KR20140026068A - Method for predicting distance to empty of electric vehicle - Google Patents
Method for predicting distance to empty of electric vehicle Download PDFInfo
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- KR20140026068A KR20140026068A KR1020120093009A KR20120093009A KR20140026068A KR 20140026068 A KR20140026068 A KR 20140026068A KR 1020120093009 A KR1020120093009 A KR 1020120093009A KR 20120093009 A KR20120093009 A KR 20120093009A KR 20140026068 A KR20140026068 A KR 20140026068A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/545—Temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
- B60Y2200/00—Type of vehicle
- B60Y2200/90—Vehicles comprising electric prime movers
- B60Y2200/91—Electric vehicles
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
Description
본 발명은 운행 시작부터 끝까지 더욱 정확한 잔존주행거리 정보를 제공할 수 있는 전기자동차의 잔존주행거리 예측 방법에 관한 것이다.
The present invention relates to a method of predicting the remaining driving distance of an electric vehicle that can provide more accurate remaining driving distance information from the start to the end of the operation.
최근 들어 가솔린, 디젤, LPG 등의 연료를 동력원으로 사용하는 자동차에는 중요한 차량 운행 정보 중 하나로서 연료량에 따라 주행가능한 잔존주행거리(Distance to empty: DTE)에 대한 정보를 운전자에게 알려줌으로써 사용자에게 편의를 제공하고 있다.Recently, as a vehicle that uses fuels such as gasoline, diesel, and LPG as a power source, one of the important vehicle driving information is to provide the driver with information on the distance to empty (DTE) that can be driven according to the amount of fuel. Providing.
특히, 배터리에 저장된 전기에너지를 동력원으로 사용하는 순수 전기자동차는 가솔린 등을 주동력원으로 사용하는 일반 차량과 달리 배터리의 잔존 충전량에만 의존하여 주행하여야 하므로, 잔존주행거리 정보는 운전자에게 매우 중요한 정보이고, 그 정확도가 매우 중시되는 항목 중 하나이다.In particular, pure electric vehicles that use the electric energy stored in the battery as a power source, unlike general vehicles that use gasoline or the like as a main power source, must travel only on the remaining charge of the battery, so the remaining mileage information is very important for the driver. This is one of the items where accuracy is very important.
상기 잔존주행거리는 운전자의 성향에 따라 크게 변동되며, 운전자의 성향은 측정하는 것이 불가능하므로 예측만이 가능한 정보이다.The remaining driving distance varies greatly depending on the driver's propensity, and since the driver's propensity cannot be measured, it is only information that can be predicted.
따라서, 전기자동차의 잔존주행거리의 정확한 예측을 위하여, 배터리 잔존 충전량의 추정 기술이 우선 선행되어야 하고, 이후에 정확한 잔존주행거리 예측 방법이 요구된다.Therefore, in order to accurately predict the remaining driving distance of the electric vehicle, a technique for estimating the remaining battery charge must be preceded first, and then an accurate remaining driving distance prediction method is required.
한편, 상기 잔존주행거리의 예측 시 운전자의 운전 성향을 파악하기 위해 누적연비(여기서는 km/SOC)을 측정하게 되는데, 전기자동차에서 배터리의 누적연비를 측정함에 있어서 누적 시간이 늘어날수록 누적연비 측정에 대한 정확도가 증대되지만, 운행 초기에는 일반 차량에서와 같은 공인 연비와 주행 직전 학습 연비 등에 대한 실예가 존재하지 않아 누적연비 측정에 대한 오차가 크게 발생하여 잔존주행거리 정보에 대한 초기 오차 문제가 있었다.Meanwhile, the cumulative fuel economy (here, km / SOC) is measured to determine the driver's driving tendency when predicting the remaining mileage. In the electric vehicle, the cumulative fuel consumption is measured as the cumulative time increases. Although the accuracy is increased in the early stage of driving, there are no examples of certified fuel economy and learning fuel efficiency just before driving, and there is a large error in the measurement of cumulative fuel consumption, resulting in an initial error problem of the remaining mileage information.
