및 선행차량과의 평균거리가 포함될 수 있다.The information collection step collects a plurality of pieces of operation information including at least an average speed in a vehicle operated by a driver of a specific vehicle. The plurality of pieces of operation information include a depression rate of the vehicle, a throttle position sensor change amount, The sensor change amount, the change in the brake operation amount, and the average distance between the cycle and the preceding vehicle.
그리고, 상기 수집된 정보를 뉴럴 네트워크(Neural Network) 제어를 통해 분석하여 운전자의 운전성향을 학습하는 단계를 실행한다(S52).Then, the collected information is analyzed through a neural network control to learn the driving behavior of the driver (S52).
상기 운전성향 학습단계는 상기 정보 수집단계에서 수집된 다수의 운전정보들이 각각 뉴럴 알고리즘에 의해 최적화되고, 상기 특정한 차량 운전자의 운전성향으로 분석하여, 적어도 1가지 이상의 학습운전성향등급으로 분류되어 학습이 이루어진다.Wherein the driving tendency learning step comprises: a plurality of driving information collected in the information collecting step is optimized by a neural algorithm, analyzed by the driving propensity of the specific vehicle driver, classified into at least one learning driving tendency class, .
또한, 상기 운전성향 학습단계에서 분류되어 학습된 상기 차량 운전자의 학습운전성향등급이 기 설정된 임의의 차량 운전자의 적어도 1개 이상의 설정등급분류에 매칭되는지 여부가 비교 판단되는 운전성향 판단단계를 포함한다.In addition, the driving characteristic determining step includes a driving characteristic determining step in which it is determined whether or not the learning driving tendency class of the vehicle driver classified and learned in the driving tendency learning step is matched with at least one setting class classification of a predetermined vehicle driver .
상기 운전성향 학습단계에서, 상기 학습운전성향등급은 스포츠 변속모드와, 상기 스포츠 변속모드에 비해 부드러운 운전성향과, 상기 스포츠 변속모드에 비해 거친 운전성향의 3 가지 모드로 설정된다.In the driving propensity learning step, the learning operation propensity level is set to three modes: a sport shift mode, a smooth operation propensity in comparison with the sport speed change mode, and a rough propensity to operate in comparison with the sports speed change mode.
그리고, 상기 운전성향 학습단계에서 매칭이 되는 설정운전성향등급이 상기 차량 운전자의 학습운전성향등급과 일치되면 운전성향 학습을 완료한다(S53).If the set driving behavior propensity that matches in the driving propensity learning step matches the learning driving propensity of the driver, the driving propensity learning is completed (S53).
상기 뉴럴 네트워크에 의한 학습이 완료되면, 운전성향 결정단계로 진행하고, 학습이 완료되지 않은 경우에는 S51 단계로 리턴한다. When learning by the neural network is completed, the process proceeds to the driving propensity determination step, and when learning is not completed, the process returns to step S51.
상기 운전성향 결정단계는 상기 설정운전성향등급을 분석하여 상기 차량 운전자의 학습운전성향등급과 일치되면, 상기 일치되는 운전성향등급이 엔진 아이들 회전수 제어를 위한 등급으로 대체된다(S54).The operation propensity determining step analyzes the set operation propensity level, and if the operating propensity level matches the learning operation propensity level of the vehicle driver, the operation propensity level is replaced with a grade for controlling the engine idle speed (S54).
그 다음, 상기 대체된 차량 운전자의 운전성향등급에 맞춰 상기 차량의 엔진이 제어되는 운전자의 운전성향 적용단계를 실행한다.Then, the driving behavior applying step of the driver whose engine of the vehicle is controlled is executed according to the driving propensity level of the replaced vehicle driver.
한편, 상기 운전성향 적용단계에서의 아이들 회전수는 상기 대체된 차량 운전자의 운전성향등급에 따른 수정 아이들 토크에 엔진의 마진 토크(Margin Torque)가 함께 고려된다(S55).Meanwhile, the idle revolutions of the driving tendency application step are considered together with the corrected idle torque according to the driving tendency of the replaced vehicle driver (S55).
상기 마진 토크(Margin Torque)는 엔진의 스톨 토크(Stall Torque)에 일정한 마진 값(Margin Value)을 합산하여 산출된다.The marginal torque is calculated by summing a certain margin value with the stall torque of the engine.