이와 같은 문제점을 해결하기 위해 본 출원인이 특허출원하여 등록받은 특허발명으로서, 등록특허 제1154307호에는 전기자동차의 운행 초기에 주행 직전(충전 직전)의 학습 누적연비와 현재 주행중인 실시간 누적연비 간의 반영 비율을 상대적으로 변화시켜 잔존주행거리를 예측함으로써, 운행 초기 시 이전 주행패턴과 현재 주행패턴간의 초기 오차를 줄일 수 있는 전기자동차의 잔존주행거리 적응형 초기값 설정 장치 및 방법이 개시되어 있다.In order to solve such a problem, the present invention is a patent application registered and applied for by the present applicant, and Patent No. 1154307 reflects the cumulative fuel efficiency of learning immediately before driving (just before charging) and the real-time cumulative fuel consumption that is currently driving. Disclosed is an apparatus and method for setting an adaptive initial value of a residual driving distance of an electric vehicle that can reduce an initial error between a previous driving pattern and a current driving pattern at the beginning of driving by predicting a remaining driving distance by relatively changing a ratio.
그러나, 상기 특허의 경우 배터리의 초기 온도를 고려하지 않아 운행 초기의 잔존주행거리 예측 오차를 줄이는 효과가 감소되는 문제점이 있다.However, the patent does not consider the initial temperature of the battery, there is a problem that the effect of reducing the residual mileage prediction error during the initial operation is reduced.
예를 들면, 배터리는 초기 온도에 따라 도 1에 도시한 바와 같이 에너지량의 차이가 발생하여 겨울철에 외기온이 상온인 상태에서 주행한 이후 배터리를 충전하고, 밤새 영하의 외기 온도에서 방치하게 되면 배터리 온도가 상온일 때와의 에너지 차이로 인해 과거 학습 누적연비에 의한 잔존주행거리 예측값과 실주행거리간의 편차가 발생하는 문제점이 있다.
For example, as shown in Figure 1 according to the initial temperature, the battery is a difference in the amount of energy occurs in the winter when the ambient temperature is running at room temperature after charging the battery, if the battery is left at minus outside temperature overnight Due to the energy difference between the temperature and the room temperature, there is a problem that a deviation occurs between the predicted remaining distance and the actual running distance due to past learning cumulative fuel economy.
본 발명은 상기한 문제점을 해결하기 위해 발명한 것으로서, 배터리의 잔존 배터리용량으로 주행가능한 잔존주행거리를 예측할 때 배터리의 초기 온도를 반영함으로써, 기존의 배터리 초기 온도에 따른 운행 초기의 잔존주행거리 예측 오차 문제를 해소할 수 있는 전기자동차의 잔존주행거리 예측 방법을 제공하는데 그 목적이 있다.
The present invention has been invented to solve the above-mentioned problems, and reflects the initial temperature of the battery when estimating the remaining mileage that can be driven by the remaining battery capacity of the battery, thereby predicting the remaining mileage at the beginning of operation according to the existing battery initial temperature. The purpose of the present invention is to provide a method for estimating the remaining driving distance of an electric vehicle that can solve the error problem.
상기 목적을 달성하기 위하여 본 발명에 따른 전기자동차의 잔존주행거리 예측 방법은 차량 시동 시 배터리의 초기 온도를 측정하는 단계; 상기 단계에서 측정된 배터리의 초기 온도 입력에 따른 주행 초기 온도별 학습연비를 이미 저장된 온도별 연비 테이블로부터 선택적으로 사용하는 단계; 주행 중 누적연비를 실시간으로 계산하여 저장하는 단계; 상기 저장된 배터리의 초기 온도 입력에 따른 주행 초기 온도별 학습연비와 주행 중 누적연비를 상대 비율로 조합하여 최종 누적연비를 계산하는 단계; 및 상기 최종 누적연비를 이용하여 잔존주행거리 초기값을 설정하는 단계;로 이루어지고, 잔존주행거리 예측 시 배터리의 초기 온도를 반영하여 초기 예측오차를 방지할 수 있는 것을 특징으로 한다.In order to achieve the above object, the remaining driving distance prediction method of an electric vehicle according to the present invention comprises the steps of measuring the initial temperature of the battery when the vehicle starts; Selectively using the learning fuel efficiency for each driving initial temperature according to the initial temperature input of the battery measured in the step from a previously stored temperature-specific fuel economy table; Calculating and storing the cumulative fuel economy while driving in real time; Calculating a final cumulative fuel economy by combining learning fuel economy by driving initial temperature according to an initial temperature input of the stored battery and cumulative fuel economy while driving at a relative ratio; And setting an initial residual distance by using the final cumulative fuel economy. The initial estimated error may be prevented by reflecting an initial temperature of the battery when predicting the residual traveling distance.
상기 주행 초기 온도별 학습연비는 기 저장된 초기 온도 입력에 따른 테이블 값으로부터 사용할 수 있도록 된 것을 특징으로 한다.
The learning fuel economy for each driving initial temperature may be used from a table value according to a pre-stored initial temperature input.
본 발명에 따른 전기자동차의 잔존주행거리 예측 방법의 장점을 설명하면 다음과 같다.The advantages of the method for predicting the remaining driving distance of an electric vehicle according to the present invention are as follows.
첫째로, 누적연비를 통해 운전자의 성향을 실시간으로 적용하여 전기자동차의 잔존주행거리를 예측함에 있어서 차량 시동 시 배터리의 초기 온도 입력에 따른 주행 초기 온도별 학습연비 테이블로부터 초기 학습누적연비를 사용함으로써, 충전 직전과 차량 시동 시 배터리 초기 온도의 차이로 인하여 과거 학습누적연비에 의한 주행 거리 예측값과 실 주행거리 간의 편차가 발생하지 않아 잔존주행거리 예측의 정확성을 극대화시킬 수 있다.First, by using the cumulative fuel economy to estimate the driving distance of the electric vehicle by applying the driver's propensity in real time, by using the initial learning accumulation fuel consumption from the learning fuel consumption table for each initial temperature according to the initial temperature input of the battery when the vehicle starts. Therefore, due to the difference in the initial temperature of the battery immediately before charging and when the vehicle is started, the deviation between the mileage prediction value and the actual mileage due to the past accumulated accumulation fuel does not occur, thereby maximizing the accuracy of the remaining mileage prediction.
둘째로, 배터리 충전에서 자유롭지 못한 운전자들에게 전기자동차의 정확한 사용가능정보를 운행 초기부터 제공함으로써 차량에 대한 신뢰도를 증가시켜 구매 의용 및 다양한 IT 정보기기와의 융합으로 편리성을 제공할 수 있다.
Second, by providing accurate usable information of electric vehicles from the beginning of operation to drivers who are not free from battery charging, it is possible to provide convenience by increasing the reliability of the vehicle and fusion with various IT information devices.
도 1은 배터리의 온도별 배터리 에너지를 보여주는 그래프
도 2는 본 발명의 일실시예에 따른 배터리 초기 온도에 따른 전기차 잔존주행거리 초기값 설정 방법을 보여주는 순서도
도 3은 본 발명의 일실시예에 따른 주행 초기 온도별 연비 학습 실시예를 보여주는 순서도1 is a graph showing battery energy by temperature of a battery
2 is a flowchart illustrating a method for setting an initial value of an electric vehicle remaining distance according to an initial temperature of a battery according to an exemplary embodiment of the present invention.
Figure 3 is a flow chart showing an embodiment of the fuel economy learning according to the initial temperature driving in accordance with an embodiment of the present invention
이하, 첨부한 도면을 참조하여 본 발명의 바람직한 실시예를 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자가 용이하게 실시할 수 있도록 상세하게 설명하기로 한다.Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention.
본 발명은 전기자동차의 배터리의 잔존 배터리 충전용량으로 주행 가능한 거리를 예측함에 있어서 초기 오차를 개선하여 운행 시작부터 끝까지 더욱 정확한 잔존주행거리를 예측할 수 있는 전기자동차의 잔존주행거리 예측 방법에 관한 것이다.The present invention relates to a method for estimating the remaining mileage of an electric vehicle that can predict a more accurate remaining mileage from the start to the end by improving the initial error in estimating the distance that can be driven by the remaining battery charge capacity of the battery of the electric vehicle.
여기서, 본 발명은 본 출원인이 출원한 제1154307호에 근거하여 충전 직전 학습연비와 현재 주행 중 누적연비를 계산할 수 있고, 다만 상기 등록특허에서 충전 직전 학습연비 계산 시 배터리 초기온도를 반영한 점에 특징이 있다.Here, the present invention can calculate the learning fuel consumption immediately before charging and the cumulative fuel consumption during the current driving, based on the No. 1154307 filed by the present applicant, but the characteristics of the point that reflects the initial temperature of the battery when calculating the learning fuel consumption immediately before charging There is this.
본 발명에 따른 전기자동차의 잔존주행거리 예측 방법은 누적연비를 통해 운전자의 성향을 실시간 학습(파악)하여 전기차의 잔존주행거리를 예측한다.In the method of estimating the remaining driving distance of an electric vehicle according to the present invention, the driver's propensity is learned in real time through the cumulative fuel efficiency to predict the remaining driving distance of the electric vehicle.