상기 마진 토크(Margin Torque)에 해당하는 아이들 회전수를 상기 운전성향결정단계에서 대체된 차량 운전자의 운전성향등급에 해당하는 수정 아이들 토크에 합산하여, 상기 합산된 아이들 토크에 따르는 회전수로 엔진을 제어한다(S56).An idle speed corresponding to the margin torque is added to a corrected idle torque corresponding to an operation propensity level of the vehicle driver replaced in the operation disposition determining step, (S56).
도 4는 본 실시예에서 비교 기준이 되는 기 설정된 운전자성향등급을 나타낸다.FIG. 4 shows a predetermined driver tendency class which is a comparison criterion in this embodiment.
도시된 바와 같이, 상기 운전성향 학습단계에서 기준이 되는 상기 학습운전성향등급은 상기 스포츠 변속모드인 제3등급을 기준으로, 상기 스포츠 변속모드에 비해 부드러운 운전성향은 제1등급과, 제2등급의 두 단계로 구분되고, 상기 스포츠 변속모드에 비해 거친 운전성향은 제4등급과, 제5등급의 두 단계로 구분됨으로써, 제1등급 내지 제5등급으로 설정될 수 있다.As shown in the figure, the learning operation tendency grade, which is a reference in the driving tendency learning step, is based on the third class, which is the sports shift mode, and the smooth driving tendency is lower than the first, And the rough driving tendency as compared with the sport shift mode is divided into the first to fifth grades by being divided into the fourth grade and the fifth grade.
이때, 상기 스포츠 변속모드의 전환이 이루어지려는 시점이 기준이 되어 스포츠 변속모드로 구분되고, 상기 스포츠 변속모드의 엔진 회전수에 일정한 회전수가 더해져 상기 상대적으로 거친(Wild) 운전성향으로 구분되고, 상기 스포츠 변속모드의 엔진 회전수에 일정한 회전수가 차감되어져 상기 상대적으로 부드러운(Mild) 운전성향의 3가지 모드로 구분된다.At this time, a time point when the sport shift mode is to be switched is regarded as a reference and classified into a sport shift mode, a certain number of revolutions is added to the engine speed of the sport shift mode, The engine speed of the sport shift mode is reduced by a predetermined number of revolutions, and the relatively smooth (Mild) driving tendency is classified into three modes.
이때, 상기 일정한 회전수의 더함 또는 차감은 상기 엔진의 50 rpm으로 정해진다.At this time, the addition or subtraction of the constant number of revolutions is set to 50 rpm of the engine.
도5는 도4에 나타낸 회전수 등급과 차량 성능에 대한 관계를 나타낸 그래프이다.5 is a graph showing the relationship between the number of revolutions shown in FIG. 4 and vehicle performance.
도시된 바와 같이, 노멀 등급(3 등급)을 기준으로 운전성향이 거친(Wild) 성향인 4 등급 및 5 등급에서는 차량의 초기 구동력의 확보에 유리하고, 부드러운(Mild) 성향인 1 등급 및 2 등급에서는 연비에 유리한 특징이 있다.As shown in the figure, in the fourth grade and the fifth grade, which tend to be wild in the driving tendency based on the normal grade (grade 3), it is advantageous in securing the initial driving force of the vehicle and the grade 1 and
따라서, 상기한 본 발명에 따른 운전자 성향 자동 매칭 방법에 의하면, 운전자가 부드럽고(Mild), 저속 및 저가속 위주의 운전성향을 나타내는 경우에는 아이들 회전수(Idle rpm)를 줄여 연비를 높일 수 있도록 하고, 운전자가 거칠고(Wild), 고속 및 고가속 위주의 운전성향을 나타내는 경우에는 아이들 회전수(Idle rpm)를 높여 충분한 초기 기동력을 확보할 수 있도록 다단계로 제어가 가능하다.Therefore, according to the driver tendency automatic matching method according to the present invention, when the driver exhibits a tendency to drive smoothly (Mild), low speed, and low speed, the idle rpm can be reduced to increase the fuel efficiency , The driver is rough, and the driving tendency is focused on high speed and high speed, it is possible to control the multi-stage so as to secure a sufficient initial driving force by raising the idle speed (Idle rpm).