상기 누적연비의 계산방법은 1초 동안 배터리 충전량(SOC) 변화량과 1초 동안 실제 이동거리 변화량을 매초 마다 누적하여 계산된다. The method of calculating the cumulative fuel economy is calculated by accumulating the SOC change amount for 1 second and the actual movement distance change amount for 1 second.
여기서, 본 발명은 전기자동차의 잔존주행거리를 예측함에 있어 초기 오차를 개선하기 위해 배터리 초기 온도에 따른 주행 초기 온도별 학습연비를 별도의 저장장치(EEPROM)에 저장하고, 차량 시동 시 배터리의 초기온도 입력에 따른 주행 초기 온도별 학습연비와 현재 주행중 누적연비의 반영비율을 상대적으로 변화시켜 최대한 이전 주행패턴과 현재 주행패턴의 괴리와 오차를 줄일 수 있다.Here, the present invention stores the learning fuel consumption for each initial driving temperature according to the initial battery temperature in a separate storage device (EEPROM) in order to improve the initial error in estimating the remaining driving distance of the electric vehicle, the initial of the battery at the start of the vehicle It is possible to reduce the deviation and error between the previous driving pattern and the current driving pattern as much as possible by changing the learning fuel efficiency by the initial temperature for driving and the reflecting ratio of the cumulative fuel consumption during the current driving.
상기 배터리 초기 온도에 따른 주행 초기 온도별 학습 연비는 배터리 초기 온도에 따라 별도의 메모리에 테이블값으로 주행 전에 저장되며, 주행 중 및 주행 완료 후에도 온도별 학습 연비를 상기 메모리에 저장하여 기존의 테이블값을 업데이트시킴으로써, 차량 시동 시 배터리 초기 온도에 따른 잔존주행거리 예측시 배터리의 초기 온도를 반영할 수 있다.The learning fuel economy by driving initial temperature according to the battery initial temperature is stored before driving as a table value in a separate memory according to the battery initial temperature, and the learning fuel efficiency by temperature is stored in the memory even during driving and after driving is completed. By updating the, it is possible to reflect the initial temperature of the battery when predicting the remaining driving distance according to the initial temperature of the battery when the vehicle starts.
본 발명의 일실시예에 따른 전기자동차의 잔존주행거리 예측 방법을 설명하기로 한다.A method of predicting the remaining driving distance of an electric vehicle according to an embodiment of the present invention will be described.
첨부한 도 2는 본 발명의 일실시예에 따른 배터리 초기 온도에 따른 전기차 잔존주행거리 초기값 설정 방법을 보여주는 순서도이고, 도 3은 본 발명의 일실시예에 따른 주행 초기 온도별 연비 학습 실시예를 보여주는 순서도이다.2 is a flowchart illustrating a method of setting an initial value of an electric vehicle remaining driving distance according to an initial temperature of a battery according to an embodiment of the present invention, and FIG. 3 is a fuel efficiency learning example of driving initial temperature according to an embodiment of the present invention. Is a flowchart showing.
미리 배터리 초기 온도에 따른 주행 초기 온도별 학습연비를 별도의 메모리에 저장해 둔다.In advance, the fuel economy for each initial driving temperature according to the initial battery temperature is stored in a separate memory.
상기 메모리에 저장된 테이블에는 온도별로 연비가 각각 저장되어 있으며, 차량 시동시 배터리 온도센서를 이용하여 배터리의 초기온도를 측정하고, 배터리제어부는 배터리 온도센서를 통해 감지된 배터리 초기온도에 따라 테이블에 저장된 값들 중에서 어느 하나를 선택하여 초기 학습연비로 설정한다.Fuel consumption is stored for each temperature in the table stored in the memory, and when the vehicle is started, the initial temperature of the battery is measured by using a battery temperature sensor, and the battery controller is stored in the table according to the initial temperature of the battery detected by the battery temperature sensor. Select one of the values and set it as the initial learning fuel economy.
그 다음, 주행 중 누적연비를 학습한다.Then, learn cumulative fuel economy while driving.
주행 중 누적연비는 1초 동안 SOC 변화량과 1초 동안 실제 이동거리 변화량을 매초마다 누적하여 계산한다.The cumulative fuel economy during driving is calculated by accumulating SOC variation for 1 second and actual movement distance variation for 1 second.
차량 시동시 배터리 초기온도 입력에 따른 초기 학습연비와 주행중 누적연비를 상대적 비율, 즉 SOC가 1% 변동될 때마다 상대적으로 9:1,8:2,…0:10 으로 변화시켜 최종 누적연비를 계산한다.The relative ratio of the initial learning fuel consumption and the accumulated fuel consumption during driving according to the initial battery temperature input at the start of the vehicle, that is, when the SOC changes by 1%, is relatively 9: 1,8: 2,... Change to 0:10 to calculate the final cumulative fuel economy.
예를 들어 차량 시동 시 누적 SOC 량에서 10% 소모되는 시점까지 1% 변동될 때마다 초기 학습연비에 학습연비 비율(Gain1)을 곱하고 현재 주행중 누적연비에 누적연비 비율(Gain2)을 곱한 후, 상기 상대적 비율로 초기 학습연비와 현재 주행중 누적연비를 조합하여 최종 누적연비를 계산한다.For example, every 1% change from the cumulative SOC amount to the point at which 10% is consumed when the vehicle is started, multiplies the initial fuel economy by the learning fuel ratio (Gain1) and multiplies the current fuel accumulation by the cumulative fuel ratio (Gain2). The final cumulative fuel economy is calculated by combining the initial learning fuel economy with the cumulative fuel economy while driving.
다시 말해서, 차량 시동 시 누적 SOC 량이 80%인 상태에서 79%로 변동될 때 주행 초기 온도별 학습연비에 9를 곱하고, 현재 주행중 누적연비에 1을 곱하여, 이와 같은 9:1의 비율로 주행 초기 온도별 학습연비와 현재 주행중 누적연비를 조합하여 최종 누적연비를 계산한다.In other words, when the cumulative SOC amount at the start of the vehicle changes from 79% to 79%, the learning fuel efficiency for each initial driving temperature is multiplied by 9, and the current fuel accumulation during driving is multiplied by 1, such that the initial driving temperature is 9: 1. The final cumulative fuel economy is calculated by combining the learning fuel economy of each star with the current fuel accumulation.
계속해서, 상기 누적 SOC 량이 79%에서 78%로 변동될 때 주행 초기 온도별 학습연비에 8을 곱하고, 현재 주행중 누적연비에 2를 곱하여, 8:2의 비율로 주행 초기 온도별 학습연비와 현재 주행중 누적연비를 조합하여 최종 누적연비를 계산한다.Subsequently, when the cumulative SOC amount varies from 79% to 78%, the learning fuel efficiency by driving initial temperature is multiplied by 8 and the cumulative fuel consumption by current is multiplied by 2, and the learning fuel consumption by driving initial temperature and current driving are at a ratio of 8: 2. The final cumulative fuel economy is calculated by combining the cumulative fuel economy.
이와 같이 상기 누적 SOC 량이 1% 변동될 때마다 주행 초기 온도별 학습연비의 상대비율은 10에서 0까지 1씩 감소시키고, 현재 주행중 누적연비의 상대비율은 0에서 10까지 1씩 증대시키는 방식으로 주행 초기 온도별 학습연비와 현재 주행중 누적연비를 상대적으로 변화시켜 최종 누적 연비를 계산한다.As described above, whenever the cumulative SOC amount fluctuates by 1%, the relative ratio of learning fuel economy by initial driving temperature decreases by 1 from 10 to 0, and the relative ratio of cumulative fuel consumption during current driving increases by 1 from 0 to 10. The final cumulative fuel economy is calculated by changing the learning fuel efficiency by temperature and the cumulative fuel consumption while driving.
상기와 같이 계산된 최종 누적연비는 초기 잔존주행거리를 계산하는 통상의 로직에 입력되어 초기 잔존주행거리를 정확하게 계산하는데 사용된다.The final cumulative fuel economy calculated as described above is input to conventional logic for calculating the initial remaining driving distance and used to accurately calculate the initial remaining driving distance.
여기서, 주행 중은 물론 주행 완료된 후에도 배터리 온도에 따른 연비를 메모리의 테이블값에 저장하여 그 다음 초기 시동 시 배터리 온도에 따른 누적연비를 잔존주행거리 예측에 반영할 수 있도록 한다.Here, the fuel consumption according to the battery temperature is stored in the table value of the memory during driving as well as after the driving is completed, so that the accumulated fuel efficiency according to the battery temperature may be reflected in the remaining driving distance prediction at the next initial start-up.