뿐만 아니라, 운전자의 성향에 맞는 기동을 실시 함으로써, 경제성과 운전성을 동시에 만족시킬 수 있으며, 아이들 회전수 결정시 마진 토크(Margin Torque)와 스톨 토크(Stall Torque)를 함께 고려함으로써, 엔진의 스톨(Stall)이 방지될 수 있는 효과가 있다.
In addition, it is possible to satisfy both the economical efficiency and the driving performance simultaneously by performing the maneuver according to the driver's tendency, and by considering the marginal torque and the stall torque when determining the idle speed, (Stall) can be prevented.
11: 차속센서 12: 가속도 센서
13: 쓰로틀 포지션 센서 14: 액셀 포지션 센서
15: 브레이크 센서 16: 차간 거리센서
20: ECU 21: 뉴럴 네트워크 제어부
22: 운전성향 등급맵 30: ISC 구동부11: vehicle speed sensor 12: acceleration sensor
13: Throttle position sensor 14: Acceleration position sensor
15: Brake sensor 16: Inter-vehicle distance sensor
20: ECU 21: Neural network control unit
22: Driving propensity level map 30: ISC driving part
Claims (10)
상기 다수의 운전정보들 각각이 뉴럴 알고리즘에 의해 최적화되어, 상기 차량 운전자의 운전성향으로 분석된 후, 적어도 한 가지 이상의 학습운전성향등급으로 분류되어 학습되는 운전성향 학습단계;
상기 차량 운전자의 학습운전성향등급이 설정운전성향등급과 매칭되는지 비교 판단되는 운전성향 판단단계;
상기 학습운전성향등급이 상기 설정운전성향등급과 일치되면, 상기 학습운전성향등급이 상기 설정운전성향등급으로 수정되는 운전성향결정단계;
상기 설정운전성향등급에 맞춰 상기 차량의 엔진의 아이들 회전수가 제어되는 운전성향 적용단계;가 포함되어 수행되며,
상기 운전성향 학습단계에서, 상기 학습운전성향등급은 스포츠 변속모드와, 상기 스포츠 변속모드에 비해 부드러운 운전성향과, 상기 스포츠 변속모드에 비해 거친 운전성향의 3 가지 모드로 구분되는 것을 특징으로 하는 운전자 성향 자동 매칭 방법.
An information collecting step of collecting a plurality of operation information including at least an average speed in a vehicle operated by a vehicle driver;
An operation propensity learning step in which each of the plurality of operation information is optimized by a neural algorithm, analyzed as an operating propensity of the vehicle driver, and then classified and learned by at least one learning operation propensity level;
Determining whether the vehicle driver's learning driving tendency class is matched with the set driving tendency class;
A driving propensity determining step of, when the learning operation propensity level coincides with the setting operation propensity level, the learning operation propensity level being corrected to the setting operation propensity level;
And an operation propensity application step in which the number of idle revolutions of the engine of the vehicle is controlled in accordance with the set operation propensity level,
Wherein the learning operation tendency class is classified into three modes: a sport shift mode, a smooth operation tendency as compared with the sport shift mode, and a rough driving tendency as compared with the sport shift mode. Inclination automatic matching method.
The method according to claim 1, wherein in the information collection step, the plurality of operation information includes at least one of a depression rate of the vehicle, a throttle position sensor change amount, an excel position sensor change amount, And the distance between the driver and the driver.
2. The method of claim 1, wherein the learning operation tendency class is classified into a first class and a second class based on a third class, which is the sports shift mode, Wherein the rough driving tendency is divided into two grades of a fourth grade and a fifth grade as compared with the sport shift mode, and thus the first to fifth grades are classified.
The method as claimed in claim 4, wherein the third criterion is a time when the shift of the sport shift mode is to be performed.
6. The method according to claim 5, wherein the first and second grades are determined by subtracting a predetermined number of revolutions from the engine revolution number of the third grade.
The method according to claim 5, wherein the fourth and fifth grades are determined by adding a certain number of revolutions to the engine rotational speed of the third grade.
The method according to claim 6 or 7, wherein the predetermined number of rotations added to or subtracted from the third grade is 50 rpm.
The method according to claim 1, wherein, in the driving preference application step, the engine control of the vehicle is calculated by the number of revolutions considering margin torque of the engine.
The method according to claim 9, wherein the margin torque is calculated by summing a certain margin value with a stall torque of the engine.
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