그리고, 상기 차량 시동 후 주행중 배터리 충전량(SOC)이 10% 소모 완료된 후에는 누적연비 데이터가 늘어날수록 그 정확도가 향상되므로, 주행 초기 온도별 학습연비와 현재 주행중 누적연비 간의 조합을 실시하지 않고, 현재 주행중 누적연비만을 잔존주행거리 계산을 위해 사용된다.After 10% of the battery charge SOC is consumed after the vehicle is started, the accuracy thereof is improved as the cumulative fuel consumption data increases. Therefore, the present invention does not perform a combination between the learning fuel efficiency for each initial driving temperature and the cumulative fuel consumption during the current driving. Only cumulative fuel economy while driving is used to calculate the remaining mileage.
따라서, 본 발명에 의하면 누적연비를 통해 운전자의 성향을 실시간으로 적용하여 전기자동차의 잔존주행거리를 예측함에 있어서 배터리의 초기온도를 측정하여 별도의 메모리에 저장하고, 차량 시동 시 배터리의 초기온도 입력에 따른 주행 초기 온도별 학습연비를 테이블로부터 선택적으로 사용함으로써, 겨울철 외기온이 상온인 상태에서 주행한 후 배터리를 충전하고, 밤새 영하의 외기온에 방치하더라도 과거 충전 직전 학습연비에 의한 잔존주행거리 예측값과 실 주행거리 간의 편차가 발생하지 않아 잔존주행거리 예측의 정확성을 확보할 수 있다.Therefore, according to the present invention, the driver's propensity is applied in real time through cumulative fuel economy, and the initial temperature of the battery is measured and stored in a separate memory, and the initial temperature of the battery is input when the vehicle is started. By selectively using the learning fuel consumption by initial temperature according to the table from the table, the battery is charged after running in the ambient air temperature at room temperature in winter, and the remaining driving distance predicted by the previous fuel consumption immediately before charging even if it is left at minus outside temperature overnight. Since the deviation between the actual driving distances does not occur, the accuracy of the remaining driving distance prediction can be secured.
뿐만 아니라, 배터리 충전에서 자유롭지 못한 운전자들에게 전기자동차의 정확한 사용가능정보를 운행 초기부터 제공함으로써 차량에 대한 신뢰도를 증가시켜 구매 의용 및 다양한 IT 정보기기와의 융합으로 편리성을 제공할 수 있다.
In addition, by providing accurate usage information of the electric vehicle to the drivers who are not free from battery charging from the beginning of the operation, it can increase the reliability of the vehicle and provide convenience by integrating it with purchase IT and various IT information devices.
Claims (2)
상기 단계에서 측정된 배터리의 초기 온도 입력에 따른 주행 초기 온도별 학습연비를 이미 저장된 온도별 연비 테이블로부터 선택적으로 사용하는 단계;
현재 주행 중 누적연비를 실시간으로 계산하여 저장하는 단계;
상기 배터리의 초기 온도 입력에 따른 주행 초기 온도별 학습연비와 현재 주행 중 누적연비를 상대 비율로 조합하여 최종 누적연비를 계산하는 단계; 및
상기 최종 누적연비를 이용하여 잔존주행거리 초기값을 설정하는 단계;
로 이루어지며, 잔존주행거리 예측시 배터리 초기 온도를 반영하여 초기 예측 오차를 방지할 수 있도록 된 것을 특징으로 하는 전기자동차의 잔존주행거리 예측 방법.
Measuring an initial temperature of the battery at vehicle startup;
Selectively using the learning fuel efficiency for each driving initial temperature according to the initial temperature input of the battery measured in the step from a previously stored temperature-specific fuel economy table;
Calculating and storing the cumulative fuel efficiency while driving in real time;
Calculating a final cumulative fuel economy by combining a learning fuel economy for each driving initial temperature according to an initial temperature input of the battery and a cumulative fuel economy during a current driving at a relative ratio; And
Setting a residual mileage initial value using the final cumulative fuel economy;
The method for predicting the remaining driving distance of the electric vehicle, characterized in that to prevent the initial prediction error by reflecting the initial temperature of the battery when the remaining driving distance prediction.
상기 주행 초기 온도별 학습연비는 기 저장된 초기 온도 입력에 따른 테이블 값으로부터 사용할 수 있도록 된 것을 특징으로 하는 전기자동차의 잔존주행거리 예측 방법.
The method according to claim 1,
The learning mileage for each initial driving temperature can be used from a table value according to a pre-stored initial temperature input.
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